no code implementations • 30 May 2024 • Zhiyuan He, Pin-Yu Chen, Tsung-Yi Ho
In particular, the average performance of RIGID exceeds the current best training-free method by more than 25%.
no code implementations • 29 May 2024 • Xiangyu Qi, Yangsibo Huang, Yi Zeng, Edoardo Debenedetti, Jonas Geiping, Luxi He, Kaixuan Huang, Udari Madhushani, Vikash Sehwag, Weijia Shi, Boyi Wei, Tinghao Xie, Danqi Chen, Pin-Yu Chen, Jeffrey Ding, Ruoxi Jia, Jiaqi Ma, Arvind Narayanan, Weijie J Su, Mengdi Wang, Chaowei Xiao, Bo Li, Dawn Song, Peter Henderson, Prateek Mittal
The exposure of security vulnerabilities in safety-aligned language models, e. g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety and AI security.
no code implementations • 27 May 2024 • Shengyun Peng, Pin-Yu Chen, Matthew Hull, Duen Horng Chau
Safety alignment is the key to guiding the behaviors of large language models (LLMs) that are in line with human preferences and restrict harmful behaviors at inference time, but recent studies show that it can be easily compromised by finetuning with only a few adversarially designed training examples.
no code implementations • 27 May 2024 • Chia-Yi Hsu, Yu-Lin Tsai, Chih-Hsun Lin, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang
Therefore, parameter-efficient fine-tuning such as LoRA have emerged, allowing users to fine-tune LLMs without the need for considerable computing resources, with little performance degradation compared to fine-tuning all parameters.
no code implementations • 26 May 2024 • Mohammed Nowaz Rabbani Chowdhury, Meng Wang, Kaoutar El Maghraoui, Naigang Wang, Pin-Yu Chen, Christopher Carothers
The sparsely gated mixture of experts (MoE) architecture sends different inputs to different subnetworks, i. e., experts, through trainable routers.
no code implementations • 24 May 2024 • Shuai Zhang, Heshan Devaka Fernando, Miao Liu, Keerthiram Murugesan, Songtao Lu, Pin-Yu Chen, Tianyi Chen, Meng Wang
This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics.
no code implementations • 23 May 2024 • Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Chengwei Qin, Pin-Yu Chen, Eng Siong Chng, Chao Zhang
We propose an unsupervised adaptation framework, Self-TAught Recognizer (STAR), which leverages unlabeled data to enhance the robustness of automatic speech recognition (ASR) systems in diverse target domains, such as noise and accents.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 15 May 2024 • Tsuyoshi Idé, Jokin Labaien, Pin-Yu Chen
We propose a new positional encoding method for a neural network architecture called the Transformer.
no code implementations • 2 May 2024 • Zhenhan Huang, Tejaswini Pedapati, Pin-Yu Chen, Chunhen Jiang, Jianxi Gao
Our method, dubbed nasgraph, remarkably reduces the computational costs by converting neural architectures to graphs and using the average degree, a graph measure, as the proxy in lieu of the evaluation metric.
no code implementations • 24 Apr 2024 • Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung, Che-Rung Lee
Latency attacks against object detection represent a variant of adversarial attacks that aim to inflate the inference time by generating additional ghost objects in a target image.
1 code implementation • 21 Mar 2024 • Yi-Shan Lan, Pin-Yu Chen, Tsung-Yi Ho
In this paper, we propose novel semantic data augmentation methods, Novel Augmentation of New Node Attributes (NaNa), and Molecular Interactions and Geometric Upgrading (MiGu) to incorporate backbone chemical and side-chain biophysical information into protein classification tasks and a co-embedding residual learning framework.
no code implementations • 18 Mar 2024 • Payel Das, Subhajit Chaudhury, Elliot Nelson, Igor Melnyk, Sarath Swaminathan, Sihui Dai, Aurélie Lozano, Georgios Kollias, Vijil Chenthamarakshan, Jiří, Navrátil, Soham Dan, Pin-Yu Chen
Efficient and accurate updating of knowledge stored in Large Language Models (LLMs) is one of the most pressing research challenges today.
1 code implementation • 16 Mar 2024 • Andrew Geng, Pin-Yu Chen
When evaluating the performance of a pre-trained model transferred to a downstream task, it is imperative to assess not only the in-distribution (ID) accuracy of the downstream model but also its capacity to generalize and identify out-of-distribution (OOD) samples.
no code implementations • 12 Mar 2024 • Chaoyi Zhu, Jeroen Galjaard, Pin-Yu Chen, Lydia Y. Chen
As large language models (LLM) are increasingly used for text generation tasks, it is critical to audit their usages, govern their applications, and mitigate their potential harms.
no code implementations • 12 Mar 2024 • Hsin-Ju Lin, Tsu-Chun Chung, Ching-Chun Hsiao, Pin-Yu Chen, Wei-Chen Chiu, Ching-Chun Huang
Text detection is frequently used in vision-based mobile robots when they need to interpret texts in their surroundings to perform a given task.
no code implementations • 12 Mar 2024 • Hongkang Li, Shuai Zhang, Yihua Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen
Despite algorithmic efforts to improve the minority group accuracy, a theoretical generalization analysis of ERM on individual groups remains elusive.
no code implementations • 9 Mar 2024 • Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini, Sara E. Berger, Bishwaranjan Bhattacharjee, Djallel Bouneffouf, Subhajit Chaudhury, Pin-Yu Chen, Lamogha Chiazor, Elizabeth M. Daly, Rogério Abreu de Paula, Pierre Dognin, Eitan Farchi, Soumya Ghosh, Michael Hind, Raya Horesh, George Kour, Ja Young Lee, Erik Miehling, Keerthiram Murugesan, Manish Nagireddy, Inkit Padhi, David Piorkowski, Ambrish Rawat, Orna Raz, Prasanna Sattigeri, Hendrik Strobelt, Sarathkrishna Swaminathan, Christoph Tillmann, Aashka Trivedi, Kush R. Varshney, Dennis Wei, Shalisha Witherspooon, Marcel Zalmanovici
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
no code implementations • 1 Mar 2024 • Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho
Large Language Models (LLMs) are becoming a prominent generative AI tool, where the user enters a query and the LLM generates an answer.
no code implementations • 27 Feb 2024 • Shyam Marjit, Harshit Singh, Nityanand Mathur, Sayak Paul, Chia-Mu Yu, Pin-Yu Chen
In the realm of subject-driven text-to-image (T2I) generative models, recent developments like DreamBooth and BLIP-Diffusion have led to impressive results yet encounter limitations due to their intensive fine-tuning demands and substantial parameter requirements.
no code implementations • 23 Feb 2024 • Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui, Pin-Yu Chen
Despite the empirical success, the mechanics of how to train a Transformer to achieve ICL and the corresponding ICL capacity is mostly elusive due to the technical challenges of analyzing the nonconvex training problems resulting from the nonlinear self-attention and nonlinear activation in Transformers.
no code implementations • 23 Feb 2024 • Ryan L'Abbate, Anthony D'Onofrio Jr., Samuel Stein, Samuel Yen-Chi Chen, Ang Li, Pin-Yu Chen, Juntao Chen, Ying Mao
In this study, we concentrate on quantum deep learning and introduce a collaborative classical-quantum architecture called co-TenQu.
1 code implementation • 18 Feb 2024 • Yihua Zhang, Pingzhi Li, Junyuan Hong, Jiaxiang Li, Yimeng Zhang, Wenqing Zheng, Pin-Yu Chen, Jason D. Lee, Wotao Yin, Mingyi Hong, Zhangyang Wang, Sijia Liu, Tianlong Chen
In the evolving landscape of natural language processing (NLP), fine-tuning pre-trained Large Language Models (LLMs) with first-order (FO) optimizers like SGD and Adam has become standard.
no code implementations • 8 Feb 2024 • Chen Chen, Ruizhe Li, Yuchen Hu, Sabato Marco Siniscalchi, Pin-Yu Chen, EnSiong Chng, Chao-Han Huck Yang
Recent studies have successfully shown that large language models (LLMs) can be successfully used for generative error correction (GER) on top of the automatic speech recognition (ASR) output.
Audio-Visual Speech Recognition Automatic Speech Recognition +3
1 code implementation • 7 Feb 2024 • Shashank Kotyan, Po-Yuan Mao, Pin-Yu Chen, Danilo Vasconcellos Vargas
Our EvoSeed framework uses auxiliary Conditional Diffusion and Classifier models to operate in a black-box setting.
1 code implementation • 2 Feb 2024 • Bharat Runwal, Tejaswini Pedapati, Pin-Yu Chen
Building upon this insight, in this work, we propose a novel density loss that encourages higher activation sparsity (equivalently, lower activation density) in the pre-trained models.
1 code implementation • 24 Jan 2024 • Ming-Chang Chiu, Yingfei Wang, Yen-Ju Kuo, Pin-Yu Chen
We take another angle to investigate color contrast's impact, beyond skin tones, on malignancy detection in skin disease datasets: We hypothesize that in addition to skin tones, the color difference between the lesion area and skin also plays a role in malignancy detection performance of dermatology AI models.
1 code implementation • 19 Jan 2024 • Yuchen Hu, Chen Chen, Chao-Han Huck Yang, Ruizhe Li, Chao Zhang, Pin-Yu Chen, EnSiong Chng
To this end, we propose to extract a language-space noise embedding from the N-best list to represent the noise conditions of source speech, which can promote the denoising process in GER.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +6
1 code implementation • 10 Jan 2024 • Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Yuan Li, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bertie Vidgen, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao liu, Heng Ji, Hongyi Wang, huan zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, Joaquin Vanschoren, John Mitchell, Kai Shu, Kaidi Xu, Kai-Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin-Yu Chen, Quanquan Gu, ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, William Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yong Chen, Yue Zhao
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
no code implementations • 28 Nov 2023 • Ming-Yu Chung, Sheng-Yen Chou, Chia-Mu Yu, Pin-Yu Chen, Sy-Yen Kuo, Tsung-Yi Ho
Dataset distillation offers a potential means to enhance data efficiency in deep learning.
1 code implementation • 27 Nov 2023 • Shengwei An, Sheng-Yen Chou, Kaiyuan Zhang, QiuLing Xu, Guanhong Tao, Guangyu Shen, Siyuan Cheng, Shiqing Ma, Pin-Yu Chen, Tsung-Yi Ho, Xiangyu Zhang
Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training.
no code implementations • 20 Nov 2023 • Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hung Chen, Marcel Worring
The aim of video summarization is to shorten videos automatically while retaining the key information necessary to convey the overall story.
no code implementations • 24 Oct 2023 • Shuai Zhang, Hongkang Li, Meng Wang, Miao Liu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Keerthiram Murugesan, Subhajit Chaudhury
This paper provides the first theoretical convergence and sample complexity analysis of the practical setting of DQNs with $\epsilon$-greedy policy.
1 code implementation • 16 Oct 2023 • Yu-Lin Tsai, Chia-Yi Hsu, Chulin Xie, Chih-Hsun Lin, Jia-You Chen, Bo Li, Pin-Yu Chen, Chia-Mu Yu, Chun-Ying Huang
While efforts have been made to mitigate such problems, either by implementing a safety filter at the evaluation stage or by fine-tuning models to eliminate undesirable concepts or styles, the effectiveness of these safety measures in dealing with a wide range of prompts remains largely unexplored.
1 code implementation • 12 Oct 2023 • Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Sijia Liu, Tsung-Yi Ho
To bridge this gap, we propose AutoVP, an end-to-end expandable framework for automating VP design choices, along with 12 downstream image-classification tasks that can serve as a holistic VP-performance benchmark.
1 code implementation • 5 Oct 2023 • Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, Peter Henderson
Optimizing large language models (LLMs) for downstream use cases often involves the customization of pre-trained LLMs through further fine-tuning.
2 code implementations • 3 Oct 2023 • Ming Jin, Shiyu Wang, Lintao Ma, Zhixuan Chu, James Y. Zhang, Xiaoming Shi, Pin-Yu Chen, Yuxuan Liang, Yuan-Fang Li, Shirui Pan, Qingsong Wen
We begin by reprogramming the input time series with text prototypes before feeding it into the frozen LLM to align the two modalities.
1 code implementation • NeurIPS 2023 • Chen Chen, Yuchen Hu, Chao-Han Huck Yang, Sabato Macro Siniscalchi, Pin-Yu Chen, Eng Siong Chng
We make our results publicly accessible for reproducible pipelines with released pre-trained models, thus providing a new evaluation paradigm for ASR error correction with LLMs.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 22 Sep 2023 • Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu
While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokenization procedure and vision transformer backbone, convolutional neural networks as another important and widely-adopted architecture for image data, though having contrastive-learning techniques to drive the self-supervised learning, still face the difficulty of leveraging such straightforward and general masking operation to benefit their learning process significantly.
1 code implementation • 12 Sep 2023 • Xilong Wang, Chia-Mu Yu, Pin-Yu Chen
For machine learning with tabular data, Table Transformer (TabTransformer) is a state-of-the-art neural network model, while Differential Privacy (DP) is an essential component to ensure data privacy.
1 code implementation • 12 Sep 2023 • Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu
In this work, we propose Prompting4Debugging (P4D) as a debugging and red-teaming tool that automatically finds problematic prompts for diffusion models to test the reliability of a deployed safety mechanism.
1 code implementation • 3 Sep 2023 • Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
1 code implementation • ICCV 2023 • Yihua Zhang, Ruisi Cai, Tianlong Chen, Guanhua Zhang, huan zhang, Pin-Yu Chen, Shiyu Chang, Zhangyang Wang, Sijia Liu
Since the lack of robustness has become one of the main hurdles for CNNs, in this paper we ask: How to adversarially robustify a CNN-based MoE model?
1 code implementation • 24 Jul 2023 • Neel Bhandari, Pin-Yu Chen
Language Models today provide a high accuracy across a large number of downstream tasks.
no code implementations • 4 Jul 2023 • Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen, Andrew Brown, Marcel Worring
Multi-modal video summarization has a video input and a text-based query input.
1 code implementation • 29 Jun 2023 • Hao-Lun Sun, Lei Hsiung, Nandhini Chandramoorthy, Pin-Yu Chen, Tsung-Yi Ho
To address this challenge, we introduce NeuralFuse, a novel add-on module that addresses the accuracy-energy tradeoff in low-voltage regimes by learning input transformations to generate error-resistant data representations.
1 code implementation • NeurIPS 2023 • Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho
This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs.
1 code implementation • 7 Jun 2023 • Mohammed Nowaz Rabbani Chowdhury, Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen
In deep learning, mixture-of-experts (MoE) activates one or few experts (sub-networks) on a per-sample or per-token basis, resulting in significant computation reduction.
no code implementations • 26 May 2023 • Jokin Labaien, Tsuyoshi Idé, Pin-Yu Chen, Ekhi Zugasti, Xabier De Carlos
This paper addresses the task of anomaly diagnosis when the underlying data generation process has a complex spatio-temporal (ST) dependency.
no code implementations • 25 May 2023 • Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen, Baharan Mirzasoleiman
However, supervised CL is prone to collapsing representations of subclasses within a class by not capturing all their features, and unsupervised CL may suppress harder class-relevant features by focusing on learning easy class-irrelevant features; both significantly compromise representation quality.
no code implementations • 18 May 2023 • Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh
Variational quantum circuit (VQC) is a promising approach for implementing quantum neural networks on noisy intermediate-scale quantum (NISQ) devices.
no code implementations • 30 Apr 2023 • Jia-Hong Huang, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hung Chen, Marcel Worring
In this work, a Causal Explainer, dubbed Causalainer, is proposed to address this issue.
no code implementations • 24 Apr 2023 • Sarwan Ali, Babatunde Bello, Prakash Chourasia, Ria Thazhe Punathil, Pin-Yu Chen, Imdad Ullah Khan, Murray Patterson
Understanding the host-specificity of different families of viruses sheds light on the origin of, e. g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans.
no code implementations • 19 Apr 2023 • Zaitang Li, Pin-Yu Chen, Tsung-Yi Ho
Formally, GREAT Score carries the physical meaning of a global statistic capturing a mean certified attack-proof perturbation level over all samples drawn from a generative model.
no code implementations • 11 Apr 2023 • Erh-Chung Chen, Pin-Yu Chen, I-Hsin Chung, Che-Rung Lee
Nowadays, the deployment of deep learning-based applications is an essential task owing to the increasing demands on intelligent services.
1 code implementation • ICCV 2023 • Yizhe Li, Yu-Lin Tsai, Xuebin Ren, Chia-Mu Yu, Pin-Yu Chen
Visual Prompting (VP) is an emerging and powerful technique that allows sample-efficient adaptation to downstream tasks by engineering a well-trained frozen source model.
1 code implementation • 3 Mar 2023 • Dennis Wei, Haoze Wu, Min Wu, Pin-Yu Chen, Clark Barrett, Eitan Farchi
The softmax function is a ubiquitous component at the output of neural networks and increasingly in intermediate layers as well.
no code implementations • 21 Feb 2023 • Sihui Dai, Saeed Mahloujifar, Chong Xiang, Vikash Sehwag, Pin-Yu Chen, Prateek Mittal
Using our framework, we present the first leaderboard, MultiRobustBench, for benchmarking multiattack evaluation which captures performance across attack types and attack strengths.
no code implementations • 12 Feb 2023 • Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen
Based on a data model characterizing both label-relevant and label-irrelevant tokens, this paper provides the first theoretical analysis of training a shallow ViT, i. e., one self-attention layer followed by a two-layer perceptron, for a classification task.
no code implementations • 6 Feb 2023 • Shuai Zhang, Meng Wang, Pin-Yu Chen, Sijia Liu, Songtao Lu, Miao Liu
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and storage costs.
no code implementations • 26 Jan 2023 • Alex Gu, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel
Interpreting machine learning models is challenging but crucial for ensuring the safety of deep networks in autonomous driving systems.
no code implementations • 8 Jan 2023 • Pin-Yu Chen, Payel Das
With the advancements in machine learning (ML) methods and compute resources, artificial intelligence (AI) empowered systems are becoming a prevailing technology.
no code implementations • 5 Jan 2023 • Ria Vinod, Pin-Yu Chen, Payel Das
To this end, we reprogram an off-the-shelf pre-trained English language transformer and benchmark it on a set of protein physicochemical prediction tasks (secondary structure, stability, homology, stability) as well as on a biomedically relevant set of protein function prediction tasks (antimicrobial, toxicity, antibody affinity).
no code implementations • 19 Dec 2022 • Zichong Li, Pin-Yu Chen, Sijia Liu, Songtao Lu, Yangyang Xu
In this paper, we design and analyze stochastic inexact augmented Lagrangian methods (Stoc-iALM) to solve problems involving a nonconvex composite (i. e. smooth+nonsmooth) objective and nonconvex smooth functional constraints.
no code implementations • 16 Dec 2022 • Ming-Chang Chiu, Yingfei Wang, Derrick Eui Gyu Kim, Pin-Yu Chen, Xuezhe Ma
It is well established in neuroscience that color vision plays an essential part in the human visual perception system.
1 code implementation • ICCV 2023 • Ming-Chang Chiu, Pin-Yu Chen, Xuezhe Ma
In this paper, we provide 20, 000 non-trivial human annotations on popular datasets as a first step to bridge gap to studying how natural semantic spurious features affect image classification, as prior works often study datasets mixing low-level features due to limitations in accessing realistic datasets.
1 code implementation • CVPR 2023 • Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho
To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks.
1 code implementation • 1 Dec 2022 • Jiajin Zhang, Hanqing Chao, Amit Dhurandhar, Pin-Yu Chen, Ali Tajer, Yangyang Xu, Pingkun Yan
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
1 code implementation • 29 Nov 2022 • Lei Hsiung, Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho
With the advancement of deep learning technology, neural networks have demonstrated their excellent ability to provide accurate predictions in many tasks.
1 code implementation • CVPR 2023 • Aochuan Chen, Yuguang Yao, Pin-Yu Chen, Yihua Zhang, Sijia Liu
As highlighted below, we show that when reprogramming an ImageNet-pretrained ResNet-18 to 13 target tasks, our method outperforms baselines by a substantial margin, e. g., 7. 9% and 6. 7% accuracy improvements in transfer learning to the target Flowers102 and CIFAR100 datasets.
no code implementations • 2 Nov 2022 • Jhih-Cing Huang, Yu-Lin Tsai, Chao-Han Huck Yang, Cheng-Fang Su, Chia-Mu Yu, Pin-Yu Chen, Sy-Yen Kuo
Recently, quantum classifiers have been found to be vulnerable to adversarial attacks, in which quantum classifiers are deceived by imperceptible noises, leading to misclassification.
1 code implementation • 2 Nov 2022 • Tsun-An Hsieh, Chao-Han Huck Yang, Pin-Yu Chen, Sabato Marco Siniscalchi, Yu Tsao
This study addresses the speech enhancement (SE) task within the causal inference paradigm by modeling the noise presence as an intervention.
1 code implementation • 2 Nov 2022 • Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen, Alexander Lerch
In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) classification based on the concept of Neural Model Reprogramming (NMR).
1 code implementation • 27 Oct 2022 • Elvin Lo, Pin-Yu Chen
Molecule optimization is an important problem in chemical discovery and has been approached using many techniques, including generative modeling, reinforcement learning, genetic algorithms, and much more.
1 code implementation • 23 Oct 2022 • Kaiyuan Zhang, Guanhong Tao, QiuLing Xu, Siyuan Cheng, Shengwei An, Yingqi Liu, Shiwei Feng, Guangyu Shen, Pin-Yu Chen, Shiqing Ma, Xiangyu Zhang
In this work, we theoretically analyze the connection among cross-entropy loss, attack success rate, and clean accuracy in this setting.
2 code implementations • 12 Oct 2022 • Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, Sijia Liu
In this work, we leverage visual prompting (VP) to improve adversarial robustness of a fixed, pre-trained model at testing time.
no code implementations • 7 Oct 2022 • Zhixu Du, Jingwei Sun, Ang Li, Pin-Yu Chen, Jianyi Zhang, Hai "Helen" Li, Yiran Chen
We also show that layer normalization is a better choice in FL which can mitigate the external covariate shift and improve the performance of the global model.
no code implementations • 6 Oct 2022 • Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra, Payel Das, Luca Daniel
Given a pretrained model, the representations of data synthesized from the Gaussian mixture are used to compare with our reference to infer the quality.
1 code implementation • 5 Oct 2022 • Igor Melnyk, Vijil Chenthamarakshan, Pin-Yu Chen, Payel Das, Amit Dhurandhar, Inkit Padhi, Devleena Das
Results on antibody design benchmarks show that our model on low-resourced antibody sequence dataset provides highly diverse CDR sequences, up to more than a two-fold increase of diversity over the baselines, without losing structural integrity and naturalness.
no code implementations • 23 Sep 2022 • Yung-Chen Tang, Pin-Yu Chen, Tsung-Yi Ho
Neural network calibration is an essential task in deep learning to ensure consistency between the confidence of model prediction and the true correctness likelihood.
no code implementations • 8 Sep 2022 • Chulin Xie, Yunhui Long, Pin-Yu Chen, Qinbin Li, Arash Nourian, Sanmi Koyejo, Bo Li
We then provide two robustness certification criteria: certified prediction and certified attack inefficacy for DPFL on both user and instance levels.
no code implementations • 31 Aug 2022 • Zhiyuan He, Yijun Yang, Pin-Yu Chen, Qiang Xu, Tsung-Yi Ho
Empowered by the robust relation net built on SSL, we found that BEYOND outperforms baselines in terms of both detection ability and speed.
no code implementations • 10 Aug 2022 • Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Additionally, each process $i\in\{1, \dots, K\}$ has a private parameter $\alpha_i$.
1 code implementation • 20 Jul 2022 • Chulin Xie, Pin-Yu Chen, Qinbin Li, Arash Nourian, Ce Zhang, Bo Li
To address these challenges, in this paper, we introduce a VFL framework with multiple heads (VIM), which takes the separate contribution of each client into account, and enables an efficient decomposition of the VFL optimization objective to sub-objectives that can be iteratively tackled by the server and the clients on their own.
1 code implementation • 18 Jul 2022 • Sarwan Ali, Bikram Sahoo, Alexander Zelikovskiy, Pin-Yu Chen, Murray Patterson
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 genome -- millions of sequences and counting.
1 code implementation • 16 Jul 2022 • Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho
Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e. g., perturbations bounded in Lp ball.
no code implementations • 7 Jul 2022 • Hongkang Li, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data.
1 code implementation • 24 Jun 2022 • Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
This highlights that the performance of DG methods on a few benchmark datasets may not be representative of their performance on unseen domains in the wild.
1 code implementation • 15 Jun 2022 • Tianlong Chen, huan zhang, Zhenyu Zhang, Shiyu Chang, Sijia Liu, Pin-Yu Chen, Zhangyang Wang
Certifiable robustness is a highly desirable property for adopting deep neural networks (DNNs) in safety-critical scenarios, but often demands tedious computations to establish.
2 code implementations • 13 Jun 2022 • Gaoyuan Zhang, Songtao Lu, Yihua Zhang, Xiangyi Chen, Pin-Yu Chen, Quanfu Fan, Lee Martie, Lior Horesh, Mingyi Hong, Sijia Liu
Spurred by that, we propose distributed adversarial training (DAT), a large-batch adversarial training framework implemented over multiple machines.
1 code implementation • 8 Jun 2022 • Momin Abbas, Quan Xiao, Lisha Chen, Pin-Yu Chen, Tianyi Chen
Model-agnostic meta learning (MAML) is currently one of the dominating approaches for few-shot meta-learning.
1 code implementation • 8 Jun 2022 • Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Min-Hsiu Hsieh
In this work, we first put forth an end-to-end quantum neural network, TTN-VQC, which consists of a quantum tensor network based on a tensor-train network (TTN) for dimensionality reduction and a VQC for functional regression.
no code implementations • 20 May 2022 • N. Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai
Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics.
1 code implementation • NAACL 2022 • Yong Xie, Dakuo Wang, Pin-Yu Chen, JinJun Xiong, Sijia Liu, Sanmi Koyejo
More and more investors and machine learning models rely on social media (e. g., Twitter and Reddit) to gather real-time information and sentiment to predict stock price movements.
no code implementations • 8 Apr 2022 • Abolaji D. Adesoji, Pin-Yu Chen
In recent years, Machine-Learning (ML)-driven approaches have been widely used in scientific discovery domains.
no code implementations • 29 Mar 2022 • Chao-Han Huck Yang, I-Te Danny Hung, Yi-Chieh Liu, Pin-Yu Chen
In this work, we incorporate this binary information of "existence of noise" as treatment into image classification tasks to improve prediction accuracy by jointly estimating their treatment effects.
1 code implementation • 11 Mar 2022 • Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen, Javier Tejedor
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance.
no code implementations • 1 Mar 2022 • Celia Cintas, Payel Das, Brian Quanz, Girmaw Abebe Tadesse, Skyler Speakman, Pin-Yu Chen
We propose group-based subset scanning to identify, quantify, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of the generative models.
1 code implementation • 22 Feb 2022 • Pin-Yu Chen
In data-rich domains such as vision, language, and speech, deep learning prevails to deliver high-performance task-specific models and can even learn general task-agnostic representations for efficient finetuning to downstream tasks.
no code implementations • 17 Feb 2022 • Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Yu Tsao, Pin-Yu Chen
Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
no code implementations • 15 Feb 2022 • Pin-Yu Chen, Sijia Liu
Adversarial robustness studies the worst-case performance of a machine learning model to ensure safety and reliability.
1 code implementation • CVPR 2023 • Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$-ball to composite semantic perturbations, such as the combination of Hue, Saturation, Brightness, Contrast, and Rotation.
1 code implementation • 2 Feb 2022 • Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labeled data can be difficult to obtain in many applications.
no code implementations • 21 Jan 2022 • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
no code implementations • 11 Jan 2022 • Chunheng Jiang, Tejaswini Pedapati, Pin-Yu Chen, Yizhou Sun, Jianxi Gao
To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$.
no code implementations • 8 Dec 2021 • Ching-Yun Ko, Jeet Mohapatra, Sijia Liu, Pin-Yu Chen, Luca Daniel, Lily Weng
With the integrated framework, we achieve up to 6\% improvement on the standard accuracy and 17\% improvement on the robust accuracy.
no code implementations • NeurIPS 2021 • Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
no code implementations • 1 Dec 2021 • Jiachen Sun, Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Dan Hendrycks, Jihun Hamm, Z. Morley Mao
To alleviate this issue, we propose a novel data augmentation scheme, FourierMix, that produces augmentations to improve the spectral coverage of the training data.
no code implementations • NeurIPS 2021 • Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
1 code implementation • NeurIPS 2021 • Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
no code implementations • 29 Nov 2021 • Chao-Han Huck Yang, Zhengling Qi, Yifan Cui, Pin-Yu Chen
Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications.
no code implementations • 28 Nov 2021 • Yu-Hsuan Li, Tzu-Yin Chao, Ching-Chun Huang, Pin-Yu Chen, Wei-Chen Chiu
Basically, given only a small set of detectors that are learned to recognize some manually annotated attributes (i. e., the seen attributes), we aim to synthesize the detectors of novel attributes in a zero-shot learning manner.
no code implementations • AAAI Workshop AdvML 2022 • Chia-Hung Yuan, Pin-Yu Chen, Chia-Mu Yu
A plethora of attack methods have been proposed to generate adversarial examples, among which the iterative methods have been demonstrated the ability to find a strong attack.
no code implementations • NeurIPS 2021 • Arpan Mukherjee, Ali Tajer, Pin-Yu Chen, Payel Das
Owing to the adversarial contamination of the rewards, each arm's mean is only partially identifiable.
2 code implementations • NeurIPS 2021 • Lijie Fan, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Chuang Gan
We show that AdvCL is able to enhance cross-task robustness transferability without loss of model accuracy and finetuning efficiency.
1 code implementation • 26 Oct 2021 • Xiao Jin, Pin-Yu Chen, Chia-Yi Hsu, Chia-Mu Yu, Tianyi Chen
We name our proposed method as catastrophic data leakage in vertical federated learning (CAFE).
no code implementations • 22 Oct 2021 • Rulin Shao, JinFeng Yi, Pin-Yu Chen, Cho-Jui Hsieh
Our comprehensive analysis shows several novel insights that (1) With KDIGA, students can preserve or even exceed the adversarial robustness of the teacher model, even when their models have fundamentally different architectures; (2) KDIGA enables robustness to transfer to pre-trained students, such as KD from an adversarially trained ResNet to a pre-trained ViT, without loss of clean accuracy; and (3) Our derived local linearity bounds for characterizing adversarial robustness in KD are consistent with the empirical results.
no code implementations • 19 Oct 2021 • Yunchuan Liu, Lei Yang, Amir Ghasemkhani, Hanif Livani, Virgilio A. Centeno, Pin-Yu Chen, Junshan Zhang
Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e. g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers.
no code implementations • 12 Oct 2021 • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong
Moreover, when the algorithm for training a pruned neural network is specified as an (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned weights in the hidden layer.
1 code implementation • 8 Oct 2021 • Hao Yen, Pin-Jui Ku, Chao-Han Huck Yang, Hu Hu, Sabato Marco Siniscalchi, Pin-Yu Chen, Yu Tsao
In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system.
no code implementations • 6 Oct 2021 • Jun Qi, Chao-Han Huck Yang, Pin-Yu Chen
The advent of noisy intermediate-scale quantum (NISQ) computers raises a crucial challenge to design quantum neural networks for fully quantum learning tasks.
no code implementations • ICLR 2022 • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited.
no code implementations • ICLR 2022 • Keerthiram Murugesan, Vijay Sadashivaiah, Ronny Luss, Karthikeyan Shanmugam, Pin-Yu Chen, Amit Dhurandhar
Knowledge transfer between heterogeneous source and target networks and tasks has received a lot of attention in recent times as large amounts of quality labelled data can be difficult to obtain in many applications.
no code implementations • 29 Sep 2021 • Washington Garcia, Pin-Yu Chen, Somesh Jha, Hamilton Scott Clouse, Kevin R. B. Butler
It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors.
no code implementations • 29 Sep 2021 • Sarwan Ali, Bikram Sahoo, Pin-Yu Chen, Murray Patterson
The rapid spread of the COVID-19 pandemic has resulted in an unprecedented amount of sequence data of the SARS-CoV-2 viral genome --- millions of sequences and counting.
no code implementations • 29 Sep 2021 • Chulin Xie, Yunhui Long, Pin-Yu Chen, Krishnaram Kenthapadi, Bo Li
Federated learning (FL) provides an efficient training paradigm to jointly train a global model leveraging data from distributed users.
no code implementations • 29 Sep 2021 • Wang Zhang, Lam M. Nguyen, Subhro Das, Pin-Yu Chen, Sijia Liu, Alexandre Megretski, Luca Daniel, Tsui-Wei Weng
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
no code implementations • 24 Sep 2021 • Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilovic, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
As artificial intelligence and machine learning algorithms become increasingly prevalent in society, multiple stakeholders are calling for these algorithms to provide explanations.
no code implementations • 4 Sep 2021 • Chang-Sheng Lin, Chia-Yi Hsu, Pin-Yu Chen, Chia-Mu Yu
The Cycle-GAN is used to generate adversarial makeup, and the architecture of the victimized classifier is VGG 16.
1 code implementation • NeurIPS 2021 • Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Unsupervised domain adaptation (UDA) enables cross-domain learning without target domain labels by transferring knowledge from a labeled source domain whose distribution differs from that of the target.
1 code implementation • ICLR 2022 • Chia-Hsiang Kao, Wei-Chen Chiu, Pin-Yu Chen
Model-agnostic meta-learning (MAML) is one of the most popular and widely adopted meta-learning algorithms, achieving remarkable success in various learning problems.
1 code implementation • 24 Jun 2021 • Yue Cao, Payel Das, Vijil Chenthamarakshan, Pin-Yu Chen, Igor Melnyk, Yang shen
Designing novel protein sequences for a desired 3D topological fold is a fundamental yet non-trivial task in protein engineering.
no code implementations • ICML Workshop AML 2021 • Yun-Yun Tsai, Lei Hsiung, Pin-Yu Chen, Tsung-Yi Ho
We then propose generalized adversarial training (GAT) to extend model robustness from $\ell_{p}$ norm to composite semantic perturbations, such as Hue, Saturation, Brightness, Contrast, and Rotation.
no code implementations • ICML Workshop AML 2021 • Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
However, the limited effect of poisoning is restricted to the setting where training and test data are from the same distribution.
3 code implementations • 17 Jun 2021 • Chao-Han Huck Yang, Yun-Yun Tsai, Pin-Yu Chen
Learning to classify time series with limited data is a practical yet challenging problem.
1 code implementation • 15 Jun 2021 • Chulin Xie, Minghao Chen, Pin-Yu Chen, Bo Li
Our method exploits clipping and smoothing on model parameters to control the global model smoothness, which yields a sample-wise robustness certification on backdoors with limited magnitude.
no code implementations • NeurIPS 2021 • Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
However, despite these successes, the recent Predicting Generalization in Deep Learning (PGDL) NeurIPS 2020 competition suggests that there is a need for more robust and efficient measures of network generalization.
no code implementations • 20 May 2021 • Jaydeep Borkar, Pin-Yu Chen
We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine. com, picpurify. com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API.
1 code implementation • 17 May 2021 • Sayak Paul, Pin-Yu Chen
Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy.
no code implementations • 14 May 2021 • Siyue Wang, Xiao Wang, Pin-Yu Chen, Pu Zhao, Xue Lin
This paper proposes Characteristic Examples for effectively fingerprinting deep neural networks, featuring high-robustness to the base model against model pruning as well as low-transferability to unassociated models.
no code implementations • NeurIPS 2020 • Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei zhang, Kailash Gopalakrishnan
Large-scale distributed training of Deep Neural Networks (DNNs) on state-of-the-art platforms is expected to be severely communication constrained.
no code implementations • 8 Apr 2021 • Yair Schiff, Brian Quanz, Payel Das, Pin-Yu Chen
The field of Deep Learning is rich with empirical evidence of human-like performance on a variety of regression, classification, and control tasks.
no code implementations • 1 Apr 2021 • Celia Cintas, Payel Das, Brian Quanz, Skyler Speakman, Victor Akinwande, Pin-Yu Chen
We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models.
1 code implementation • 29 Mar 2021 • Rulin Shao, Zhouxing Shi, JinFeng Yi, Pin-Yu Chen, Cho-Jui Hsieh
Following the success in advancing natural language processing and understanding, transformers are expected to bring revolutionary changes to computer vision.
1 code implementation • 5 Mar 2021 • Omid Aramoon, Pin-Yu Chen, Gang Qu
Engineering a top-notch deep learning model is an expensive procedure that involves collecting data, hiring human resources with expertise in machine learning, and providing high computational resources.
no code implementations • 4 Mar 2021 • Washington Garcia, Pin-Yu Chen, Somesh Jha, Scott Clouse, Kevin R. B. Butler
It was recently shown in the gradient-level setting that regular adversarial examples leave the data manifold, while their on-manifold counterparts are in fact generalization errors.
no code implementations • 3 Mar 2021 • Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen
Studying the sensitivity of weight perturbation in neural networks and its impacts on model performance, including generalization and robustness, is an active research topic due to its implications on a wide range of machine learning tasks such as model compression, generalization gap assessment, and adversarial attacks.
1 code implementation • 2 Mar 2021 • Chia-Yi Hsu, Pin-Yu Chen, Songtao Lu, Sijia Liu, Chia-Mu Yu
In this paper, we propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation.
no code implementations • 25 Feb 2021 • Chun-Chih Teng, Pin-Yu Chen, Wei-Chen Chiu
We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with sufficient source data and a few target data.
no code implementations • 23 Feb 2021 • Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu, Pin-Yu Chen
In this paper, we formalize the notion of non-singular adversarial robustness for neural networks through the lens of joint perturbations to data inputs as well as model weights.
1 code implementation • ICLR 2021 • Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang
Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning.
1 code implementation • 18 Feb 2021 • Chao-Han Huck Yang, I-Te Danny Hung, Yi Ouyang, Pin-Yu Chen
Deep reinforcement learning (DRL) has demonstrated impressive performance in various gaming simulators and real-world applications.
no code implementations • 10 Feb 2021 • Omid Aramoon, Pin-Yu Chen, Gang Qu, Yuan Tian
Due to its distributed methodology alongside its privacy-preserving features, Federated Learning (FL) is vulnerable to training time adversarial attacks.
no code implementations • 1 Feb 2021 • Akhilan Boopathy, Tsui-Wei Weng, Sijia Liu, Pin-Yu Chen, Gaoyuan Zhang, Luca Daniel
Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees.
no code implementations • 1 Feb 2021 • Syed Zawad, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian, Feng Yan
Data heterogeneity has been identified as one of the key features in federated learning but often overlooked in the lens of robustness to adversarial attacks.
no code implementations • 30 Jan 2021 • Maurício Gruppi, Sibel Adali, Pin-Yu Chen
The goal of LSC is to characterize and quantify language variations with respect to word meaning, to measure how distinct two language sources are (that is, people or language models).
no code implementations • 13 Jan 2021 • Yiqin Yu, Pin-Yu Chen, Yuan Zhou, Jing Mei
With the successful adoption of machine learning on electronic health records (EHRs), numerous computational models have been deployed to address a variety of clinical problems.
no code implementations • 7 Jan 2021 • Rulin Shao, Zhouxing Shi, JinFeng Yi, Pin-Yu Chen, Cho-Jui Hsieh
At the second stage, we design and apply a highly transferable adversarial attack for text CAPTCHAs to better obstruct CAPTCHA solvers.
no code implementations • NeurIPS 2021 • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong
Moreover, as the algorithm for training a sparse neural network is specified as (accelerated) stochastic gradient descent algorithm, we theoretically show that the number of samples required for achieving zero generalization error is proportional to the number of the non-pruned model weights in the hidden layer.
no code implementations • 1 Jan 2021 • Xiao Jin, Ruijie Du, Pin-Yu Chen, Tianyi Chen
In this paper, we revisit this defense premise and propose an advanced data leakage attack to efficiently recover batch data from the shared aggregated gradients.
no code implementations • 1 Jan 2021 • Norman Joseph Tatro, Payel Das, Pin-Yu Chen, Vijil Chenthamarakshan, Rongjie Lai
Empowered by the disentangled latent space learning, the extrinsic latent embedding is successfully used for classification or property prediction of different drugs bound to a specific protein.
1 code implementation • 22 Dec 2020 • Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy machine learning systems.
no code implementations • 21 Dec 2020 • Pranay Sharma, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Xue Lin, Pramod K. Varshney
In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations.
no code implementations • 7 Dec 2020 • Ria Vinod, Pin-Yu Chen, Payel Das
Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations.
1 code implementation • SEMEVAL 2020 • Maurício Gruppi, Sibel Adali, Pin-Yu Chen
Our results show evidence that the number of landmarks used for alignment has a directimpact on the predictive performance of the model.
1 code implementation • CVPR 2021 • Akshay Mehra, Bhavya Kailkhura, Pin-Yu Chen, Jihun Hamm
Moreover, our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods such as Gaussian data augmentation\cite{cohen2019certified}, MACER\cite{zhai2020macer}, and SmoothAdv\cite{salman2019provably} that achieve high certified adversarial robustness.
1 code implementation • 3 Nov 2020 • Samuel Hoffman, Vijil Chenthamarakshan, Kahini Wadhawan, Pin-Yu Chen, Payel Das
Machine learning based methods have shown potential for optimizing existing molecules with more desirable properties, a critical step towards accelerating new chemical discovery.
2 code implementations • 26 Oct 2020 • Chao-Han Huck Yang, Jun Qi, Samuel Yen-Chi Chen, Pin-Yu Chen, Sabato Marco Siniscalchi, Xiaoli Ma, Chin-Hui Lee
Testing on the Google Speech Commands Dataset, the proposed QCNN encoder attains a competitive accuracy of 95. 12% in a decentralized model, which is better than the previous architectures using centralized RNN models with convolutional features.
Ranked #1 on Keyword Spotting on Google Speech Commands (10-keyword Speech Commands dataset metric)
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • NeurIPS 2020 • Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel
We also provide a framework that generalizes the calculation for certification using higher-order information.
1 code implementation • NeurIPS 2020 • N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Yet, current curve finding algorithms do not consider the influence of symmetry in the loss surface created by model weight permutations.
1 code implementation • ECCV 2020 • Ren Wang, Gaoyuan Zhang, Sijia Liu, Pin-Yu Chen, JinJun Xiong, Meng Wang
When the training data are maliciously tampered, the predictions of the acquired deep neural network (DNN) can be manipulated by an adversary known as the Trojan attack (or poisoning backdoor attack).
no code implementations • ICML 2020 • Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho
Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data.
BIG-bench Machine Learning Diabetic Retinopathy Detection +1
1 code implementation • ICML 2020 • Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Cynthia Liu, Pin-Yu Chen, Shiyu Chang, Luca Daniel
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability (namely, making network interpretation maps visually similar), or interpretability is itself susceptible to adversarial attacks.
no code implementations • ICML 2020 • Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen, JinJun Xiong
In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems.
no code implementations • 23 Jun 2020 • Orlando Romero, Subhro Das, Pin-Yu Chen, Sérgio Pequito
Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications.
no code implementations • 11 Jun 2020 • Sijia Liu, Pin-Yu Chen, Bhavya Kailkhura, Gaoyuan Zhang, Alfred Hero, Pramod K. Varshney
Zeroth-order (ZO) optimization is a subset of gradient-free optimization that emerges in many signal processing and machine learning applications.
2 code implementations • 22 May 2020 • Payel Das, Tom Sercu, Kahini Wadhawan, Inkit Padhi, Sebastian Gehrmann, Flaviu Cipcigan, Vijil Chenthamarakshan, Hendrik Strobelt, Cicero dos Santos, Pin-Yu Chen, Yi Yan Yang, Jeremy Tan, James Hedrick, Jason Crain, Aleksandra Mojsilovic
De novo therapeutic design is challenged by a vast chemical repertoire and multiple constraints, e. g., high broad-spectrum potency and low toxicity.
2 code implementations • ICLR 2020 • Chulin Xie, Keli Huang, Pin-Yu Chen, Bo Li
Compared to standard centralized backdoors, we show that DBA is substantially more persistent and stealthy against FL on diverse datasets such as finance and image data.
3 code implementations • ICLR 2020 • Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy, Xue Lin
In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness.
no code implementations • 31 Mar 2020 • Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Xiaoli Ma, Chin-Hui Lee
Recent studies have highlighted adversarial examples as ubiquitous threats to the deep neural network (DNN) based speech recognition systems.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 2 Mar 2020 • Jeet Mohapatra, Ching-Yun Ko, Tsui-Wei, Weng, Sijia Liu, Pin-Yu Chen, Luca Daniel
The fragility of modern machine learning models has drawn a considerable amount of attention from both academia and the public.
no code implementations • 26 Feb 2020 • Kaidi Xu, Sijia Liu, Pin-Yu Chen, Pu Zhao, Xue Lin
Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks.
no code implementations • 25 Feb 2020 • Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin
To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner.
no code implementations • 20 Feb 2020 • Chao-Han Huck Yang, Jun Qi, Pin-Yu Chen, Yi Ouyang, I-Te Danny Hung, Chin-Hui Lee, Xiaoli Ma
Recent deep neural networks based techniques, especially those equipped with the ability of self-adaptation in the system level such as deep reinforcement learning (DRL), are shown to possess many advantages of optimizing robot learning systems (e. g., autonomous navigation and continuous robot arm control.)
no code implementations • 19 Feb 2020 • Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, Peter Chin
Designing effective defense against adversarial attacks is a crucial topic as deep neural networks have been proliferated rapidly in many security-critical domains such as malware detection and self-driving cars.
no code implementations • 18 Feb 2020 • Xiao Wang, Siyue Wang, Pin-Yu Chen, Xue Lin, Peter Chin
Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models.
1 code implementation • 18 Feb 2020 • Pu Zhao, Pin-Yu Chen, Siyue Wang, Xue Lin
Despite the great achievements of the modern deep neural networks (DNNs), the vulnerability/robustness of state-of-the-art DNNs raises security concerns in many application domains requiring high reliability.
no code implementations • 17 Feb 2020 • Minhao Cheng, Qi Lei, Pin-Yu Chen, Inderjit Dhillon, Cho-Jui Hsieh
Adversarial training has become one of the most effective methods for improving robustness of neural networks.
1 code implementation • 9 Feb 2020 • Yunan Ye, Hengzhi Pei, Boxin Wang, Pin-Yu Chen, Yada Zhu, Jun Xiao, Bo Li
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
1 code implementation • 19 Dec 2019 • Jeet Mohapatra, Tsui-Wei, Weng, Pin-Yu Chen, Sijia Liu, Luca Daniel
Verifying robustness of neural networks given a specified threat model is a fundamental yet challenging task.
1 code implementation • ECCV 2020 • Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, Xue Lin
To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.
no code implementations • ICML 2020 • Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, Kush R. Varshney
Moreover, the same classifier yields the lack of a trade-off with respect to ideal distributions while yielding a trade-off when accuracy is measured with respect to the given (possibly biased) dataset.
no code implementations • 25 Sep 2019 • N. Joseph Tatro, Pin-Yu Chen, Payel Das, Igor Melnyk, Prasanna Sattigeri, Rongjie Lai
Empirically, this initialization is critical for efficiently learning a simple, planar, low-loss curve between networks that successfully generalizes.
no code implementations • 25 Sep 2019 • Minhao Cheng, Pin-Yu Chen, Sijia Liu, Shiyu Chang, Cho-Jui Hsieh, Payel Das
Enhancing model robustness under new and even adversarial environments is a crucial milestone toward building trustworthy and reliable machine learning systems.
no code implementations • 25 Sep 2019 • Akhilan Boopathy, Sijia Liu, Gaoyuan Zhang, Pin-Yu Chen, Shiyu Chang, Luca Daniel
Recent works have empirically shown that there exist adversarial examples that can be hidden from neural network interpretability, and interpretability is itself susceptible to adversarial attacks.
no code implementations • 25 Sep 2019 • Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Luca Daniel
We propose that many common certified defenses can be viewed under a unified framework of regularization.
no code implementations • 25 Sep 2019 • Jingkang Wang, Tianyun Zhang, Sijia Liu, Pin-Yu Chen, Jiacen Xu, Makan Fardad, Bo Li
The worst-case training principle that minimizes the maximal adversarial loss, also known as adversarial training (AT), has shown to be a state-of-the-art approach for enhancing adversarial robustness against norm-ball bounded input perturbations.
1 code implementation • ICLR 2020 • Minhao Cheng, Simranjit Singh, Patrick Chen, Pin-Yu Chen, Sijia Liu, Cho-Jui Hsieh
We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input.
2 code implementations • 6 Sep 2019 • Vijay Arya, Rachel K. E. Bellamy, Pin-Yu Chen, Amit Dhurandhar, Michael Hind, Samuel C. Hoffman, Stephanie Houde, Q. Vera Liao, Ronny Luss, Aleksandra Mojsilović, Sami Mourad, Pablo Pedemonte, Ramya Raghavendra, John Richards, Prasanna Sattigeri, Karthikeyan Shanmugam, Moninder Singh, Kush R. Varshney, Dennis Wei, Yunfeng Zhang
Equally important, we provide a taxonomy to help entities requiring explanations to navigate the space of explanation methods, not only those in the toolkit but also in the broader literature on explainability.
no code implementations • IEEE Access 2019 • Zhining Liu, Weiyi Liu, Pin-Yu Chen, Chenyi Zhuang, Chengyun Song
Graph neural networks (GNNs) have recently made remarkable breakthroughs in the paradigm of learning with graph-structured data.
Ranked #39 on Node Classification on Citeseer
1 code implementation • 20 Aug 2019 • Xiao Wang, Siyue Wang, Pin-Yu Chen, Yanzhi Wang, Brian Kulis, Xue Lin, Peter Chin
However, one critical drawback of current defenses is that the robustness enhancement is at the cost of noticeable performance degradation on legitimate data, e. g., large drop in test accuracy.