Search Results for author: Max Tegmark

Found 33 papers, 13 papers with code

Poisson Flow Generative Models

1 code implementation22 Sep 2022 Yilun Xu, Ziming Liu, Max Tegmark, Tommi Jaakkola

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).

Image Generation

AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations

no code implementations23 Mar 2022 Ziming Liu, Varun Madhavan, Max Tegmark

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).

BIG-bench Machine Learning

Biological error correction codes generate fault-tolerant neural networks

no code implementations25 Feb 2022 Alexander Zlokapa, Andrew K. Tan, John M. Martyn, Ila R. Fiete, Max Tegmark, Isaac L. Chuang

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?

Machine-learning hidden symmetries

no code implementations20 Sep 2021 Ziming Liu, Max Tegmark

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.

BIG-bench Machine Learning

Machine-Learning Non-Conservative Dynamics for New-Physics Detection

no code implementations31 May 2021 Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu

Energy conservation is a basic physics principle, the breakdown of which often implies new physics.

BIG-bench Machine Learning

AI Poincaré: Machine Learning Conservation Laws from Trajectories

no code implementations9 Nov 2020 Ziming Liu, Max Tegmark

We present AI Poincar\'e, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems.

BIG-bench Machine Learning

AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity

2 code implementations NeurIPS 2020 Silviu-Marian Udrescu, Andrew Tan, Jiahai Feng, Orisvaldo Neto, Tailin Wu, Max Tegmark

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.

Symbolic Regression Two-sample testing

Symbolic Pregression: Discovering Physical Laws from Distorted Video

no code implementations19 May 2020 Silviu-Marian Udrescu, Max Tegmark

We present a method for unsupervised learning of equations of motion for objects in raw and optionally distorted unlabeled video.

Symbolic Regression

Foreground modelling via Gaussian process regression: an application to HERA data

no code implementations13 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

Pareto-optimal data compression for binary classification tasks

1 code implementation23 Aug 2019 Max Tegmark, Tailin Wu

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.

Classification Data Compression +3

Learnability for the Information Bottleneck

no code implementations ICLR Workshop LLD 2019 Tailin Wu, Ian Fischer, Isaac L. Chuang, Max Tegmark

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.

Representation Learning

AI Feynman: a Physics-Inspired Method for Symbolic Regression

1 code implementation27 May 2019 Silviu-Marian Udrescu, Max Tegmark

A core challenge for both physics and artificial intellicence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function.

Symbolic Regression

Latent Representations of Dynamical Systems: When Two is Better Than One

no code implementations9 Feb 2019 Max Tegmark

A popular approach for predicting the future of dynamical systems involves mapping them into a lower-dimensional "latent space" where prediction is easier.

Toward an AI Physicist for Unsupervised Learning

1 code implementation24 Oct 2018 Tailin Wu, Max Tegmark

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.

Meta-learning autoencoders for few-shot prediction

1 code implementation26 Jul 2018 Tailin Wu, John Peurifoy, Isaac L. Chuang, Max Tegmark

Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges.


Nanophotonic Particle Simulation and Inverse Design Using Artificial Neural Networks

1 code implementation18 Oct 2017 John Peurifoy, Yichen Shen, Li Jing, Yi Yang, Fidel Cano-Renteria, Brendan Delacy, Max Tegmark, John D. Joannopoulos, Marin Soljacic

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles.

Computational Physics Applied Physics Optics

Gated Orthogonal Recurrent Units: On Learning to Forget

1 code implementation8 Jun 2017 Li Jing, Caglar Gulcehre, John Peurifoy, Yichen Shen, Max Tegmark, Marin Soljačić, Yoshua Bengio

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)

Denoising Question Answering

The power of deeper networks for expressing natural functions

no code implementations ICLR 2018 David Rolnick, Max Tegmark

It is well-known that neural networks are universal approximators, but that deeper networks tend in practice to be more powerful than shallower ones.

Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs

6 code implementations ICML 2017 Li Jing, Yichen Shen, Tena Dubček, John Peurifoy, Scott Skirlo, Yann Lecun, Max Tegmark, Marin Soljačić

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.


Why does deep and cheap learning work so well?

no code implementations29 Aug 2016 Henry W. Lin, Max Tegmark, David Rolnick

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.

Criticality in Formal Languages and Statistical Physics

no code implementations21 Jun 2016 Henry W. Lin, Max Tegmark

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.

An Improved Model of Diffuse Galactic Radio Emission from 10 MHz to 5 THz

3 code implementations16 May 2016 Haoxuan Zheng, Max Tegmark, Joshua S. Dillon, Doyeon A. Kim, Adrian Liu, Abraham Neben, Justin Jonas, Patricia Reich, Wolfgang Reich

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

Research Priorities for Robust and Beneficial Artificial Intelligence

no code implementations10 Feb 2016 Stuart Russell, Daniel Dewey, Max Tegmark

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.

Friendly Artificial Intelligence: the Physics Challenge

no code implementations2 Sep 2014 Max Tegmark

Relentless progress in artificial intelligence (AI) is increasingly raising concerns that machines will replace humans on the job market, and perhaps altogether.

A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz

2 code implementations12 Feb 2008 Angelica de Oliveira-Costa, Max Tegmark, B. M. Gaensler, Justin Jonas, T. L. Landecker, Patricia Reich

Understanding diffuse Galactic radio emission is interesting both in its own right and for minimizing foreground contamination of cosmological measurements.

Optical Character Recognition

Separating the Early Universe from the Late Universe: cosmological parameter estimation beyond the black box

1 code implementation2 Jul 2002 Max Tegmark, Matias Zaldarriaga

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.

The importance of quantum decoherence in brain processes

no code implementations5 Jul 1999 Max Tegmark

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.

On the dimensionality of spacetime

no code implementations25 Feb 1997 Max Tegmark

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

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