Search Results for author:
Found 22 papers, 7 papers with code
Secure Semantic Communication for Image Transmission in the Presence of Eavesdroppers
To address this issue, we propose a novel secure semantic communication (SemCom) approach for image transmission, which integrates steganography technology to conceal private information within non-private images (host images).
MESEN: Exploit Multimodal Data to Design Unimodal Human Activity Recognition with Few Labels
Human activity recognition (HAR) will be an essential function of various emerging applications.
Intention-aware Denoising Diffusion Model for Trajectory Prediction
To decrease the inference time, we reduce the variable dimensions in the intention-aware diffusion process and restrict the initial distribution of the action-aware diffusion process, which leads to fewer diffusion steps.
Large Language Model Guided Knowledge Distillation for Time Series Anomaly Detection
To overcome the limitation, we propose \textbf{AnomalyLLM}, a knowledge distillation-based time series anomaly detection approach where the student network is trained to mimic the features of the large language model (LLM)-based teacher network that is pretrained on large-scale datasets.
TreeMIL: A Multi-instance Learning Framework for Time Series Anomaly Detection with Inexact Supervision
Then, the subsequence features are extracted to determine the presence of collective anomalies.
Label-Free Multivariate Time Series Anomaly Detection
In this paper, we propose MTGFlow, an unsupervised anomaly detection approach for MTS anomaly detection via dynamic Graph and entity-aware normalizing Flow.
Deep Learning Enabled Semantic Communication Systems for Video Transmission
Semantic communication has emerged as a promising approach for improving efficient transmission in the next generation of wireless networks.
Confidant: Customizing Transformer-based LLMs via Collaborative Edge Training
Transformer-based large language models (LLMs) have demonstrated impressive capabilities in a variety of natural language processing (NLP) tasks.
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
Moreover, we propose a bit-level computation-efficient data compression scheme to compress the data to be transmitted between devices during training.
AnomalyCLIP: Object-agnostic Prompt Learning for Zero-shot Anomaly Detection
It is a crucial task when training data is not accessible due to various concerns, eg, data privacy, yet it is challenging since the models need to generalize to anomalies across different domains where the appearance of foreground objects, abnormal regions, and background features, such as defects/tumors on different products/organs, can vary significantly.
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture
Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers.
$α$ DARTS Once More: Enhancing Differentiable Architecture Search by Masked Image Modeling
Differentiable architecture search (DARTS) has been a mainstream direction in automatic machine learning.
Ranked #10 on
Neural Architecture Search
on NAS-Bench-201, CIFAR-100
Detecting Multivariate Time Series Anomalies with Zero Known Label
Multivariate time series anomaly detection has been extensively studied under the semi-supervised setting, where a training dataset with all normal instances is required.
NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations
In this paper, we explore for the first time the possibility of using implicit neural representations for autonomous 3D scene reconstruction by addressing two key challenges: 1) seeking a criterion to measure the quality of the candidate viewpoints for the view planning based on the new representations, and 2) learning the criterion from data that can generalize to different scenes instead of a hand-crafting one.
AsyncFedED: Asynchronous Federated Learning with Euclidean Distance based Adaptive Weight Aggregation
In an asynchronous federated learning framework, the server updates the global model once it receives an update from a client instead of waiting for all the updates to arrive as in the synchronous setting.
Semantic-preserved Communication System for Highly Efficient Speech Transmission
Deep learning (DL) based semantic communication methods have been explored for the efficient transmission of images, text, and speech in recent years.
Wireless Transmission of Images With The Assistance of Multi-level Semantic Information
In particular, the proposed model includes a multilevel semantic feature extractor, that extracts both the highlevel semantic information, such as the text semantics and the segmentation semantics, and the low-level semantic information, such as local spatial details of the images.
Semantic-aware Speech to Text Transmission with Redundancy Removal
We also propose a two-stage training scheme, which speeds up the training of the proposed DL model.
Generalized Global Ranking-Aware Neural Architecture Ranker for Efficient Image Classifier Search
The NAR explores the quality tiers of the search space globally and classifies each individual to the tier they belong to according to its global ranking.
FTPipeHD: A Fault-Tolerant Pipeline-Parallel Distributed Training Framework for Heterogeneous Edge Devices
Our numerical results demonstrate that FTPipeHD is 6. 8x faster in training than the state of the art method when the computing capacity of the best device is 10x greater than the worst one.
Fairness-aware Outlier Ensemble
Meanwhile, we propose a post-processing framework to tune the original ensemble results through a stacking process so that we can achieve a trade off between fairness and detection performance.
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection
Vision-based dynamic pedestrian intrusion detection (PID), judging whether pedestrians intrude an area-of-interest (AoI) by a moving camera, is an important task in mobile surveillance.
