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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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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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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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05/22/2020
Accelerate Cycle-Level Full-System Simulation of Multi-Core RISC-V Systems with Binary Translation
It has always been difficult to balance the accuracy and performance of ...
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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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05/16/2019
Fast TLB Simulation for RISC-V Systems
Address translation and protection play important roles in today's proce...
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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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03/04/2019
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
The Winograd or Cook-Toom class of algorithms help to reduce the overall...
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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
