Iterative refinement (IR) is a popular scheme for solving a linear syste...
The discovery of scientific formulae that parsimoniously explain natural...
Nudging is a behavioral strategy aimed at influencing people's thoughts ...
Automated planning is concerned with developing efficient algorithms to
...
The concept of Artificial Intelligence has gained a lot of attention ove...
Large Language Models (LLMs) have been the subject of active research,
s...
This study concerns the formulation and application of Bayesian optimal
...
Considering Grover's Search Algorithm (GSA) with the standard diffuser s...
One-way functions (OWF) are one of the most essential cryptographic
prim...
Topological data analysis (TDA) is a powerful technique for extracting
c...
Current deep neural networks (DNNs) are vulnerable to adversarial attack...
Learning data representations under uncertainty is an important task tha...
The boundary operator is a linear operator that acts on a collection of
...
Current AI systems lack several important human capabilities, such as
ad...
AI systems have seen dramatic advancement in recent years, bringing many...
Scientists have long aimed to discover meaningful equations which accura...
Quantum computing offers the potential of exponential speedups for certa...
Epistemic Planning (EP) refers to an automated planning setting where th...
This paper describes a set of rational filtering algorithms to compute a...
In recent years, a variety of randomized constructions of sketching matr...
We implement a Quantum Autoencoder (QAE) as a quantum circuit capable of...
We create classical (non-quantum) dynamic data structures supporting que...
This paper considers the problem of updating the rank-k truncated Singul...
This paper proposes a research direction to advance AI which draws
inspi...
In this study, we address three important challenges related to the COVI...
The Symbolic Regression (SR) problem, where the goal is to find a regres...
In this era of big data, data analytics and machine learning, it is
impe...
With the emergence of quantum computing and quantum networks, many
commu...
Many irregular domains such as social networks, financial transactions,
...
To understand the fundamental trade-offs between training stability, tem...
We propose a tensor neural network (t-NN) framework that offers an excit...
Mathematical models are used extensively for diverse tasks including
ana...
In this study we introduce a new technique for symbolic regression that
...
From linear classifiers to neural networks, image classification has bee...
We consider a class of misspecified dynamical models where the governing...
Hessian-free training has become a popular parallel second or- der
optim...