Clusters energy usage patterns from smart meters.
problem Identify and group similar energy usage profiles.
method Clustering time-series data from smart meters.
result Accurate grouping of similar energy usage patterns.
On-device research index
arXiv research
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.
169,341 papers · 148 categories
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48 results for “energy method”
Discrete knot energiesmath.GT
The chapter discusses discrete knot energies for computational and geometric modeling.
problem Creating efficient and consistent discrete models for knots.
method Introducing Möbius energy, integral Menger curvature, and thickness as discrete knot energies.
result These discrete energies behave similarly to the original model and facilitate computational methods.
New method assesses energy storage value beyond cost reduction.
problem Improving energy storage value beyond cost reduction.
method Market potential method to evaluate and compare energy storage technologies.
result High-cost hydrogen storage can be more valuable than low-cost hydrogen storage.
Energy savings for DNN inference on resource-constrained devices.
problem Energy efficiency in deep learning inference for constrained devices.
method Efficiently searches through equivalent DNN graphs to find the one with the least execution cost.
result Achieves 24% energy savings with minimal performance impact.
Improves sample quality of generative models using energy-based methods.
problem Low sample quality in generative models.
method Constructs an energy function on latent space, trains an energy-based model, and generates improved samples.
result Significant improvement in sample quality with minimal computational overhead.
Energy methods solve Dirac-type equations in 2D Minkowski space.
problem Solving Dirac-type equations in 2D Minkowski space.
method Energy methods for linear and nonlinear equations.
result Existence results for Dirac-type equations.
CityTFT models urban building energy using a data-driven approach.
problem Current UBEM methods are time-consuming and based on physics.
method CityTFT uses a TFT framework with an augmented loss function.
result CityTFT predicts energy demands with high accuracy.
End-to-end learning improves SPENs for structured prediction.
problem Improving accuracy in structured prediction models.
method Discriminative training of SPENs with backpropagation through gradient-based optimization.
result End-to-end SPENs outperform structured SVM methods.
Paper proposes energy-efficient DNN training methods.
problem Energy-constrained deployment of deep neural networks.
method Weighted sparse projection and layer input masking integrated into DNN training.
result Framework provides higher accuracy with same or lower energy budgets.
FEAT estimates free energy using adaptive transports.
problem Estimating free energy across scientific domains.
method Uses learned transports and stochastic interpolants.
result Provides consistent, minimum-variance estimators.
Paper benchmarks and customizes energy forecasting methods.
problem Energy forecasting challenges and differences from traditional time series.
method Collected large-scale load datasets and renewable energy datasets. Developed feature engineering and customized loss functions.
result Comprehensive evaluation of 21 forecasting methods in energy datasets.
Stock mechanics: a general theory and method of energy conservation with applications on DJIAphysics.soc-ph
A new method, based on the original theory of conservation of sum of kinetic and potential energy defined for prices is proposed and applied on Dow Jones Industrials Average (DJIA). The general trends averaged over months or years gave a roughly conserved total energy, with three different potential energies, i.e. posi…
Paper presents an energy-efficient RL method for sensor networks.
problem Energy consumption in sensor networks for health monitoring.
method Adaptive Reinforcement Learning framework using SARSA algorithm.
result Achieves performance enhancement and energy savings over time.
New method learns latent energy models using particle algorithms.
problem Learning latent variable models with energy priors.
method Continuous-time SDEs for MMLE, particle-based discretization.
result Practical algorithm converges to solve MMLE problem.
VAV method optimizes learning rate for faster, stable SGD convergence.
problem Optimizing learning rate for efficient and stable machine learning models.
method Energy-based self-adaptive learning rate with auxiliary variable .
result VAV method achieves faster convergence and superior stability with larger learning rates.
New algorithms improve learning deep energy models.
problem Learning deep energy models efficiently and accurately.
method Proposed new algorithms combining GAN-style methods with traditional energy-based learning.
result SteinCD performs well in test likelihood, SteinGAN in generating realistic images.
New method reduces energy consumption of Hoeffding trees by up to 92%.
problem Inefficient energy consumption of Hoeffding trees due to fixed parameters.
method nmin adaptation for Hoeffding trees to adapt nmin parameter dynamically.
result VFDT-nmin consumes up to 92% less energy than CVFDT, trading off a few percent of accuracy.
A new parametric method studies Willmore flows and energy quantization.
problem Understanding Willmore flows and their singularities.
method Parametric approach to Willmore gradient flows.
result For small-energy weak immersions, a unique solution exists.
Targeted free energy estimation via learned mappingsphysics.comp-ph
New method uses neural networks to improve free energy estimation.
problem Estimating free energy differences using FEP is limited by insufficient overlap between distributions.
method Developed a neural network to parameterize a high-dimensional mapping in configuration space.
result Demonstrated substantial variance reduction in free energy estimates.
Current state-of-the-art discrete optimization methods struggle behind when it comes to challenging contrast-enhancing discrete energies (i.e., favoring different labels for neighboring variables). This work suggests a multiscale approach for these challenging problems. Deriving an algebraic representation allows us to…
Proves smooth critical points of Möbius energy are analytic.
problem Analyticity of critical points of Möbius energy.
method Cauchy's method of majorants and gradient decomposition.
result Smooth critical points of Möbius energy are analytic.
Energy Matching unifies flow matching and energy-based models for generative modeling.
problem Inability of flow-based models to integrate partial observations and priors.
method Energy Matching framework that integrates flow matching and energy-based models.
result Substantially outperforms existing EBMs on CIFAR-10 and ImageNet generation.
Nonholonomic Newmark methodmath.NA
Generalizes Newmark methods for nonholonomic systems.
problem Energy behavior in nonholonomic systems.
method Nonholonomic exponential map to generalize Newmark methods.
result Composition of two Newmark methods can improve energy behavior.
Proves energy expression on Poincaré-Einstein spaces.
problem Computing renormalized Yang-Mills energy on Poincaré-Einstein manifolds.
method Generalizes Chang-Qing-Yang method for renormalized volumes and uses scattering theory for Schrödinger operators.
result Agrees with anomaly boundary integrand in seven dimensions.
On the regularity of critical points for O'Hara's knot energies: From smoothness to analyticitymath.AP
Analyticity of critical points for O'Hara's knot energies proved.
problem Analyzing the regularity of critical points for O'Hara's knot energies.
method Cauchy's method of majorants and a Möbius energy-inspired gradient decomposition.
result Smooth critical points of O'Hara's knot energies are analytic.
Quantum vacuum energy (Casimir energy) is reviewed for a mathematical audience as a topic in spectral theory. Then some one-dimensional systems are solved exactly, in terms of closed classical paths and periodic orbits. The relations among local spectral densities, energy densities, global eigenvalue densities, and tot…
New optimization method for sampling from unknown density measures.
problem Sampling from measures with unknown normalization constants.
method Mollified Interaction Energy Descent (MIED) method.
result Gradient flow of MIE converges to chi-square divergence.
Novel segmentation method for energy game-theoretic frameworks using graphical lasso.
problem Difficulty in computing utility functions for high-player energy game-theoretic frameworks.
method Graphical Lasso based approach to cluster features leading to energy usage behaviors.
result Characteristic clusters demonstrating different energy usage behaviors identified.
Extends Penrose's method to null shells with pressure and energy flux.
problem Constructing null thin shells with arbitrary gravitational/matter content.
method Derive locally Lipschitz metric and coordinate transformation.
result Example of null shell with non-trivial energy density, flux, and pressure in Minkowski space.
Two regularization methods show similar results for Riesz energies of submanifolds.
problem Analyzing Riesz energies of submanifolds in Euclidean space.
method Comparing Hadamard's finite part and analytic continuation for regularization.
result Regularization techniques give similar results for Riesz energies.
Improved neural architecture optimization for energy efficiency.
problem Designing energy-efficient deep learning networks for mobile and edge devices.
method Incorporates energy cost in splitting process and uses a scalable stochastic gradient algorithm to speed up the splitting.
result Trains highly accurate and energy-efficient networks on challenging datasets like ImageNet.
Divergence Triangle for Joint Training of Generator Model, Energy-based Model, and Inference Modelstat.ML
Unified framework for training generator, energy model, and inference model.
problem Training of generator, energy model, and inference model in a unified probabilistic formulation.
method Divergence Triangle framework integrating variational learning, adversarial learning, wake-sleep algorithm, and contrastive divergence.
result Unified training of generator, energy model, and inference model without costly Markov chain Monte Carlo methods.
A two-step market clearing method for local energy trading among prosumers and consumers.
problem Integrating distributed energy resources into local energy markets.
method Feeder-based market with Two-StepMarket Clearing (2SMC) mechanism.
result Maximizes market surplus and correct incentives for prosumers and consumers.
Study a flow for curve energy, proving existence for various p.
problem Evolution of closed curves under energy minimization.
method Second order flow decreasing p-elastic energy, proving existence via minimizing movements.
result Existence of solutions for , long-time existence for .
Physics aware machine learning for micromagnetic energy minimization: recent algorithmic developmentsphysics.comp-ph
This work develops machine learning for micromagnetic energy minimization.
problem Minimizing Gibbs free energy in full 3D micromagnetic simulations.
method Advanced machine learning techniques, including Physics-Informed Neural Networks (PINNs) and Extreme Learning Machines (ELMs), with reformulated bounds and optimization schemes.
result Competitive performance of machine learning methods compared to traditional numerical approaches.
AEGD optimizes non-convex functions with dynamic energy updates.
problem Optimizing non-convex functions efficiently and robustly.
method Adaptive Gradient Descent (AEGD) with a dynamically updated energy variable.
result AEGD achieves energy-dependent convergence rates for both convex and non-convex objectives.
New method uses nearest neighbors quantile filter for probabilistic energy forecasting.
problem Creating accurate probabilistic energy forecasts using complex data mining techniques.
method Uses a new nearest neighbors quantile filter to create quantile regressions without a non-differentiable cost function.
result Demonstrates superior performance in Global Energy Forecasting Competition 2014.
Variational principle to regularize machine-learned density functionals: the non-interacting kinetic-energy functionalphysics.chem-ph
New method trains deep neural networks for non-interacting kinetic-energy functionals in DFT.
problem Lack of exact relationship between electron density and non-interacting kinetic energy.
method Variational principle to regularize machine-learned density functionals.
result Excellent results on kinetic-energy functionals for various systems.
The paper finds curves minimizing elastic energy pinned at endpoints.
problem Finding curves that minimize elastic energy with fixed endpoints.
method Applying the shooting method to identify and classify critical points.
result Critical points consist of wavelike elasticae, and minimizers have no loops or interior inflection points.
This paper prioritizes experience replay in robotics using energy-based principles.
problem Randomly replaying experience in HER leads to inefficient learning.
method Developed an energy-based framework to prioritize hindsight experience in robotic manipulation tasks.
result EBP outperforms state-of-the-art approaches in robotic manipulation tasks.
The paper improves energy contract pricing models by incorporating jumps and varying parameters.
problem Inaccurate pricing of energy contracts using the Black-Scholes-Merton model.
method Integrates regime switching and time-changed Levy processes with a two-state Markov chain.
result Improved accuracy in pricing energy contracts through a new model.
New method proves regularity for small energy solutions of Yang-Mills-Higgs equations.
problem Proving regularity for small energy solutions of Yang-Mills-Higgs equations.
method Improved Kato inequality and Weitzenböck formulae.
result Obtains bounded curvature without Coulomb gauges.
Proposes a new learning method for RBMs that combines strengths of forward and reverse KLD.
problem Underfitting and mode-collapse issues in RBM learning.
method Ratio divergence learning using target energy.
result Significantly outperforms other learning methods in energy function fitting, mode-covering, and stability.
A new method using energy distance for ensemble and scenario reduction.
problem Solving complex dynamic and stochastic programs, especially in energy systems.
method Proposes a new method based on energy distance for ensemble and scenario reduction.
result Reduced scenario sets exhibit better statistical properties for energy distance than Wasserstein distance.
ShotgunCSP predicts crystal structures using machine learning, achieving high accuracy with minimal computation.
problem Predicting stable or metastable crystal structures of large systems.
method Noniterative screening using transfer learning and generative models.
result ShotgunCSP achieves 93.3% accuracy in benchmark tests with 90 different crystal structures.
Paper proposes a neural network for disaggregating appliance-level energy consumption.
problem Estimating appliance-level electricity consumption from a single meter.
method Adapts a neural network to classify operational state changes of appliances.
result Competitive performance compared to existing methods in simulated experiments.
The paper extends energy-based models to learn nonlinear probability maps from energies.
problem Learning accurate probabilistic models with wide probability ranges.
method Generalizes energy-based models to include a nonlinear map from energies to unnormalized probabilities, learned from data.
result The generalized model accurately captures neural activity distributions with large probability ranges.
The paper proposes using energy disaggregation techniques to detect flexible demand in buildings.
problem Real-time detection of flexible demand in buildings.
method The paper investigates the use of existing classification methods and proposes the use of Restricted Boltzmann Machine for feature extraction.
result The proposed approach shows robustness and good generalization capabilities with at least 96% accuracy.