Publications related to CrOC: Cross-View Online Clustering for Dense Visual Representation Learning

Related publications (27)

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

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 ...

SAGE PUBLICATIONS LTD2025

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

Volkan Cevher, Fanghui Liu, Zhenyu Zhu

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 ...

2025

Learning to Assemble with Alternative Plans

Gabriel Rémi Vallat, Maryam Kamgarpour, Stefana Parascho, Ziqi Wang, Jingwen Wang

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 ...

Association for Computing Machinery (ACM)2025

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

Fernando Porté Agel, Jiannong Fang, Wenlong Liao

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 ...

2025

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

Christel Marie Tartini-Chappuis

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 ...

EPFL2025

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

Liza Carol Andrea Dunbar

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 ...

IOP Publishing Ltd2025

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Demetri Psaltis, Christophe Moser, Niyazi Ulas Dinç, Ilker Oguz, Mustafa Yildirim, Jih-Liang Hsieh

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 ...

Walter de Gruyter GmbH2025

Accurate neural quantum states for interacting lattice bosons

Giuseppe Carleo, Zakari Denis

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 ...

Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften2025

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

Auke Ijspeert, Astha Gupta

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, ...

IOP Publishing Ltd2025

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

Clara Lucía Galimberti

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 ...

EPFL2024

RAPNet: Resolution-Adaptive and Predictive Early Exit Network for Efficient Image Recognition

Yung-Cheng Chiang

Deploying compute-intensive deep neural networks (DNNs) on resource-constrained end devices has become a prominent trend, enabling localized intelligence. However, efficiently deploying these DNNs at scale poses challenges. To address this, extensive resea ...

2024

Sequential CNN-based Enhancement of Ultrafast Ultrasound Imaging for Sparse Arrays

Jean-Philippe Thiran, Roser Vinals Terres

With the emergence of ultra-portable ultrasound systems, the need to reduce cost and complexity of ultrasound systems has intensified. Sparse arrays, employing fewer transducer elements to acquire images, offer a viable solution by effectively minimizing d ...

European Signal Processing Conference, EUSIPCO2024

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

Aude Billard, Kunpeng Yao, Farshad Khadivar, Xiao Gao

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 ...

2024

Gibbs sampling the posterior of neural networks

Lenka Zdeborová, Emanuele Troiani, Giovanni Piccioli

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 ...

Iop Publishing Ltd2024

Exploring “dark-matter” protein folds using deep learning

Irina Morozova, Pierre Vandergheynst, Bruno Emanuel Ferreira De Sousa Correia, Andreas Loukas, Sandrine Madeleine Suzanne Georgeon, Stéphane Rosset, Zander Harteveld, Alexandra Krina Van Hall-Beauvais, Casper Alexander Goverde

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 ...

2024

Safe Deep Neural Networks

Kyle Michael Matoba

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 ...

EPFL2024

Quantitative convergence rates for scaling limit of SPDEs with transport noise

Lucio Galeati

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 ...

Academic Press Inc Elsevier Science2024

Infinite-width limit of deep linear neural networks

Maria Colombo, Xavier Fernandez-Real Girona, Lénaïc Chizat

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 ...

Hoboken2024

Digital Twin Generalization with Meta and Geometric Deep Learning

Olga Fink, Raffael Pascal Theiler

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 ...

Prognostics and Health Management Society2024

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

Jiancheng Yang

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 ...

FRONTIERS MEDIA SA2024