1 code implementation • EMNLP 2021 • Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle G. Lee, Anish Acharya, Rajiv Ratn Shah
Code-switching is the communication phenomenon where the speakers switch between different languages during a conversation.
no code implementations • 8 Feb 2024 • Anish Acharya, Sujay Sanghavi
Positive Unlabeled (PU) learning refers to the task of learning a binary classifier given a few labeled positive samples, and a set of unlabeled samples (which could be positive or negative).
no code implementations • 1 Jun 2022 • Anish Acharya, Sujay Sanghavi, Li Jing, Bhargav Bhushanam, Dhruv Choudhary, Michael Rabbat, Inderjit Dhillon
We extend this paradigm to the classical positive unlabeled (PU) setting, where the task is to learn a binary classifier given only a few labeled positive samples, and (often) a large amount of unlabeled samples (which could be positive or negative).
no code implementations • 29 Mar 2022 • Debanjan Mahata, Navneet Agarwal, Dibya Gautam, Amardeep Kumar, Swapnil Parekh, Yaman Kumar Singla, Anish Acharya, Rajiv Ratn Shah
Identifying keyphrases (KPs) from text documents is a fundamental task in natural language processing and information retrieval.
no code implementations • 7 Jul 2021 • Anish Acharya, Rudrajit Das
In this paper we study test time decoding; an ubiquitous step in almost all sequential text generation task spanning across a wide array of natural language processing (NLP) problems.
2 code implementations • 16 Jun 2021 • Anish Acharya, Abolfazl Hashemi, Prateek Jain, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu
Geometric median (\textsc{Gm}) is a classical method in statistics for achieving a robust estimation of the uncorrupted data; under gross corruption, it achieves the optimal breakdown point of 0. 5.
Ranked #20 on Image Classification on MNIST (Accuracy metric)
no code implementations • 5 Jun 2021 • Jay Whang, Alliot Nagle, Anish Acharya, Hyeji Kim, Alexandros G. Dimakis
Distributed source coding (DSC) is the task of encoding an input in the absence of correlated side information that is only available to the decoder.
no code implementations • NAACL 2021 • Anish Acharya, Suranjit Adhikari, Sanchit Agarwal, Vincent Auvray, Nehal Belgamwar, Arijit Biswas, Shubhra Chandra, Tagyoung Chung, Maryam Fazel-Zarandi, Raefer Gabriel, Shuyang Gao, Rahul Goel, Dilek Hakkani-Tur, Jan Jezabek, Abhay Jha, Jiun-Yu Kao, Prakash Krishnan, Peter Ku, Anuj Goyal, Chien-Wei Lin, Qing Liu, Arindam Mandal, Angeliki Metallinou, Vishal Naik, Yi Pan, Shachi Paul, Vittorio Perera, Abhishek Sethi, Minmin Shen, Nikko Strom, Eddie Wang
Finally, we evaluate our system using a typical movie ticket booking task and show that the dialogue simulator is an essential component of the system that leads to over $50\%$ improvement in turn-level action signature prediction accuracy.
no code implementations • 17 Apr 2021 • Laiba Mehnaz, Debanjan Mahata, Rakesh Gosangi, Uma Sushmitha Gunturi, Riya Jain, Gauri Gupta, Amardeep Kumar, Isabelle Lee, Anish Acharya, Rajiv Ratn Shah
Towards this objective, we introduce abstractive summarization of Hindi-English code-switched conversations and develop the first code-switched conversation summarization dataset - GupShup, which contains over 6, 831 conversations in Hindi-English and their corresponding human-annotated summaries in English and Hindi-English.
no code implementations • 7 Dec 2020 • Rudrajit Das, Anish Acharya, Abolfazl Hashemi, Sujay Sanghavi, Inderjit S. Dhillon, Ufuk Topcu
We propose \texttt{FedGLOMO}, a novel federated learning (FL) algorithm with an iteration complexity of $\mathcal{O}(\epsilon^{-1. 5})$ to converge to an $\epsilon$-stationary point (i. e., $\mathbb{E}[\|\nabla f(\bm{x})\|^2] \leq \epsilon$) for smooth non-convex functions -- under arbitrary client heterogeneity and compressed communication -- compared to the $\mathcal{O}(\epsilon^{-2})$ complexity of most prior works.
1 code implementation • 20 Nov 2020 • Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo, Sujay Sanghavi, Inderjit Dhillon
In this paper, we show that, in such compressed decentralized optimization settings, there are benefits to having {\em multiple} gossip steps between subsequent gradient iterations, even when the cost of doing so is appropriately accounted for e. g. by means of reducing the precision of compressed information.
no code implementations • 19 Dec 2018 • Anish Acharya
The main idea conveyed in this article is to come up with a new feature selection scheme that does the classification job elegantly and with high accuracy but with simpler but wisely chosen small number of features thus being less prone to over-fitting.
no code implementations • 1 Nov 2018 • Anish Acharya, Rahul Goel, Angeliki Metallinou, Inderjit Dhillon
Empirically, we show that the proposed method can achieve 90% compression with minimal impact in accuracy for sentence classification tasks, and outperforms alternative methods like fixed-point quantization or offline word embedding compression.
1 code implementation • 7 May 2016 • Anish Acharya, Uddipan Mukherjee, Charless Fowlkes
Estimates of image gradients play a ubiquitous role in image segmentation and classification problems since gradients directly relate to the boundaries or the edges of a scene.
no code implementations • 16 Dec 2014 • Anish Acharya
At this moment Autonomous cars are probably the biggest and most talked about technology in the Robotics Research Community.
no code implementations • 27 Jun 2014 • Anish Acharya
This article provides a step by step development of designing a Object Detection scheme using the HOG based Feature Pyramid aligned with the concept of Template Matching.