Zobrazeno 1 - 10
of 3 376
pro vyhledávání: '"GUPTA, SUNIL"'
Autor:
Nguyen, Tri Minh, Tawfik, Sherif Abdulkader, Tran, Truyen, Gupta, Sunil, Rana, Santu, Venkatesh, Svetha
Discovering new solid-state materials requires rapidly exploring the vast space of crystal structures and locating stable regions. Generating stable materials with desired properties and compositions is extremely difficult as we search for very small
Externí odkaz:
http://arxiv.org/abs/2411.04323
While most generative models show achievements in image data generation, few are developed for tabular data generation. Recently, due to success of large language models (LLM) in diverse tasks, they have also been used for tabular data generation. Ho
Externí odkaz:
http://arxiv.org/abs/2410.21717
Effective decision-making in partially observable environments demands robust memory management. Despite their success in supervised learning, current deep-learning memory models struggle in reinforcement learning environments that are partially obse
Externí odkaz:
http://arxiv.org/abs/2410.10132
The objective of active level set estimation for a black-box function is to precisely identify regions where the function values exceed or fall below a specified threshold by iteratively performing function evaluations to gather more information abou
Externí odkaz:
http://arxiv.org/abs/2410.05660
This paper presents two models of neural-networks and their training applicable to neural networks of arbitrary width, depth and topology, assuming only finite-energy neural activations; and a novel representor theory for neural networks in terms of
Externí odkaz:
http://arxiv.org/abs/2405.15254
Autor:
A V, Arun Kumar, Shilton, Alistair, Gupta, Sunil, Rana, Santu, Greenhill, Stewart, Venkatesh, Svetha
Experimental (design) optimization is a key driver in designing and discovering new products and processes. Bayesian Optimization (BO) is an effective tool for optimizing expensive and black-box experimental design processes. While Bayesian optimizat
Externí odkaz:
http://arxiv.org/abs/2402.17343
A common problem encountered in many real-world applications is level set estimation where the goal is to determine the region in the function domain where the function is above or below a given threshold. When the function is black-box and expensive
Externí odkaz:
http://arxiv.org/abs/2402.16237
Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In many real-world scenarios, this approach is not practical due to the hi
Externí odkaz:
http://arxiv.org/abs/2402.10765
Black-box optimization is a powerful approach for discovering global optima in noisy and expensive black-box functions, a problem widely encountered in real-world scenarios. Recently, there has been a growing interest in leveraging domain knowledge t
Externí odkaz:
http://arxiv.org/abs/2402.03243
Autor:
Tawfik, Sherif Abdulkader, Nguyen, Tri Minh, Russo, Salvy P., Tran, Truyen, Gupta, Sunil, Venkatesh, Svetha
At the heart of the flourishing field of machine learning potentials are graph neural networks, where deep learning is interwoven with physics-informed machine learning (PIML) architectures. Various PIML models, upon training with density functional
Externí odkaz:
http://arxiv.org/abs/2402.10931