arXiv Machine Learning
Sep 10

Learning Kernels by Alignment for Multiclass Bayes Classification

The paper introduces a framework that learns the kernel used in kernel methods through alignment, leveraging the Collaborative Learning and Inference (CLaI) approach. It demonstrates that CLaI can be interpreted as a kernel alignment process and that its inference stage is equivalent to kernel Bayes classification with Parzen-window density estimation. By replacing cosine similarity with a learned Mahalanobis distance, the authors extend CLaI to multiclass classification, achieving higher accuracy, faster convergence, and lower calibration error on datasets such as CIFAR-10, PathMNIST, and SleepEDF, while also showing connections to Gaussian processes and competitive calibration in sepsis prediction.

By Hollan Haule, Alfredo Gonzalez-Sulser, Javier Escudero
arXiv Machine Learning
Jun 9

Similarity-Distance-Magnitude Activations

arXiv:2509. 12760v5 Announce Type: replace Abstract: We introduce the Similarity-Distance-Magnitude (SDM) activation function, a more robust and interpretable formulation of the standard softmax activation function, adding Similarity (i.

By Allen Schmaltz
arXiv Statistics ML
Oct 28

A Mechanistic Interpretation of Prescribed-Parameter Convolutional Kernels

The paper investigates why prescribed‑parameter convolutional kernels, such as those used in ROCKET and its variants, perform well on time‑series classification. It analyzes the proportion of positive values (PPV) pooling operator, proving exact invariance to circular translations and quantifying deviations under finite boundaries. Experiments on the UCR collection show that increasing kernel count yields diminishing returns, while longer kernels raise effective dimensionality and distance correlation, with optimal test performance at intermediate lengths.

By Jorge Marco Blanco, Rub\'en Cuevas Rum\'in