Zobrazeno 1 - 10
of 9 646
pro vyhledávání: '"Litvak, A."'
Autor:
Saraogi, Divita, Bala, Suman, Joshi, Jitendra, Iyyani, Shabnam, Bhalerao, Varun, Aditya, J Venkata, Svinkin, D. S., Frederiks, D. D., Lysenko, A. L., Ridnaia, A. V., Kozyrev, A. S., Golovin, D. V., Mitrofanov, I. G., Litvak, M. L., Sanin, A. B., Chattopadyay, Tanmoy, Gupta, Soumya, Waratkar, Gaurav, Bhattacharya, Dipankar, Vadawal, Santosh, Dewangan, Gulab
We present results of a comprehensive analysis of the polarization characteristics of GRB 200503A and GRB 201009A observed with the Cadmium Zinc Telluride Imager (CZTI) on board AstroSat. Despite these GRBs being reasonably bright, they were missed b
Externí odkaz:
http://arxiv.org/abs/2411.00410
Based on observations in the web-graph, the power-law hypothesis states that PageRank has a power-law distribution with the same exponent as the in-degree. While this hypothesis has been analytically verified for many random graph models, such as dir
Externí odkaz:
http://arxiv.org/abs/2407.13730
Autor:
Arman, Andrii, Litvak, Alexander E.
We improve some upper bounds for minimal dispersion on the cube and torus. /Our new ingredient is an improvement of a probabilistic lemma used to obtain upper bounds for dispersion in several previous works. Our new lemma combines a random and non-ra
Externí odkaz:
http://arxiv.org/abs/2406.02757
Publikováno v:
Scientific Reports, volume 14, Article number: 11866 (2024)
We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks,
Externí odkaz:
http://arxiv.org/abs/2404.00793
In this paper, we investigate the conditions under which link analysis algorithms prevent minority groups from reaching high ranking slots. We find that the most common link-based algorithms using centrality metrics, such as PageRank and HITS, can re
Externí odkaz:
http://arxiv.org/abs/2402.13787
Autor:
Li, Yiyang, Li, Lei, Hu, Dingxin, Hao, Xueyi, Litvak, Marina, Vanetik, Natalia, Zhou, Yanquan
Improving factual consistency in abstractive summarization has been a focus of current research. One promising approach is the post-editing method. However, previous works have yet to make sufficient use of factual factors in summaries and suffers fr
Externí odkaz:
http://arxiv.org/abs/2402.08581
Autor:
Li, Zizhang, Litvak, Dor, Li, Ruining, Zhang, Yunzhi, Jakab, Tomas, Rupprecht, Christian, Wu, Shangzhe, Vedaldi, Andrea, Wu, Jiajun
Learning 3D models of all animals on the Earth requires massively scaling up existing solutions. With this ultimate goal in mind, we develop 3D-Fauna, an approach that learns a pan-category deformable 3D animal model for more than 100 animal species
Externí odkaz:
http://arxiv.org/abs/2401.02400
We present the class of projection methods for community detection that generalizes many popular community detection methods. In this framework, we represent each clustering (partition) by a vector on a high-dimensional hypersphere. A community detec
Externí odkaz:
http://arxiv.org/abs/2312.14568
We introduce a new method for learning a generative model of articulated 3D animal motions from raw, unlabeled online videos. Unlike existing approaches for 3D motion synthesis, our model requires no pose annotations or parametric shape models for tr
Externí odkaz:
http://arxiv.org/abs/2312.13604
We provide the first useful and rigorous analysis of ensemble sampling for the stochastic linear bandit setting. In particular, we show that, under standard assumptions, for a $d$-dimensional stochastic linear bandit with an interaction horizon $T$,
Externí odkaz:
http://arxiv.org/abs/2311.08376