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08/28/2023
Continual Learning with Dynamic Sparse Training: Exploring Algorithms for Effective Model Updates
Continual learning (CL) refers to the ability of an intelligent system t...
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06/21/2023
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Dynamic Sparse Training (DST) is a rapidly evolving area of research tha...
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03/10/2023
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Feature selection that selects an informative subset of variables from d...
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02/13/2023
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Tomorrow's robots will need to distinguish useful information from noise...
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12/19/2022
Dynamic Sparse Network for Time Series Classification: Learning What to "see”
The receptive field (RF), which determines the region of time series to ...
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11/28/2022
You Can Have Better Graph Neural Networks by Not Training Weights at All: Finding Untrained GNNs Tickets
Recent works have impressively demonstrated that there exists a subnetwo...
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11/26/2022
Where to Pay Attention in Sparse Training for Feature Selection?
A new line of research for feature selection based on neural networks ha...
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07/08/2022
Memory-free Online Change-point Detection: A Novel Neural Network Approach
Change-point detection (CPD), which detects abrupt changes in the data d...
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05/30/2022
Superposing Many Tickets into One: A Performance Booster for Sparse Neural Network Training
Recent works on sparse neural network training (sparse training) have sh...
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02/05/2022
The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training
Random pruning is arguably the most naive way to attain sparsity in neur...
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10/11/2021
Addressing the Stability-Plasticity Dilemma via Knowledge-Aware Continual Learning
Continual learning agents should incrementally learn a sequence of tasks...
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06/28/2021
FreeTickets: Accurate, Robust and Efficient Deep Ensemble by Training with Dynamic Sparsity
Recent works on sparse neural networks have demonstrated that it is poss...
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06/19/2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Works on lottery ticket hypothesis (LTH) and single-shot network pruning...
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06/08/2021
Dynamic Sparse Training for Deep Reinforcement Learning
Deep reinforcement learning has achieved significant success in many dec...
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03/02/2021
Sparse Training Theory for Scalable and Efficient Agents
A fundamental task for artificial intelligence is learning. Deep Neural ...
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02/04/2021
Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training
In this paper, we introduce a new perspective on training deep neural ne...
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02/02/2021
Truly Sparse Neural Networks at Scale
Recently, sparse training methods have started to be established as a de...
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01/28/2021
Self-Attention Meta-Learner for Continual Learning
Continual learning aims to provide intelligent agents capable of learnin...
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01/22/2021
Selfish Sparse RNN Training
Sparse neural networks have been widely applied to reduce the necessary ...
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01/15/2021
Learning Invariant Representation for Continual Learning
Continual learning aims to provide intelligent agents that are capable o...
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12/01/2020
Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders
Major complications arise from the recent increase in the amount of high...
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07/15/2020
SpaceNet: Make Free Space For Continual Learning
The continual learning (CL) paradigm aims to enable neural networks to l...
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06/24/2020
Topological Insights in Sparse Neural Networks
Sparse neural networks are effective approaches to reduce the resource r...
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02/10/2020
Novelty Producing Synaptic Plasticity
A learning process with the plasticity property often requires reinforce...
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06/27/2019
On improving deep learning generalization with adaptive sparse connectivity
Large neural networks are very successful in various tasks. However, wit...
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04/02/2019
Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions
A fundamental aspect of learning in biological neural networks (BNNs) is...
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03/22/2019
Learning with Delayed Synaptic Plasticity
The plasticity property of biological neural networks allows them to per...
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03/17/2019
Evolving and Understanding Sparse Deep Neural Networks using Cosine Similarity
Training sparse neural networks with adaptive connectivity is an active ...
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01/26/2019
Intrinsically Sparse Long Short-Term Memory Networks
Long Short-Term Memory (LSTM) has achieved state-of-the-art performances...
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01/26/2019
Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware
Microarray gene expression has widely attracted the eyes of the public a...
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04/19/2018
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number ...
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04/18/2018
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach
Deep learning, even if it is very successful nowadays, traditionally nee...
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07/18/2017
On-line Building Energy Optimization using Deep Reinforcement Learning
Unprecedented high volumes of data are becoming available with the growt...
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07/15/2017
Evolutionary Training of Sparse Artificial Neural Networks: A Network Science Perspective
Through the success of deep learning, Artificial Neural Networks (ANNs) ...
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10/18/2016
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data
Conceived in the early 1990s, Experience Replay (ER) has been shown to b...
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04/20/2016
A topological insight into restricted Boltzmann machines
Restricted Boltzmann Machines (RBMs) and models derived from them have b...
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04/20/2016
