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pro vyhledávání: '"A. Moitra"'
An important task in high-dimensional statistics is learning the parameters or dependency structure of an undirected graphical model, or Markov random field (MRF). Much of the prior work on this problem assumes access to i.i.d. samples from the MRF d
Externí odkaz:
http://arxiv.org/abs/2409.05284
Publikováno v:
Applied Physics Reviews, 2024
This review explores the intersection of bio-plausible artificial intelligence in the form of Spiking Neural Networks (SNNs) with the analog In-Memory Computing (IMC) domain, highlighting their collective potential for low-power edge computing enviro
Externí odkaz:
http://arxiv.org/abs/2408.12767
Due to the high computation overhead of Vision Transformers (ViTs), In-memory Computing architectures are being researched towards energy-efficient deployment in edge-computing scenarios. Prior works have proposed efficient algorithm-hardware co-desi
Externí odkaz:
http://arxiv.org/abs/2408.12742
Autor:
Moitra, Upamanyu
We consider trajectories of massless particles in the presence of charged black holes in asymptotically AdS spacetimes in arbitrary dimensions. We study the properties of the photon ring in the (near-)extremal limit and show that the photon ring can
Externí odkaz:
http://arxiv.org/abs/2408.08308
Autor:
Dabholkar, Atish, Moitra, Upamanyu
We construct $\mathbb{Z}_N$ orbifolds of the ten-dimensional heterotic string theories appropriate for implementing the stringy replica method for the calculation of quantum entanglement entropy. A novel feature for the heterotic string is that the g
Externí odkaz:
http://arxiv.org/abs/2407.17553
Autor:
Golowich, Noah, Moitra, Ankur
In this paper, we study the offline RL problem with linear function approximation. Our main structural assumption is that the MDP has low inherent Bellman error, which stipulates that linear value functions have linear Bellman backups with respect to
Externí odkaz:
http://arxiv.org/abs/2406.11686
Autor:
Golowich, Noah, Moitra, Ankur
One of the most natural approaches to reinforcement learning (RL) with function approximation is value iteration, which inductively generates approximations to the optimal value function by solving a sequence of regression problems. To ensure the suc
Externí odkaz:
http://arxiv.org/abs/2406.11640
Autor:
Golowich, Noah, Moitra, Ankur
Motivated by the problem of detecting AI-generated text, we consider the problem of watermarking the output of language models with provable guarantees. We aim for watermarks which satisfy: (a) undetectability, a cryptographic notion introduced by Ch
Externí odkaz:
http://arxiv.org/abs/2406.02633
We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply $e^{-\mathrm{i} Ht}$ for an unknown local Hamiltonian $H = \sum_{a = 1}^m \lambda_a E_a$ on $n$ qubits, the goal is to recover $H$. This probl
Externí odkaz:
http://arxiv.org/abs/2405.00082
In this expository note we show that the learning parities with noise (LPN) assumption is robust to weak dependencies in the noise distribution of small batches of samples. This provides a partial converse to the linearization technique of [AG11]. Th
Externí odkaz:
http://arxiv.org/abs/2404.11325