52 citations · 153 across the 67 of their papers we have counts for
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An Empirical Investigation of the Role of Pre-training in Lifelong Learning
Sanket Vaibhav Mehta, Darshan Patil, Sarath Chandar +1
The lifelong learning paradigm in machine learning is an attractive alternative to the more prominent isolated learning scheme not only due to its resemblance to biological learnin…
Scaling Laws for the Few-Shot Adaptation of Pre-trained Image Classifiers
Gabriele Prato, Simon Guiroy, Ethan Caballero +2
Empirical science of neural scaling laws is a rapidly growing area of significant importance to the future of machine learning, particularly in the light of recent breakthroughs ac…
Local Structure Matters Most: Perturbation Study in NLU
Louis Clouatre, Prasanna Parthasarathi, Amal Zouaq +1
Recent research analyzing the sensitivity of natural language understanding models to word-order perturbations has shown that neural models are surprisingly insensitive to the orde…
Memory Augmented Optimizers for Deep Learning
Paul-Aymeric McRae, Prasanna Parthasarathi, Mahmoud Assran +1
Popular approaches for minimizing loss in data-driven learning often involve an abstraction or an explicit retention of the history of gradients for efficient parameter updates. Th…
Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?
Prasanna Parthasarathi, Joelle Pineau, Sarath Chandar
Predicting the next utterance in dialogue is contingent on encoding of users' input text to generate appropriate and relevant response in data-driven approaches. Although the seman…
A Brief Study on the Effects of Training Generative Dialogue Models with a Semantic loss
Prasanna Parthasarathi, Mohamed Abdelsalam, Joelle Pineau +1
Neural models trained for next utterance generation in dialogue task learn to mimic the n-gram sequences in the training set with training objectives like negative log-likelihood (…
A Survey of Data Augmentation Approaches for NLP
Steven Y. Feng, Varun Gangal, Jason Wei +4
Data augmentation has recently seen increased interest in NLP due to more work in low-resource domains, new tasks, and the popularity of large-scale neural networks that require la…
Continuous Coordination As a Realistic Scenario for Lifelong Learning
Hadi Nekoei, Akilesh Badrinaaraayanan, Aaron Courville +1
Current deep reinforcement learning (RL) algorithms are still highly task-specific and lack the ability to generalize to new environments. Lifelong learning (LLL), however, aims at…