Papers Tensor Networks
“Tensor Networks” 태그가 달린 논문 226편 · 필터 해제
Enhancing Symbolic Machine Learning by Subsymbolic Representations
The goal of neuro-symbolic AI is to integrate symbolic and subsymbolic AI approaches, to overcome the limitations of either. Prominent systems include Logic Tensor Networks (LTN) or DeepProbLog, which offer neural predic…
Tensor NetworksTensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHC
The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum…
Anomaly DetectionQuantum Machine LearningTensor NetworksDomain-Aware Tensor Network Structure Search
Tensor networks (TNs) provide efficient representations of high-dimensional data, yet identification of the optimal TN structures, the so called tensor network structure search (TN-SS) problem, remains a challenge. Curre…
Tensor NetworksA tensor network approach for chaotic time series prediction
Making accurate predictions of chaotic time series is a complex challenge. Reservoir computing, a neuromorphic-inspired approach, has emerged as a powerful tool for this task. It exploits the memory and nonlinearity of d…
Computational EfficiencyTensor NetworksTime SeriesTime Series PredictionSaten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models
The efficient implementation of large language models (LLMs) is crucial for deployment on resource-constrained devices. Low-rank tensor compression techniques, such as tensor-train (TT) networks, have been widely studied…
Model CompressionTensor NetworksTensorRL-QAS: Reinforcement learning with tensor networks for scalable quantum architecture search
Variational quantum algorithms hold the promise to address meaningful quantum problems already on noisy intermediate-scale quantum hardware, but they face the challenge of designing quantum circuits that both solve the t…
Reinforcement Learning (RL)Tensor NetworksBoosting Binomial Exotic Option Pricing with Tensor Networks
Pricing of exotic financial derivatives, such as Asian and multi-asset American basket options, poses significant challenges for standard numerical methods such as binomial trees or Monte Carlo methods. While the former …
Tensor NetworksHMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems
Quantum machine learning for spin and molecular systems faces critical challenges of scarce labeled data and computationally expensive simulations. To address these limitations, we introduce Hamiltonian-Masked Autoencodi…
Few-Shot LearningQuantum Machine LearningTensor NetworksTransfer LearningExplaining Anomalies with Tensor Networks
Tensor networks, a class of variational quantum many-body wave functions have attracted considerable research interest across many disciplines, including classical machine learning. Recently, Aizpurua et al. demonstrated…
Anomaly DetectionTensor NetworksTrainable Quantum Neural Network for Multiclass Image Classification with the Power of Pre-trained Tree Tensor Networks
Tree tensor networks (TTNs) offer powerful models for image classification. While these TTN image classifiers already show excellent performance on classical hardware, embedding them into quantum neural networks (QNNs) m…
Classificationimage-classificationImage ClassificationTensor NetworksPlastic tensor networks for interpretable generative modeling
A structural optimization scheme for a single-layer nonnegative adaptive tensor tree (NATT) that models a target probability distribution is proposed. The NATT scheme, by construction, has the advantage that it is interp…
Tensor NetworksSymDQN: Symbolic Knowledge and Reasoning in Neural Network-based Reinforcement Learning
We propose a learning architecture that allows symbolic control and guidance in reinforcement learning with deep neural networks. We introduce SymDQN, a novel modular approach that augments the existing Dueling Deep Q-Ne…
reinforcement-learningReinforcement LearningTensor NetworksCompositionality Unlocks Deep Interpretable Models
We propose $\chi$-net, an intrinsically interpretable architecture combining the compositional multilinear structure of tensor networks with the expressivity and efficiency of deep neural networks. $\chi$-nets retain equ…
Model CompressionTensor NetworksQuantum Methods for Managing Ambiguity in Natural Language Processing
The Categorical Compositional Distributional (DisCoCat) framework models meaning in natural language using the mathematical framework of quantum theory, expressed as formal diagrams. DisCoCat diagrams can be associated w…
Tensor NetworksCombining Local Symmetry Exploitation and Reinforcement Learning for Optimised Probabilistic Inference -- A Work In Progress
Efficient probabilistic inference by variable elimination in graphical models requires an optimal elimination order. However, finding an optimal order is a challenging combinatorial optimisation problem for models with a…
Tensor NetworksTensor-based Model Reduction and Identification for Generalized Memory Polynomial
Power amplifiers (PAs) are essential components in wireless communication systems, and the design of their behavioral models has been an important research topic for many years. The widely used generalized memory polynom…
Tensor Networkstn4ml: Tensor Network Training and Customization for Machine Learning
Tensor Networks have emerged as a prominent alternative to neural networks for addressing Machine Learning challenges in foundational sciences, paving the way for their applications to real-life problems. This paper intr…
Tensor NetworksGenerative-enhanced optimization for knapsack problems: an industry-relevant study
Optimization is a crucial task in various industries such as logistics, aviation, manufacturing, chemical, pharmaceutical, and insurance, where finding the best solution to a problem can result in significant cost saving…
Tensor NetworksvalidRegularized dynamical parametric approximation of stiff evolution problems
Evolutionary deep neural networks have emerged as a rapidly growing field of research. This paper studies numerical integrators for such and other classes of nonlinear parametrizations $ u(t) = \Phi(\theta(t)) $, where t…
Tensor NetworksTensor Network Estimation of Distribution Algorithms
Tensor networks are a tool first employed in the context of many-body quantum physics that now have a wide range of uses across the computational sciences, from numerical methods to machine learning. Methods integrating …
Tensor Networks