arXiv research

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

Machine learning speeds up CRTM model predictions for weather forecasting.

Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal resolution while in situ autonomous probes supply high vertical resolution, but horiz…

We investigate unification of two systems of identical elements having different dimensions which may be of interest for both physics and economics. Characteristic parameters as well as explicit formulae for the temperature (in economics - capital turnover) and dimension of the united system are obtained as functions o…

Gradient-based method prunes large models to create transferable architectures.

Methane is considered being a good choice as a propellant for future reusable launch systems. However, the heat transfer prediction for supercritical methane flowing in cooling channels of a regeneratively cooled combustion chamber is challenging. Because accurate heat transfer predictions are essential to design relia…

Dual ML approach predicts peak temperatures in AFSD, improving process optimization.

Distillation affects some classes more than others, impacting fairness and bias.

Recent studies have shown that the aggregated dynamic flexibility of an ensemble of thermostatic loads can be modeled in the form of a virtual battery. The existing methods for computing the virtual battery parameters require the knowledge of the first-principle models and parameter values of the loads in the ensemble.…

Thermal dynamics modeling has been a critical issue in building heating, ventilation, and air-conditioning (HVAC) systems, which can significantly affect the control and maintenance strategies. Due to the uniqueness of each specific building, traditional thermal dynamics modeling approaches heavily depending on physics…

Data-driven methods link graphon limits to random walks and spectral clustering.

Temperature scaling improves model uncertainty but not diversity in LLMs.

Study local sensitivity of HDD and CDD temperature derivatives prices.

We introduce a temperature into the exponential function and replace the softmax output layer of neural nets by a high temperature generalization. Similarly, the logarithm in the log loss we use for training is replaced by a low temperature logarithm. By tuning the two temperatures we create loss functions that are non…

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 …

The paper analyzes extreme temperature forecasting using machine learning models.

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

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

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.

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…

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

Researchers derive the relation between temperature and volatility in ideal agent systems.

New method estimates Nishimori temperature for node classification in weighted graphs.

Cold posteriors improve Bayesian neural networks by reducing overestimation of aleatoric uncertainty.

Temperature scaling fails for distributions with class overlaps, while Mixup improves calibration.

We present a model of financial markets originally proposed for a turbulent flow, as a dynamic basis of its intermittent behavior. Time evolution of the price change is assumed to be described by Brownian motion in a power-law potential, where the `temperature' fluctuates slowly. The model generally yields a fat-tailed…

Improved PDMP samplers for multi-modal distributions using tempering.

Unified framework for critical scaling of inverse temperature in self-attention.

Model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. The uncertainty does not represent the model error well. In this paper, temperature scaling is extended to dropout variational inference to calibrate model uncertainty. Expected uncertainty calibration erro…

CNN detects phase transitions in Potts models without prior knowledge.

We prove uniqueness of solutions to complex Monge-Ampère equations for small temperature.

TemperatureGAN generates hourly atmospheric temperature data with high fidelity.

Meta-SAC automatically tunes SAC's entropy temperature for better exploration.

We develop a variant of multiclass logistic regression that is significantly more robust to noise. The algorithm has one weight vector per class and the surrogate loss is a function of the linear activations (one per class). The surrogate loss of an example with linear activation vector $\mathbf{a}$ and class $c$ has t…

CNN improves medium-range temperature forecasts with limited resources.