We interpret the data points as electrical charges on the $z=0$ hyperplane in a space augmented with an additional dimension $z$, generating a high-dimensional electric field (the gradient of the solution to Poisson equation).
We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set.
We present a machine learning algorithm that discovers conservation laws from differential equations, both numerically (parametrized as neural networks) and symbolically, ensuring their functional independence (a non-linear generalization of linear independence).
It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons?
We present an automated method for finding hidden symmetries, defined as symmetries that become manifest only in a new coordinate system that must be discovered.
Energy conservation is a basic physics principle, the breakdown of which often implies new physics.
We present AI Poincar\'e, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems.
We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity.
We present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video.
no code implementations • 13 Apr 2020 • Abhik Ghosh, Florent Mertens, Gianni Bernardi, Mário G. Santos, Nicholas S. Kern, Christopher L. Carilli, Trienko L. Grobler, Léon V. E. Koopmans, Daniel C. Jacobs, Adrian Liu, Aaron R. Parsons, Miguel F. Morales, James E. Aguirre, Joshua S. Dillon, Bryna J. Hazelton, Oleg M. Smirnov, Bharat K. Gehlot, Siyanda Matika, Paul Alexander, Zaki S. Ali, Adam P. Beardsley, Roshan K. Benefo, Tashalee S. Billings, Judd D. Bowman, Richard F. Bradley, Carina Cheng, Paul M. Chichura, David R. DeBoer, Eloy de Lera Acedo, Aaron Ewall-Wice, Gcobisa Fadana, Nicolas Fagnoni, Austin F. Fortino, Randall Fritz, Steve R. Furlanetto, Samavarti Gallardo, Brian Glendenning, Deepthi Gorthi, Bradley Greig, Jasper Grobbelaar, Jack Hickish, Alec Josaitis, Austin Julius, Amy S. Igarashi, MacCalvin Kariseb, Saul A. Kohn, Matthew Kolopanis, Telalo Lekalake, Anita Loots, David MacMahon, Lourence Malan, Cresshim Malgas, Matthys Maree, Zachary E. Martinot, Nathan Mathison, Eunice Matsetela, Andrei Mesinger, Abraham R. Neben, Bojan Nikolic, Chuneeta D. Nunhokee, Nipanjana Patra, Samantha Pieterse, Nima Razavi-Ghods, Jon Ringuette, James Robnett, Kathryn Rosie, Raddwine Sell, Craig Smith, Angelo Syce, Max Tegmark, Nithyanandan Thyagarajan, Peter K. G. Williams, Haoxuan Zheng
The key challenge in the observation of the redshifted 21-cm signal from cosmic reionization is its separation from the much brighter foreground emission.
Cosmology and Nongalactic Astrophysics
The goal of lossy data compression is to reduce the storage cost of a data set $X$ while retaining as much information as possible about something ($Y$) that you care about.
However, in practice, not only is $\beta$ chosen empirically without theoretical guidance, there is also a lack of theoretical understanding between $\beta$, learnability, the intrinsic nature of the dataset and model capacity.
A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.
no code implementations • 30 Apr 2019 • Ricardo Vinuesa, Hossein Azizpour, Iolanda Leite, Madeline Balaam, Virginia Dignum, Sami Domisch, Anna Felländer, Simone Langhans, Max Tegmark, Francesco Fuso Nerini
We find that AI can support the achievement of 128 targets across all SDGs, but it may also inhibit 58 targets.
A popular approach for predicting the future of dynamical systems involves mapping them into a lower-dimensional "latent space" where prediction is easier.
We investigate opportunities and challenges for improving unsupervised machine learning using four common strategies with a long history in physics: divide-and-conquer, Occam's razor, unification and lifelong learning.
Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.
We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles.
Computational Physics Applied Physics Optics
We present a novel recurrent neural network (RNN) based model that combines the remembering ability of unitary RNNs with the ability of gated RNNs to effectively forget redundant/irrelevant information in its memory.
Ranked #7 on Question Answering on bAbi (Accuracy (trained on 1k) metric)
Using unitary (instead of general) matrices in artificial neural networks (ANNs) is a promising way to solve the gradient explosion/vanishing problem, as well as to enable ANNs to learn long-term correlations in the data.
We show how the success of deep learning could depend not only on mathematics but also on physics: although well-known mathematical theorems guarantee that neural networks can approximate arbitrary functions well, the class of functions of practical interest can frequently be approximated through "cheap learning" with exponentially fewer parameters than generic ones.
We show that the mutual information between two symbols, as a function of the number of symbols between the two, decays exponentially in any probabilistic regular grammar, but can decay like a power law for a context-free grammar.
We present an improved Global Sky Model (GSM) of diffuse galactic radio emission from 10 MHz to 5 THz, whose uses include foreground modeling for CMB and 21 cm cosmology.
Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls.
Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether.
Understanding diffuse Galactic radio emission is interesting both in its own right and for minimizing foreground contamination of cosmological measurements.
We present a method for measuring the cosmic matter budget without assumptions about speculative Early Universe physics, and for measuring the primordial power spectrum P*(k) non-parametrically, either by combining CMB and LSS information or by using CMB polarization.
Based on a calculation of neural decoherence rates, we argue that that the degrees of freedom of the human brain that relate to cognitive processes should be thought of as a classical rather than quantum system, i. e., that there is nothing fundamentally wrong with the current classical approach to neural network simulations.
Some superstring theories have more than one effective low-energy limit, corresponding to classical spacetimes with different dimensionalities.
General Relativity and Quantum Cosmology High Energy Physics - Phenomenology High Energy Physics - Theory