Search Results for author:
Found 21 papers, 5 papers with code
Dynamic Stashing Quantization for Efficient Transformer Training
Large Language Models (LLMs) have demonstrated impressive performance on a range of Natural Language Processing (NLP) tasks.
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks
These defences work by inspecting the training data, the model, or the integrity of the training procedure.
Augmentation Backdoors
It is well known that backdoors can be inserted into machine learning models through serving a modified dataset to train on.
Efficient Adversarial Training With Data Pruning
In this paper we demonstrate data pruning-a method for increasing adversarial training efficiency through data sub-sampling. We empirically show that data pruning leads to improvements in convergence and reliability of adversarial training, albeit with different levels of utility degradation.
Architectural Backdoors in Neural Networks
Machine learning is vulnerable to adversarial manipulation.
Model Architecture Adaption for Bayesian Neural Networks
Bayesian Neural Networks (BNNs) offer a mathematically grounded framework to quantify the uncertainty of model predictions but come with a prohibitive computation cost for both training and inference.
DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning
First, we introduce a time-adaptive quantization algorithm that increases the quantization level as training progresses.
Rapid Model Architecture Adaption for Meta-Learning
H-Meta-NAS shows a Pareto dominance compared to a variety of NAS and manual baselines in popular few-shot learning benchmarks with various hardware platforms and constraints.
Nudge Attacks on Point-Cloud DNNs
The wide adaption of 3D point-cloud data in safety-critical applications such as autonomous driving makes adversarial samples a real threat.
Learned Low Precision Graph Neural Networks
LPGNAS learns the optimal architecture coupled with the best quantisation strategy for different components in the GNN automatically using back-propagation in a single search round.
Sponge Examples: Energy-Latency Attacks on Neural Networks
The high energy costs of neural network training and inference led to the use of acceleration hardware such as GPUs and TPUs.
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs).
Towards Certifiable Adversarial Sample Detection
Convolutional Neural Networks (CNNs) are deployed in more and more classification systems, but adversarial samples can be maliciously crafted to trick them, and are becoming a real threat.
Automatic Generation of Multi-precision Multi-arithmetic CNN Accelerators for FPGAs
Furthermore, we show how Tomato produces implementations of networks with various sizes running on single or multiple FPGAs.
Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information
In this work, we show how such samples can be generalised from White-box and Grey-box attacks to a strong Black-box case, where the attacker has no knowledge of the agents, their training parameters and their training methods.
Focused Quantization for Sparse CNNs
In ResNet-50, we achieved a 18. 08x CR with only 0. 24% loss in top-5 accuracy, outperforming existing compression methods.
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 compute complexity of many modern deep convolutional neural networks (CNNs).
Sitatapatra: Blocking the Transfer of Adversarial Samples
Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision.
The Taboo Trap: Behavioural Detection of Adversarial Samples
Most existing detection mechanisms against adversarial attacksimpose significant costs, either by using additional classifiers to spot adversarial samples, or by requiring the DNN to be restructured.
Dynamic Channel Pruning: Feature Boosting and Suppression
Making deep convolutional neural networks more accurate typically comes at the cost of increased computational and memory resources.
To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression
We, therefore, investigate the extent to which adversarial samples are transferable between uncompressed and compressed DNNs.
