Accurate neural quantum states for interacting lattice bosons

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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 ...

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, ...

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 ...

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 ...

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 ...

In less than a century, electronic computers have become essential to everyday life and have transformed scientific research. Their use in solving scientific problems has integrated theoretical and experimental approaches, often blurring the lines between ...

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 ...

A study of the anomalous couplings of the Higgs boson to vector bosons, including CP-violation effects, has been conducted using its production and decay in the WW channel. This analysis is performed on proton-proton collision data collected with the CMS d ...

A combination of the results of several searches for the electroweak production of the supersymmetric partners of standard model bosons, and of charged leptons, is presented. All searches use proton-proton collision data at root s = 13 TeV recorded with th ...

De novo protein design explores uncharted sequence and structure space to generate novel proteins not sampled by evolution. A main challenge in de novo design involves crafting “designable” structural templates to guide the sequence searches toward adoptin ...

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 ...

This paper studies the infinite-width limit of deep linear neural networks (NNs) initialized with random parameters. We obtain that, when the number of parameters diverges, the training dynamics converge (in a precise sense) to the dynamics obtained from a ...

A search is described for the production of a pair of bottom-type vectorlike quarks (B VLQs) with mass greater than 1000 GeV. Each B VLQ decays into a b quark and a Higgs boson, a b quark and a Z boson, or a t quark and a W boson. This analysis considers b ...

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 ...

We consider on the torus the scaling limit of stochastic 2D (inviscid) fluid dynamics equations with transport noise to deterministic viscous equations. Quantitative estimates on the convergence rates are provided by combining analytic and probabilistic ar ...

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 ...

Purpose The aim of this study was to develop a novel approach for predicting the expression status of Epidermal Growth Factor Receptor (EGFR) and its subtypes in patients with Non-Small Cell Lung Cancer (NSCLC) using a Three-Dimensional Convolutional Neura ...

Dexterous in-hand manipulation in robotics, particularly with multi-fingered robotic hands, poses significant challenges due to the intricate avoidance of collisions among fingers and the object being manipulated. Collision-free paths for all fingers must ...

Accurate neural quantum states for interacting lattice bosons

Chattez avec Graph Search

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

Learning to Assemble with Alternative Plans

Using deep reinforcement learning to investigate stretch feedback during swimming of the lamprey

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

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

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

Simulating Interacting Quantum Systems with Near-Term Quantum Computers

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

Gibbs sampling the posterior of neural networks

Constraints on anomalous Higgs boson couplings from its production and decay using the WW channel in proton-proton collisions at √s=13 TeV

Combined search for electroweak production of winos, binos, higgsinos, and sleptons in proton-proton collisions at √s=13 TeV

Exploring “dark-matter” protein folds using deep learning

Safe Deep Neural Networks

Infinite-width limit of deep linear neural networks

Search for bottom-type vectorlike quark pair production in dileptonic and fully hadronic final states in proton-proton collisions at √s=13 TeV

Digital Twin Generalization with Meta and Geometric Deep Learning

Quantitative convergence rates for scaling limit of SPDEs with transport noise

On the number of regions of piecewise linear neural networks

Advancing EGFR mutation subtypes prediction in NSCLC by combining 3D pretrained ConvNeXt, radiomics, and clinical features

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