Zobrazeno 1 - 10
of 15 701
pro vyhledávání: '"Shivam, A."'
Autor:
Duary, Sarthak, Upadhyay, Shivam
We revisit the flat limit of AdS/CFT from the point of view of geodesics in AdS. We show that the flat space scattering amplitudes can be constructed from operator insertions where the geodesics of the particles corresponding to the operators hit the
Externí odkaz:
http://arxiv.org/abs/2411.08540
Autor:
Soni, Aditya, Das, Mayukh, Parayil, Anjaly, Ghosh, Supriyo, Shandilya, Shivam, Cheng, Ching-An, Gopal, Vishak, Khairy, Sami, Mittag, Gabriel, Hosseinkashi, Yasaman, Bansal, Chetan
The difficulty of exploring and training online on real production systems limits the scope of real-time online data/feedback-driven decision making. The most feasible approach is to adopt offline reinforcement learning from limited trajectory sample
Externí odkaz:
http://arxiv.org/abs/2411.06815
Autor:
Acharya, Anurag, Sharma, Shivam, Cosbey, Robin, Subramanian, Megha, Howland, Scott, Glenski, Maria
A proliferation of Large Language Models (the GPT series, BLOOM, LLaMA, and more) are driving forward novel development of multipurpose AI for a variety of tasks, particularly natural language processing (NLP) tasks. These models demonstrate strong p
Externí odkaz:
http://arxiv.org/abs/2411.03542
We propose a general framework for end-to-end learning of data structures. Our framework adapts to the underlying data distribution and provides fine-grained control over query and space complexity. Crucially, the data structure is learned from scrat
Externí odkaz:
http://arxiv.org/abs/2411.03253
Current vision systems typically assign fixed-length representations to images, regardless of the information content. This contrasts with human intelligence - and even large language models - which allocate varying representational capacities based
Externí odkaz:
http://arxiv.org/abs/2411.02393
Autor:
Doherty, Kevin, Kallina, Emma, Moylan, Kayley, Silva, María Paula, Karimian, Sajjad, Shumsher, Shivam, Brennan, Rob
We find ourselves on the ever-shifting cusp of an AI revolution -- with potentially metamorphic implications for the future practice of healthcare. For many, such innovations cannot come quickly enough; as healthcare systems worldwide struggle to kee
Externí odkaz:
http://arxiv.org/abs/2411.02067
The emergent dynamics of complex systems often arise from the internal dynamical interactions among different elements and hence is to be modeled using multiple variables that represent the different dynamical processes. When such systems are to be s
Externí odkaz:
http://arxiv.org/abs/2411.01201
Autor:
Georgiev, Kristian, Rinberg, Roy, Park, Sung Min, Garg, Shivam, Ilyas, Andrew, Madry, Aleksander, Neel, Seth
Machine unlearning -- efficiently removing the effect of a small "forget set" of training data on a pre-trained machine learning model -- has recently attracted significant research interest. Despite this interest, however, recent work shows that exi
Externí odkaz:
http://arxiv.org/abs/2410.23232
The comparative study of Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers applied to level and flow control in coupled tank systems is presented in this research work. The coupled tank system, characterized by its non
Externí odkaz:
http://arxiv.org/abs/2410.22176
Autor:
Gupta, Taneesh, Shandilya, Shivam, Zhang, Xuchao, Ghosh, Supriyo, Bansal, Chetan, Yao, Huaxiu, Rajmohan, Saravan
The use of large language models (LLMs) as evaluators has garnered significant attention due to their potential to rival human-level evaluations in long-form response assessments. However, current LLM evaluators rely heavily on static, human-defined
Externí odkaz:
http://arxiv.org/abs/2410.21545