What You See is Not What You Get: Neural Partial Differential Equations and The Illusion of Learning

Autor: Mohan, Arvind, Chattopadhyay, Ashesh, Miller, Jonah

Differentiable Programming for scientific machine learning (SciML) has recently seen considerable interest and success, as it directly embeds neural networks inside PDEs, often called as NeuralPDEs, derived from first principle physics. Therefore, th

Externí odkaz: http://arxiv.org/abs/2411.15101

Entanglement Classification of Arbitrary Three-Qubit States via Artificial Neural Networks

Autor: Singh, Jorawar, Gulati, Vaishali, Dorai, Kavita, Arvind

We design and successfully implement artificial neural networks (ANNs) to detect and classify entanglement for three-qubit systems using limited state features. The overall design principle is a feed forward neural network (FFNN), with the output lay

Externí odkaz: http://arxiv.org/abs/2411.11330

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery

Autor: John, Peter St., Lin, Dejun, Binder, Polina, Greaves, Malcolm, Shah, Vega, John, John St., Lange, Adrian, Hsu, Patrick, Illango, Rajesh, Ramanathan, Arvind, Anandkumar, Anima, Brookes, David H, Busia, Akosua, Mahajan, Abhishaike, Malina, Stephen, Prasad, Neha, Sinai, Sam, Edwards, Lindsay, Gaudelet, Thomas, Regep, Cristian, Steinegger, Martin, Rost, Burkhard, Brace, Alexander, Hippe, Kyle, Naef, Luca, Kamata, Keisuke, Armstrong, George, Boyd, Kevin, Cao, Zhonglin, Chou, Han-Yi, Chu, Simon, Costa, Allan dos Santos, Darabi, Sajad, Dawson, Eric, Didi, Kieran, Fu, Cong, Geiger, Mario, Gill, Michelle, Hsu, Darren, Kaushik, Gagan, Korshunova, Maria, Kothen-Hill, Steven, Lee, Youhan, Liu, Meng, Livne, Micha, McClure, Zachary, Mitchell, Jonathan, Moradzadeh, Alireza, Mosafi, Ohad, Nashed, Youssef, Paliwal, Saee, Peng, Yuxing, Rabhi, Sara, Ramezanghorbani, Farhad, Reidenbach, Danny, Ricketts, Camir, Roland, Brian, Shah, Kushal, Shimko, Tyler, Sirelkhatim, Hassan, Srinivasan, Savitha, Stern, Abraham C, Toczydlowska, Dorota, Veccham, Srimukh Prasad, Venanzi, Niccolò Alberto Elia, Vorontsov, Anton, Wilber, Jared, Wilkinson, Isabel, Wong, Wei Jing, Xue, Eva, Ye, Cory, Yu, Xin, Zhang, Yang, Zhou, Guoqing, Zandstein, Becca, Dallago, Christian, Trentini, Bruno, Kucukbenli, Emine, Rvachov, Timur, Calleja, Eddie, Israeli, Johnny, Clifford, Harry, Haukioja, Risto, Haemel, Nicholas, Tretina, Kyle, Tadimeti, Neha, Costa, Anthony B

Artificial Intelligence models encoding biology and chemistry are opening new routes to high-throughput and high-quality in-silico drug development. However, their training increasingly relies on computational scale, with recent protein language mode

Externí odkaz: http://arxiv.org/abs/2411.10548

Evolution of different orders of coherence of a three-qubit system and their protection via dynamical decoupling on an NMR quantum processor

Autor: Gautam, Akanksha, Dorai, Kavita, Arvind

We generate different orders of quantum coherence in a three-qubit NMR system and study their dynamics in the presence of inherent noise. Robust dynamical decoupling (DD) sequences are applied to preserve the different coherence orders. Initially, DD

Externí odkaz: http://arxiv.org/abs/2411.07187

Toward Cultural Interpretability: A Linguistic Anthropological Framework for Describing and Evaluating Large Language Models (LLMs)

Autor: Jones, Graham M., Satran, Shai, Satyanarayan, Arvind

This article proposes a new integration of linguistic anthropology and machine learning (ML) around convergent interests in both the underpinnings of language and making language technologies more socially responsible. While linguistic anthropology f

Externí odkaz: http://arxiv.org/abs/2411.05200