Gaussian Process Surrogate Models for Neural Networks

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets.Comment: Proceedings of UAI 202

Decentralized Reinforcement Learning: Global Decision-Making via Local Economic Transactions

This paper seeks to establish a framework for directing a society of simple, specialized, self-interested agents to solve what traditionally are posed as monolithic single-agent sequential decision problems. What makes it challenging to use a decentralized approach to collectively optimize a central objective is the difficulty in characterizing the equilibrium strategy profile of non-cooperative games. To overcome this challenge, we design a mechanism for defining the learning environment of each agent for which we know that the optimal solution for the global objective coincides with a Nash equilibrium strategy profile of the agents optimizing their own local objectives. The society functions as an economy of agents that learn the credit assignment process itself by buying and selling to each other the right to operate on the environment state. We derive a class of decentralized reinforcement learning algorithms that are broadly applicable not only to standard reinforcement learning but also for selecting options in semi-MDPs and dynamically composing computation graphs. Lastly, we demonstrate the potential advantages of a society's inherent modular structure for more efficient transfer learning.Comment: 18 pages, 13 figures, accepted to the International Conference on Machine Learning (ICML) 202

Rainwater isotopes in central Vietnam controlled by two oceanic moisture sources and rainout effects

The interpretation of palaeoclimate archives based on oxygen isotopes depends critically on a detailed understanding of processes controlling the isotopic composition of precipitation. In the summer monsoonal realm, like Southeast Asia, seasonally and interannually depleted oxygen isotope ratios in precipitation have been linked to the summer monsoon strength. However, in some regions, such as central Vietnam, the majority of precipitation falls outside the summer monsoon period. We investigate processes controlling stable isotopes in precipitation from central Vietnam by combining moisture uptake calculations with monthly stable isotope data observed over five years. We find that the isotopic seasonal cycle in this region is driven by a shift in moisture source from the Indian Ocean to the South China Sea. This shift is reflected in oxygen isotope ratios with low values (− 8 to − 10‰) during summer and high values during spring/winter (0 to − 3‰), while 70% of the annual rainfall occurs during autumn. Interannual changes in precipitation isotopes in central Vietnam are governed by the timing of the seasonal onset and withdrawal of the Intertropical Convergence Zone, which controls the amount of vapour contributed from each source

Is the electrostatic force between a point charge and a neutral metallic object always attractive?

We give an example of a geometry in which the electrostatic force between a point charge and a neutral metallic object is repulsive. The example consists of a point charge centered above a thin metallic hemisphere, positioned concave up. We show that this geometry has a repulsive regime using both a simple analytical argument and an exact calculation for an analogous two-dimensional geometry. Analogues of this geometry-induced repulsion can appear in many other contexts, including Casimir systems.Comment: 7 pages, 7 figure

Orbitally excited and hybrid mesons from the lattice

We discuss in general the construction of gauge-invariant non-local meson operators on the lattice. We use such operators to study the $P$- and $D$-wave mesons as well as hybrid mesons in quenched QCD, with quark masses near the strange quark mass. The resulting spectra are compared with experiment for the orbital excitations. For the states produced by gluonic excitations (hybrid mesons) we find evidence of mixing for non-exotic quantum numbers. We give predictions for masses of the spin-exotic hybrid mesons with $J^{PC}=1^{-+},\ 0^{+-}$, and$2^{+-}$.Comment: 31 pages, LATEX, 8 postscript figures. Reference adde