13,119 research outputs found
AI Feynman: a Physics-Inspired Method for Symbolic Regression
A core challenge for both physics and artificial intellicence (AI) is
symbolic regression: finding a symbolic expression that matches data from an
unknown function. Although this problem is likely to be NP-hard in principle,
functions of practical interest often exhibit symmetries, separability,
compositionality and other simplifying properties. In this spirit, we develop a
recursive multidimensional symbolic regression algorithm that combines neural
network fitting with a suite of physics-inspired techniques. We apply it to 100
equations from the Feynman Lectures on Physics, and it discovers all of them,
while previous publicly available software cracks only 71; for a more difficult
test set, we improve the state of the art success rate from 15% to 90%.Comment: 15 pages, 2 figs. Our code is available at
https://github.com/SJ001/AI-Feynman and our Feynman Symbolic Regression
Database for benchmarking can be downloaded at
https://space.mit.edu/home/tegmark/aifeynman.htm
Noise-enhanced computation in a model of a cortical column
Varied sensory systems use noise in order to enhance detection of weak
signals. It has been conjectured in the literature that this effect, known as
stochastic resonance, may take place in central cognitive processes such as the
memory retrieval of arithmetical multiplication. We show in a simplified model
of cortical tissue, that complex arithmetical calculations can be carried out
and are enhanced in the presence of a stochastic background. The performance is
shown to be positively correlated to the susceptibility of the network, defined
as its sensitivity to a variation of the mean of its inputs. For nontrivial
arithmetic tasks such as multiplication, stochastic resonance is an emergent
property of the microcircuitry of the model network
Variational Hamiltonian Monte Carlo via Score Matching
Traditionally, the field of computational Bayesian statistics has been
divided into two main subfields: variational methods and Markov chain Monte
Carlo (MCMC). In recent years, however, several methods have been proposed
based on combining variational Bayesian inference and MCMC simulation in order
to improve their overall accuracy and computational efficiency. This marriage
of fast evaluation and flexible approximation provides a promising means of
designing scalable Bayesian inference methods. In this paper, we explore the
possibility of incorporating variational approximation into a state-of-the-art
MCMC method, Hamiltonian Monte Carlo (HMC), to reduce the required gradient
computation in the simulation of Hamiltonian flow, which is the bottleneck for
many applications of HMC in big data problems. To this end, we use a {\it
free-form} approximation induced by a fast and flexible surrogate function
based on single-hidden layer feedforward neural networks. The surrogate
provides sufficiently accurate approximation while allowing for fast
exploration of parameter space, resulting in an efficient approximate inference
algorithm. We demonstrate the advantages of our method on both synthetic and
real data problems
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