Sarath Chandar

IIRC: Incremental Implicitly-Refined Classification

Jan 11, 2021
Mohamed Abdelsalam, Mojtaba Faramarzi, Shagun Sodhani, Sarath Chandar

Maximum Reward Formulation In Reinforcement Learning

Oct 08, 2020
Sai Krishna Gottipati, Yashaswi Pathak, Rohan Nuttall, Sahir, Raviteja Chunduru, Ahmed Touati, Sriram Ganapathi Subramanian, Matthew E. Taylor, Sarath Chandar

* 13 pages, 5 figures

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MLMLM: Link Prediction with Mean Likelihood Masked Language Model

Sep 15, 2020
Louis Clouatre, Philippe Trempe, Amal Zouaq, Sarath Chandar

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How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics

Aug 24, 2020
Prasanna Parthasarathi, Joelle Pineau, Sarath Chandar

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Slot Contrastive Networks: A Contrastive Approach for Representing Objects

Jul 18, 2020
Evan Racah, Sarath Chandar

* Presented at ICML 2020 Workshop: Object-Oriented Learning (OOL): Perception, Representation, and Reasoning

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The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

Jul 07, 2020
Harm van Seijen, Hadi Nekoei, Evan Racah, Sarath Chandar

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PatchUp: A Regularization Technique for Convolutional Neural Networks

Jun 14, 2020
Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan, Vikas Verma, Sarath Chandar

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Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

May 20, 2020
Sai Krishna Gottipati, Boris Sattarov, Sufeng Niu, Yashaswi Pathak, Haoran Wei, Shengchao Liu, Karam M. J. Thomas, Simon Blackburn, Connor W. Coley, Jian Tang, Sarath Chandar, Yoshua Bengio

* added the statistics of top-100 compounds used logP metric with scaled components added values of the initial reactants to the box plots some values in tables are recalculated due to the inconsistent environments on different machines. corresponding benchmarks were rerun with the requirements on github. no significant changes in the results. corrected figures in the Appendix

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Towards Lossless Encoding of Sentences

Jun 04, 2019
Gabriele Prato, Mathieu Duchesneau, Sarath Chandar, Alain Tapp

* Accepted at ACL 2019

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Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study

Jun 04, 2019
Chinnadhurai Sankar, Sandeep Subramanian, Christopher Pal, Sarath Chandar, Yoshua Bengio

* To appear at ACL 2019

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Structure Learning for Neural Module Networks

May 27, 2019
Vardaan Pahuja, Jie Fu, Sarath Chandar, Christopher J. Pal

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The Hanabi Challenge: A New Frontier for AI Research

Feb 01, 2019
Nolan Bard, Jakob N. Foerster, Sarath Chandar, Neil Burch, Marc Lanctot, H. Francis Song, Emilio Parisotto, Vincent Dumoulin, Subhodeep Moitra, Edward Hughes, Iain Dunning, Shibl Mourad, Hugo Larochelle, Marc G. Bellemare, Michael Bowling

* 37 pages, 5 figures, submitted to Artificial Intelligence

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Environments for Lifelong Reinforcement Learning

Dec 06, 2018
Khimya Khetarpal, Shagun Sodhani, Sarath Chandar, Doina Precup

* Accepted at 2nd Continual Learning Workshop, Neural Information Processing Systems (NeurIPS) 2018

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On Training Recurrent Neural Networks for Lifelong Learning

Nov 16, 2018
Shagun Sodhani, Sarath Chandar, Yoshua Bengio

* Accepted at the Continual Learning Workshop , NIPS 2018

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Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

Oct 04, 2018
Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra, Karthik Sankaranarayanan, Sarath Chandar

* Accepted in AAAI'18

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Language Expansion In Text-Based Games

May 17, 2018
Ghulam Ahmed Ansari, Sagar J P, Sarath Chandar, Balaraman Ravindran

* 9 pages. arXiv admin note: text overlap with arXiv:1511.06295 by other authors

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A Deep Reinforcement Learning Chatbot (Short Version)

Jan 20, 2018
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeswar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio

* 9 pages, 1 figure, 2 tables; presented at NIPS 2017, Conversational AI: "Today's Practice and Tomorrow's Potential" Workshop

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A Deep Reinforcement Learning Chatbot

Nov 05, 2017
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain, Saizheng Zhang, Zhouhan Lin, Sandeep Subramanian, Taesup Kim, Michael Pieper, Sarath Chandar, Nan Rosemary Ke, Sai Rajeshwar, Alexandre de Brebisson, Jose M. R. Sotelo, Dendi Suhubdy, Vincent Michalski, Alexandre Nguyen, Joelle Pineau, Yoshua Bengio

* 40 pages, 9 figures, 11 tables

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Sarath Chandar

IIRC: Incremental Implicitly-Refined Classification

Maximum Reward Formulation In Reinforcement Learning

MLMLM: Link Prediction with Mean Likelihood Masked Language Model

How To Evaluate Your Dialogue System: Probe Tasks as an Alternative for Token-level Evaluation Metrics

Slot Contrastive Networks: A Contrastive Approach for Representing Objects

The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning

PatchUp: A Regularization Technique for Convolutional Neural Networks

Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

Towards Lossless Encoding of Sentences

Do Neural Dialog Systems Use the Conversation History Effectively? An Empirical Study

Structure Learning for Neural Module Networks

The Hanabi Challenge: A New Frontier for AI Research

Environments for Lifelong Reinforcement Learning

On Training Recurrent Neural Networks for Lifelong Learning

Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph

Language Expansion In Text-Based Games

A Deep Reinforcement Learning Chatbot (Short Version)

A Deep Reinforcement Learning Chatbot

Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes

GuessWhat?! Visual object discovery through multi-modal dialogue

Memory Augmented Neural Networks with Wormhole Connections

Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Hierarchical Memory Networks

Clustering is Efficient for Approximate Maximum Inner Product Search

Correlational Neural Networks

TSEB: More Efficient Thompson Sampling for Policy Learning