arXiv research

The paper addresses over-fitting in deep learning models trained on imbalanced data.

Paper tackles robust classification under class-dependent domain shift.

Study reveals class-dependent effects in perturbation-based feature attribution metrics for time series classification.

Regularization and data augmentation can be class-dependent, leading to poor performance on some classes.

Proposes a game-theoretic approach for class-dependent rationalization.

Adversarial training is a useful approach to promote the learning of transferable representations across the source and target domains, which has been widely applied for domain adaptation (DA) tasks based on deep neural networks. Until very recently, existing adversarial domain adaptation (ADA) methods ignore the usefu…

Develops NPSVC++ to improve NPSVC performance through representation learning.

We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and mult…

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.

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.

Tautological classes, or generalised Miller-Morita-Mumford classes, are basic characteristic classes of smooth fibre bundles, and have recently been used to describe the rational cohomology of classifying spaces of diffeomorphism groups for several types of manifolds. We show that rationally tautological classes depend…

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…

The paper extends spacetime topology results using codimension 2 null cut locus properties.

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.

This paper calibrates uncertainty in dropout variational inference models.

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.

We construct a family of hyperbolic link complements by gluing tangles along totally geodesic four-punctured spheres, then investigate the commensurability relation among its members. Those with different volume are incommensurable, distinguished by their scissors congruence classes. Mutation produces arbitrarily large…

This paper considers the existence of conformally compact Einstein metrics on 4-manifolds. A reasonably complete understanding is obtained for the existence of such metrics with prescribed conformal infinity, when the conformal infinity is of positive scalar curvature. We find in particular that general solvability in …

We show that the index of an elliptic Fourier integral operator associated to a contact diffeomorphism $φ$ of cosphere bundles of two Riemannian manifolds X and Y is given by $\int_{B^*X}\hat{A}(T^*X)\expθ - \int_{B^*Y}\hat{A}(T^*Y)\expθ$. Here $B^*$ stands for the unit coball bundle and $θ$ is a certain characteristic…

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

CNN detects phase transitions in Potts models without prior knowledge.

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