The semantic landscape paradigm for neural networks

18 July 2023

Papers citing "The semantic landscape paradigm for neural networks"

11 / 11 papers shown

Title
Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability Ziming Liu Eric Gan Max Tegmark 57 40 0 04 May 2023
Omnigrok: Grokking Beyond Algorithmic Data Ziming Liu Eric J. Michaud Max Tegmark 68 81 0 03 Oct 2022
In-context Learning and Induction Heads Catherine Olsson Nelson Elhage Neel Nanda Nicholas Joseph Nova Dassarma ... Tom B. Brown Jack Clark Jared Kaplan Sam McCandlish C. Olah 296 494 0 24 Sep 2022
Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit Boaz Barak Benjamin L. Edelman Surbhi Goel Sham Kakade Eran Malach Cyril Zhang 62 126 0 18 Jul 2022
Training Compute-Optimal Large Language Models Jordan Hoffmann Sebastian Borgeaud A. Mensch Elena Buchatskaya Trevor Cai ... Karen Simonyan Erich Elsen Jack W. Rae Oriol Vinyals Laurent Sifre AI4TS 116 1,915 0 29 Mar 2022
Program Synthesis with Large Language Models Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski ... Ellen Jiang Carrie J. Cai Michael Terry Quoc V. Le Charles Sutton ELM AIMat ReCod ALM 88 1,846 0 16 Aug 2021
The Principles of Deep Learning Theory Daniel A. Roberts Sho Yaida Boris Hanin FaML PINN GNN 34 245 0 18 Jun 2021
Explaining Neural Scaling Laws Yasaman Bahri Ethan Dyer Jared Kaplan Jaehoon Lee Utkarsh Sharma 46 256 0 12 Feb 2021
Understanding the Role of Individual Units in a Deep Neural Network David Bau Jun-Yan Zhu Hendrik Strobelt Àgata Lapedriza Bolei Zhou Antonio Torralba GAN 42 446 0 10 Sep 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 447 4,662 0 23 Jan 2020
A Constructive Prediction of the Generalization Error Across Scales Jonathan S. Rosenfeld Amir Rosenfeld Yonatan Belinkov Nir Shavit 63 210 0 27 Sep 2019