A CNN with attention and shared latent space recovers SFHs and metallicities from spectro-photometric data with ~0.12 dex age and ~0.03 dex metallicity dispersion while running thousands of times faster than full spectral fitting.
Statistical Properties of the log-cosh Loss Function Used in Machine Learning
7 Pith papers cite this work, alongside 26 external citations. Polarity classification is still indexing.
7
Pith papers citing it
26
external citations · Pith
abstract
This paper analyzes a popular loss function used in machine learning called the log-cosh loss function. A number of papers have been published using this loss function but, to date, no statistical analysis has been presented in the literature. In this paper, we present the distribution function from which the log-cosh loss arises. We compare it to a similar distribution, called the Cauchy distribution, and carry out various statistical procedures that characterize its properties. In particular, we examine its associated pdf, cdf, likelihood function and Fisher information. Side-by-side we consider the Cauchy and Cosh distributions as well as the MLE of the location parameter with asymptotic bias, asymptotic variance, and confidence intervals. We also provide a comparison of robust estimators from several other loss functions, including the Huber loss function and the rank dispersion function. Further, we examine the use of the log-cosh function for quantile regression. In particular, we identify a quantile distribution function from which a maximum likelihood estimator for quantile regression can be derived. Finally, we compare a quantile M-estimator based on log-cosh with robust monotonicity against another approach to quantile regression based on convolutional smoothing.
years
2026 7representative citing papers
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An approximating full-conformal region for multi-task RKHS regression is given that contains the exact region, with volume upper bounds (known covariance) and empirical gains over split conformal.
SHEAP introduces a GPU-accelerated JAX framework for AGN spectral decomposition that achieves ~100x speedup over pPXF with 85-100% parameter agreement within 0.3 dex on four test samples.
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Determining star formation histories and age-metallicity relations with convolutional neural networks
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Eigenvalue Calibration for Semantic Embeddings of Large Language Models
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Transformer-based machine learning using low-level calorimeter signals for collimated photon identification at collider experiments
Cell-level Transformers classify collimated ALP photon-jets versus single photons with AUC 0.98 and regress diphoton mass to ~64 MeV, beating shower-shape and other ML baselines in an ATLAS-like GEANT4 simulation.
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Approximate full-conformal multi-task regression with reproducing kernels
An approximating full-conformal region for multi-task RKHS regression is given that contains the exact region, with volume upper bounds (known covariance) and empirical gains over split conformal.
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Spectral Handling and Estimation of AGN Parameters (SHEAP), The first AGN fitting GPU-based code
SHEAP introduces a GPU-accelerated JAX framework for AGN spectral decomposition that achieves ~100x speedup over pPXF with 85-100% parameter agreement within 0.3 dex on four test samples.
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UniRTL: Unifying Code and Graph for Robust RTL Representation Learning
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Alternate loss functions and regression models that achieve robustness to outliers by modulating the learning rate
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