Zobrazeno 1 - 10
of 2 128
pro vyhledávání: '"GUPTA, ANKIT"'
Autor:
Gupta, Ankit, Burada, P. S.
We numerically investigate the diffusive behavior of active Brownian particles in a two-dimensional confined channel filled with soft obstacles, whose softness is controlled by a parameter $K$. Here, active particles are subjected to external bias $F
Externí odkaz:
http://arxiv.org/abs/2410.16223
Stochastic models for chemical reaction networks are increasingly popular in systems and synthetic biology. These models formulate the reaction dynamics as Continuous-Time Markov Chains (CTMCs) whose propensities are parameterized by a vector $\theta
Externí odkaz:
http://arxiv.org/abs/2410.11471
Autor:
Bunne, Charlotte, Roohani, Yusuf, Rosen, Yanay, Gupta, Ankit, Zhang, Xikun, Roed, Marcel, Alexandrov, Theo, AlQuraishi, Mohammed, Brennan, Patricia, Burkhardt, Daniel B., Califano, Andrea, Cool, Jonah, Dernburg, Abby F., Ewing, Kirsty, Fox, Emily B., Haury, Matthias, Herr, Amy E., Horvitz, Eric, Hsu, Patrick D., Jain, Viren, Johnson, Gregory R., Kalil, Thomas, Kelley, David R., Kelley, Shana O., Kreshuk, Anna, Mitchison, Tim, Otte, Stephani, Shendure, Jay, Sofroniew, Nicholas J., Theis, Fabian, Theodoris, Christina V., Upadhyayula, Srigokul, Valer, Marc, Wang, Bo, Xing, Eric, Yeung-Levy, Serena, Zitnik, Marinka, Karaletsos, Theofanis, Regev, Aviv, Lundberg, Emma, Leskovec, Jure, Quake, Stephen R.
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intell
Externí odkaz:
http://arxiv.org/abs/2409.11654
In this work, we propose an energy efficient neuromorphic receiver to replace multiple signal-processing blocks at the receiver by a Spiking Neural Network (SNN) based module, called SpikingRx. We propose a deep convolutional SNN with spike-element-w
Externí odkaz:
http://arxiv.org/abs/2409.05610
Autor:
Wolfson, Tomer, Geva, Mor, Gupta, Ankit, Gardner, Matt, Goldberg, Yoav, Deutch, Daniel, Berant, Jonathan
Publikováno v:
Transactions of the Association for Computational Linguistics, Vol 8, Pp 183-198 (2020)
Understanding natural language questions entails the ability to break down a question into the requisite steps for computing its answer. In this work, we introduce a Question Decomposition Meaning Representation (QDMR) for questions. QDMR constitutes
Externí odkaz:
https://doaj.org/article/60cbcde23f554385b42cd09ded9966fe
Motivated by the increasing popularity of overparameterized Stochastic Differential Equations (SDEs) like Neural SDEs, Wang, Blanchet and Glynn recently introduced the generator gradient estimator, a novel unbiased stochastic gradient estimator for S
Externí odkaz:
http://arxiv.org/abs/2407.20196
The emergence of industrial-scale speech recognition (ASR) models such as Whisper and USM, trained on 1M hours of weakly labelled and 12M hours of audio only proprietary data respectively, has led to a stronger need for large scale public ASR corpora
Externí odkaz:
http://arxiv.org/abs/2402.00235
Stochastic filtering is a vibrant area of research in both control theory and statistics, with broad applications in many scientific fields. Despite its extensive historical development, there still lacks an effective method for joint parameter-state
Externí odkaz:
http://arxiv.org/abs/2311.00836
Modeling long-range dependencies across sequences is a longstanding goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences. However, these impressive empirical ga
Externí odkaz:
http://arxiv.org/abs/2310.02980
We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances. A data-set consisting o
Externí odkaz:
http://arxiv.org/abs/2309.01814