Zobrazeno 1 - 10
of 29 346
pro vyhledávání: '"VARSHNEY, A."'
Autor:
Guo, Hangzhi, Venkit, Pranav Narayanan, Jang, Eunchae, Srinath, Mukund, Zhang, Wenbo, Mingole, Bonam, Gupta, Vipul, Varshney, Kush R., Sundar, S. Shyam, Yadav, Amulya
The widespread adoption of large language models (LLMs) and generative AI (GenAI) tools across diverse applications has amplified the importance of addressing societal biases inherent within these technologies. While the NLP community has extensively
Externí odkaz:
http://arxiv.org/abs/2410.15467
Autor:
Kaur, Rishemjit, Zhang, Shuchen, Berwal, Bhavika, Ray, Sonalika, Kumar, Ritesh, Varshney, Lav R.
Herbs and spices each contain about 3000 phytochemicals on average and there is much traditional knowledge on their health benefits. However, there is a lack of systematic study to understand the relationship among herbs and spices, their phytochemic
Externí odkaz:
http://arxiv.org/abs/2410.17286
Autor:
Pattanaik, Anay, Varshney, Lav R.
This paper considers an online reinforcement learning algorithm that leverages pre-collected data (passive memory) from the environment for online interaction. We show that using passive memory improves performance and further provide theoretical gua
Externí odkaz:
http://arxiv.org/abs/2410.14665
Autor:
Varshney, Ayush K., Torra, Vicenç
Privacy regulations like the GDPR in Europe and the CCPA in the US allow users the right to remove their data ML applications. Machine unlearning addresses this by modifying the ML parameters in order to forget the influence of a specific data point
Externí odkaz:
http://arxiv.org/abs/2410.09947
Autor:
Varshney, Prateek, Pilanci, Mert
Deploying large and complex deep neural networks on resource-constrained edge devices poses significant challenges due to their computational demands and the complexities of non-convex optimization. Traditional compression methods such as distillatio
Externí odkaz:
http://arxiv.org/abs/2410.06567
Noise, traditionally considered a nuisance in computational systems, is reconsidered for its unexpected and counter-intuitive benefits across a wide spectrum of domains, including nonlinear information processing, signal processing, image processing,
Externí odkaz:
http://arxiv.org/abs/2410.06348
Autor:
Nayak, Anuj K., Varshney, Lav R.
Recent empirical studies show three phenomena with increasing size of language models: compute-optimal size scaling, emergent capabilities, and performance plateauing. We present a simple unified mathematical framework to explain all of these languag
Externí odkaz:
http://arxiv.org/abs/2410.01243
Autor:
Rawat, Ambrish, Schoepf, Stefan, Zizzo, Giulio, Cornacchia, Giandomenico, Hameed, Muhammad Zaid, Fraser, Kieran, Miehling, Erik, Buesser, Beat, Daly, Elizabeth M., Purcell, Mark, Sattigeri, Prasanna, Chen, Pin-Yu, Varshney, Kush R.
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal system
Externí odkaz:
http://arxiv.org/abs/2409.15398
Autor:
Varshney, Harsh, Agarwal, Amit
The Nernst and Seebeck effects are crucial for thermoelectric energy harvesting. However, the linear anomalous Nernst effect requires magnetic materials with intrinsically broken time-reversal symmetry. In non-magnetic systems, the dominant transvers
Externí odkaz:
http://arxiv.org/abs/2409.11108
This paper introduces a federated learning framework that enables over-the-air computation via digital communications, using a new joint source-channel coding scheme. Without relying on channel state information at devices, this scheme employs lattic
Externí odkaz:
http://arxiv.org/abs/2409.06343