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Found 34 papers, 4 papers with code
Lightweight Unsupervised Federated Learning with Pretrained Vision Language Model
To address these two inherent challenges in supervised federated learning, we propose a novel lightweight unsupervised federated learning approach that leverages unlabeled data on each client to perform lightweight model training and communication by harnessing pretrained vision-language models, such as CLIP.
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling
Moreover, a case study in the station clustering based on real passenger flow data is conducted, with quite valuable insights discovered.
Image-based Novel Fault Detection with Deep Learning Classifiers using Hierarchical Labels
One important characteristic of modern fault classification systems is the ability to flag the system when faced with previously unseen fault types.
Hierarchical Multi-label Classification for Fine-level Event Extraction from Aviation Accident Reports
This article argues that we can identify the events more accurately by leveraging the event taxonomy.
DS-NeRV: Implicit Neural Video Representation with Decomposed Static and Dynamic Codes
Implicit neural representations for video (NeRV) have recently become a novel way for high-quality video representation.
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
Our 13B model (ChipGPT-FT) has a pass rate improvement compared with GPT-3. 5 in Verilog generation and outperforms in EDA script (i. e., SiliconCompiler) generation with only 200 EDA script data.
Learning-driven Physically-aware Large-scale Circuit Gate Sizing
Gate sizing plays an important role in timing optimization after physical design.
A Crosstalk-Aware Timing Prediction Method in Routing
With shrinking interconnect spacing in advanced technology nodes, existing timing predictions become less precise due to the challenging quantification of crosstalk-induced delay.
Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering
In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically.
Personalized Tucker Decomposition: Modeling Commonality and Peculiarity on Tensor Data
We propose personalized Tucker decomposition (perTucker) to address the limitations of traditional tensor decomposition methods in capturing heterogeneity across different datasets.
Tensor Dirichlet Process Multinomial Mixture Model for Passenger Trajectory Clustering
In this paper, we propose a novel Tensor Dirichlet Process Multinomial Mixture model (Tensor-DPMM), which is designed to preserve the multi-mode and hierarchical structure of the multi-dimensional trip information via tensor, and cluster them in a unified one-step manner.
Learning to Simulate Natural Language Feedback for Interactive Semantic Parsing
In this work, we propose a new task of simulating NL feedback for interactive semantic parsing.
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation
We first split the unlabeled training set in the target domain into a pseudo-labeled confident subset and an unlabeled less-confident subset according to the prediction confidence scores from the pre-trained source model.
Source-Free Domain Adaptation
Unsupervised Domain Adaptation
Learning on Large-scale Text-attributed Graphs via Variational Inference
In this paper, we propose an efficient and effective solution to learning on large text-attributed graphs by fusing graph structure and language learning with a variational Expectation-Maximization (EM) framework, called GLEM.
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Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints
Neural Networks (NNs) have been widely {used in supervised learning} due to their ability to model complex nonlinear patterns, often presented in high-dimensional data such as images and text.
Adaptive Partially-Observed Sequential Change Detection and Isolation
High-dimensional data has become popular due to the easy accessibility of sensors in modern industrial applications.
Adaptive Resources Allocation CUSUM for Binomial Count Data Monitoring with Application to COVID-19 Hotspot Detection
In this paper, we present an efficient statistical method (denoted as "Adaptive Resources Allocation CUSUM") to robustly and efficiently detect the hotspot with limited sampling resources.
GAN-Based Multi-View Video Coding with Spatio-Temporal EPI Reconstruction
At the decoder side, we combine the SI and adjacent viewpoints to reconstruct intermediate views using the GAN generator.
ANTLER: Bayesian Nonlinear Tensor Learning and Modeler for Unstructured, Varying-Size Point Cloud Data
Unstructured point clouds with varying sizes are increasingly acquired in a variety of environments through laser triangulation or Light Detection and Ranging (LiDAR).
Deep Spatio-temporal Sparse Decomposition for Trend Prediction and Anomaly Detection in Cardiac Electrical Conduction
Electrical conduction among cardiac tissue is commonly modeled with partial differential equations, i. e., reaction-diffusion equation, where the reaction term describes cellular stimulation and diffusion term describes electrical propagation.
Deep Multistage Multi-Task Learning for Quality Prediction of Multistage Manufacturing Systems
In multistage manufacturing systems, modeling multiple quality indices based on the process sensing variables is important.
Filters for ISI Suppression in Molecular Communication via Diffusion
The extent that ISI and noise are suppressed in an MCvD system determines its effectiveness, especially at a high data rate.
Covariance Distributions in Single Particle Tracking
This suggests that the origin of the theory-experiment discrepancy is associated with localization noise, which influences only the first two covariances.
Biological Physics
Partially Observable Online Change Detection via Smooth-Sparse Decomposition
We consider online change detection of high dimensional data streams with sparse changes, where only a subset of data streams can be observed at each sensing time point due to limited sensing capacities.
Automatic Storage Structure Selection for hybrid Workload
Therefore, the users of the database need to select the storage engine and design data model according to the workload encountered.
Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing
The use of video-imaging data for in-line process monitoring applications has become more and more popular in the industry.
Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile
Spatiotemporal data is very common in many applications, such as manufacturing systems and transportation systems.
Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction
Low-rank tensor decomposition and completion have attracted significant interest from academia given the ubiquity of tensor data.
Toward a Better Monitoring Statistic for Profile Monitoring via Variational Autoencoders
To do so, we formulate nonlinear and probabilistic extensions of the monitoring statistics from classical approaches as the expected reconstruction error (ERE) and the KL-divergence (KLD) based monitoring statistics.
AKM$^2$D : An Adaptive Framework for Online Sensing and Anomaly Quantification
In point-based sensing systems such as coordinate measuring machines (CMM) and laser ultrasonics where complete sensing is impractical due to the high sensing time and cost, adaptive sensing through a systematic exploration is vital for online inspection and anomaly quantification.
Dynamic Network Embedding via Incremental Skip-gram with Negative Sampling
Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario.
Multiple profiles sensor-based monitoring and anomaly detection
However, there are several challenges in developing an effective process monitoring system: (i) data streams generated by multiple sensors are high-dimensional profiles; (ii) sensor signals are affected by noise due to system-inherent variations; (iii) signals of different sensors have cluster-wise features; and (iv) an anomaly may cause only sparse changes of sensor signals.
Structured Point Cloud Data Analysis via Regularized Tensor Regression for Process Modeling and Optimization
Advanced 3D metrology technologies such as Coordinate Measuring Machine (CMM) and laser 3D scanners have facilitated the collection of massive point cloud data, beneficial for process monitoring, control and optimization.
Dynamic Multivariate Functional Data Modeling via Sparse Subspace Learning
Multivariate functional data from a complex system are naturally high-dimensional and have complex cross-correlation structure.
