Zobrazeno 1 - 10
of 7 167
pro vyhledávání: '"P. Murty"'
Autor:
Bradshaw, Peter, Cao, Tianyue, Chen, Atlas, Dean, Braden, Gan, Siyu, Garcia, Ramon I., Krishnaiyer, Amit, McCourt, Grace, Murty, Arvind
We study the paintability, an on-line version of choosability, of complete multipartite graphs. We do this by considering an equivalent chip game introduced by Duraj, Gutowski, and Kozik. We consider complete multipartite graphs with $ n $ parts of s
Externí odkaz:
http://arxiv.org/abs/2411.19462
While compositional accounts of human language understanding are based on a hierarchical tree-like process, neural models like transformers lack a direct inductive bias for such tree structures. Introducing syntactic inductive biases could unlock mor
Externí odkaz:
http://arxiv.org/abs/2411.18885
Models that rely on subword tokenization have significant drawbacks, such as sensitivity to character-level noise like spelling errors and inconsistent compression rates across different languages and scripts. While character- or byte-level models li
Externí odkaz:
http://arxiv.org/abs/2410.20771
We introduce NNetscape Navigator (NNetnav), a method for training web agents entirely through synthetic demonstrations. These demonstrations are collected by first interacting with a browser to generate trajectory rollouts, which are then retroactive
Externí odkaz:
http://arxiv.org/abs/2410.02907
Autor:
Murty, M. Ram, Prasad, A. Narayan
Current concepts of neural networks have emerged over two centuries of progress beginning with the neural doctrine to the idea of neural cell assemblies. Presently the model of neural networks involves distributed neural circuits of nodes, hubs, and
Externí odkaz:
http://arxiv.org/abs/2407.18924
Following natural language instructions by executing actions in digital environments (e.g. web-browsers and REST APIs) is a challenging task for language model (LM) agents. Unfortunately, LM agents often fail to generalize to new environments without
Externí odkaz:
http://arxiv.org/abs/2403.08140
Autor:
Luo, Xiaoliang, Rechardt, Akilles, Sun, Guangzhi, Nejad, Kevin K., Yáñez, Felipe, Yilmaz, Bati, Lee, Kangjoo, Cohen, Alexandra O., Borghesani, Valentina, Pashkov, Anton, Marinazzo, Daniele, Nicholas, Jonathan, Salatiello, Alessandro, Sucholutsky, Ilia, Minervini, Pasquale, Razavi, Sepehr, Rocca, Roberta, Yusifov, Elkhan, Okalova, Tereza, Gu, Nianlong, Ferianc, Martin, Khona, Mikail, Patil, Kaustubh R., Lee, Pui-Shee, Mata, Rui, Myers, Nicholas E., Bizley, Jennifer K, Musslick, Sebastian, Bilgin, Isil Poyraz, Niso, Guiomar, Ales, Justin M., Gaebler, Michael, Murty, N Apurva Ratan, Loued-Khenissi, Leyla, Behler, Anna, Hall, Chloe M., Dafflon, Jessica, Bao, Sherry Dongqi, Love, Bradley C.
Scientific discoveries often hinge on synthesizing decades of research, a task that potentially outstrips human information processing capacities. Large language models (LLMs) offer a solution. LLMs trained on the vast scientific literature could pot
Externí odkaz:
http://arxiv.org/abs/2403.03230
Autor:
Song, Alexander, Kottapalli, Sai Nikhilesh Murty, Goyal, Rahul, Schölkopf, Bernhard, Fischer, Peer
Optical approaches have made great strides towards the goal of high-speed, energy-efficient computing necessary for modern deep learning and AI applications. Read-in and read-out of data, however, limit the overall performance of existing approaches.
Externí odkaz:
http://arxiv.org/abs/2402.01988
Autor:
Mehrabian, Abbas, Anand, Ankit, Kim, Hyunjik, Sonnerat, Nicolas, Balog, Matej, Comanici, Gheorghe, Berariu, Tudor, Lee, Andrew, Ruoss, Anian, Bulanova, Anna, Toyama, Daniel, Blackwell, Sam, Paredes, Bernardino Romera, Veličković, Petar, Orseau, Laurent, Lee, Joonkyung, Naredla, Anurag Murty, Precup, Doina, Wagner, Adam Zsolt
This work studies a central extremal graph theory problem inspired by a 1975 conjecture of Erd\H{o}s, which aims to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles. We formulate this pro
Externí odkaz:
http://arxiv.org/abs/2311.03583
Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism. Consequently, Transformer language models poorly capture long-tail recursive struc
Externí odkaz:
http://arxiv.org/abs/2310.19089