Search Results for author:
Found 27 papers, 8 papers with code
S2DNAS: Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
To this end, based on a given CNN model, we first generate a CNN architecture space in which each architecture is a multi-stage CNN generated from the given model using some predefined transformations.
Towards Multi-modal Transformers in Federated Learning
Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement.
LLM Inference Unveiled: Survey and Roofline Model Insights
Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques.
ASVD: Activation-aware Singular Value Decomposition for Compressing Large Language Models
This paper explores a new post-hoc training-free compression paradigm for compressing Large Language Models (LLMs) to facilitate their wider adoption in various computing environments.
FedPerfix: Towards Partial Model Personalization of Vision Transformers in Federated Learning
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments.
RPTQ: Reorder-based Post-training Quantization for Large Language Models
In this paper, we identify that the challenge in quantizing activations in LLMs arises from varying ranges across channels, rather than solely the presence of outliers.
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures.
Latency-aware Spatial-wise Dynamic Networks
The latency prediction model can efficiently estimate the inference latency of dynamic networks by simultaneously considering algorithms, scheduling strategies, and hardware properties.
Conquering the Communication Constraints to Enable Large Pre-Trained Models in Federated Learning
Recently, the use of small pre-trained models has been shown effective in federated learning optimization and improving convergence.
A Survey of Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning
Video anomaly detection aims to identify abnormal events that occurred in videos.
PTQ4ViT: Post-Training Quantization Framework for Vision Transformers with Twin Uniform Quantization
We observe the distributions of activation values after softmax and GELU functions are quite different from the Gaussian distribution.
GNNear: Accelerating Full-Batch Training of Graph Neural Networks with Near-Memory Processing
However, to realize efficient GNN training is challenging, especially on large graphs.
Energon: Towards Efficient Acceleration of Transformers Using Dynamic Sparse Attention
To enable such an algorithm with lower latency and better energy efficiency, we also propose an Energon co-processor architecture.
PTQ-SL: Exploring the Sub-layerwise Post-training Quantization
Network quantization is a powerful technique to compress convolutional neural networks.
GNNSampler: Bridging the Gap between Sampling Algorithms of GNN and Hardware
Extensive experiments show that our method is universal to mainstream sampling algorithms and helps significantly reduce the training time, especially in large-scale graphs.
BlockGNN: Towards Efficient GNN Acceleration Using Block-Circulant Weight Matrices
At the hardware design level, we propose a pipelined CirCore architecture, which supports efficient block-circulant matrices computation.
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference
The inference of a input sample can exit from early stage if the prediction of the stage is confident enough.
S2DNAS:Transforming Static CNN Model for Dynamic Inference via Neural Architecture Search
Recently, dynamic inference has emerged as a promising way to reduce the computational cost of deep convolutional neural network (CNN).
Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics
Based on this framework, we demonstrate that SGLD can prevent the information leakage of the training dataset to a certain extent.
Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection
In this paper, we aim to understand the generalization properties of generative adversarial networks (GANs) from a new perspective of privacy protection.
Deep Learning Detection of Inaccurate Smart Electricity Meters: A Case Study
Detecting inaccurate smart meters and targeting them for replacement can save significant resources.
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Specifically, we use homomorphic encryption to protect a client's raw data and use Bayesian neural networks to protect the DNN weights in a cloud server.
P3SGD: Patient Privacy Preserving SGD for Regularizing Deep CNNs in Pathological Image Classification
(2) privacy leakage: the model trained using a conventional method may involuntarily reveal the private information of the patients in the training dataset.
A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting
In this paper, a new forecasting model based on a convolution neural network and LightGBM is constructed.
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Given an input image from a specified stain, several generators are first applied to estimate its appearances in other staining methods, and a classifier follows to combine visual cues from different stains for prediction (whether it is pathological, or which type of pathology it has).
SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution
In this paper, we build a super resolution perceptual generative adversarial network (SRPGAN) framework for SISR tasks.
