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06/07/2023
Optimal Transport Model Distributional Robustness
Distributional robustness is a promising framework for training deep lea...
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05/26/2023
AIBugHunter: A Practical Tool for Predicting, Classifying and Repairing Software Vulnerabilities
Many ML-based approaches have been proposed to automatically detect, loc...
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05/26/2023
Learning to Quantize Vulnerability Patterns and Match to Locate Statement-Level Vulnerabilities
Deep learning (DL) models have become increasingly popular in identifyin...
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05/25/2023
Learning Directed Graphical Models with Optimal Transport
Estimating the parameters of a probabilistic directed graphical model fr...
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05/17/2023
Sharpness Shift-Aware Self-Supervised Learning
Self-supervised learning aims to extract meaningful features from unlabe...
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04/26/2023
Generating Adversarial Examples with Task Oriented Multi-Objective Optimization
Deep learning models, even the-state-of-the-art ones, are highly vulnera...
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04/21/2023
Hyperbolic Geometry in Computer Vision: A Survey
Hyperbolic geometry, a Riemannian manifold endowed with constant section...
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02/12/2023
Vector Quantized Wasserstein Auto-Encoder
Learning deep discrete latent presentations offers a promise of better s...
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02/06/2023
Flat Seeking Bayesian Neural Networks
Bayesian Neural Networks (BNNs) offer a probabilistic interpretation for...
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12/05/2022
Multiple Perturbation Attack: Attack Pixelwise Under Different â„“_p-norms For Better Adversarial Performance
Adversarial machine learning has been both a major concern and a hot top...
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11/30/2022
Continual Learning with Optimal Transport based Mixture Model
Online Class Incremental learning (CIL) is a challenging setting in Cont...
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11/24/2022
Improving Multi-task Learning via Seeking Task-based Flat Regions
Multi-Task Learning (MTL) is a widely-used and powerful learning paradig...
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10/14/2022
Vision Transformer Visualization: What Neurons Tell and How Neurons Behave?
Recently vision transformers (ViT) have been applied successfully for va...
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09/27/2022
Learning to Counter: Stochastic Feature-based Learning for Diverse Counterfactual Explanations
Interpretable machine learning seeks to understand the reasoning process...
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09/20/2022
An Information-Theoretic and Contrastive Learning-based Approach for Identifying Code Statements Causing Software Vulnerability
Software vulnerabilities existing in a program or function of computer s...
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09/19/2022
Cross Project Software Vulnerability Detection via Domain Adaptation and Max-Margin Principle
Software vulnerabilities (SVs) have become a common, serious and crucial...
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07/07/2022
An Additive Instance-Wise Approach to Multi-class Model Interpretation
Interpretable machine learning offers insights into what factors drive a...
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06/09/2022
STNDT: Modeling Neural Population Activity with a Spatiotemporal Transformer
Modeling neural population dynamics underlying noisy single-trial spikin...
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06/04/2022
Stochastic Multiple Target Sampling Gradient Descent
Sampling from an unnormalized target distribution is an essential proble...
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03/01/2022
Global-Local Regularization Via Distributional Robustness
Despite superior performance in many situations, deep neural networks ar...
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02/27/2022
A Unified Wasserstein Distributional Robustness Framework for Adversarial Training
It is well-known that deep neural networks (DNNs) are susceptible to adv...
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11/27/2021
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources
Domain adaptation (DA) benefits from the rigorous theoretical works that...
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10/21/2021
Supporting Massive DLRM Inference Through Software Defined Memory
Deep Learning Recommendation Models (DLRM) are widespread, account for a...
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10/14/2021
ReGVD: Revisiting Graph Neural Networks for Vulnerability Detection
Identifying vulnerabilities in the source code is essential to protect t...
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05/13/2021
SyntheticFur dataset for neural rendering
We introduce a new dataset called SyntheticFur built specifically for ma...
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04/27/2021
Text Generation with Deep Variational GAN
Generating realistic sequences is a central task in many machine learnin...
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04/27/2021
Improved and Efficient Text Adversarial Attacks using Target Information
There has been recently a growing interest in studying adversarial examp...
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02/11/2021
BoMb-OT: On Batch of Mini-batches Optimal Transport
Mini-batch optimal transport (m-OT) has been successfully used in practi...
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01/25/2021
Understanding and Achieving Efficient Robustness with Adversarial Contrastive Learning
Contrastive learning (CL) has recently emerged as an effective approach ...
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12/04/2020
Offset Curves Loss for Imbalanced Problem in Medical Segmentation
Medical image segmentation has played an important role in medical analy...
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11/05/2020
Explain by Evidence: An Explainable Memory-based Neural Network for Question Answering
Interpretability and explainability of deep neural networks are challeng...
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10/14/2020
Explain2Attack: Text Adversarial Attacks via Cross-Domain Interpretability
Training robust deep learning models for down-stream tasks is a critical...
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10/13/2020
Towards Understanding Pixel Vulnerability under Adversarial Attacks for Images
Deep neural network image classifiers are reported to be susceptible to ...
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09/21/2020
Improving Ensemble Robustness by Collaboratively Promoting and Demoting Adversarial Robustness
Ensemble-based adversarial training is a principled approach to achieve ...
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08/12/2020
Neural Sinkhorn Topic Model
In this paper, we present a new topic modelling approach via the theory ...
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07/10/2020
Improving Adversarial Robustness by Enforcing Local and Global Compactness
The fact that deep neural networks are susceptible to crafted perturbati...
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04/16/2020
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation
One of the challenging problems in sequence generation tasks is the opti...
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10/03/2019
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions
Deep neural network image classifiers are reported to be susceptible to ...
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01/25/2019
When Can Neural Networks Learn Connected Decision Regions?
Previous work has questioned the conditions under which the decision reg...
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11/15/2018
Theoretical Perspective of Deep Domain Adaptation
Deep domain adaptation has recently undergone a big success. Compared wi...
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11/06/2017
KGAN: How to Break The Minimax Game in GAN
Generative Adversarial Networks (GANs) were intuitively and attractively...
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09/19/2017
Analogical-based Bayesian Optimization
Some real-world problems revolve to solve the optimization problem _x∈Xf...
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09/12/2017
Dual Discriminator Generative Adversarial Nets
We propose in this paper a novel approach to tackle the problem of mode ...
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08/16/2017
Geometric Enclosing Networks
Training model to generate data has increasingly attracted research atte...
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08/08/2017
Multi-Generator Generative Adversarial Nets
We propose a new approach to train the Generative Adversarial Nets (GANs...
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04/22/2016
