Papers Tensor Decomposition
“Tensor Decomposition” 태그가 달린 논문 618편 · 필터 해제
Flow-Through Tensors: A Unified Computational Graph Architecture for Multi-Layer Transportation Network Optimization
Modern transportation network modeling increasingly involves the integration of diverse methodologies including sensor-based forecasting, reinforcement learning, classical flow optimization, and demand modeling that have…
Tensor DecompositionCooperative Bistatic ISAC Systems for Low-Altitude Economy
The burgeoning low-altitude economy (LAE) necessitates integrated sensing and communication (ISAC) systems capable of high-accuracy multi-target localization and velocity estimation under hardware and coverage constraint…
Computational EfficiencyIntegrated sensing and communicationISACTensor DecompositionA Scalable Factorization Approach for High-Order Structured Tensor Recovery
Tensor decompositions, which represent an $N$-order tensor using approximately $N$ factors of much smaller dimensions, can significantly reduce the number of parameters. This is particularly beneficial for high-order ten…
Tensor DecompositionDMRS-Based Uplink Channel Estimation for MU-MIMO Systems with Location-Specific SCSI Acquisition
With the growing number of users in multi-user multiple-input multiple-output (MU-MIMO) systems, demodulation reference signals (DMRSs) are efficiently multiplexed in the code domain via orthogonal cover codes (OCC) to e…
Tensor DecompositionLRSLAM: Low-rank Representation of Signed Distance Fields in Dense Visual SLAM System
Simultaneous Localization and Mapping (SLAM) has been crucial across various domains, including autonomous driving, mobile robotics, and mixed reality. Dense visual SLAM, leveraging RGB-D camera systems, offers advantage…
Autonomous DrivingMixed RealitySimultaneous Localization and MappingTensor DecompositionsparseGeoHOPCA: A Geometric Solution to Sparse Higher-Order PCA Without Covariance Estimation
We propose sparseGeoHOPCA, a novel framework for sparse higher-order principal component analysis (SHOPCA) that introduces a geometric perspective to high-dimensional tensor decomposition. By unfolding the input tensor a…
Computational EfficiencyImage ReconstructionTensor DecompositionGraph signal aware decomposition of dynamic networks via latent graphs
Dynamics on and of networks refer to changes in topology and node-associated signals, respectively and are pervasive in many socio-technological systems, including social, biological, and infrastructure networks. Due to …
Tensor DecompositionMetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
We present MetaTT, a unified Tensor Train (TT) adapter framework for global low-rank fine-tuning of pre-trained transformers. Unlike LoRA, which fine-tunes each weight matrix independently, MetaTT uses a single shared TT…
Language ModelingLanguage Modellingparameter-efficient fine-tuningTensor DecompositionDouble Low-Rank 4D Tensor Decomposition for Circular RIS-Aided mmWave MIMO-NOMA System Channel Estimation in Mobility Scenarios
Channel estimation is not only essential to highly reliable data transmission and massive device access but also an important component of the integrated sensing and communication (ISAC) in the sixth-generation (6G) mobi…
Integrated sensing and communicationISACparameter estimationTensor DecompositionJoint Channel and Symbol Estimation for Communication Systems with Movable Antennas
Communication systems aided by movable antennas have been the subject of recent research due to their potentially increased spatial degrees of freedom offered by optimizing the antenna positioning at the transmitter and/…
Tensor DecompositionRevisit CP Tensor Decomposition: Statistical Optimality and Fast Convergence
Canonical Polyadic (CP) tensor decomposition is a fundamental technique for analyzing high-dimensional tensor data. While the Alternating Least Squares (ALS) algorithm is widely used for computing CP decomposition due to…
Tensor DecompositionPatch-based Reconstruction for Unsupervised Dynamic MRI using Learnable Tensor Function with Implicit Neural Representation
Dynamic MRI plays a vital role in clinical practice by capturing both spatial details and dynamic motion, but its high spatiotemporal resolution is often limited by long scan times. Deep learning (DL)-based methods have …
Tensor DecompositionMaking deep neural networks work for medical audio: representation, compression and domain adaptation
This thesis addresses the technical challenges of applying machine learning to understand and interpret medical audio signals. The sounds of our lungs, heart, and voice convey vital information about our health. Yet, in …
Domain AdaptationModel CompressionTensor DecompositionTransfer LearningLatentLLM: Attention-Aware Joint Tensor Compression
Modern foundation models such as large language models (LLMs) and large multi-modal models (LMMs) require a massive amount of computational and memory resources. We propose a new framework to convert such LLMs/LMMs into …
Model CompressionTensor DecompositionIdentifiability of Nonnegative Tucker Decompositions -- Part I: Theory
Tensor decompositions have become a central tool in data science, with applications in areas such as data analysis, signal processing, and machine learning. A key property of many tensor decompositions, such as the canon…
Tensor DecompositionAuto Tensor Singular Value Thresholding: A Non-Iterative and Rank-Free Framework for Tensor Denoising
In modern data-driven tasks such as classification, optimization, and forecasting, mitigating the effects of intrinsic noise is crucial for improving predictive accuracy. While numerous denoising techniques have been dev…
Computational EfficiencyDenoisingTensor DecompositionD-Tracker: Modeling Interest Diffusion in Social Activity Tensor Data Streams
Large quantities of social activity data, such as weekly web search volumes and the number of new infections with infectious diseases, reflect peoples' interests and activities. It is important to discover temporal patte…
Tensor DecompositionA Double-Norm Aggregated Tensor Latent Factorization Model for Temporal-Aware Traffic Speed Imputation
In intelligent transportation systems (ITS), traffic management departments rely on sensors, cameras, and GPS devices to collect real-time traffic data. Traffic speed data is often incomplete due to sensor failures, data…
ImputationTensor DecompositionMultiscale Tensor Summation Factorization as a New Neural Network Layer (MTS Layer) for Multidimensional Data Processing
Multilayer perceptrons (MLP), or fully connected artificial neural networks, are known for performing vector-matrix multiplications using learnable weight matrices; however, their practical application in many machine le…
Tensor DecompositionCross-Frequency Implicit Neural Representation with Self-Evolving Parameters
Implicit neural representation (INR) has emerged as a powerful paradigm for visual data representation. However, classical INR methods represent data in the original space mixed with different frequency components, and s…
Cloud RemovalDenoisingTensor Decomposition