Papers tensor algebra
“tensor algebra” 태그가 달린 논문 41편 · 필터 해제
On Tensor-based Polynomial Hamiltonian Systems
It is known that a linear system with a system matrix A constitutes a Hamiltonian system with a quadratic Hamiltonian if and only if A is a Hamiltonian matrix. This provides a straightforward method to verify whether a l…
tensor algebraPLAIN: Scalable Estimation Architecture for Integrated Sensing and Communication
Integrated sensing and communication (ISAC) is envisioned be to one of the paradigms upon which next-generation mobile networks will be built, extending localization and tracking capabilities, as well as giving birth to …
compressed sensingIntegrated sensing and communicationISACmultidimensional harmonic retrieval+4Flexible Moment-Invariant Bases from Irreducible Tensors
Moment invariants are a powerful tool for the generation of rotation-invariant descriptors needed for many applications in pattern detection, classification, and machine learning. A set of invariants is optimal if it is …
tensor algebraLocal Attention Mechanism: Boosting the Transformer Architecture for Long-Sequence Time Series Forecasting
Transformers have become the leading choice in natural language processing over other deep learning architectures. This trend has also permeated the field of time series analysis, especially for long-horizon forecasting,…
tensor algebraTime SeriesTime Series AnalysisTime Series ForecastingCompressing Structured Tensor Algebra
Tensor algebra is a crucial component for data-intensive workloads such as machine learning and scientific computing. As the complexity of data grows, scientists often encounter a dilemma between the highly specialized d…
Code Generationtensor algebraCATBench: A Compiler Autotuning Benchmarking Suite for Black-box Optimization
Bayesian optimization is a powerful method for automating tuning of compilers. The complex landscape of autotuning provides a myriad of rarely considered structural challenges for black-box optimizers, and the lack of st…
Bayesian OptimizationBenchmarkingCompiler Optimizationtensor algebraOptimal Kernel Orchestration for Tensor Programs with Korch
Kernel orchestration is the task of mapping the computation defined in different operators of a deep neural network (DNN) to the execution of GPU kernels on modern hardware platforms. Prior approaches optimize kernel orc…
DiversityGPUtensor algebraSignature volatility models: pricing and hedging with Fourier
We consider a stochastic volatility model where the dynamics of the volatility are given by a possibly infinite linear combination of the elements of the time extended signature of a Brownian motion. First, we show that …
tensor algebraEquation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
Discovering governing equations from data is important to many scientific and engineering applications. Despite promising successes, existing methods are still challenged by data sparsity and noise issues, both of which …
Equation Discoveryregressiontensor algebraUncertainty QuantificationLinear to multi-linear algebra and systems using tensors
In past few decades, tensor algebra also known as multi-linear algebra has been developed and customized as a tool to be used for various engineering applications. In particular, with the help of a special form of tensor…
tensor algebraTensor NetworksGraph Tensor Networks: An Intuitive Framework for Designing Large-Scale Neural Learning Systems on Multiple Domains
Despite the omnipresence of tensors and tensor operations in modern deep learning, the use of tensor mathematics to formally design and describe neural networks is still under-explored within the deep learning community.…
Deep Learningtensor algebraTensor NetworksReservoir kernels and Volterra series
A universal kernel is constructed whose sections approximate any causal and time-invariant filter in the fading memory category with inputs and outputs in a finite-dimensional Euclidean space. This kernel is built using …
tensor algebraGraph-Regularized Tensor Regression: A Domain-Aware Framework for Interpretable Multi-Way Financial Modelling
Analytics of financial data is inherently a Big Data paradigm, as such data are collected over many assets, asset classes, countries, and time periods. This represents a challenge for modern machine learning models, as t…
regressiontensor algebraTensor DecompositionOLLIE: Derivation-based Tensor Program Optimizer
Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressio…
GPUtensor algebratntorch: Tensor Network Learning with PyTorch
We present tntorch, a tensor learning framework that supports multiple decompositions (including Candecomp/Parafac, Tucker, and Tensor Train) under a unified interface. With our library, the user can learn and handle low…
GPUtensor algebraSparseloop: An Analytical Approach To Sparse Tensor Accelerator Modeling
In recent years, many accelerators have been proposed to efficiently process sparse tensor algebra applications (e.g., sparse neural networks). However, these proposals are single points in a large and diverse design spa…
tensor algebraLoopStack: a Lightweight Tensor Algebra Compiler Stack
We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack enables us to compile entire neural network…
BIG-bench Machine Learningtensor algebraCollage: Seamless Integration of Deep Learning Backends with Automatic Placement
The strong demand for efficient and performant deployment of Deep Learning (DL) applications prompts the rapid development of a rich DL ecosystem. To keep up with this fast advancement, it is crucial for modern DL framew…
CPUDeep LearningGPUtensor algebraNonnegative Tucker Decomposition with Beta-divergence for Music Structure Analysis of Audio Signals
Nonnegative Tucker decomposition (NTD), a tensor decomposition model, has received increased interest in the recent years because of its ability to blindly extract meaningful patterns, in particular in Music Information …
Information RetrievalMusic Information RetrievalRetrievaltensor algebra+1TOD: GPU-accelerated Outlier Detection via Tensor Operations
Outlier detection (OD) is a key learning task for finding rare and deviant data samples, with many time-critical applications such as fraud detection and intrusion detection. In this work, we propose TOD, the first tenso…
CPUFraud DetectionGPUIntrusion Detection+3