no code implementations • 7 Apr 2024 • Andi Zhang, Tim Z. Xiao, Weiyang Liu, Robert Bamler, Damon Wischik
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection.
no code implementations • 1 Jun 2023 • Andi Zhang, Mingtian Zhang, Damon Wischik
We propose a probabilistic perspective on adversarial examples.
no code implementations • 26 May 2023 • Rui Sun, Andi Zhang, Haiming Zhang, Jinke Ren, Yao Zhu, Ruimao Zhang, Shuguang Cui, Zhen Li
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network Out-of-Distribution Detection +1
no code implementations • 23 Oct 2022 • Andi Zhang, Damon Wischik
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD.
no code implementations • 8 Jun 2022 • Mingtian Zhang, Andi Zhang, Tim Z. Xiao, Yitong Sun, Steven McDonagh
In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 2 Jun 2022 • Donghui Li, Jia Liu, Fang Liu, Wenhua Zhang, Andi Zhang, Wenfei Gao, Jiao Shi
With the better representation capability of optical images, we propose to enrich SAR images with generated optical images via the generative adversative network (GAN) trained by numerous SAR and optical images.
2 code implementations • NeurIPS 2021 • Mingtian Zhang, Andi Zhang, Steven McDonagh
Out-of-distribution (OOD) detection and lossless compression constitute two problems that can be solved by the training of probabilistic models on a first dataset with subsequent likelihood evaluation on a second dataset, where data distributions differ.
Out-of-Distribution Generalization Out of Distribution (OOD) Detection
no code implementations • 1 Sep 2021 • Ran Guan, Andi Zhang, Mengchao Li, Yongliang Wang
In indoor positioning, signal fluctuation is highly location-dependent.
no code implementations • ICLR Workshop SSL-RL 2021 • Mingtian Zhang, Peter Noel Hayes, Tim Z. Xiao, Andi Zhang, David Barber
We introduce a new model-based reinforcement learning framework that aims to tackle environments with high dimensional state spaces.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • EMNLP 2017 • Yang Feng, Shiyue Zhang, Andi Zhang, Dong Wang, Andrew Abel
Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs.
no code implementations • ACL 2017 • Jiyuan Zhang, Yang Feng, Dong Wang, Yang Wang, Andrew Abel, Shiyue Zhang, Andi Zhang
It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism.