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07/24/2023
Towards Generalising Neural Topical Representations
Topic models have evolved from conventional Bayesian probabilistic model...
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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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05/06/2023
Active Continual Learning: Labelling Queries in a Sequence of Tasks
Acquiring new knowledge without forgetting what has been learned in a se...
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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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01/18/2023
Class Enhancement Losses with Pseudo Labels for Zero-shot Semantic Segmentation
Recent mask proposal models have significantly improved the performance ...
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12/05/2022
Multiple Perturbation Attack: Attack Pixelwise Under Different ℓ_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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10/20/2022
Can Domains Be Transferred Across Languages in Multi-Domain Multilingual Neural Machine Translation?
Previous works mostly focus on either multilingual or multi-domain aspec...
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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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09/19/2022
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
Although two-stage Vector Quantized (VQ) generative models allow for syn...
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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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06/04/2022
Stochastic Multiple Target Sampling Gradient Descent
Sampling from an unnormalized target distribution is an essential proble...
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04/05/2022
High-Quality Pluralistic Image Completion via Code Shared VQGAN
PICNet pioneered the generation of multiple and diverse results for imag...
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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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02/22/2022
Sobolev Transport: A Scalable Metric for Probability Measures with Graph Metrics
Optimal transport (OT) is a popular measure to compare probability distr...
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12/16/2021
Two-view Graph Neural Networks for Knowledge Graph Completion
In this paper, we introduce a novel GNN-based knowledge graph embedding ...
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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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10/29/2021
Model Fusion of Heterogeneous Neural Networks via Cross-Layer Alignment
Layer-wise model fusion via optimal transport, named OTFusion, applies s...
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10/18/2021
Exploiting Domain-Specific Features to Enhance Domain Generalization
Domain Generalization (DG) aims to train a model, from multiple observed...
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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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09/09/2021
Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection
This paper considers the unsupervised domain adaptation problem for neur...
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08/03/2021
Elastic Architecture Search for Diverse Tasks with Different Resources
We study a new challenging problem of efficient deployment for diverse t...
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07/24/2021
Multi-Label Image Classification with Contrastive Learning
Recently, as an effective way of learning latent representations, contra...
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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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04/15/2021
Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
We introduce a novel embedding model, named NoKE, which aims to integrat...
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02/28/2021
Topic Modelling Meets Deep Neural Networks: A Survey
Topic modelling has been a successful technique for text analysis for al...
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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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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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10/05/2020
Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models
Recent work has shown the importance of adaptation of broad-coverage con...
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09/26/2020
QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings
We propose a simple and effective embedding model, named QuatRE, to lear...
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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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08/12/2020
Quaternion Graph Neural Networks
We consider reducing model parameters and moving beyond the Euclidean sp...
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08/06/2020