Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher.
Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?
Some links on this page may take you to nonfederal websites. Their policies may differ from this site.

A long standing open problem in the theory of neural networks is the development of quantitative methods to estimate and compare the capabilities of different architectures. Here we define the capacity of an architecture by the binary logarithm of the number of functions it can compute, as the synaptic weights are varied. The capacity provides an upper bound on the number of bits that can be extracted from the training data and stored in the architecture during learning. We study the capacity of layered, fullyconnected, architectures of linear threshold neurons with L layers and show that in essence the capacity is given by a cubic polynomial in the layer sizes. In proving the main result, we also develop new techniques (multiplexing, enrichment, and stacking) as well as new bounds on the capacity of finite sets. We use the main result to identify architectures with maximal or minimal capacity under a number of natural constraints. This leads to the notion of structural regularization for deep architectures. While in general, everything else being equal, shallow networks compute more functions than deep networks, the functions computed by deep networks are more regular and “interesting".more » « less

Free, publiclyaccessible full text available March 1, 2024

Recently, Approximate Policy Iteration (API) algorithms have achieved superhuman proficiency in twoplayer zerosum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these twoplayer games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodidactic Iteration is able to learn how to solve the Rubik’s Cube without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.more » « less

Free, publiclyaccessible full text available June 1, 2024

Free, publiclyaccessible full text available March 1, 2024

Free, publiclyaccessible full text available February 1, 2024

Abstract The Pandora Software Development Kit and algorithm libraries provide patternrecognition logic essential to the reconstruction of particle interactions in liquid argon time projection chamber detectors. Pandora is the primary event reconstruction software used at ProtoDUNESP, a prototype for the Deep Underground Neutrino Experiment far detector. ProtoDUNESP, located at CERN, is exposed to a chargedparticle test beam. This paper gives an overview of the Pandora reconstruction algorithms and how they have been tailored for use at ProtoDUNESP. In complex events with numerous cosmicray and beam background particles, the simulated reconstruction and identification efficiency for triggered testbeam particles is above 80% for the majority of particle type and beam momentum combinations. Specifically, simulated 1 GeV/ c charged pions and protons are correctly reconstructed and identified with efficiencies of 86.1 $$\pm 0.6$$ ± 0.6 % and 84.1 $$\pm 0.6$$ ± 0.6 %, respectively. The efficiencies measured for testbeam data are shown to be within 5% of those predicted by the simulation.more » « lessFree, publiclyaccessible full text available July 1, 2024

Free, publiclyaccessible full text available June 1, 2024