R
Rohan Taori
Researcher at University of California, Berkeley
Publications - 15
Citations - 657
Rohan Taori is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Robustness (computer science). The author has an hindex of 6, co-authored 10 publications receiving 309 citations. Previous affiliations of Rohan Taori include Stanford University.
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Measuring Robustness to Natural Distribution Shifts in Image Classification
TL;DR: It is found that there is often little to no transfer of robustness from current synthetic to natural distribution shift, and the results indicate that distribution shifts arising in real data are currently an open research problem.
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Targeted Adversarial Examples for Black Box Audio Systems
TL;DR: The authors adopted a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the task, achieving 89.25% targeted attack similarity after 3000 generations while maintaining 94.6% audio file similarity.
Proceedings ArticleDOI
Targeted Adversarial Examples for Black Box Audio Systems
TL;DR: This paper adopts a black-box approach to adversarial generation, combining the approaches of both genetic algorithms and gradient estimation to solve the ASR fooling task.
Proceedings Article
Measuring Robustness to Natural Distribution Shifts in Image Classification
TL;DR: In this paper, the authors study how robust current ImageNet models are to distribution shifts arising from natural variations in datasets, and they find that there is often little to no transfer of robustness from current synthetic to natural distribution shift.
Posted Content
On the Opportunities and Risks of Foundation Models.
Rishi Bommasani,Drew A. Hudson,Ehsan Adeli,Russ B. Altman,Simran Arora,Sydney von Arx,Michael S. Bernstein,Jeannette Bohg,Antoine Bosselut,Emma Brunskill,Erik Brynjolfsson,Shyamal Buch,Dallas Card,Rodrigo Castellon,Niladri S. Chatterji,Annie Chen,Kathleen Creel,Jared Davis,Dora Demszky,Chris Donahue,Moussa Doumbouya,Esin Durmus,Stefano Ermon,John Etchemendy,Kawin Ethayarajh,Li Fei-Fei,Chelsea Finn,Trevor Gale,Lauren Gillespie,Karan Goel,Noah D. Goodman,Shelby Grossman,Neel Guha,Tatsunori Hashimoto,Peter Henderson,John Hewitt,Daniel E. Ho,Jenny Hong,Kyle Hsu,Jing Huang,Thomas Icard,Saahil Jain,Dan Jurafsky,Pratyusha Kalluri,Siddharth Karamcheti,Geoff Keeling,Fereshte Khani,Omar Khattab,Pang Wei Koh,Mark Krass,Ranjay Krishna,Rohith Kuditipudi,Ananya Kumar,Faisal Ladhak,Mina Lee,Tony Lee,Jure Leskovec,Isabelle Levent,Xiang Lisa Li,Xuechen Li,Tengyu Ma,Ali Ahmad Malik,Christopher D. Manning,Suvir Mirchandani,Eric Mitchell,Zanele Munyikwa,Suraj Nair,Avanika Narayan,Deepak Narayanan,Ben Newman,Allen Nie,Juan Carlos Niebles,Hamed Nilforoshan,Julian Nyarko,Giray Ogut,Laurel Orr,Isabel Papadimitriou,Joon Sung Park,Chris Piech,Eva Portelance,Christopher Potts,Aditi Raghunathan,Rob Reich,Hongyu Ren,Frieda Rong,Yusuf H. Roohani,Camilo Ruiz,Jack Ryan,Christopher Ré,Dorsa Sadigh,Shiori Sagawa,Keshav Santhanam,Andy Shih,Krishnan Srinivasan,Alex Tamkin,Rohan Taori,Armin W. Thomas,Florian Tramèr,Rose E. Wang,William Yang Wang,Bohan Wu,Jiajun Wu,Yuhuai Wu,Sang Michael Xie,Michihiro Yasunaga,Jiaxuan You,Matei Zaharia,Michael Zhang,Tianyi Zhang,Xikun Zhang,Yuhui Zhang,Lucia Zheng,Kaitlyn Zhou,Percy Liang +113 more
TL;DR: The authors provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.