Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multifingered In-Hand Manipulation

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This paper investigates the fundamental regression task of learning k neurons (a.k.a. teachers) from Gaussian input, using two-layer ReLU neural networks with width m (a.k.a. students) and m, k = O(1), trained via gradient descent under proper initializati ...

We present a reinforcement learning framework for constructing assemblies composed of rigid parts, which are commonly seen in many historical masonry buildings and bridges. Traditional construction methods for such structures often depend on dense scaffold ...

Deep neural networks (DNNs) are receiving increasing attention in wind power forecasting due to their ability to effectively capture complex patterns in wind data. However, their forecast errors are severely limited by the local optimal weight issue in opt ...

Animals have to navigate complex environments and perform intricate swimming maneuvers in the real world. To conquer these challenges, animals evolved a variety of motion control strategies. While it is known that many factors contribute to motion control, ...

Deploying energy-efficient deep neural networks on energy-constrained edge devices is an important research topic in both machine learning and circuit design communities. Both artificial neural networks (ANNs) and spiking neural networks (SNNs) have been p ...

Now is an exciting time for the domain of Earth observation (EO), with a multitude of diverse sensors looking at the planet from satellites, airplanes or drones. The volume of imagery acquired is massive, and hold great potential for a variety of applicati ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Many empirical studies have provided evidence for the emergence of algorithmic mechanisms (abilities) in the learning of language models, that lead to qualitative improvements in the model capabilities. Yet, a theoretical characterization of how such mecha ...

As one of the major threats to women's health worldwide, breast cancer requires early diagnosis and accurate classification, since they are key to optimizing therapeutic interventions and ensuring precise prognosis. Recently, deep learning has demonstrated ...

In recent years, neural quantum states have emerged as a powerful variational approach, achieving state-of-the-art accuracy when representing the ground-state wave function of a great variety of quantum many-body systems, including spin lattices, interacti ...

Plasma shape control is a fundamental tool for effective tokamak operation, allowing improved plasma performance and the pursuit of challenging scientific objectives. In this framework, the Tokamak á Configuration Variable (TCV) of the École Polytechnique ...

Sign Language conveys information through multiple channels composed of manual (handshape, hand movement) and non-manual (facial expression, mouthing, body posture) components. Sign language assessment involves giving granular feedback to a learner, in ter ...

The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, in ...

This thesis addresses key challenges in developing adaptive lower-limb exoskeletons to enhance balance, mobility, and user autonomy during daily living activities. It presents a comprehensive exploration of three critical aspects: push recovery during stan ...

In preparation for error field (EF) identification experiments in ITER, a series of experiments have been conducted at JET to test the portability of the non-disruptive compass scan, a method originally designed and tested in DIII-D (Paz-Soldan et al 2022 ...

Neurons in the human amygdala and hippocampus are classically thought to encode a person’s identity invariant to visual features. However, it remains largely unknown how visual information from higher visual cortical areas is translated into such a semanti ...

The work presented in this thesis lies at the intersection of Machine Learning (ML) and control theory. The first part is dedicated to the design of Deep Neural Network (DNN) architectures with specific numerical properties, inspired by dynamical systems m ...

In this paper, we study sampling from a posterior derived from a neural network. We propose a new probabilistic model consisting of adding noise at every pre- and post-activation in the network, arguing that the resulting posterior can be sampled using an ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Deep digital twins (DDTs) are deep neural networks that encode the behavior of complex physical systems. DDTs are excellent system representations due to their ability to continuously adapt to operational changes and their capability to capture complex rel ...

Enhancing Dexterity in Confined Spaces: Real-Time Motion Planning for Multifingered In-Hand Manipulation

Chattez avec Graph Search

How Gradient Descent Balances Features: A Dynamical Analysis For Two-Layer Neural Networks

Learning to Assemble with Alternative Plans

Improving the accuracy and interpretability of neural networks for wind power forecasting

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Eflop: a Sparsity-aware Metric for Evaluating Computational Cost in Spiking and Non-spiking Neural Networks

Interact with Earth Observation Images using AI: Transparent Methods and Evaluation for Visual Question Answering

Training hybrid neural networks with multimode optical nonlinearities using digital twins

A phase transition between positional and semantic learning in a solvable model of dot-product attention

Mspo: a Machine Learning Hyperparameter Optimization Method for Enhanced Breast Cancer Image Classification

Accurate neural quantum states for interacting lattice bosons

Design and implementation of a model-based hierarchical architecture for plasma shape control in the TCV tokamak

Posterior-Based Analysis of Spatio-Temporal Features for Sign Language Assessment

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

Partial Assistance with Lower-Limb Exoskeletons to Enhance Gait and Balance in Daily Living Activities

Lessons Learned Executing the Non-disruptive Compass Scan Method at Jet Towards Error Field Identification Experiments in Iter

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

Navigating the Loop: From Control Systems to Neural Networks and Back

Gibbs sampling the posterior of neural networks

Safe Deep Neural Networks

Digital Twin Generalization with Meta and Geometric Deep Learning