Zobrazeno 1 - 10
of 49 574
pro vyhledávání: '"PARMAR, A"'
Autor:
Anderson, Eric, Fritz, Jonathan, Lee, Austin, Li, Bohou, Lindblad, Mark, Lindeman, Henry, Meyer, Alex, Parmar, Parth, Ranade, Tanvi, Shah, Mehul A., Sowell, Benjamin, Tecuci, Dan, Thapliyal, Vinayak, Welsh, Matt
LLMs demonstrate an uncanny ability to process unstructured data, and as such, have the potential to go beyond search and run complex, semantic analyses at scale. We describe the design of an unstructured analytics system, Aryn, and the tenets and us
Externí odkaz:
http://arxiv.org/abs/2409.00847
The feedback between mechanical and chemical signals plays a key role in controlling many biological processes and collective cell behavior. Here we focus on the emergence of spatiotemporal density waves in a one-dimensional "cell train." Combining a
Externí odkaz:
http://arxiv.org/abs/2408.02806
In the domain of black-box model extraction, conventional methods reliant on soft labels or surrogate datasets struggle with scaling to high-dimensional input spaces and managing the complexity of an extensive array of interrelated classes. In this w
Externí odkaz:
http://arxiv.org/abs/2408.02140
Autor:
Tyagi, Nemika, Parmar, Mihir, Kulkarni, Mohith, RRV, Aswin, Patel, Nisarg, Nakamura, Mutsumi, Mitra, Arindam, Baral, Chitta
Solving grid puzzles involves a significant amount of logical reasoning. Hence, it is a good domain to evaluate the reasoning capability of a model which can then guide us to improve the reasoning ability of models. However, most existing works evalu
Externí odkaz:
http://arxiv.org/abs/2407.14790
The recent strides in artificial intelligence (AI) and machine learning (ML) have propelled the rise of TinyML, a paradigm enabling AI computations at the edge without dependence on cloud connections. While TinyML offers real-time data analysis and s
Externí odkaz:
http://arxiv.org/abs/2407.11599
As language models have scaled both their number of parameters and pretraining dataset sizes, the computational cost for pretraining has become intractable except for the most well-resourced teams. This increasing cost makes it ever more important to
Externí odkaz:
http://arxiv.org/abs/2407.07263
Autor:
Parmar, Jupinder, Prabhumoye, Shrimai, Jennings, Joseph, Liu, Bo, Jhunjhunwala, Aastha, Wang, Zhilin, Patwary, Mostofa, Shoeybi, Mohammad, Catanzaro, Bryan
The impressive capabilities of recent language models can be largely attributed to the multi-trillion token pretraining datasets that they are trained on. However, model developers fail to disclose their construction methodology which has lead to a l
Externí odkaz:
http://arxiv.org/abs/2407.06380
Autor:
Parmar, Mihir, Deilamsalehy, Hanieh, Dernoncourt, Franck, Yoon, Seunghyun, Rossi, Ryan A., Bui, Trung
Extractive summarization plays a pivotal role in natural language processing due to its wide-range applications in summarizing diverse content efficiently, while also being faithful to the original content. Despite significant advancement achieved in
Externí odkaz:
http://arxiv.org/abs/2407.04855
Hypothesis- Stacks of clay platelets in aqueous suspension swell and break up gradually, with the exfoliated clay particles eventually self-assembling to form transient, fragile, gel-like networks. As a consequence of this physical aging phenomenon,
Externí odkaz:
http://arxiv.org/abs/2407.01396
Autor:
Patel, Nisarg, Kulkarni, Mohith, Parmar, Mihir, Budhiraja, Aashna, Nakamura, Mutsumi, Varshney, Neeraj, Baral, Chitta
As Large Language Models (LLMs) continue to exhibit remarkable performance in natural language understanding tasks, there is a crucial need to measure their ability for human-like multi-step logical reasoning. Existing logical reasoning evaluation be
Externí odkaz:
http://arxiv.org/abs/2406.17169