A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Deep learning depends on tuning layers near critical points.
Gradient estimation techniques applied to programs with randomness in high energy physics.
Study shows energy levels on hyperbolic surfaces follow GOE fluctuations.
We use matricial free energy to regularize autoencoders, producing Gaussian-like codes.
Study smooth linear statistics on random covers of hyperbolic surfaces, showing central limit and variance results.
Machine Learning (ML) algorithms, like Convolutional Neural Networks (CNN), Support Vector Machines (SVM), etc. have become widespread and can achieve high statistical performance. However their accuracy decreases significantly in energy-constrained mobile and embedded systems space, where all computations need to be c…
Paper improves DNN accelerator robustness against bit errors with energy savings.
A neural network model minimizes region-based free energy for faster inference in MRFs.
EB-RANSAC uses energy-based model for robust estimation without complex sampling.
Energy-efficient sampling for machine learning using magnetic tunnel junctions.
New algorithm nearly achieves ground state free energy of SK model.
In this paper we are concerned with the learnability of energies from data obtained by observing time evolutions of their critical points starting at random initial equilibria. As a byproduct of our theoretical framework we introduce the novel concept of mean-field limit of critical point evolutions and of their energy…
We explore a new method for discrete-time control problems using randomization and entropy.
New method speeds up sampling of Markov random fields.
CLuP achieves near optimal ground state energies for positive and negative Hopfield models.
New insights into simple kernel smoothing reveal surprising asymptotics.
Dynamic probabilistic forecasts guide optimal decisions in uncertain processes.
In this thesis a connection between the worlds of discrete and continuous conformal geometry is explored. Specifically, a disk pattern production theroem is proved using an energy which measures how ``uniform'' the angle data of a triangulation is, see also math.DG/0002150. Then this energy is averaged over all the Del…
We prove that a random group of the graph model associated with a sequence of expanders has fixed-point property for a certain class of CAT(0) spaces. We use Gromov's criterion for fixed-point property in terms of the growth of n-step energy of equivariant maps from a finitely generated group into a CAT(0) space, to wh…
New method clusters directed and undirected graphs without losing directional information.
There have lately been several suggestions for parametrized distances on a graph that generalize the shortest path distance and the commute time or resistance distance. The need for developing such distances has risen from the observation that the above-mentioned common distances in many situations fail to take into ac…
Develops tests for conditional symmetry under group actions.
Study free energy in spherical spin glasses, proving universality dichotomy.
In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was replayed at random, without considering which episode might be the most valuable for learning. In this paper, we develop an energy-based fr…
Paper optimizes energy trading on DA markets using RL.
Machine learning model predicts DFT total energy to complete basis set limit.
The paper improves energy contract pricing models by incorporating jumps and varying parameters.
We briefly review statistical models for the probability distribution of money developed in the econophysics literature since the late 1990s. In these models, economic transactions are modeled as random transfers of money between the agents in payment for goods and services. We focus on conceptual foundations for this …
Loopy and generalized belief propagation are popular algorithms for approximate inference in Markov random fields and Bayesian networks. Fixed points of these algorithms correspond to extrema of the Bethe and Kikuchi free energy. However, belief propagation does not always converge, which explains the need for approach…
Based on forward curves modelled as Hilbert-space valued processes, we analyse the pricing of various options relevant in energy markets. In particular, we connect empirical evidence about energy forward prices known from the literature to propose stochastic models. Forward prices can be represented as linear functions…
A new method using energy distance for ensemble and scenario reduction.
We study the volume distribution of nodal domains of random band-limited functions on generic manifolds, and find that in the high energy limit a typical instance obeys a deterministic universal law, independent of the manifold. Some of the basic qualitative properties of this law, such as its support, monotonicity and…
In recent years, multi-access edge computing (MEC) is a key enabler for handling the massive expansion of Internet of Things (IoT) applications and services. However, energy consumption of a MEC network depends on volatile tasks that induces risk for energy demand estimations. As an energy supplier, a microgrid can fac…
Active inference minimizes expected free energy for optimal behavior.
Random harmonic maps into spheres converge to a specific metric under strong convergence of representations.
In this paper we address the problem of finding the most probable state of a discrete Markov random field (MRF), also known as the MRF energy minimization problem. The task is known to be NP-hard in general and its practical importance motivates numerous approximate algorithms. We propose a submodular relaxation approa…
We present a new proximal bundle method for Maximum-A-Posteriori (MAP) inference in structured energy minimization problems. The method optimizes a Lagrangean relaxation of the original energy minimization problem using a multi plane block-coordinate Frank-Wolfe method that takes advantage of the specific structure of …
Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab-initio calculations) and at speeds suitable for molecular dynam- ics simulation. Best performance is achieved when the known physical constraints are encoded in the mac…
Dual training method for EBMs with overparametrized neural networks.
Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use neural network architectures to define energy functions that can capture arbitrary dependencies among parts of structured outputs. Prior work used gradient descent for inference, relaxing the structured output to a set of continuous variables a…
In structured output prediction tasks, labeling ground-truth training output is often expensive. However, for many tasks, even when the true output is unknown, we can evaluate predictions using a scalar reward function, which may be easily assembled from human knowledge or non-differentiable pipelines. But searching th…
RBM models reveal how hidden unit tail behavior affects pattern reconstruction.
Experimental fractal landscape dynamics observed in emulsions.
Paper compares econometric models with machine learning for energy forecasting.
Study shows visual feedback and monetary incentives reduce plugload energy consumption in commercial buildings.
Many models of market dynamics make use of the idea of wealth exchanges among economic agents. A simple analogy compares the wealth in a society with the energy in a physical system, and the trade between agents to the energy exchange between molecules during collisions. However, while in physical systems the equiparti…
New algorithm reduces costs in wind energy systems by minimizing decision changes.
Crowdsourcing has been successfully applied in many domains including astronomy, cryptography and biology. In order to test its potential for useful application in a Smart Grid context, this paper investigates the extent to which a crowd can contribute predictive hypotheses to a model of residential electric energy con…