research
∙
07/07/2023
Optimized Path Planning for USVs under Ocean Currents
The proposed work focuses on the path planning for Unmanned Surface Vehi...
share
research
∙
06/25/2023
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto metho...
research
∙
06/18/2023
Graph Ladling: Shockingly Simple Parallel GNN Training without Intermediate Communication
Graphs are omnipresent and GNNs are a powerful family of neural networks...
research
∙
06/18/2023
Instant Soup: Cheap Pruning Ensembles in A Single Pass Can Draw Lottery Tickets from Large Models
Large pre-trained transformers have been receiving explosive attention i...
research
∙
06/06/2023
The Emergence of Essential Sparsity in Large Pre-trained Models: The Weights that Matter
Large pre-trained transformers are show-stealer in modern-day deep learn...
research
∙
05/30/2023
Dynamic Sparsity Is Channel-Level Sparsity Learner
Sparse training has received an upsurging interest in machine learning d...
research
∙
05/30/2023
Are Large Kernels Better Teachers than Transformers for ConvNets?
This paper reveals a new appeal of the recently emerged large-kernel Con...
research
∙
03/10/2023
Supervised Feature Selection with Neuron Evolution in Sparse Neural Networks
Feature selection that selects an informative subset of variables from d...
research
∙
03/03/2023
Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!
Sparse Neural Networks (SNNs) have received voluminous attention predomi...
research
∙
03/02/2023
Sparse MoE as the New Dropout: Scaling Dense and Self-Slimmable Transformers
Despite their remarkable achievement, gigantic transformers encounter si...
research
∙
02/06/2023
Ten Lessons We Have Learned in the New "Sparseland": A Short Handbook for Sparse Neural Network Researchers
This article does not propose any novel algorithm or new hardware for sp...
research
∙
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 ...
research
∙
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...
research
∙
08/23/2022
Lottery Pools: Winning More by Interpolating Tickets without Increasing Training or Inference Cost
Lottery tickets (LTs) is able to discover accurate and sparse subnetwork...
research
∙
07/07/2022
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Transformers have quickly shined in the computer vision world since the ...
research
∙
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...
research
∙
03/05/2022
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance
Generative adversarial networks (GANs) have received an upsurging intere...
research
∙
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...
research
∙
01/27/2022
Achieving Personalized Federated Learning with Sparse Local Models
Federated learning (FL) is vulnerable to heterogeneously distributed dat...
research
∙
07/07/2021
Hierarchical Semantic Segmentation using Psychometric Learning
Assigning meaning to parts of image data is the goal of semantic image s...
research
∙
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...
research
∙
06/19/2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Works on lottery ticket hypothesis (LTH) and single-shot network pruning...
research
∙
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...
research
∙
01/22/2021
Selfish Sparse RNN Training
Sparse neural networks have been widely applied to reduce the necessary ...
research
∙
06/24/2020
Topological Insights in Sparse Neural Networks
Sparse neural networks are effective approaches to reduce the resource r...
research
∙
06/27/2019
On improving deep learning generalization with adaptive sparse connectivity
Large neural networks are very successful in various tasks. However, wit...
research
∙
01/26/2019
Intrinsically Sparse Long Short-Term Memory Networks
Long Short-Term Memory (LSTM) has achieved state-of-the-art performances...
research
∙
01/26/2019
