Search Results for author:
Found 28 papers, 7 papers with code
Beyond Slow Signs in High-fidelity Model Extraction
Our study evaluates the feasibility of parameter extraction methods of Carlini et al. [1] further enhanced by Canales-Mart\'inez et al. [2] for models trained on standard benchmarks.
Inverse Constitutional AI: Compressing Preferences into Principles
In constitutional AI, a set of principles (or constitution) is used to provide feedback and fine-tune AI models.
Locking Machine Learning Models into Hardware
We demonstrate that locking mechanisms are feasible by either targeting efficiency of model representations, such making models incompatible with quantisation, or tie the model's operation on specific characteristics of hardware, such as number of cycles for arithmetic operations.
Architectural Neural Backdoors from First Principles
In this work we construct an arbitrary trigger detector which can be used to backdoor an architecture with no human supervision.
LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation
Test stimuli generation has been a crucial but labor-intensive task in hardware design verification.
Human-Producible Adversarial Examples
Visual adversarial examples have so far been restricted to pixel-level image manipulations in the digital world, or have required sophisticated equipment such as 2D or 3D printers to be produced in the physical real world.
Revisiting Automated Prompting: Are We Actually Doing Better?
Current literature demonstrates that Large Language Models (LLMs) are great few-shot learners, and prompting significantly increases their performance on a range of downstream tasks in a few-shot learning setting.
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 backdoors are impossible to detect during the training or data preparation processes, because they are not yet present.
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.
