Adversarial Confidence Estimation Networks for Robust Stereo Matching

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SuperAnimal pretrained pose estimation models for behavioral analysis

Alexander Mathis, Mackenzie Mathis, Shaokai Ye, Steffen Schneider, Jessy Lauer, Tian Qiu, Maxime Michel André Vidal, Anastasiia Filippova

Quantification of behavior is critical in diverse applications from neuroscience, veterinary medicine to animal conservation. A common key step for behavioral analysis is first extracting relevant keypoints on animals, known as pose estimation. However, re ...

Nature Research2024

Programming the scalable optical learning operator with spatial-spectral optimization

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Carlo Gigli, Jih-Liang Hsieh, Yi Zhou Ju

Electronic computers have evolved drastically over the past years with an ever-growing demand for improved performance. However, the transfer of information from memory and high energy consumption have emerged as issues that require solutions. Optical tech ...

2024

Discovering meaningful units from text sequences

Melika Behjati

In recent years, the field of Natural Language Processing has seen significant revolution by the introduction of Transformers, a stack of multiple layers of attention and non-linearity, capable of performing almost any task and the backbone for large found ...

EPFL2024

Distill-SODA: Distilling Self-Supervised Vision Transformer for Source-Free Open-Set Domain Adaptation in Computational Pathology

Jean-Philippe Thiran, Seyedbehzad Bozorgtabar, Guillaume Marc Georges Vray, Devavrat Tomar

Developing computational pathology models is essential for reducing manual tissue typing from whole slide images, transferring knowledge from the source domain to an unlabeled, shifted target domain, and identifying unseen categories. We propose a practica ...

Institute of Electrical and Electronics Engineers2024

Data-driven frequency-based feedforward control design for a robotic arm joint

Alireza Karimi, Philippe Louis Schuchert

Next-generation motion control systems require fast and precise control, but advanced control strategies often rely on complex and expensive models. This paper proposes a data-driven approach to tune controllers for a joint used in a robotic arm. This is a ...

IEEE2024

Information propagation in Gaussian processes on multilayer networks

Giorgio Nicoletti

Complex systems with multiple processes evolving on different temporal scales are naturally described by multilayer networks, where each layer represents a different timescale. In this work, we show how the multilayer structure shapes the generation and pr ...

2024

BlueCelluLab: Biologically Detailed Neural Network Experimentation API

Henry Markram, Eilif Benjamin Muller, Werner Alfons Hilda Van Geit, Lida Kanari, Mustafa Anil Tuncel, Darshan Mandge, Aurélien Tristan Jaquier

The NEURON simulator, established in 1984 and continuously developed since, stands as the preeminent tool for neuron simulation within computational neuroscience. Its widespread adoption and compatibility with computational clusters and supercomputers unde ...

The Open Journal2024

Exact Sampling for Classical and Quantum Many-Body Systems

Dian Wu

Many-body systems at low temperature have revealed non-trivial phases of materials, such as spin liquids, which have found applications in the evolving fields of superconductivity, nanoelectronics, and quantum computing. Their exponentially large state spa ...

EPFL2024