Deep Learning
Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.
Papers
JetLOV: Enhancing Jet Tree Tagging through Neural Network Learning of Optimal LundNet Variables
Mauricio A. Diaz, Giorgio Cerro, Jacan Chaplais, Srinandan Dasmahapatra, Stefano Moretti
Finding Foundation Models for Time Series Classification with a PreText Task
Ali Ismail-Fawaz, Maxime Devanne, Stefano Berretti, Jonathan Weber, Germain Forestier
Towards Interpretable Classification of Leukocytes based on Deep Learning
Stefan Röhrl, Johannes Groll, Manuel Lengl, Simon Schumann, Christian Klenk, Dominik Heim, Martin Knopp, Oliver Hayden, Klaus Diepold
Deep Learning for Automatic Strain Quantification in Arrhythmogenic Right Ventricular Cardiomyopathy
Laura Alvarez-Florez, Jörg Sander, Mimount Bourfiss, Fleur V. Y. Tjong, Birgitta K. Velthuis, Ivana Išgum
Federated Transformed Learning for a Circular, Secure, and Tiny AI
Weisi Guo, Schyler Sun, Bin Li, Sam Blakeman
Deep Learning as a Method for Inversion of NMR Signals
Julian B. B. Beckmann, Mick D. Mantle, Andrew J. Sederman, Lynn F. Gladden
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies
Shabnam Daghaghi, Benjamin Coleman, Benito Geordie, Anshumali Shrivastava
Learning principle and mathematical realization of the learning mechanism in the brain
Taisuke Katayose
Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging
Ekin Yagis, Shahab Aslani, Yashvardhan Jain, Yang Zhou, Shahrokh Rahmani, Joseph Brunet, Alexandre Bellier, Christopher Werlein, Maximilian Ackermann, Danny Jonigk, Paul Tafforeau, Peter D Lee, Claire Walsh
Differentiable Visual Computing for Inverse Problems and Machine Learning
Andrew Spielberg, Fangcheng Zhong, Konstantinos Rematas, Krishna Murthy Jatavallabhula, Cengiz Oztireli, Tzu-Mao Li, Derek Nowrouzezahrai
VALUED -- Vision and Logical Understanding Evaluation Dataset
Soumadeep Saha, Saptarshi Saha, Utpal Garain
Deep learning-based detection of morphological features associated with hypoxia in H&E breast cancer whole slide images
Petru Manescu, Joseph Geradts, Delmiro Fernandez-Reyes
Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods
Saman Yazdannik, Morteza Tayefi, Mojtaba Farrokh
Neural Network Pruning by Gradient Descent
Zhang Zhang, Ruyi Tao, Jiang Zhang
Mapping "Brain Coral" Regions on Mars using Deep Learning
Kyle A. Pearson, Eldar Noe, Daniel Zhao, Alphan Altinok, Alex Morgan
Identifying DNA Sequence Motifs Using Deep Learning
Asmita Poddar, Vladimir Uzun, Elizabeth Tunbridge, Wilfried Haerty, Alejo Nevado-Holgado
Ovarian Cancer Data Analysis using Deep Learning: A Systematic Review from the Perspectives of Key Features of Data Analysis and AI Assurance
Muta Tah Hira, Mohammad A. Razzaque, Mosharraf Sarker