
Integration of Data and Theory for Accelerated Derivable Symbolic Discovery
Scientists have long aimed to discover meaningful equations which accura...
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Quantum Topological Data Analysis with Linear Depth and Exponential Speedup
Quantum computing offers the potential of exponential speedups for certa...
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EPDDL: A Standardized Way of Defining Epistemic Planning Problems
Epistemic Planning (EP) refers to an automated planning setting where th...
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Fast randomized nonHermitian eigensolver based on rational filtering and matrix partitioning
This paper describes a set of rational filtering algorithms to compute a...
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Sparse graph based sketching for fast numerical linear algebra
In recent years, a variety of randomized constructions of sketching matr...
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Denoising quantum states with Quantum Autoencoders – Theory and Applications
We implement a Quantum Autoencoder (QAE) as a quantum circuit capable of...
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QuantumInspired Algorithms from Randomized Numerical Linear Algebra
We create classical (nonquantum) dynamic data structures supporting que...
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Projection techniques to update the truncated SVD of evolving matrices
This paper considers the problem of updating the rankk truncated Singul...
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Thinking Fast and Slow in AI
This paper proposes a research direction to advance AI which draws inspi...
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Dynamic graph based epidemiological model for COVID19 contact tracing data analysis and optimal testing prescription
In this study, we address three important challenges related to the COVI...
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Symbolic Regression using MixedInteger Nonlinear Optimization
The Symbolic Regression (SR) problem, where the goal is to find a regres...
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TensorTensor Products for Optimal Representation and Compression
In this era of big data, data analytics and machine learning, it is impe...
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Communication over Continuous Quantum Secure Dialogue using EinsteinPodolskyRosen States
With the emergence of quantum computing and quantum networks, many commu...
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Tensor Graph Convolutional Networks for Prediction on Dynamic Graphs
Many irregular domains such as social networks, financial transactions, ...
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Recurrent Neural Networks in the Eye of Differential Equations
To understand the fundamental tradeoffs between training stability, tem...
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Stable Tensor Neural Networks for Rapid Deep Learning
We propose a tensor neural network (tNN) framework that offers an excit...
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Should You Derive, Or Let the Data Drive? An Optimization Framework for Hybrid FirstPrinciples DataDriven Modeling
Mathematical models are used extensively for diverse tasks including ana...
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Globally Optimal Symbolic Regression
In this study we introduce a new technique for symbolic regression that ...
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Image classification using local tensor singular value decompositions
From linear classifiers to neural networks, image classification has bee...
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Experimental Design for NonParametric Correction of Misspecified Dynamical Models
We consider a class of misspecified dynamical models where the governing...
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Accelerating Hessianfree optimization for deep neural networks by implicit preconditioning and sampling
Hessianfree training has become a popular parallel second or der optim...
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Lior Horesh
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