Deconstructing Distributions: A Pointwise Framework of Learning

20 February 2022

Papers citing "Deconstructing Distributions: A Pointwise Framework of Learning"

8 / 8 papers shown

Title
A picture of the space of typical learnable tasks Rahul Ramesh J. Mao Itay Griniasty Rubing Yang H. Teoh M. Transtrum J. Sethna Pratik Chaudhari SSL DRL 36 5 0 31 Oct 2022
Metadata Archaeology: Unearthing Data Subsets by Leveraging Training Dynamics Shoaib Ahmed Siddiqui Nitarshan Rajkumar Tegan Maharaj David M. Krueger Sara Hooker 42 27 0 20 Sep 2022
The Value of Out-of-Distribution Data Ashwin De Silva Rahul Ramesh Carey E. Priebe Pratik Chaudhari Joshua T. Vogelstein OODD 19 11 0 23 Aug 2022
Measuring Coding Challenge Competence With APPS Dan Hendrycks Steven Basart Saurav Kadavath Mantas Mazeika Akul Arora ... Collin Burns Samir Puranik Horace He D. Song Jacob Steinhardt ELM AIMat ALM 208 624 0 20 May 2021
Estimating Example Difficulty Using Variance of Gradients Chirag Agarwal Daniel D'souza Sara Hooker 208 107 0 26 Aug 2020
Calibration of Pre-trained Transformers Shrey Desai Greg Durrett UQLM 243 289 0 17 Mar 2020
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 246 4,489 0 23 Jan 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles Balaji Lakshminarayanan Alexander Pritzel Charles Blundell UQCV BDL 276 5,661 0 05 Dec 2016