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08/23/2023
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Graph generation poses a significant challenge as it involves predicting...
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06/08/2023
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer
Given the increasing volume and quality of genomics data, extracting new...
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06/08/2023
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Graphs are widely used to encapsulate a variety of data formats, but rea...
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05/27/2023
The Curse of Recursion: Training on Generated Data Makes Models Forget
Stable Diffusion revolutionised image creation from descriptive text. GP...
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04/07/2023
Revisiting Automated Prompting: Are We Actually Doing Better?
Current literature demonstrates that Large Language Models (LLMs) are gr...
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03/09/2023
Dynamic Stashing Quantization for Efficient Transformer Training
Large Language Models (LLMs) have demonstrated impressive performance on...
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12/20/2022
Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation
Open software supply chain attacks, once successful, can exact heavy cos...
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10/05/2022
Revisiting Structured Dropout
Large neural networks are often overparameterised and prone to overfitti...
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10/02/2022
DARTFormer: Finding The Best Type Of Attention
Given the wide and ever growing range of different efficient Transformer...
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10/02/2022
Wide Attention Is The Way Forward For Transformers
The Transformer is an extremely powerful and prominent deep learning arc...
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09/30/2022
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks
Early backdoor attacks against machine learning set off an arms race in ...
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09/29/2022
Augmentation Backdoors
Data augmentation is used extensively to improve model generalisation. H...
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07/01/2022
Efficient Adversarial Training With Data Pruning
Neural networks are susceptible to adversarial examples-small input pert...
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06/15/2022
Architectural Backdoors in Neural Networks
Machine learning is vulnerable to adversarial manipulation. Previous lit...
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02/09/2022
Model Architecture Adaption for Bayesian Neural Networks
Bayesian Neural Networks (BNNs) offer a mathematically grounded framewor...
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10/31/2021
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
Federated Learning (FL) is a powerful technique for training a model on ...
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09/10/2021
Rapid Model Architecture Adaption for Meta-Learning
Network Architecture Search (NAS) methods have recently gathered much at...
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06/01/2021
Markpainting: Adversarial Machine Learning meets Inpainting
Inpainting is a learned interpolation technique that is based on generat...
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04/19/2021
Manipulating SGD with Data Ordering Attacks
Machine learning is vulnerable to a wide variety of different attacks. I...
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11/22/2020
Nudge Attacks on Point-Cloud DNNs
The wide adaption of 3D point-cloud data in safety-critical applications...
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09/19/2020
Learned Low Precision Graph Neural Networks
Deep Graph Neural Networks (GNNs) show promising performance on a range ...
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06/05/2020
Sponge Examples: Energy-Latency Attacks on Neural Networks
The high energy costs of neural network training and inference led to th...
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03/21/2020
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) fo...
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02/20/2020
Towards Certifiable Adversarial Sample Detection
Convolutional Neural Networks (CNNs) are deployed in more and more class...
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10/21/2019
Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs
Modern deep Convolutional Neural Networks (CNNs) are computationally dem...
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09/06/2019
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
Recent research on reinforcement learning has shown that trained agents ...
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03/07/2019
Efficient and Effective Quantization for Sparse DNNs
Deep convolutional neural networks (CNNs) are powerful tools for a wide ...
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01/23/2019
Sitatapatra: Blocking the Transfer of Adversarial Samples
Convolutional Neural Networks (CNNs) are widely used to solve classifica...
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11/18/2018
The Taboo Trap: Behavioural Detection of Adversarial Samples
Deep Neural Networks (DNNs) have become a powerful tool for a wide range...
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10/12/2018
Dynamic Channel Pruning: Feature Boosting and Suppression
Making deep convolutional neural networks more accurate typically comes ...
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09/29/2018