b'Yiren Zhao'

research

∙ 08/23/2023

Will More Expressive Graph Neural Networks do Better on Generative Tasks?

Graph generation poses a significant challenge as it involves predicting...

0 Xiandong Zou, et al. ∙

research

∙ 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...

0 Zehui Li, et al. ∙

research

∙ 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...

0 Zehui Li, et al. ∙

research

∙ 05/27/2023

The Curse of Recursion: Training on Generated Data Makes Models Forget

Stable Diffusion revolutionised image creation from descriptive text. GP...

0 Ilia Shumailov, et al. ∙

research

∙ 04/07/2023

Revisiting Automated Prompting: Are We Actually Doing Better?

Current literature demonstrates that Large Language Models (LLMs) are gr...

0 Yulin Zhou, et al. ∙

research

∙ 03/09/2023

Dynamic Stashing Quantization for Efficient Transformer Training

Large Language Models (LLMs) have demonstrated impressive performance on...

0 Guo Yang, et al. ∙

research

∙ 12/20/2022

Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation

Open software supply chain attacks, once successful, can exact heavy cos...

0 Tianrui Qin, et al. ∙

research

∙ 10/05/2022

Revisiting Structured Dropout

Large neural networks are often overparameterised and prone to overfitti...

0 Yiren Zhao, et al. ∙

research

∙ 10/02/2022

DARTFormer: Finding The Best Type Of Attention

Given the wide and ever growing range of different efficient Transformer...

0 Jason Ross Brown, et al. ∙

research

∙ 10/02/2022

Wide Attention Is The Way Forward For Transformers

The Transformer is an extremely powerful and prominent deep learning arc...

0 Jason Ross Brown, et al. ∙

research

∙ 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 ...

0 Tim Clifford, et al. ∙

research

∙ 09/29/2022

Augmentation Backdoors

Data augmentation is used extensively to improve model generalisation. H...

0 Joseph Rance, et al. ∙

research

∙ 07/01/2022

Efficient Adversarial Training With Data Pruning

Neural networks are susceptible to adversarial examples-small input pert...

0 Maximilian Kaufmann, et al. ∙

research

∙ 06/15/2022

Architectural Backdoors in Neural Networks

Machine learning is vulnerable to adversarial manipulation. Previous lit...

0 Mikel Bober-Irizar, et al. ∙

research

∙ 02/09/2022

Model Architecture Adaption for Bayesian Neural Networks

Bayesian Neural Networks (BNNs) offer a mathematically grounded framewor...

0 Duo Wang, et al. ∙

research

∙ 10/31/2021

DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning

Federated Learning (FL) is a powerful technique for training a model on ...

0 Robert Hönig, et al. ∙

research

∙ 09/10/2021

Rapid Model Architecture Adaption for Meta-Learning

Network Architecture Search (NAS) methods have recently gathered much at...

0 Yiren Zhao, et al. ∙

research

∙ 06/01/2021

Markpainting: Adversarial Machine Learning meets Inpainting

Inpainting is a learned interpolation technique that is based on generat...

0 David Khachaturov, et al. ∙

research

∙ 04/19/2021

Manipulating SGD with Data Ordering Attacks

Machine learning is vulnerable to a wide variety of different attacks. I...

14 Ilia Shumailov, et al. ∙

research

∙ 11/22/2020

Nudge Attacks on Point-Cloud DNNs

The wide adaption of 3D point-cloud data in safety-critical applications...

0 Yiren Zhao, et al. ∙

research

∙ 09/19/2020

Learned Low Precision Graph Neural Networks

Deep Graph Neural Networks (GNNs) show promising performance on a range ...

0 Yiren Zhao, et al. ∙

research

∙ 06/05/2020

Sponge Examples: Energy-Latency Attacks on Neural Networks

The high energy costs of neural network training and inference led to th...

8 Ilia Shumailov, et al. ∙

research

∙ 03/21/2020

Probabilistic Dual Network Architecture Search on Graphs

We present the first differentiable Network Architecture Search (NAS) fo...

0 Yiren Zhao, et al. ∙

research

∙ 02/20/2020

Towards Certifiable Adversarial Sample Detection

Convolutional Neural Networks (CNNs) are deployed in more and more class...

0 Ilia Shumailov, et al. ∙

research

∙ 10/21/2019

Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs

Modern deep Convolutional Neural Networks (CNNs) are computationally dem...

0 Yiren Zhao, et al. ∙

research

∙ 09/06/2019

Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information

Recent research on reinforcement learning has shown that trained agents ...

0 Yiren Zhao, et al. ∙

research

∙ 03/07/2019

Efficient and Effective Quantization for Sparse DNNs

Deep convolutional neural networks (CNNs) are powerful tools for a wide ...

0 Yiren Zhao, et al. ∙

research

∙ 01/23/2019

Sitatapatra: Blocking the Transfer of Adversarial Samples

Convolutional Neural Networks (CNNs) are widely used to solve classifica...

0 Ilia Shumailov, et al. ∙

research

∙ 11/18/2018

The Taboo Trap: Behavioural Detection of Adversarial Samples

Deep Neural Networks (DNNs) have become a powerful tool for a wide range...

0 Ilia Shumailov, et al. ∙

research

∙ 10/12/2018

Dynamic Channel Pruning: Feature Boosting and Suppression

Making deep convolutional neural networks more accurate typically comes ...

0 Xitong Gao, et al. ∙

research

∙ 09/29/2018

To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression

As deep neural networks (DNNs) become widely used, pruned and quantised ...

0 Yiren Zhao, et al. ∙

Yiren Zhao

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