arXiv research

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

Bayesian neural networks improve performance at finite temperature.

In CJKLS quandle cohomology is used to produce invariants for particular embeddings of codimension two; 2-cocycles give to invariants for (classical) knots and 3-cocycles give rise to invariants for knotted surfaces. This is done by way of a notion of coloring of a diagram. Also, these invariants have the form of state…

Real-time machine learning predicts AM process temperatures accurately.

Bayesian framework quantifies uncertainty in portfolio temperature alignment.

The study shows black hole horizons at low temperatures have limited topology.

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 …

The paper analyzes extreme temperature forecasting using machine learning models.

New statistical test for change-point detection using relative entropy.

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 neural networks to efficiently approximate Bayesian inference for complex models.

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…

Study evaluates and compares numerical differentiation methods on three case studies.

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.

Combines ocean surface and interior data to study ocean dynamics.

The number of colorings of a knot diagram by a quandle has been shown to be a knot invariant by CJKLS using quandle cohomology methods. In a previous paper by the second named author, the CJKLS invariant was refined and, in particular, it was shown that the number of colorings is an invariant directly without resorting…

This paper calibrates uncertainty in dropout variational inference models.

Study finds phase transition in context-sensitive language model with short-range interactions.

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.

CNN detects phase transitions in Potts models without prior knowledge.

Paper studies convergence of Mean-Field GDA dynamics for MNE of continuous games.

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

TemperatureGAN generates hourly atmospheric temperature data with high fidelity.

This study evaluates machine learning models for precise temperature estimation in PMSMs.