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pro vyhledávání: '"GUPTA, DEEPAK"'
Systems switching between different dynamical phases is an ubiquitous phenomenon. The general understanding of such a process is limited. To this end, we present a general expression that captures fluctuations of a system exhibiting a switching mecha
Externí odkaz:
http://arxiv.org/abs/2408.16643
Bioimpedance a Diagnostic Tool for Tobacco Induced Oral Lesions: a Mixed Model cross-sectional study
Introduction: Electrical impedance spectroscopy (EIS) has recently developed as a novel diagnostic device for screening and evaluating cervical dysplasia, prostate cancer, breast cancer and basal cell carcinoma. The current study aimed to validate an
Externí odkaz:
http://arxiv.org/abs/2408.11886
Low-rank approximations, of the weight and feature space can enhance the performance of deep learning models, whether in terms of improving generalization or reducing the latency of inference. However, there is no clear consensus yet on \emph{how}, \
Externí odkaz:
http://arxiv.org/abs/2405.13039
Autor:
Lin, Ying-Chun, Neville, Jennifer, Stokes, Jack W., Yang, Longqi, Safavi, Tara, Wan, Mengting, Counts, Scott, Suri, Siddharth, Andersen, Reid, Xu, Xiaofeng, Gupta, Deepak, Jauhar, Sujay Kumar, Song, Xia, Buscher, Georg, Tiwary, Saurabh, Hecht, Brent, Teevan, Jaime
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems. Users express their satisfaction or dissatisfaction with diverse conversational patterns in bo
Externí odkaz:
http://arxiv.org/abs/2403.12388
Conventional scaling of neural networks typically involves designing a base network and growing different dimensions like width, depth, etc. of the same by some predefined scaling factors. We introduce an automated scaling approach leveraging second-
Externí odkaz:
http://arxiv.org/abs/2402.12418
Despite the impressive performance of LLMs, their widespread adoption faces challenges due to substantial computational and memory requirements during inference. Recent advancements in model compression and system-level optimization methods aim to en
Externí odkaz:
http://arxiv.org/abs/2402.01799
Partial resetting, whereby a state variable $x(t)$ is reset at random times to a value $a x (t)$, $0\leq a \leq 1$, generalizes conventional resetting by introducing the resetting strength $a$ as a parameter. Partial resetting generates a broad famil
Externí odkaz:
http://arxiv.org/abs/2401.11919
Data sharding, a technique for partitioning and distributing data among multiple servers or nodes, offers enhancements in the scalability, performance, and fault tolerance of extensive distributed systems. Nonetheless, this strategy introduces novel
Externí odkaz:
http://arxiv.org/abs/2405.00004
Due to the substantial scale of Large Language Models (LLMs), the direct application of conventional compression methodologies proves impractical. The computational demands associated with even minimal gradient updates present challenges, particularl
Externí odkaz:
http://arxiv.org/abs/2312.07046