Publications related to Calibration of high-precision flexure parallel robots

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

EPFL2024

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

Here we provide the neural data, activation and predictions for the best models and result dataframes of our article "Task-driven neural network models predict neural dynamics of proprioception". It contains the behavioral and neural experimental data (cu ...

EPFL Infoscience2024

Investigating neural resource allocation in the sensorimotor control of extra limbs

Giulia Dominijanni

The rise of robotic body augmentation brings forth new developments that will transform robotics, human-machine interaction, and wearable electronics. Extra robotic limbs, although building upon restorative technologies, bring their own set of challenges i ...

EPFL2024

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

Alexander Mathis, Alberto Silvio Chiappa, Alessandro Marin Vargas, Axel Bisi

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 ...

EPFL Infoscience2024

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Romain Christophe Rémy Fleury, Janez Rus

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

2024

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

Small-scale robotic devices for medical interventions in the brain

Mahmut Selman Sakar, Lorenzo Francesco John Noseda

This article summarizes the recent advancements in the design, fabrication, and control of microrobotic devices for the diagnosis and treatment of brain disorders. With a focus on diverse actuation methods, we discuss how advancements in materials science ...

2024

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Martin Jaggi, Vinitra Swamy, Jibril Albachir Frej, Julian Thomas Blackwell

Interpretability for neural networks is a trade-off between three key requirements: 1) faithfulness of the explanation (i.e., how perfectly it explains the prediction), 2) understandability of the explanation by humans, and 3) model performance. Most exist ...

2024

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Pakshal Narendra Bohra

In inverse problems, the task is to reconstruct an unknown signal from its possibly noise-corrupted measurements. Penalized-likelihood-based estimation and Bayesian estimation are two powerful statistical paradigms for the resolution of such problems. They ...

EPFL2024

Driving and suppressing the human language network using large language models

Martin Schrimpf

Transformer models such as GPT generate human-like language and are predictive of human brain responses to language. Here, using functional-MRI-measured brain responses to 1,000 diverse sentences, we first show that a GPT-based encoding model can predict t ...

Berlin2024

Calibration of high-precision flexure parallel robots

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Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Investigating neural resource allocation in the sensorimotor control of extra limbs

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Small-scale robotic devices for medical interventions in the brain

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Driving and suppressing the human language network using large language models

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Task-driven neural network models predict neural dynamics of proprioception: Experimental data, activations and predictions of neural network models

Investigating neural resource allocation in the sensorimotor control of extra limbs

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

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Small-scale robotic devices for medical interventions in the brain

InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts

Statistical Inference for Inverse Problems: From Sparsity-Based Methods to Neural Networks

Driving and suppressing the human language network using large language models