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Found 12 papers, 6 papers with code
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.
Root-Cause Analysis of Activation Cascade Differences in Brain Networks
The question we focus on is: if we are given such activation cascades for two groups, say A and B (e. g. Controls versus a mental disorder), what is the smallest set of brain connectivity (graph edge weight) changes that are sufficient to explain the observed differences in the activation cascades between the two groups?
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.
PHEW: Paths with Higher Edge-Weights give ''winning tickets'' without training data
Then, we show that Paths with Higher Edge-Weights (PHEW) at initialization have higher loss gradient magnitude, resulting in more efficient training.
PHEW: Constructing Sparse Networks that Learn Fast and Generalize Well without Training Data
Our work is based on a recently proposed decomposition of the Neural Tangent Kernel (NTK) that has decoupled the dynamics of the training process into a data-dependent component and an architecture-dependent kernel - the latter referred to as Path Kernel.
Evolution of Hierarchical Structure & Reuse in iGEM Synthetic DNA Sequences
Contrary to Evo-Lexis, in iGEM the amount of reuse decreases during the timeline of the dataset.
Unsupervised Progressive Learning and the STAM Architecture
We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed.
Unsupervised Continual Learning and Self-Taught Associative Memory Hierarchies
We first pose the Unsupervised Continual Learning (UCL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time.
A neuro-inspired architecture for unsupervised continual learning based on online clustering and hierarchical predictive coding
We propose that the Continual Learning desiderata can be achieved through a neuro-inspired architecture, grounded on Mountcastle's cortical column hypothesis.
Emergence and Evolution of Hierarchical Structure in Complex Systems
It is well known that many complex systems, both in technology and nature, exhibit hierarchical modularity: smaller modules, each of them providing a certain function, are used within larger modules that perform more complex functions.
δ-MAPS: From spatio-temporal data to a weighted and lagged network between functional domains
The proposed edge inference method examines the statistical significance of each lagged cross-correlation between two domains, infers a range of lag values for each edge, and assigns a weight to each edge based on the covariance of the two domains.
Other Computer Science
Lexis: An Optimization Framework for Discovering the Hierarchical Structure of Sequential Data
We also consider the problem of identifying the set of intermediate nodes (substrings) that collectively form the "core" of a Lexis-DAG, which is important in the analysis of Lexis-DAGs.
