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Found 6 papers, 4 papers with code
Class-Incremental Continual Learning for General Purpose Healthcare Models
In this work, we investigate the performance of continual learning models on four different medical imaging scenarios involving ten classification datasets from diverse modalities, clinical specialties, and hospitals.
SHARP: Sparsity and Hidden Activation RePlay for Neuro-Inspired Continual Learning
Inspired by how our brain consolidates memories, a powerful strategy in CL is replay, which involves training the DNN on a mixture of new and all seen classes.
System Design for an Integrated Lifelong Reinforcement Learning Agent for Real-Time Strategy Games
In this paper, we introduce the Lifelong Reinforcement Learning Components Framework (L2RLCF), which standardizes L2RL systems and assimilates different continual learning components (each addressing different aspects of the lifelong learning problem) into a unified system.
NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks
The goal of continual learning (CL) is to learn different tasks over time.
MGN-Net: a multi-view graph normalizer for integrating heterogeneous biological network populations
With the recent technological advances, biological datasets, often represented by networks (i. e., graphs) of interacting entities, proliferate with unprecedented complexity and heterogeneity.
Deep Graph Normalizer: A Geometric Deep Learning Approach for Estimating Connectional Brain Templates
Particularly, estimating a well-centered and representative CBT for populations of multi-view brain networks (MVBN) is more challenging since these networks sit on complex manifolds and there is no easy way to fuse different heterogeneous network views.
