Tootfinder

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Sai Puneeth Reddy Gottam, Haoming Zhang, Eivydas Keras
https://arxiv.org/abs/2509.08333

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data…

NewsReX: A More Efficient Approach to News Recommendation with Keras 3 and JAX
Igor L. R. Azevedo, Toyotaro Suzumura, Yuichiro Yasui
https://arxiv.org/abs/2508.21572 https://

NewsReX: A More Efficient Approach to News Recommendation with Keras 3 and JAX
Reproducing and comparing results in news recommendation research has become increasingly difficult. This is due to a fragmented ecosystem of diverse codebases, varied configurations, and mainly due to resource-intensive models. We introduce NewsReX, an open-source library designed to streamline this process. Our key contribution is a modern implementation built on Keras 3 and JAX, which provides an increase in computational efficiency. Experiments show that NewsReX is faster than current imple…

Machine learning analysis of Photometric data from the Dark Energy Survey
Elcio Abdalla, Filipe B. Abdalla, Alessandro Marins, Amilcar Queiroz, Rafael M. Ribeiro, Alex S. C. Souza
https://arxiv.org/abs/2508.10191

Machine learning analysis of Photometric data from the Dark Energy Survey
In order to retrieve cosmological parameters from photometric surveys, we need to estimate the distribution of the photometric redshift in the sky with excellent accuracy. We use and apply three different machine learning methods to publicly available Dark Energy Survey data release 2 (DR2): a) Artificial Neural Network for photometric redshifts (ANNz2); b) Gaussian processes for photometric redshifts (GPz); and c) Keras, a deep learning application programming interface in Python. We compare t…