Zobrazeno 1 - 10
of 15 475
pro vyhledávání: '"Nayyar AS"'
Autor:
Huber, Patrick, Einolghozati, Arash, Conway, Rylan, Narang, Kanika, Smith, Matt, Nayyar, Waqar, Sagar, Adithya, Aly, Ahmed, Shrivastava, Akshat
Distilling conversational skills into Small Language Models (SLMs) with approximately 1 billion parameters presents significant challenges. Firstly, SLMs have limited capacity in their model parameters to learn extensive knowledge compared to larger
Externí odkaz:
http://arxiv.org/abs/2408.11219
Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a scenario where the imitator relies solely on observed behavior and cannot
Externí odkaz:
http://arxiv.org/abs/2408.09125
Autor:
Akkiraju, Rama, Xu, Anbang, Bora, Deepak, Yu, Tan, An, Lu, Seth, Vishal, Shukla, Aaditya, Gundecha, Pritam, Mehta, Hridhay, Jha, Ashwin, Raj, Prithvi, Balasubramanian, Abhinav, Maram, Murali, Muthusamy, Guru, Annepally, Shivakesh Reddy, Knowles, Sidney, Du, Min, Burnett, Nick, Javiya, Sean, Marannan, Ashok, Kumari, Mamta, Jha, Surbhi, Dereszenski, Ethan, Chakraborty, Anupam, Ranjan, Subhash, Terfai, Amina, Surya, Anoop, Mercer, Tracey, Thanigachalam, Vinodh Kumar, Bar, Tamar, Krishnan, Sanjana, Kilaru, Samy, Jaksic, Jasmine, Algarici, Nave, Liberman, Jacob, Conway, Joey, Nayyar, Sonu, Boitano, Justin
Enterprise chatbots, powered by generative AI, are emerging as key applications to enhance employee productivity. Retrieval Augmented Generation (RAG), Large Language Models (LLMs), and orchestration frameworks like Langchain and Llamaindex are cruci
Externí odkaz:
http://arxiv.org/abs/2407.07858
The fruit identification process involves analyzing and categorizing different types of fruits based on their visual characteristics. This activity can be achieved using a range of methodologies, encompassing manual examination, conventional computer
Externí odkaz:
http://arxiv.org/abs/2406.01869
Autor:
Chou, Kuan-Yen, Prabhu, Santhosh, Subramanian, Giri, Zhou, Wenxuan, Nayyar, Aanand, Godfrey, Brighten, Caesar, Matthew
Data plane verification has grown into a powerful tool to ensure network correctness. However, existing monolithic data plane models have high memory requirements with large networks, and the existing method of scaling out is too limited in expressiv
Externí odkaz:
http://arxiv.org/abs/2405.20982
In this paper, we introduce the constrained best mixed arm identification (CBMAI) problem with a fixed budget. This is a pure exploration problem in a stochastic finite armed bandit model. Each arm is associated with a reward and multiple types of co
Externí odkaz:
http://arxiv.org/abs/2405.15090
Autor:
Dobhal, Daksh, Nagpal, Jayesh, Karia, Rushang, Verma, Pulkit, Nayyar, Rashmeet Kaur, Shah, Naman, Srivastava, Siddharth
Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts the complexities of robot planning can be challenging. This work presents an open-so
Externí odkaz:
http://arxiv.org/abs/2404.00808
We consider the problem of designing a control policy for an infinite-horizon discounted cost Markov decision process $\mathcal{M}$ when we only have access to an approximate model $\hat{\mathcal{M}}$. How well does an optimal policy $\hat{\pi}^{\sta
Externí odkaz:
http://arxiv.org/abs/2402.08813
Autor:
Xu, Weijie, Huang, Zicheng, Hu, Wenxiang, Fang, Xi, Cherukuri, Rajesh Kumar, Nayyar, Naumaan, Malandri, Lorenzo, Sengamedu, Srinivasan H.
Publikováno v:
EACL 2024
Recent advancements in Large Language Models (LLMs) have been reshaping Natural Language Processing (NLP) task in several domains. Their use in the field of Human Resources (HR) has still room for expansions and could be beneficial for several time c
Externí odkaz:
http://arxiv.org/abs/2402.01018
Learning in POMDPs is known to be significantly harder than MDPs. In this paper, we consider the online learning problem for episodic POMDPs with unknown transition and observation models. We propose a Posterior Sampling-based reinforcement learning
Externí odkaz:
http://arxiv.org/abs/2310.10107