Zobrazeno 1 - 10
of 274
pro vyhledávání: '"RAVINDRAN, BALARAMAN"'
As machine learning gets deployed more and more widely, and model sizes continue to grow, improving computational efficiency during model inference has become a key challenge. In many commonly used model architectures, including Transformers, a signi
Externí odkaz:
http://arxiv.org/abs/2412.00408
Autor:
Parthasarathy, Ambreesh, Phalnikar, Aditya, Krishnan, Gokul S, Jauhar, Ameen, Ravindran, Balaraman
This paper forms the second of a two-part series on the value of a participatory approach to AI development and deployment. The first paper had crafted a principled, as well as pragmatic, justification for deploying participatory methods in these two
Externí odkaz:
http://arxiv.org/abs/2407.13103
Autor:
Parthasarathy, Ambreesh, Phalnikar, Aditya, Jauhar, Ameen, Somayajula, Dhruv, Krishnan, Gokul S, Ravindran, Balaraman
The widespread adoption of Artificial Intelligence (AI) technologies in the public and private sectors has resulted in them significantly impacting the lives of people in new and unexpected ways. In this context, it becomes important to inquire how t
Externí odkaz:
http://arxiv.org/abs/2407.13100
Autor:
Dogra, Atharvan, Pillutla, Krishna, Deshpande, Ameet, Sai, Ananya B, Nay, John, Rajpurohit, Tanmay, Kalyan, Ashwin, Ravindran, Balaraman
We explore the ability of large language model (LLM)-based agents to engage in subtle deception such as strategically phrasing and intentionally manipulating information to misguide and deceive other agents. This harmful behavior can be hard to detec
Externí odkaz:
http://arxiv.org/abs/2405.04325
Autor:
Tripathi, Yogesh, Donakanti, Raghav, Girhepuje, Sahil, Kavathekar, Ishan, Vedula, Bhaskara Hanuma, Krishnan, Gokul S, Goyal, Shreya, Goel, Anmol, Ravindran, Balaraman, Kumaraguru, Ponnurangam
Recent advancements in language technology and Artificial Intelligence have resulted in numerous Language Models being proposed to perform various tasks in the legal domain ranging from predicting judgments to generating summaries. Despite their imme
Externí odkaz:
http://arxiv.org/abs/2402.10567
Autor:
Krishnan, Gokul S, Padi, Sarala, Greenberg, Craig S., Ravindran, Balaraman, Manoch, Dinesh, Sriram, Ram D.
Emotion Recognition in Conversations (ERC) is a critical aspect of affective computing, and it has many practical applications in healthcare, education, chatbots, and social media platforms. Earlier approaches for ERC analysis involved modeling both
Externí odkaz:
http://arxiv.org/abs/2312.03756
Autor:
Gurukar, Saket, Venkatakrishnan, Shaileshh Bojja, Ravindran, Balaraman, Parthasarathy, Srinivasan
Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance
Externí odkaz:
http://arxiv.org/abs/2306.14357
Model predictive control (MPC) is a popular approach for trajectory optimization in practical robotics applications. MPC policies can optimize trajectory parameters under kinodynamic and safety constraints and provide guarantees on safety, optimality
Externí odkaz:
http://arxiv.org/abs/2305.19111
Classification model selection is a process of identifying a suitable model class for a given classification task on a dataset. Traditionally, model selection is based on cross-validation, meta-learning, and user preferences, which are often time-con
Externí odkaz:
http://arxiv.org/abs/2305.13926
Multi-agent reinforcement learning (MARL) methods often suffer from high sample complexity, limiting their use in real-world problems where data is sparse or expensive to collect. Although latent-variable world models have been employed to address th
Externí odkaz:
http://arxiv.org/abs/2304.06011