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09/19/2023
Amplifying Pathological Detection in EEG Signaling Pathways through Cross-Dataset Transfer Learning
Pathology diagnosis based on EEG signals and decoding brain activity hol...
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08/08/2023
Continual Pre-Training of Large Language Models: How to (re)warm your model?
Large language models (LLMs) are routinely pre-trained on billions of to...
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07/11/2023
GOKU-UI: Ubiquitous Inference through Attention and Multiple Shooting for Continuous-time Generative Models
Scientific Machine Learning (SciML) is a burgeoning field that synergist...
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06/23/2023
Predicting Grokking Long Before it Happens: A look into the loss landscape of models which grok
This paper focuses on predicting the occurrence of grokking in neural ne...
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04/26/2023
Towards ethical multimodal systems
The impact of artificial intelligence systems on our society is increasi...
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02/02/2023
A Survey on Compositional Generalization in Applications
The field of compositional generalization is currently experiencing a re...
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11/09/2022
Knowledge Distillation for Federated Learning: a Practical Guide
Federated Learning (FL) enables the training of Deep Learning models wit...
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10/26/2022
Broken Neural Scaling Laws
We present a smoothly broken power law functional form that accurately m...
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10/08/2022
Cognitive Models as Simulators: The Case of Moral Decision-Making
To achieve desirable performance, current AI systems often require huge ...
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10/06/2022
Towards Out-of-Distribution Adversarial Robustness
Adversarial robustness continues to be a major challenge for deep learni...
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07/10/2022
Scaling the Number of Tasks in Continual Learning
Standard gradient descent algorithms applied to sequences of tasks are k...
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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...
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04/04/2022
APP: Anytime Progressive Pruning
With the latest advances in deep learning, there has been a lot of focus...
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03/18/2022
WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks
Machine learning models often fail to generalize well under distribution...
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01/31/2022
Towards Scaling Difference Target Propagation by Learning Backprop Targets
The development of biologically-plausible learning algorithms is importa...
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01/28/2022
Gradient Masked Averaging for Federated Learning
Federated learning is an emerging paradigm that permits a large number o...
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12/13/2021
Continual Learning In Environments With Polynomial Mixing Times
The mixing time of the Markov chain induced by a policy limits performan...
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10/18/2021
Compositional Attention: Disentangling Search and Retrieval
Multi-head, key-value attention is the backbone of the widely successful...
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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...
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08/27/2021
Approximate Bayesian Optimisation for Neural Networks
A body of work has been done to automate machine learning algorithm to h...
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08/02/2021
Sequoia: A Software Framework to Unify Continual Learning Research
The field of Continual Learning (CL) seeks to develop algorithms that ac...
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07/20/2021
Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deform...
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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...
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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...
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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...
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04/21/2021
Gradient Masked Federated Optimization
Federated Averaging (FedAVG) has become the most popular federated learn...
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04/14/2021
Towards Causal Federated Learning For Enhanced Robustness and Privacy
Federated Learning is an emerging privacy-preserving distributed machine...
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04/04/2021
Understanding Continual Learning Settings with Data Distribution Drift Analysis
Classical machine learning algorithms often assume that the data are dra...
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12/25/2020
Towards Continual Reinforcement Learning: A Review and Perspectives
In this article, we aim to provide a literature review of different form...
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10/15/2020
Double-Linear Thompson Sampling for Context-Attentive Bandits
In this paper, we analyze and extend an online learning framework known ...
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06/08/2020
Adversarial Feature Desensitization
Deep neural networks can now perform many tasks that were once thought t...
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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...
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03/31/2020
Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation
Unpaired Image-to-Image Translation (I2IT) tasks often suffer from lack ...
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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 ...
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06/21/2019
Reinforcement Learning Models of Human Behavior: Reward Processing in Mental Disorders
Drawing an inspiration from behavioral studies of human decision making,...
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04/19/2019
Continual Learning with Self-Organizing Maps
Despite remarkable successes achieved by modern neural networks in a wid...
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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...
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06/24/2018
Beyond Backprop: Alternating Minimization with co-Activation Memory
We propose a novel online algorithm for training deep feedforward neural...
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05/24/2018
Learning Nonlinear Brain Dynamics: van der Pol Meets LSTM
Many real-world data sets, especially in biology, are produced by highly...
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04/26/2018
Dialogue Modeling Via Hash Functions: Applications to Psychotherapy
We propose a novel machine-learning framework for dialogue modeling whic...
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02/03/2018
Adaptive Representation Selection in Contextual Bandit with Unlabeled History
We consider an extension of the contextual bandit setting, motivated by ...
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12/01/2017
Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks
Smart systems that can accurately diagnose patients with mental disorder...
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11/10/2017
Efficient Representation for Natural Language Processing via Kernelized Hashcodes
Kernel similarity functions have been successfully applied in classifica...
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06/07/2017
Bandit Models of Human Behavior: Reward Processing in Mental Disorders
Drawing an inspiration from behavioral studies of human decision making,...
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05/10/2017
Context Attentive Bandits: Contextual Bandit with Restricted Context
We consider a novel formulation of the multi-armed bandit model, which w...
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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...
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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 ...
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02/02/2016
Mental State Recognition via Wearable EEG
The increasing quality and affordability of consumer electroencephalogra...
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02/06/2013
A Scheme for Approximating Probabilistic Inference
This paper describes a class of probabilistic approximation algorithms b...
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01/30/2013