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08/15/2023
MOLE: MOdular Learning FramEwork via Mutual Information Maximization
This paper is to introduce an asynchronous and local learning framework ...
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06/25/2023
Enhancing Adversarial Training via Reweighting Optimization Trajectory
Despite the fact that adversarial training has become the de facto metho...
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05/23/2023
DF2M: An Explainable Deep Bayesian Nonparametric Model for High-Dimensional Functional Time Series
In this paper, we present Deep Functional Factor Model (DF2M), a Bayesia...
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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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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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12/16/2021
Semantic-Based Few-Shot Learning by Interactive Psychometric Testing
Few-shot classification tasks aim to classify images in query sets based...
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12/11/2021
A Comparative Study on Robust Graph Neural Networks to Structural Noises
Graph neural networks (GNNs) learn node representations by passing and a...
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10/01/2021
Calibrated Adversarial Training
Adversarial training is an approach of increasing the robustness of mode...
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07/18/2021
A Survey on Role-Oriented Network Embedding
Recently, Network Embedding (NE) has become one of the most attractive r...
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07/06/2021
On Generalization of Graph Autoencoders with Adversarial Training
Adversarial training is an approach for increasing model's resilience ag...
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04/19/2021
Direction-Aggregated Attack for Transferable Adversarial Examples
Deep neural networks are vulnerable to adversarial examples that are cra...
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04/16/2021
Hop-Count Based Self-Supervised Anomaly Detection on Attributed Networks
Recent years have witnessed an upsurge of interest in the problem of ano...
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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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11/07/2020
Bridging the Performance Gap between FGSM and PGD Adversarial Training
Deep learning achieves state-of-the-art performance in many tasks but ex...
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09/30/2020
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
Effectively detecting anomalous nodes in attributed networks is crucial ...
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12/24/2019
Simultaneous Identification of Tweet Purpose and Position
Tweet classification has attracted considerable attention recently. Most...
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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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05/25/2018