Zobrazeno 1 - 10
of 37 352
pro vyhledávání: '"P. Aditya"'
Stochastic Gradient Descent (SGD) is the main approach to optimizing neural networks. Several generalization properties of deep networks, such as convergence to a flatter minima, are believed to arise from SGD. This article explores the causality asp
Externí odkaz:
http://arxiv.org/abs/2412.03035
Large language models (LLMs) are often equipped with multi-sample decoding strategies. An LLM implicitly defines an arithmetic code book, facilitating efficient and embarrassingly parallelizable \textbf{arithmetic sampling} to produce multiple sample
Externí odkaz:
http://arxiv.org/abs/2411.06251
The growing need for accurate and efficient 3D identification of tumors, particularly in liver segmentation, has spurred considerable research into deep learning models. While many existing architectures offer strong performance, they often face chal
Externí odkaz:
http://arxiv.org/abs/2412.19713
Autor:
Agarwal, Aditya, Deshpande, Rahul
The increase in wall-pressure fluctuations with increasing friction Reynolds number ($Re_{\tau}$) of a turbulent boundary layer (TBL) is well known in the literature. However, very few studies have investigated the $Re_{\tau}$-variation of the source
Externí odkaz:
http://arxiv.org/abs/2412.19474
Autor:
Kashi, Aditya, Lu, Hao, Brewer, Wesley, Rogers, David, Matheson, Michael, Shankar, Mallikarjun, Wang, Feiyi
The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities,
Externí odkaz:
http://arxiv.org/abs/2412.19322
We present Chunked Augmented Generation (CAG), an architecture specifically designed to overcome the context window limitations of Google Chrome's built-in Gemini Nano model. While Chrome's integration of Gemini Nano represents a significant advancem
Externí odkaz:
http://arxiv.org/abs/2412.18708
Autor:
Jignasu, Anushrut, Herron, Ethan, Jiang, Zhanhong, Sarkar, Soumik, Hegde, Chinmay, Ganapathysubramanian, Baskar, Balu, Aditya, Krishnamurthy, Adarsh
We present STITCH, a novel approach for neural implicit surface reconstruction of a sparse and irregularly spaced point cloud while enforcing topological constraints (such as having a single connected component). We develop a new differentiable frame
Externí odkaz:
http://arxiv.org/abs/2412.18696
Autor:
Natu, Aditya M., HosseinNia, S. Hassan
Nanopositioning systems frequently encounter limitations in control bandwidth due to their lightly damped resonance behavior. This paper presents a novel Non-Minimum-Phase Resonant Controller (NRC) aimed at active damping control within dual closed-l
Externí odkaz:
http://arxiv.org/abs/2412.18374
Given the ease of creating synthetic data from machine learning models, new models can be potentially trained on synthetic data generated by previous models. This recursive training process raises concerns about the long-term impact on model quality.
Externí odkaz:
http://arxiv.org/abs/2412.17646
Large Language Models (LLMs) are increasingly being deployed in applications such as chatbots, code editors, and conversational agents. A key feature of LLMs is their ability to engage in multi-turn interactions with humans or external tools, enablin
Externí odkaz:
http://arxiv.org/abs/2412.16434