Zobrazeno 1 - 10
of 12 814
pro vyhledávání: '"A., Apoorva"'
Jackknife instrumental variable estimation (JIVE) is a classic method to leverage many weak instrumental variables (IVs) to estimate linear structural models, overcoming the bias of standard methods like two-stage least squares. In this paper, we ext
Externí odkaz:
http://arxiv.org/abs/2406.14140
Autor:
Safai, Apoorva, Jonaitis, Erin, Langhough, Rebecca E, Buckingham, William R, Johnson, Sterling C., Powell, W. Ryan, Kind, Amy J. H., Bendlin, Barbara B., Tiwari, Pallavi
Neighborhood disadvantage is associated with worse health and cognitive outcomes. Morphological similarity network (MSN) is a promising approach to elucidate cortical network patterns underlying complex cognitive functions. We hypothesized that MSNs
Externí odkaz:
http://arxiv.org/abs/2406.13822
Autor:
Kalluri, Shareef Babu, Singh, Prachi, Chowdhuri, Pratik Roy, Kulkarni, Apoorva, Baghel, Shikha, Hegde, Pradyoth, Sontakke, Swapnil, T, Deepak K, Prasanna, S. R. Mahadeva, Vijayasenan, Deepu, Ganapathy, Sriram
The DIarization of SPeaker and LAnguage in Conversational Environments (DISPLACE) 2024 challenge is the second in the series of DISPLACE challenges, which involves tasks of speaker diarization (SD) and language diarization (LD) on a challenging multi
Externí odkaz:
http://arxiv.org/abs/2406.09494
Stochastic dominance is an important concept in probability theory, econometrics and social choice theory for robustly modeling agents' preferences between random outcomes. While many works have been dedicated to the univariate case, little has been
Externí odkaz:
http://arxiv.org/abs/2406.06425
Autor:
Tennyson, Jonathan, Yurchenko, Sergei N., Zhang, Jingxin, Bowesman, Charles A., Brady, Ryan P., Buldyreva, Jeanna, Chubb, Katy L., Gamache, Robert R., Gorman, Maire N., Guest, Elizabeth R., Hill, Christian, Kefala, Kyriaki, Lynas-Gray, A. E., Mellor, Thomas M., McKemmish, Laura K., Mitev, Georgi B., Mizus, Irina I., Owens, Alec, Peng, Zhijian, Perri, Armando N., Pezzella, Marco, Polyansky, Oleg L., Qu, Qianwei, Semenov, Mikhail, Smola, Oleksiy, Solokov, Andrei, Somogyi, Wilfrid, Upadhyay, Apoorva, Wright, Samuel O. M., Zobov, Nikolai F.
The ExoMol database (www.exomol.com) provides molecular data for spectroscopic studies of hot atmospheres. These data are widely used to model atmospheres of exoplanets, cool stars and other astronomical objects, as well as a variety of terrestrial a
Externí odkaz:
http://arxiv.org/abs/2406.06347
Autor:
Melnyk, Igor, Mroueh, Youssef, Belgodere, Brian, Rigotti, Mattia, Nitsure, Apoorva, Yurochkin, Mikhail, Greenewald, Kristjan, Navratil, Jiri, Ross, Jerret
Current LLM alignment techniques use pairwise human preferences at a sample level, and as such, they do not imply an alignment on the distributional level. We propose in this paper Alignment via Optimal Transport (AOT), a novel method for distributio
Externí odkaz:
http://arxiv.org/abs/2406.05882
Autor:
Vijayaraghavan, Prashanth, Shi, Luyao, Ambrogio, Stefano, Mackin, Charles, Nitsure, Apoorva, Beymer, David, Degan, Ehsan
With the unprecedented advancements in Large Language Models (LLMs), their application domains have expanded to include code generation tasks across various programming languages. While significant progress has been made in enhancing LLMs for popular
Externí odkaz:
http://arxiv.org/abs/2406.04379
Gas samples relevant to health and environment typically contain a plethora of molecular species that span a huge concentration dynamic range. High-concentration molecules impose a strong absorption background that hinders robust identification of lo
Externí odkaz:
http://arxiv.org/abs/2406.03609
Autor:
Harne, Sarthak, Choudhury, Monjoy Narayan, Rao, Madhav, Srikanth, TK, Mehrotra, Seema, Vashisht, Apoorva, Basu, Aarushi, Sodhi, Manjit
The limited availability of psychologists necessitates efficient identification of individuals requiring urgent mental healthcare. This study explores the use of Natural Language Processing (NLP) pipelines to analyze text data from online mental heal
Externí odkaz:
http://arxiv.org/abs/2406.00314
Modern motion planners for autonomous driving frequently use imitation learning (IL) to draw from expert driving logs. Although IL benefits from its ability to glean nuanced and multi-modal human driving behaviors from large datasets, the resulting p
Externí odkaz:
http://arxiv.org/abs/2405.11139