research
∙
06/25/2023
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto metho...
share
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
∙
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
∙
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
∙
12/16/2021
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Few-shot classification tasks aim to classify images in query sets based...
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
∙
06/19/2021
Sparse Training via Boosting Pruning Plasticity with Neuroregeneration
Works on lottery ticket hypothesis (LTH) and single-shot network pruning...
research
∙
05/28/2021
Linear-Time Self Attention with Codeword Histogram for Efficient Recommendation
Self-attention has become increasingly popular in a variety of sequence ...
research
∙
05/28/2021
Rethinking Lifelong Sequential Recommendation with Incremental Multi-Interest Attention
Sequential recommendation plays an increasingly important role in many e...
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
∙
04/14/2020
Knowledge Elicitation using Deep Metric Learning and Psychometric Testing
Knowledge present in a domain is well expressed as relationships between...
research
∙
03/10/2020