Papers Tensor Networks
“Tensor Networks” 태그가 달린 논문 226편 · 필터 해제
Efficient Finite Initialization for Tensorized Neural Networks
We present a novel method for initializing layers of tensorized neural networks in a way that avoids the explosion of the parameters of the matrix it emulates. The method is intended for layers with a high number of node…
Tensor NetworksDetecting Violations of Differential Privacy for Quantum Algorithms
Quantum algorithms for solving a wide range of practical problems have been proposed in the last ten years, such as data search and analysis, product recommendation, and credit scoring. The concern about privacy and othe…
Product RecommendationQuantum Machine LearningTensor NetworksSymbolically integrating tensor networks over various random tensors by the second version of Python RTNI
We are upgrading the Python-version of RTNI, which symbolically integrates tensor networks over the Haar-distributed unitary matrices. Now, PyRTNI2 can treat the Haar-distributed orthogonal matrices and the real and comp…
Tensor NetworksFuzzy Logic Visual Network (FLVN): A neuro-symbolic approach for visual features matching
Neuro-symbolic integration aims at harnessing the power of symbolic knowledge representation combined with the learning capabilities of deep neural networks. In particular, Logic Tensor Networks (LTNs) allow to incorpora…
Tensor NetworksZero-Shot LearningQuaternion tensor left ring decomposition and application for color image inpainting
In recent years, tensor networks have emerged as powerful tools for solving large-scale optimization problems. One of the most promising tensor networks is the tensor ring (TR) decomposition, which achieves circular dime…
Image InpaintingTensor NetworksConvolutions and More as Einsum: A Tensor Network Perspective with Advances for Second-Order Methods
Despite their simple intuition, convolutions are more tedious to analyze than dense layers, which complicates the transfer of theoretical and algorithmic ideas to convolutions. We simplify convolutions by viewing them as…
Second-order methodsTensor NetworkslogLTN: Differentiable Fuzzy Logic in the Logarithm Space
The AI community is increasingly focused on merging logic with deep learning to create Neuro-Symbolic (NeSy) paradigms and assist neural approaches with symbolic knowledge. A significant trend in the literature involves …
Tensor NetworksDistributive Pre-Training of Generative Modeling Using Matrix-Product States
Tensor networks have recently found applications in machine learning for both supervised learning and unsupervised learning. The most common approaches for training these models are gradient descent methods. In this work…
Density EstimationTensor NetworksTensorKrowch: Smooth integration of tensor networks in machine learning
Tensor networks are factorizations of high-dimensional tensors into networks of smaller tensors. They have applications in physics and mathematics, and recently have been proposed as promising machine learning architectu…
Tensor NetworksMachine learning with tree tensor networks, CP rank constraints, and tensor dropout
Tensor networks developed in the context of condensed matter physics try to approximate order-$N$ tensors with a reduced number of degrees of freedom that is only polynomial in $N$ and arranged as a network of partially …
image-classificationImage ClassificationTensor NetworksCombining Monte Carlo and Tensor-network Methods for Partial Differential Equations via Sketching
In this paper, we propose a general framework for solving high-dimensional partial differential equations with tensor networks. Our approach uses Monte-Carlo simulations to update the solution and re-estimates the new so…
Tensor NetworksCompressing neural network by tensor network with exponentially fewer variational parameters
Neural network (NN) designed for challenging machine learning tasks is in general a highly nonlinear mapping that contains massive variational parameters. High complexity of NN, if unbounded or unconstrained, might unpre…
Tensor NetworksContinual Reasoning: Non-Monotonic Reasoning in Neurosymbolic AI using Continual Learning
Despite the extensive investment and impressive recent progress at reasoning by similarity, deep learning continues to struggle with more complex forms of reasoning such as non-monotonic and commonsense reasoning. Non-mo…
Continual LearningRelational ReasoningTensor NetworksTensorizing flows: a tool for variational inference
Fueled by the expressive power of deep neural networks, normalizing flows have achieved spectacular success in generative modeling, or learning to draw new samples from a distribution given a finite dataset of training s…
Tensor NetworksVariational InferenceAdaptively Topological Tensor Network for Multi-view Subspace Clustering
Multi-view subspace clustering methods have employed learned self-representation tensors from different tensor decompositions to exploit low rank information. However, the data structures embedded with self-representatio…
ClusteringMulti-view Subspace ClusteringTensor DecompositionTensor NetworksApplication of quantum-inspired generative models to small molecular datasets
Quantum and quantum-inspired machine learning has emerged as a promising and challenging research field due to the increased popularity of quantum computing, especially with near-term devices. Theoretical contributions p…
Generative Adversarial NetworkTensor NetworksLinear 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 NetworksElegansNet: a brief scientific report and initial experiments
This research report introduces ElegansNet, a neural network that mimics real-world neuronal network circuitry, with the goal of better understanding the interplay between connectome topology and deep learning systems. T…
Deep Learningimage-classificationImage ClassificationTensor NetworksANTN: Bridging Autoregressive Neural Networks and Tensor Networks for Quantum Many-Body Simulation
Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of t…
Inductive BiasTensor NetworksInterval Logic Tensor Networks
In this paper, we introduce Interval Real Logic (IRL), a two-sorted logic that interprets knowledge such as sequential properties (traces) and event properties using sequences of real-featured data. We interpret connecti…
Tensor Networks