Sarath Chandar

research

∙ 08/03/2022

Improving Meta-Learning Generalization with Activation-Based Early-Stopping

Meta-Learning algorithms for few-shot learning aim to train neural netwo...

Simon Guiroy, et al. ∙

research

∙ 07/10/2022

An Introduction to Lifelong Supervised Learning

This primer is an attempt to provide a detailed summary of the different...

Shagun Sodhani, et al. ∙

research

∙ 04/25/2022

Towards Evaluating Adaptivity of Model-Based Reinforcement Learning Methods

In recent years, a growing number of deep model-based reinforcement lear...

Yi Wan, et al. ∙

research

∙ 02/01/2022

Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers

Much of recent Deep Reinforcement Learning success is owed to the neural...

Amir Ardalan Kalantari, et al. ∙

research

∙ 12/16/2021

An Empirical Investigation of the Role of Pre-training in Lifelong Learning

The lifelong learning paradigm in machine learning is an attractive alte...

Sanket Vaibhav Mehta, et al. ∙

research

∙ 10/13/2021

Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers

Empirical science of neural scaling laws is a rapidly growing area of si...

Gabriele Prato, et al. ∙

research

∙ 08/10/2021

Post-hoc Interpretability for Neural NLP: A Survey

Natural Language Processing (NLP) models have become increasingly more c...

Andreas Madsen, et al. ∙

research

∙ 07/29/2021

Demystifying Neural Language Models' Insensitivity to Word-Order

Recent research analyzing the sensitivity of natural language understand...

Louis Clouâtre, et al. ∙

research

∙ 06/20/2021

Memory Augmented Optimizers for Deep Learning

Popular approaches for minimizing loss in data-driven learning often inv...

Paul-Aymeric McRae, et al. ∙

research

∙ 06/20/2021

Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?

Predicting the next utterance in dialogue is contingent on encoding of u...

Prasanna Parthasarathi, et al. ∙

research

∙ 06/20/2021

A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss

Neural models trained for next utterance generation in dialogue task lea...

Prasanna Parthasarathi, et al. ∙

research

∙ 05/11/2021

TAG: Task-based Accumulated Gradients for Lifelong learning

When an agent encounters a continual stream of new tasks in the lifelong...

Pranshu Malviya, et al. ∙

research

∙ 03/04/2021

Continuous Coordination As a Realistic Scenario for Lifelong Learning

Current deep reinforcement learning (RL) algorithms are still highly tas...

Hadi Nekoei, et al. ∙

research

∙ 12/23/2020

IIRC: Incremental Implicitly-Refined Classification

We introduce the "Incremental Implicitly-Refined Classi-fication (IIRC)"...

Mohamed Abdelsalam, et al. ∙

research

∙ 10/08/2020

Maximum Reward Formulation In Reinforcement Learning

Reinforcement learning (RL) algorithms typically deal with maximizing th...

Sai Krishna Gottipati, et al. ∙

research

∙ 09/15/2020

MLMLM: Link Prediction with Mean Likelihood Masked Language Model

Knowledge Bases (KBs) are easy to query, verifiable, and interpretable. ...

Louis Clouâtre, et al. ∙

research

∙ 08/24/2020

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

Though generative dialogue modeling is widely seen as a language modelin...

Prasanna Parthasarathi, et al. ∙

research

∙ 07/18/2020

Slot Contrastive Networks: A Contrastive Approach for Representing Objects

Unsupervised extraction of objects from low-level visual data is an impo...

Evan Racah, et al. ∙

research

∙ 07/07/2020

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

Deep model-based Reinforcement Learning (RL) has the potential to substa...

Harm van Seijen, et al. ∙

research

∙ 06/14/2020

PatchUp: A Regularization Technique for Convolutional Neural Networks

Large capacity deep learning models are often prone to a high generaliza...

Mojtaba Faramarzi, et al. ∙

research

∙ 04/26/2020

Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning

Over the last decade, there has been significant progress in the field o...

Sai Krishna Gottipati, et al. ∙

research

∙ 06/04/2019

Towards Lossless Encoding of Sentences

A lot of work has been done in the field of image compression via machin...

Gabriele Prato, et al. ∙

research

∙ 06/04/2019

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

Neural generative models have been become increasingly popular when buil...

Chinnadhurai Sankar, et al. ∙

research

∙ 05/27/2019

Structure Learning for Neural Module Networks

Neural Module Networks, originally proposed for the task of visual quest...

Vardaan Pahuja, et al. ∙

research

∙ 02/11/2019

Edge Replacement Grammars: A Formal Language Approach for Generating Graphs

Graphs are increasingly becoming ubiquitous as models for structured dat...

Revanth Reddy, et al. ∙

research

∙ 02/01/2019

The Hanabi Challenge: A New Frontier for AI Research

From the early days of computing, games have been important testbeds for...

Nolan Bard, et al. ∙

research

∙ 01/22/2019

Towards Non-saturating Recurrent Units for Modelling Long-term Dependencies

Modelling long-term dependencies is a challenge for recurrent neural net...

Sarath Chandar, et al. ∙

research

∙ 11/26/2018

Environments for Lifelong Reinforcement Learning

To achieve general artificial intelligence, reinforcement learning (RL) ...

Khimya Khetarpal, et al. ∙

research

∙ 11/16/2018

On Training Recurrent Neural Networks for Lifelong Learning

Capacity saturation and catastrophic forgetting are the central challeng...

Shagun Sodhani, et al. ∙

research

∙ 05/17/2018

Language Expansion In Text-Based Games

Text-based games are suitable test-beds for designing agents that can le...

Ghulam Ahmed Ansari, et al. ∙

research

∙ 01/31/2018

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

While conversing with chatbots, humans typically tend to ask many questi...

Amrita Saha, et al. ∙

research

∙ 01/20/2018

A Deep Reinforcement Learning Chatbot (Short Version)

We present MILABOT: a deep reinforcement learning chatbot developed by t...

Iulian V. Serban, et al. ∙

research

∙ 09/07/2017

A Deep Reinforcement Learning Chatbot

We present MILABOT: a deep reinforcement learning chatbot developed by t...

Iulian V. Serban, et al. ∙

research

∙ 01/30/2017

Memory Augmented Neural Networks with Wormhole Connections

Recent empirical results on long-term dependency tasks have shown that n...

Caglar Gulcehre, et al. ∙

research

∙ 11/23/2016

GuessWhat?! Visual object discovery through multi-modal dialogue

We introduce GuessWhat?!, a two-player guessing game as a testbed for re...

Harm de Vries, et al. ∙

research

∙ 06/30/2016

Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes

We extend neural Turing machine (NTM) model into a dynamic neural Turing...

Caglar Gulcehre, et al. ∙

research

∙ 06/15/2016

A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Interlingua based Machine Translation (MT) aims to encode multiple langu...

Amrita Saha, et al. ∙

research

∙ 05/24/2016

Hierarchical Memory Networks

Memory networks are neural networks with an explicit memory component th...

Sarath Chandar, et al. ∙

research

∙ 03/22/2016

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

Over the past decade, large-scale supervised learning corpora have enabl...

Iulian Vlad Serban, et al. ∙

research

∙ 10/13/2015

Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning

Recently there has been a lot of interest in learning common representat...

Janarthanan Rajendran, et al. ∙

research

∙ 07/28/2015

Reasoning about Linguistic Regularities in Word Embeddings using Matrix Manifolds

Recent work has explored methods for learning continuous vector space wo...

Sridhar Mahadevan, et al. ∙

research

∙ 07/21/2015

Clustering is Efficient for Approximate Maximum Inner Product Search

Efficient Maximum Inner Product Search (MIPS) is an important task that ...

Alex Auvolat, et al. ∙

research

∙ 04/27/2015

Correlational Neural Networks

Common Representation Learning (CRL), wherein different descriptions (or...

Sarath Chandar, et al. ∙

Sarath Chandar

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