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pro vyhledávání: '"Mazumdar A"'
We propose a framework for two-player infinite-dimensional games with cooperative or competitive structure. These games take the form of coupled partial differential equations in which players optimize over a space of measures, driven by either a gra
Externí odkaz:
http://arxiv.org/abs/2411.07403
We demonstrate the existence of genuine tripartite non-Gaussian entanglement in a quantum gravitational system formed by a quantum harmonic oscillator coupled to a single frequency of a quantized gravitational wave. For this purpose, we introduce a n
Externí odkaz:
http://arxiv.org/abs/2411.03293
Autor:
Elahi, Shafaq Gulzar, Schut, Martine, Dana, Andrew, Grinin, Alexey, Bose, Sougato, Mazumdar, Anupam, Geraci, Andrew
The Quantum Gravity Mediated Entanglement (QGEM) protocol offers a novel method to probe the quantumness of gravitational interactions at non-relativistic scales. This protocol leverages the Stern-Gerlach effect to create $\mathcal{O}(\sim \mu m)$ sp
Externí odkaz:
http://arxiv.org/abs/2411.02325
We provide a solution for decoherence in spatial superpositions due to scattering/collision with air molecules. This result reproduces the short- and long-wavelength limits known in the literature. We compare the decoherence rate with several existin
Externí odkaz:
http://arxiv.org/abs/2410.20910
Autor:
Li, Xiaxin, Mazumdar, Arya
In Group Testing, the objective is to identify K defective items out of N, K<
Externí odkaz:
http://arxiv.org/abs/2410.14566
In this paper, we study the data-dependent convergence and generalization behavior of gradient methods for neural networks with smooth activation. Our first result is a novel bound on the excess risk of deep networks trained by the logistic loss, via
Externí odkaz:
http://arxiv.org/abs/2410.10024
Autor:
Adamiak, Maciej, Grinblat, Yulia, Psotta, Julian, Fulman, Nir, Mazumdar, Himshikhar, Tang, Shiyu, Zipf, Alexander
This paper presents a method for detecting and estimating vehicle speeds using PlanetScope SuperDove satellite imagery, offering a scalable solution for global vehicle traffic monitoring. Conventional methods such as stationary sensors and mobile sys
Externí odkaz:
http://arxiv.org/abs/2410.14698
Standard multi-agent reinforcement learning (MARL) algorithms are vulnerable to sim-to-real gaps. To address this, distributionally robust Markov games (RMGs) have been proposed to enhance robustness in MARL by optimizing the worst-case performance w
Externí odkaz:
http://arxiv.org/abs/2409.20067
Recovering the underlying clustering of a set $U$ of $n$ points by asking pair-wise same-cluster queries has garnered significant interest in the last decade. Given a query $S \subset U$, $|S|=2$, the oracle returns yes if the points are in the same
Externí odkaz:
http://arxiv.org/abs/2409.10908
In this paper, we consider two-player zero-sum matrix and stochastic games and develop learning dynamics that are payoff-based, convergent, rational, and symmetric between the two players. Specifically, the learning dynamics for matrix games are base
Externí odkaz:
http://arxiv.org/abs/2409.01447