SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression

18 January 2024

Vladimir Loncar

Papers citing "SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression"

33 / 33 papers shown

Title
Discovering Nuclear Models from Symbolic Machine Learning Jose M. Munoz S. Udrescu Ronald F. Garcia Ruiz 78 0 0 17 Apr 2024
Symbolic Regression on FPGAs for Fast Machine Learning Inference Ho Fung Tsoi Adrian Alan Pol Vladimir Loncar E. Govorkova M. Cranmer S. Dasu P. Elmer Philip C. Harris I. Ojalvo M. Pierini 34 8 0 06 May 2023
Differentiable Genetic Programming for High-dimensional Symbolic Regression Peng Zeng Xiaotian Song Andrew Lensen Yuwei Ou Yanan Sun Mengjie Zhang Jiancheng Lv 57 3 0 18 Apr 2023
Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression Jiří Kubalík Erik Derner Robert Babuška 28 12 0 01 Feb 2023
Symbolic Regression with Fast Function Extraction and Nonlinear Least Squares Optimization Lukas Kammerer G. Kronberger M. Kommenda 43 3 0 20 Sep 2022
A computational framework for physics-informed symbolic regression with straightforward integration of domain knowledge Liron Simon Keren A. Liberzon Teddy Lazebnik 63 83 0 13 Sep 2022
The SZ flux-mass ( $Y$ - $M$ ) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback D. Wadekar L. Thiele J. Hill S. Pandey F. Villaescusa-Navarro ... M. Cranmer D. Nagai D. Anglés-Alcázar S. Ho L. Hernquist AI4CE 25 17 0 05 Sep 2022
Symbolic Regression is NP-hard M. Virgolin S. Pissis 96 64 0 03 Jul 2022
Bayesian Learning to Discover Mathematical Operations in Governing Equations of Dynamic Systems Hongpeng Zhou W. Pan 30 4 0 01 Jun 2022
SymFormer: End-to-end symbolic regression using transformer-based architecture Martin Vastl Jonáš Kulhánek Jiří Kubalík Erik Derner Robert Babuška 71 48 0 31 May 2022
End-to-end symbolic regression with transformers Pierre-Alexandre Kamienny Stéphane dÁscoli Guillaume Lample Franccois Charton 78 173 0 22 Apr 2022
Rediscovering orbital mechanics with machine learning Pablo Lemos N. Jeffrey M. Cranmer S. Ho Peter W. Battaglia PINN AI4CE 62 90 0 04 Feb 2022
Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter D. Wadekar L. Thiele F. Villaescusa-Navarro J. Hill M. Cranmer D. Spergel N. Battaglia D. Anglés-Alcázar L. Hernquist S. Ho 71 12 0 04 Jan 2022
PySINDy: A comprehensive Python package for robust sparse system identification A. Kaptanoglu Brian M. de Silva Urban Fasel Kadierdan Kaheman Andy J. Goldschmidt ... Zachary G. Nicolaou Kathleen P. Champion Jean-Christophe Loiseau J. Nathan Kutz Steven L. Brunton AI4CE 66 149 0 12 Nov 2021
Contemporary Symbolic Regression Methods and their Relative Performance William La Cava Patryk Orzechowski Bogdan Burlacu Fabrício Olivetti de Francca M. Virgolin Ying Jin M. Kommenda J. Moore 180 259 0 29 Jul 2021
SymbolicGPT: A Generative Transformer Model for Symbolic Regression Mojtaba Valipour Bowen You Maysum Panju A. Ghodsi 43 93 0 27 Jun 2021
Neural Symbolic Regression that Scales Luca Biggio Tommaso Bendinelli Alexander Neitz Aurelien Lucchi Giambattista Parascandolo 80 179 0 11 Jun 2021
Informed Equation Learning M. Werner Andrej Junginger Philipp Hennig Georg Martius 29 14 0 13 May 2021
Symbolic regression for scientific discovery: an application to wind speed forecasting Ismail Alaoui Abdellaoui S. Mehrkanoon 44 21 0 21 Feb 2021
Fast convolutional neural networks on FPGAs with hls4ml T. Aarrestad Vladimir Loncar Nicolò Ghielmetti M. Pierini S. Summers ... N. Tran Miaoyuan Liu E. Kreinar Zhenbin Wu Duc Hoang 47 108 0 13 Jan 2021
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors C. Coelho Aki Kuusela Shane Li Zhuang Hao T. Aarrestad Vladimir Loncar J. Ngadiuba M. Pierini Adrian Alan Pol S. Summers MQ 72 178 0 15 Jun 2020
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers Junjie Liu Zhe Xu Runbin Shi R. Cheung Hayden Kwok-Hay So 46 121 0 14 May 2020
Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients Brenden K. Petersen Mikel Landajuela T. Nathan Mundhenk Claudio Santiago Soo K. Kim Joanne T. Kim 52 315 0 10 Dec 2019
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery Samuel Kim Peter Y. Lu Srijon Mukherjee M. Gilbert Li Jing V. Ceperic Marin Soljacic 60 164 0 10 Dec 2019
AI Feynman: a Physics-Inspired Method for Symbolic Regression S. Udrescu Max Tegmark 158 872 0 27 May 2019
Learning Equations for Extrapolation and Control Subham S. Sahoo Christoph H. Lampert Georg Martius 43 233 0 19 Jun 2018
Fast inference of deep neural networks in FPGAs for particle physics Javier Mauricio Duarte Song Han Philip C. Harris S. Jindariani E. Kreinar ... J. Ngadiuba M. Pierini R. Rivera N. Tran Zhenbin Wu AI4CE 126 392 0 16 Apr 2018
Learning Sparse Neural Networks through $L_0$ Regularization Christos Louizos Max Welling Diederik P. Kingma 425 1,144 0 04 Dec 2017
To prune, or not to prune: exploring the efficacy of pruning for model compression Michael Zhu Suyog Gupta 191 1,276 0 05 Oct 2017
Extrapolation and learning equations Georg Martius Christoph H. Lampert 38 157 0 10 Oct 2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding Song Han Huizi Mao W. Dally 3DGS 253 8,833 0 01 Oct 2015
Deep Learning with Limited Numerical Precision Suyog Gupta A. Agrawal K. Gopalakrishnan P. Narayanan HAI 196 2,047 0 09 Feb 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 1.7K 150,039 0 22 Dec 2014