Weakly Supervised Deep Learning Methods for Biomicroscopy

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Weakly Supervised Deep Learning Methods for Biomicroscopy

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Understanding generalization and robustness in modern deep learning

Probing the latent hierarchical structure of data via diffusion models

Surface passivation and functionalisation for mass photometry

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

Incorporating Projective Geometry into Deep Learning

Domain shift, domain adaptation, and generalization: A focus on MRI

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Live-cell imaging powered by computation

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Data-driven control in atomic force microscopy using a genetic algorithm

Aggregating Spatial and Photometric Context for Photometric Stereo

Residual-based attention in physics-informed neural networks

Feature-based encoding of face identity by single neurons in the human amygdala and hippocampus

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Subsurface fluorescence time-of-flight imaging using a large-format single-photon avalanche diode sensor for tumor depth assessment

Advances on Real Time M/EEG Neural Feature Extraction

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

A Closer Look at Benchmarking Self-supervised Pre-training with Image Classification