Zobrazeno 1 - 10
of 1 199
pro vyhledávání: '"BANERJEE, ARINDAM"'
Autor:
Chang, Yi-Chia, Stewart, Adam J., Bastani, Favyen, Wolters, Piper, Kannan, Shreya, Huber, George R., Wang, Jingtong, Banerjee, Arindam
Foundation models pre-trained using self-supervised and weakly-supervised learning have shown powerful transfer learning capabilities on various downstream tasks, including language understanding, text generation, and image recognition. Recently, the
Externí odkaz:
http://arxiv.org/abs/2409.09451
Weight normalization (WeightNorm) is widely used in practice for the training of deep neural networks and modern deep learning libraries have built-in implementations of it. In this paper, we provide the first theoretical characterizations of both op
Externí odkaz:
http://arxiv.org/abs/2409.08935
Autor:
Banerjee, Arindam, Basu, Saugata
Let $\mathrm{R}$ be a real closed field, $S \subset \mathrm{R}^n$ a closed and bounded semi-algebraic set and $\mathbf{f} = (f_1,\ldots,f_p):S \rightarrow \mathrm{R}^p$ a continuous semi-algebraic map. We study the poset module structure in homology
Externí odkaz:
http://arxiv.org/abs/2407.13586
Generalization and optimization guarantees on the population loss in machine learning often rely on uniform convergence based analysis, typically based on the Rademacher complexity of the predictors. The rich representation power of modern models has
Externí odkaz:
http://arxiv.org/abs/2406.07712
Recent works have shown a reduction from contextual bandits to online regression under a realizability assumption [Foster and Rakhlin, 2020, Foster and Krishnamurthy, 2021]. In this work, we investigate the use of neural networks for such online regr
Externí odkaz:
http://arxiv.org/abs/2312.07145
Generative models have gained popularity for their potential applications in imaging science, such as image reconstruction, posterior sampling and data sharing. Flow-based generative models are particularly attractive due to their ability to tractabl
Externí odkaz:
http://arxiv.org/abs/2309.04856
Autor:
Stewart, Adam J., Lehmann, Nils, Corley, Isaac A., Wang, Yi, Chang, Yi-Chia, Braham, Nassim Ait Ali, Sehgal, Shradha, Robinson, Caleb, Banerjee, Arindam
The Landsat program is the longest-running Earth observation program in history, with 50+ years of data acquisition by 8 satellites. The multispectral imagery captured by sensors onboard these satellites is critical for a wide range of scientific fie
Externí odkaz:
http://arxiv.org/abs/2306.09424
In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decades with various applications. To solve the exploitation-exploration tr
Externí odkaz:
http://arxiv.org/abs/2305.03784