Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Aalok Sathe"'
Autor:
Abhishek M. Dedhe, Aakash A. Chowkase, Niramay V. Gogate, Manas M. Kshirsagar, Rohan Naphade, Atharv Naphade, Pranav Kulkarni, Mrunmayi Naik, Aarya Dharm, Soham Raste, Shravan Patankar, Chinmay M. Jogdeo, Aalok Sathe, Soham Kulkarni, Vibha Bapat, Rohinee Joshi, Kshitij Deshmukh, Subhash Lele, Kody J. Manke-Miller, Jessica F. Cantlon, Pranav S. Pandit
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-15 (2024)
Abstract Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In thi
Externí odkaz:
https://doaj.org/article/ad155a05400347c7bb72f04c70c23e64
Autor:
Benjamin Lipkin, Greta Tuckute, Josef Affourtit, Hannah Small, Zachary Mineroff, Hope Kean, Olessia Jouravlev, Lara Rakocevic, Brianna Pritchett, Matthew Siegelman, Caitlyn Hoeflin, Alvincé Pongos, Idan A. Blank, Melissa Kline Struhl, Anna Ivanova, Steven Shannon, Aalok Sathe, Malte Hoffmann, Alfonso Nieto-Castañón, Evelina Fedorenko
Publikováno v:
Scientific Data, Vol 9, Iss 1, Pp 1-10 (2022)
Measurement(s) Brain activity measurement Technology Type(s) fMRI Sample Characteristic - Organism Homo sapiens
Externí odkaz:
https://doaj.org/article/d8c7a2adba6a42a2a2b2277fb38ae36b
Autor:
Greta Tuckute, Aalok Sathe, Shashank Srikant, Maya Taliaferro, Mingye Wang, Martin Schrimpf, Kendrick Kay, Evelina Fedorenko
Transformer language models are today’s most accurate models of language processing in the brain. Here, using fMRI-measured brain responses to 1,000 diverse sentences, we develop a GPT-based encoding model and use this model to identify new sentenc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::edae26b6285af3d4a2a95b9f48eb308d
https://doi.org/10.1101/2023.04.16.537080
https://doi.org/10.1101/2023.04.16.537080
Autor:
Prateek Bhakta, Sara Krehbiel, Rachel Morris, Heather M. Russell, Aalok Sathe, Wesley Su, Maxine Xin
Publikováno v:
Advances in Applied Mathematics. 149:102556
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b757563a1a821536621e722dfd432511
https://doi.org/10.1016/j.aam.2023.102556
https://doi.org/10.1016/j.aam.2023.102556
Autor:
Benjamin Lipkin, Greta Tuckute, Josef Affourtit, Hannah Small, Zachary Mineroff, Hope Kean, Olessia Jouravlev, Lara Rakocevic, Brianna Pritchett, Matthew Siegelman, Caitlyn Hoeflin, Alvincé Pongos, Idan A. Blank, Melissa Kline Struhl, Anna Ivanova, Steven Shannon, Aalok Sathe, Malte Hoffmann, Alfonso Nieto-Castañón, Evelina Fedorenko
Publikováno v:
Scientific data. 9(1)
Two analytic traditions characterize fMRI language research. One relies on averaging activations across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, any given voxel/vertex in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f61fb3b3b41c9f6086832a0a8629aaae
https://pubmed.ncbi.nlm.nih.gov/36038572
https://pubmed.ncbi.nlm.nih.gov/36038572
Autor:
Benjamin Lipkin, Greta Tuckute, Josef Affourtit, Hannah Small, Zachary Mineroff, Hope Kean, Olessia Jouravlev, Lara Rakocevic, Brianna Pritchett, Matthew Siegelman, Caitlyn Hoeflin, Alvincé Pongos, Idan A. Blank, Melissa Kline Struhl, Anna Ivanova, Steven Shannon, Aalok Sathe, Malte Hoffmann, Alfonso Nieto-Castañón, Evelina Fedorenko
Two analytic traditions characterize fMRI language research. One relies on averaging activations voxel-wise across individuals. This approach has limitations: because of inter-individual variability in the locations of language areas, a location in a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5806a610550c951ff808ca52ed95901a
https://doi.org/10.1101/2022.03.06.483177
https://doi.org/10.1101/2022.03.06.483177
Publikováno v:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: System Demonstrations.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2b9a7176463cb7f11af64cc15cc5d75f
https://doi.org/10.18653/v1/2022.naacl-demo.11
https://doi.org/10.18653/v1/2022.naacl-demo.11
Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c8a235d7d4337963d12a26ff60d8aef3
Autor:
Aalok Sathe, Joonsuk Park
Publikováno v:
Proceedings of the Fourth Workshop on Fact Extraction and VERification (FEVER).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6653f00be5cbf44d0a10d6a81f6d2611
https://doi.org/10.18653/v1/2021.fever-1.11
https://doi.org/10.18653/v1/2021.fever-1.11
Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various types of sub-tasks, requiring di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a8b23364a953a6739beb1cbf9294c602