Confidence Matters: Applications to Semantic Segmentation

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In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

Dynamic aperture is an important concept for the study of non-linear beam dynamics in circular accelerators. It describes the extent of the phase-space region where a particle's motion remains bounded over a given number of turns. Understanding the feature ...

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Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

Many feedforward neural networks (NNs) generate continuous and piecewise-linear (CPWL) mappings. Specifically, they partition the input domain into regions on which the mapping is affine. The number of these so-called linear regions offers a natural metric ...

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Confidence Matters: Applications to Semantic Segmentation

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Topics in statistical physics of high-dimensional machine learning

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Optimizing Dynamic Aperture Studies with Active Learning

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Enabling Uncertainty Estimation in Iterative Neural Networks

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

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Robust NAS under adversarial training: benchmark, theory, and beyond

On the number of regions of piecewise linear neural networks

Network time series forecasting in photovoltaics power production

Predicting the long-term collective behaviour of fish pairs with deep learning