arXiv research

Study evaluates DGMs' ability to recover non-stationary Gaussian fields.

Deep models struggle with non-stationary Gaussian fields, but DDPM and score-SDE perform best.

Deep learning model predicts European weather parameters.

Study combines variational inference and transformers for seasonal climate predictions.

Deep learning improves combustor anomaly detection in gas turbines.

Investigates how extreme temperature events affect global equity portfolios.

Combines ocean surface and interior data to study ocean dynamics.

Neural network predicts weather probabilities with added uncertainty.

Study predicts wind energy potential in Gulf of Oman using climate models.

New method reconstructs past foehn occurrences using unsupervised and supervised learning.

An encoder-decoder model detects anomalies in manufacturing data.

Measures time-delay embedding for noisy, sparse data.

EagleEye detects localized density anomalies in multivariate data.

Decadal climate predictions, which are initialized with observed conditions, are characterized by two main sources of uncertainties--internal and model variabilities. Using an ensemble of climate model simulations from the CMIP5 decadal experiments, we quantified the total uncertainty associated with these predictions …

The coefficient of the logarithmic term in the entropy on even spheres is re-computed by the local technique of integrating the finite temperature energy density up to the horizon on static d--dimensional de Sitter space and thence finding the entropy by thermodynamics. Numeric evaluation yields the known answer i.e. (…

Machine learning predicts Atlantic blocking using limited data.

WeatherBench provides a dataset and metrics for comparing data-driven weather forecasts.

Study improves accuracy of weather data for real-time building simulations.

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph. Graph signal recovery implies recovery of one or multiple smooth graph signals from noisy, corrupted, or incomplete measurements. We propose a graph signal model and formulate signal recovery as a cor…

Introduces bi-temperature logistic loss for more robust training.

Temperature scaling improves model uncertainty but not diversity in LLMs.

Introduce a thermodynamically informed, temperature-transferable MLCG framework for proteins.

Study local sensitivity of HDD and CDD temperature derivatives prices.

A new method selects optimal temperature for Bayesian Deep Learning.

Climate volatility reduces economic growth, especially in poorer countries.

New adaptive temperature selection improves parallel tempering efficiency.

Logit-GFN accelerates GFlowNets training by scaling logits based on temperature.

This paper measures temperature in agent systems using volatility.

Attention temperature improves robustness of ICL in high-dimensional settings.

Trends in terrestrial temperature variability are perhaps more relevant for species viability than trends in mean temperature. In this paper, we develop methodology for estimating such trends using multi-resolution climate data from polar orbiting weather satellites. We derive two novel algorithms for computation that …

Framework reconstructs missing spatio-temporal data for extreme value prediction.

The paper analyzes extreme temperature forecasting using machine learning models.

The paper analyzes the InfoNCE loss under different temperature schedules using Langevin dynamics.

Embeds spherically symmetric space-times into 5D flat space, matching Hawking temperature.

The Gibbs algorithm's generalization error is bounded, improving with prior volume in low temperatures.

We propose a novel method to forecast the future from the present using time-reversed data.

Optimizes contrastive learning with individualized temperatures for better performance on imbalanced datasets.

New method uses RBM flows to find critical temperatures in Ising models.

The paper prices weather contracts using a complex temperature model.

A scalable Gaussian process clustering method for large datasets.

Algorithm learns Sherrington-Kirkpatrick model parameters at low temperatures.

Develops a new stochastic volatility model for temperature derivatives.

Study shows sample complexity for logistic regression with normal covariates.

The minute fluctuations of of S&P 500 and NASDAQ 100 indices display Boltzmann statistics over a wide range of positive as well as negative returns, thus allowing us to define a {\em market temperature} for either sign. With increasing time the sharp Boltzmann peak broadens into a Gaussian whose volatility $σ$ measure…

In the spirit of behavioral finance, we study the process of opinion formation among investors using a variant of the 2D Voter Model with a tunable social temperature. Further, a feedback acting on the temperature is introduced, such that social temperature reacts to market imbalances and thus becomes time dependent. I…

A spin model relating physical to financial variables is presented. This work is the first to introduce the concept of negative absolute temperature into stock market dynamics by establishing a rigorous formal analogy between physical and financial variables. Based on this model, an algorithm evaluating negative temper…

Improves anomaly detection with contaminated unlabeled data.

Machine learning and deep learning infer surface/groundwater exchange from temperature data.