b'Irina Rish'

research

∙ 04/26/2023

Towards ethical multimodal systems

The impact of artificial intelligence systems on our society is increasi...

0 Alexis Roger, et al. ∙

research

∙ 02/02/2023

A Survey on Compositional Generalization in Applications

The field of compositional generalization is currently experiencing a re...

7 Baihan Lin, et al. ∙

research

∙ 11/09/2022

Knowledge Distillation for Federated Learning: a Practical Guide

Federated Learning (FL) enables the training of Deep Learning models wit...

0 Alessio Mora, et al. ∙

research

∙ 10/26/2022

Broken Neural Scaling Laws

We present a smoothly broken power law functional form that accurately m...

0 Ethan Caballero, et al. ∙

research

∙ 10/08/2022

Cognitive Models as Simulators: The Case of Moral Decision-Making

To achieve desirable performance, current AI systems often require huge ...

0 Ardavan S. Nobandegani, et al. ∙

research

∙ 10/06/2022

Towards Out-of-Distribution Adversarial Robustness

Adversarial robustness continues to be a major challenge for deep learni...

98 Adam Ibrahim, et al. ∙

research

∙ 07/10/2022

Scaling the Number of Tasks in Continual Learning

Standard gradient descent algorithms applied to sequences of tasks are k...

9 Timothée Lesort, et al. ∙

research

∙ 04/30/2022

Foundational Models for Continual Learning: An Empirical Study of Latent Replay

Rapid development of large-scale pre-training has resulted in foundation...

12 Oleksiy Ostapenko, et al. ∙

research

∙ 04/04/2022

APP: Anytime Progressive Pruning

With the latest advances in deep learning, there has been a lot of focus...

0 Diganta Misra, et al. ∙

research

∙ 03/18/2022

WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks

Machine learning models often fail to generalize well under distribution...

0 Jean-Christophe Gagnon-Audet, et al. ∙

research

∙ 01/31/2022

Towards Scaling Difference Target Propagation by Learning Backprop Targets

The development of biologically-plausible learning algorithms is importa...

9 Maxence Ernoult, et al. ∙

research

∙ 01/28/2022

Gradient Masked Averaging for Federated Learning

Federated learning is an emerging paradigm that permits a large number o...

0 Irene Tenison, et al. ∙

research

∙ 12/13/2021

Continual Learning In Environments With Polynomial Mixing Times

The mixing time of the Markov chain induced by a policy limits performan...

0 Matthew Riemer, et al. ∙

research

∙ 10/18/2021

Compositional Attention: Disentangling Search and Retrieval

Multi-head, key-value attention is the backbone of the widely successful...

17 Sarthak Mittal, 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...

10 Gabriele Prato, et al. ∙

research

∙ 08/27/2021

Approximate Bayesian Optimisation for Neural Networks

A body of work has been done to automate machine learning algorithm to h...

0 Nadhir Hassen, et al. ∙

research

∙ 08/02/2021

Sequoia: A Software Framework to Unify Continual Learning Research

The field of Continual Learning (CL) seeks to develop algorithms that ac...

12 Fabrice Normandin, et al. ∙

research

∙ 07/20/2021

Parametric Scattering Networks

The wavelet scattering transform creates geometric invariants and deform...

0 Shanel Gauthier, et al. ∙

research

∙ 06/11/2021

Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization

The invariance principle from causality is at the heart of notable appro...

4 Kartik Ahuja, et al. ∙

research

∙ 06/04/2021

SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization

A major bottleneck in the real-world applications of machine learning mo...

8 Soroosh Shahtalebi, et al. ∙

research

∙ 06/03/2021

Continual Learning in Deep Networks: an Analysis of the Last Layer

We study how different output layer types of a deep neural network learn...

0 Timothée Lesort, et al. ∙

research

∙ 04/21/2021

Gradient Masked Federated Optimization

Federated Averaging (FedAVG) has become the most popular federated learn...

0 Irene Tenison, et al. ∙

research

∙ 04/14/2021

Towards Causal Federated Learning For Enhanced Robustness and Privacy

Federated Learning is an emerging privacy-preserving distributed machine...

0 Sreya Francis, et al. ∙

research

∙ 04/04/2021

Understanding Continual Learning Settings with Data Distribution Drift Analysis

Classical machine learning algorithms often assume that the data are dra...

18 Timothée Lesort, et al. ∙

research

∙ 12/25/2020

Towards Continual Reinforcement Learning: A Review and Perspectives

In this article, we aim to provide a literature review of different form...

0 Khimya Khetarpal, et al. ∙

research

∙ 10/15/2020

Double-Linear Thompson Sampling for Context-Attentive Bandits

In this paper, we analyze and extend an online learning framework known ...

0 Djallel Bouneffouf, et al. ∙

research

∙ 06/08/2020

Adversarial Feature Desensitization

Deep neural networks can now perform many tasks that were once thought t...

0 Pouya Bashivan, et al. ∙

research

∙ 05/10/2020

Unified Models of Human Behavioral Agents in Bandits, Contextual Bandits and RL

Artificial behavioral agents are often evaluated based on their consiste...

2 Baihan Lin, et al. ∙

research

∙ 03/31/2020

Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation

Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack ...

15 Victor Schmidt, et al. ∙

research

∙ 03/12/2020

Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning

Learning from non-stationary data remains a great challenge for machine ...

26 Massimo Caccia, et al. ∙

research

∙ 06/21/2019

Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders

Drawing an inspiration from behavioral studies of human decision making,...

0 Baihan Lin, et al. ∙

research

∙ 04/19/2019

Continual Learning with Self-Organizing Maps

Despite remarkable successes achieved by modern neural networks in a wid...

26 Pouya Bashivan, et al. ∙

research

∙ 10/29/2018

Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference

Lack of performance when it comes to continual learning over non-station...

0 Matthew Riemer, et al. ∙

research

∙ 06/24/2018

Beyond Backprop: Alternating Minimization with co-Activation Memory

We propose a novel online algorithm for training deep feedforward neural...

0 Anna Choromanska, et al. ∙

research

∙ 05/24/2018

Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM

Many real-world data sets, especially in biology, are produced by highly...

0 German Abrevaya, et al. ∙

research

∙ 04/26/2018

Dialogue Modeling Via Hash Functions: Applications to Psychotherapy

We propose a novel machine-learning framework for dialogue modeling whic...

0 Sahil Garg, et al. ∙

research

∙ 02/03/2018

Adaptive Representation Selection in Contextual Bandit with Unlabeled History

We consider an extension of the contextual bandit setting, motivated by ...

0 Baihan Lin, et al. ∙

research

∙ 12/01/2017

Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

Smart systems that can accurately diagnose patients with mental disorder...

0 Jumana Dakka, et al. ∙

research

∙ 11/10/2017

Efficient Representation for Natural Language Processing via Kernelized Hashcodes

Kernel similarity functions have been successfully applied in classifica...

0 Sahil Garg, et al. ∙

research

∙ 06/07/2017

Bandit Models of Human Behavior: Reward Processing in Mental Disorders

Drawing an inspiration from behavioral studies of human decision making,...

0 Djallel Bouneffouf, et al. ∙

research

∙ 05/10/2017

Context Attentive Bandits: Contextual Bandit with Restricted Context

We consider a novel formulation of the multi-armed bandit model, which w...

0 Djallel Bouneffouf, et al. ∙

research

∙ 01/22/2017

Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a Changing World

In this paper, we focus on online representation learning in non-station...

0 Sahil Garg, et al. ∙

research

∙ 01/06/2017

NIPS 2016 Workshop on Representation Learning in Artificial and Biological Neural Networks (MLINI 2016)

This workshop explores the interface between cognitive neuroscience and ...

0 Leila Wehbe, et al. ∙

research

∙ 02/02/2016

Mental State Recognition via Wearable EEG

The increasing quality and affordability of consumer electroencephalogra...

0 Pouya Bashivan, et al. ∙

research

∙ 02/06/2013

A Scheme for Approximating Probabilistic Inference

This paper describes a class of probabilistic approximation algorithms b...

0 Rina Dechter, et al. ∙

research

∙ 01/30/2013

Empirical Evaluation of Approximation Algorithms for Probabilistic Decoding

It was recently shown that the problem of decoding messages transmitted ...

0 Irina Rish, et al. ∙

research

∙ 07/04/2012

Efficient Test Selection in Active Diagnosis via Entropy Approximation

We consider the problem of diagnosing faults in a system represented by ...

0 Alice X. Zheng, et al. ∙

Irina Rish

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