Quantifying the probable approximation error of probabilistic inference programs

31 May 2016

Papers citing "Quantifying the probable approximation error of probabilistic inference programs"

8 / 8 papers shown

Title
Structures of Neural Network Effective Theories cCaugin Ararat Tianji Cai Cem Tekin Zhengkang Zhang 47 7 0 03 May 2023
The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers Zong-xiao Li Chong You Srinadh Bhojanapalli Daliang Li A. S. Rawat ... Kenneth Q Ye Felix Chern Felix X. Yu Ruiqi Guo Surinder Kumar MoE 25 87 0 12 Oct 2022
Laziness, Barren Plateau, and Noise in Machine Learning Junyu Liu Zexi Lin Liang Jiang 25 21 0 19 Jun 2022
The edge of chaos: quantum field theory and deep neural networks Kevin T. Grosvenor R. Jefferson 25 22 0 27 Sep 2021
Deep learning approaches to surrogates for solving the diffusion equation for mechanistic real-world simulations J. Q. Toledo-Marín Geoffrey C. Fox J. Sluka J. Glazier MedIm AI4CE 11 8 0 10 Feb 2021
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 226 4,460 0 23 Jan 2020
Building machines that adapt and compute like brains Brenden Lake J. Tenenbaum AI4CE FedML NAI AILaw 248 890 0 11 Nov 2017
Efficient Estimation of Word Representations in Vector Space Tomáš Mikolov Kai Chen G. Corrado J. Dean 3DV 228 31,253 0 16 Jan 2013