research
∙
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...
share
research
∙
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...
research
∙
08/30/2022
Beyond Supervised Continual Learning: a Review
Continual Learning (CL, sometimes also termed incremental learning) is a...
research
∙
07/10/2022
Scaling the Number of Tasks in Continual Learning
Standard gradient descent algorithms applied to sequences of tasks are k...
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...
research
∙
03/02/2022
Continual Feature Selection: Spurious Features in Continual Learning
Continual Learning (CL) is the research field addressing learning settin...
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...
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...
research
∙
04/04/2021
Understanding Continual Learning Settings with Data Distribution Drift Analysis
Classical machine learning algorithms often assume that the data are dra...
research
∙
02/11/2021
Continuum: Simple Management of Complex Continual Learning Scenarios
Continual learning is a machine learning sub-field specialized in settin...
research
∙
07/01/2020
Continual Learning: Tackling Catastrophic Forgetting in Deep Neural Networks with Replay Processes
Humans learn all their life long. They accumulate knowledge from a seque...
research
∙
12/06/2019
Regularization Shortcomings for Continual Learning
In classical machine learning, the data streamed to the algorithms is as...
research
∙
07/11/2019
DisCoRL: Continual Reinforcement Learning via Policy Distillation
In multi-task reinforcement learning there are two main challenges: at t...
research
∙
06/29/2019
Continual Learning for Robotics
Continual learning (CL) is a particular machine learning paradigm where ...
research
∙
06/11/2019
Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer
We focus on the problem of teaching a robot to solve tasks presented seq...
research
∙
01/24/2019
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Scaling end-to-end reinforcement learning to control real robots from vi...
research
∙
12/21/2018
Generative Models from the perspective of Continual Learning
Which generative model is the most suitable for Continual Learning? This...
research
∙
10/29/2018
Marginal Replay vs Conditional Replay for Continual Learning
We present a new replay-based method of continual classification learnin...
research
∙
09/25/2018
S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning
State representation learning aims at learning compact representations f...
research
∙
06/28/2018
Training Discriminative Models to Evaluate Generative Ones
Generative models are known to be difficult to assess. Recent works, esp...
research
∙
04/02/2018
Exploring to learn visual saliency: The RL-IAC approach
The problem of object localization and recognition on autonomous mobile ...
research
∙
02/12/2018
State Representation Learning for Control: An Overview
Representation learning algorithms are designed to learn abstract featur...
research
∙
09/15/2017
