Search Results for author:
Found 34 papers, 9 papers with code
CATS: Enhancing Multivariate Time Series Forecasting by Constructing Auxiliary Time Series as Exogenous Variables
For Multivariate Time Series Forecasting (MTSF), recent deep learning applications show that univariate models frequently outperform multivariate ones.
Recent Advances in Deterministic Human Motion Prediction: A Review
In recent years, with the continuous advancement of deep learning and the emergence of large-scale human motion datasets, human motion prediction technology has gradually gained prominence in various fields such as human-computer interaction, autonomous driving, sports analysis, and personnel tracking.
Which mode is better for federated learning? Centralized or Decentralized
Both centralized and decentralized approaches have shown excellent performance and great application value in federated learning (FL).
Efficient Federated Prompt Tuning for Black-box Large Pre-trained Models
With the blowout development of pre-trained models (PTMs), the efficient tuning of these models for diverse downstream applications has emerged as a pivotal research concern.
TriGait: Aligning and Fusing Skeleton and Silhouette Gait Data via a Tri-Branch Network
To fully exploit the complementary nature of the two modalities, a novel triple branch gait recognition framework, TriGait, is proposed in this paper.
Temporal Sentence Grounding in Streaming Videos
The goal of TSGSV is to evaluate the relevance between a video stream and a given sentence query.
Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedup
Adaptive optimization has achieved notable success for distributed learning while extending adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) rugged convergence due to inaccurate gradient estimation in global adaptive optimizer; (ii) client drifts exacerbated by local over-fitting with the local adaptive optimizer.
GaitASMS: Gait Recognition by Adaptive Structured Spatial Representation and Multi-Scale Temporal Aggregation
Extensive experiments conducted on two datasets demonstrate the competitive advantage of proposed method, especially in complex scenes, i. e. BG and CL.
Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training
Personalized federated learning (PFL) aims to produce the greatest personalized model for each client to face an insurmountable problem--data heterogeneity in real FL systems.
Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscape
In federated learning (FL), a cluster of local clients are chaired under the coordination of the global server and cooperatively train one model with privacy protection.
Prokaryotic genome editing based on the subtype I-B-Svi CRISPR-Cas system
Type I CRISPR-Cas systems are the most common among six types of CRISPR-Cas systems, however, non-self-targeting genome editing based on a single Cas3 of type I CRISPR-Cas systems has not been reported.
On Efficient Training of Large-Scale Deep Learning Models: A Literature Review
The field of deep learning has witnessed significant progress, particularly in computer vision (CV), natural language processing (NLP), and speech.
Visual Prompt Based Personalized Federated Learning
Then, the local model is trained on the input composed of raw data and a visual prompt to learn the distribution information contained in the prompt.
Subspace based Federated Unlearning
Federated learning (FL) enables multiple clients to train a machine learning model collaboratively without exchanging their local data.
FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracy
Federated learning is an emerging distributed machine learning framework which jointly trains a global model via a large number of local devices with data privacy protections.
Fusion of Global and Local Knowledge for Personalized Federated Learning
Personalized federated learning, as a variant of federated learning, trains customized models for clients using their heterogeneously distributed data.
Deep Graph-Level Orthogonal Hypersphere Compression for Anomaly Detection
Graph-level anomaly detection aims to identify anomalous graphs from a collection of graphs in an unsupervised manner.
Enhance Local Consistency in Federated Learning: A Multi-Step Inertial Momentum Approach
Federated learning (FL), as a collaborative distributed training paradigm with several edge computing devices under the coordination of a centralized server, is plagued by inconsistent local stationary points due to the heterogeneity of the local partial participation clients, which precipitates the local client-drifts problems and sparks off the unstable and slow convergence, especially on the aggravated heterogeneous dataset.
Improving the Model Consistency of Decentralized Federated Learning
To mitigate the privacy leakages and communication burdens of Federated Learning (FL), decentralized FL (DFL) discards the central server and each client only communicates with its neighbors in a decentralized communication network.
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network
Sufficient dimension reduction is a powerful tool to extract core information hidden in the high-dimensional data and has potentially many important applications in machine learning tasks.
Laplacian-based Cluster-Contractive t-SNE for High Dimensional Data Visualization
Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces to extract hidden and useful information or facilitate visual understanding and interpretation of the data.
A Kernel-Expanded Stochastic Neural Network
The deep neural network suffers from many fundamental issues in machine learning.
Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration
Deep learning has powered recent successes of artificial intelligence (AI).
Consistent Sparse Deep Learning: Theory and Computation
Deep learning has been the engine powering many successes of data science.
Molecular Clouds in the Second Quadrant of the Milky Way Mid-plane from l$=$104$.\!\!^{\circ}$75 to l=119$.\!\!^{\circ}$75 and b=$-$5$.\!\!^{\circ}$25 to b=5$.\!\!^{\circ}$25
About 78\% of the N-PDFs of the selected molecular clouds are well fitted with log-normal functions with only small deviations at high-densities which correspond to star-forming regions with scales of $\sim$1-5 pc in the Local arm and $\sim$5-10 pc in the Perseus arm.
Astrophysics of Galaxies
Quasi-quantized Hall response in bulk InAs
The quasi-quantized Hall effect (QQHE) is the three-dimensional (3D) counterpart of the integer quantum Hall effect (QHE), exhibited only by two-dimensional (2D) electron systems.
Mesoscale and Nanoscale Physics Other Condensed Matter
Distances to molecular clouds in the second Galactic quadrant
We present distances to 76 medium-sized molecular clouds and an extra large-scale one in the second Galactic quadrant ($104. 75^\circ <l<150. 25^\circ $ and $|b|<5. 25^\circ$), 73 of which are accurately measured for the first time.
Astrophysics of Galaxies
Deep Learning Based Equalizer for MIMO-OFDM Systems with Insufficient Cyclic Prefix
In this paper, we study the equalization design for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with insufficient cyclic prefix (CP).
Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection
Stochastic gradient Markov chain Monte Carlo (MCMC) algorithms have received much attention in Bayesian computing for big data problems, but they are only applicable to a small class of problems for which the parameter space has a fixed dimension and the log-posterior density is differentiable with respect to the parameters.
Learning-based real-time method to looking through scattering medium beyond the memory effect
Strong scattering medium brings great difficulties to optical imaging, which is also a problem in medical imaging and many other fields.
A Charge-Density-Wave Topological Semimetal
We here demonstrate that the room-temperature phase of (TaSe$_4$)$_2$I is a Weyl semimetal with 24 pairs of Weyl nodes.
Band Gap
Materials Science
Strongly Correlated Electrons
Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach
We propose a variable selection method for high dimensional regression models, which allows for complex, nonlinear, and high-order interactions among variables.
Fast Learning of Relational Dependency Networks
We describe an approach for learning both the RDN's structure and its parameters, given an input relational database: First learn a Bayesian network (BN), then transform the Bayesian network to an RDN.
Computing Multi-Relational Sufficient Statistics for Large Databases
With a naive enumeration approach, computing sufficient statistics for negative relationships is feasible only for small databases.
