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Found 42 papers, 14 papers with code
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.
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.
Markpainting: Adversarial Machine Learning meets Inpainting
Inpainting is a learned interpolation technique that is based on generative modeling and used to populate masked or missing pieces in an image; it has wide applications in picture editing and retouching.
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.
Genomic Interpreter: A Hierarchical Genomic Deep Neural Network with 1D Shifted Window Transformer
This work presents Genomic Interpreter: a novel architecture for genomic assay prediction.
Revisiting Block-based Quantisation: What is Important for Sub-8-bit LLM Inference?
In this work, we explore the statistical and learning properties of the LLM layer and attribute the bottleneck of LLM quantisation to numerical scaling offsets.
Architectural Backdoors in Neural Networks
Machine learning is vulnerable to adversarial manipulation.
Flareon: Stealthy any2any Backdoor Injection via Poisoned Augmentation
Open software supply chain attacks, once successful, can exact heavy costs in mission-critical applications.
Latent Diffusion Model for DNA Sequence Generation
On the other hand, Diffusion Models are a promising new class of generative models that are not burdened with these problems, enabling them to reach the state-of-the-art in domains such as image generation.
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.
The Curse of Recursion: Training on Generated Data Makes Models Forget
It is now clear that large language models (LLMs) are here to stay, and will bring about drastic change in the whole ecosystem of online text and images.
Manipulating SGD with Data Ordering Attacks
Machine learning is vulnerable to a wide variety of attacks.
Augmentation Backdoors
It is well known that backdoors can be inserted into machine learning models through serving a modified dataset to train on.
Task-Agnostic Graph Neural Network Evaluation via Adversarial Collaboration
Existing GNN benchmarking methods for molecular representation learning focus on comparing the GNNs' performances on some node/graph classification/regression tasks on certain datasets.
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.
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.
Sitatapatra: Blocking the Transfer of Adversarial Samples
Convolutional Neural Networks (CNNs) are widely used to solve classification tasks in computer vision.
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.
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.
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.
Probabilistic Dual Network Architecture Search on Graphs
We present the first differentiable Network Architecture Search (NAS) for Graph Neural Networks (GNNs).
Pay Attention to Features, Transfer Learn Faster CNNs
Deep convolutional neural networks are now widely deployed in vision applications, but a limited size of training data can restrict their task performance.
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.
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.
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.
FedDrop: Trajectory-weighted Dropout for Efficient Federated Learning
Federated learning (FL) enables edge clients to train collaboratively while preserving individual's data privacy.
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.
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.
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.
DARTFormer: Finding The Best Type Of Attention
Given the wide and ever growing range of different efficient Transformer attention mechanisms, it is important to identify which attention is most effective when given a task.
Wide Attention Is The Way Forward For Transformers?
We demonstrate that wide single layer Transformer models can compete with or outperform deeper ones in a variety of Natural Language Processing (NLP) tasks when both are trained from scratch.
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.
Revisiting Structured Dropout
Large neural networks are often overparameterised and prone to overfitting, Dropout is a widely used regularization technique to combat overfitting and improve model generalization.
Dynamic Stashing Quantization for Efficient Transformer Training
Large Language Models (LLMs) have demonstrated impressive performance on a range of Natural Language Processing (NLP) tasks.
Adaptive Channel Sparsity for Federated Learning Under System Heterogeneity
Owing to the non-i. i. d.
Hybrid Graph: A Unified Graph Representation with Datasets and Benchmarks for Complex Graphs
Additionally, though many Graph Neural Networks (GNNs) have been proposed for representation learning on higher-order graphs, they are usually only evaluated on simple graph datasets.
Will More Expressive Graph Neural Networks do Better on Generative Tasks?
Moreover, we show that GCPN and GraphAF with advanced GNNs can achieve state-of-the-art results across 17 other non-GNN-based graph generative approaches, such as variational autoencoders and Bayesian optimisation models, on the proposed molecular generative objectives (DRD2, Median1, Median2), which are impor- tant metrics for de-novo molecular design.
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.
LQER: Low-Rank Quantization Error Reconstruction for LLMs
Post-training quantization of Large Language Models (LLMs) is challenging.
DiscDiff: Latent Diffusion Model for DNA Sequence Generation
This paper introduces a novel framework for DNA sequence generation, comprising two key components: DiscDiff, a Latent Diffusion Model (LDM) tailored for generating discrete DNA sequences, and Absorb-Escape, a post-training algorithm designed to refine these sequences.
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.
Enhancing Real-World Complex Network Representations with Hyperedge Augmentation
These methods cannot fully address the complexities of real-world large-scale networks that often involve higher-order node relations beyond only being pairwise.
