A Latent Implicit 3D Shape Model for Multiple Levels of Detail

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Machine learning and artificial intelligence methods have achieved remarkable success, matching and even surpassing human capabilities in various complex tasks. However, many demonstrations have generally neglected a critical part of the intelligence that ...

Low-light photography produces images with low signal-to-noise ratios due to limited photons. In such conditions, common approximations like the Gaussian noise model fall short, and many denoising techniques fail to remove noise effectively. Although deep- ...

The Dynamic Aperture is an important concept for the study of non-linear beam dynamics in a circular accelerator. The DA is defined as the extent of the phase-space region in which the particle's motion remains bounded over a finite number of turns. Such a ...

This paper proposes a novel approach to improve the performance of distributed nonlinear control systems while preserving stability by leveraging Deep Neural Networks (DNNs). We build upon the Neural System Level Synthesis (Neur-SLS) framework and introduc ...

Efficient sampling and approximation of Boltzmann distributions involving large sets of binary variables, or spins, are pivotal in diverse scientific fields even beyond physics. Recent advances in generative neural networks have significantly impacted this ...

In this manuscript, we investigate the problem of how two-layer neural networks learn features from data, and improve over the kernel regime, after being trained with a single gradient descent step. Leveraging the insight from (Ba et al., 2022), we model t ...

With a growing emphasis on indoor air quality (IAQ) in educational environments, CO2 monitoring in classrooms has become commonplace. CO2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further l ...

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

Curvilinear structures are frequently observed in a variety of domains and are essential for comprehending neural circuits, detecting fractures in materials, and determining road and irrigation canal networks. It can be costly and time-consuming to manuall ...

The development of low-power wearable systems requires specialized techniques to accommodate their unique requirements and constraints. While significant advancements have been made in the inference phase of artificial intelligence, the training phase rema ...

Neural networks provide an attractive framework to monitor the water table level and the volume of stored water in porous media from seismic data in an automated, fast and cost-efficient manner. In this work, a subsurface reservoir is modeled as a coupled ...

Empirical evidence shows that memories that are frequently revisited are easy to recall, and that familiar items involve larger hippocampal representations than less familiar ones. In line with these observations, here we develop a modelling approach to pr ...

Modern machine learning models use an ever-increasing number of parameters to train (175 x 10(9) parameters for GPT-3) with large datasets to achieve better performance. Optical computing has been rediscovered as a potential solution for large-scale data p ...

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Te ...

Training accurate and robust machine learning models requires a large amount of data that is usually scattered across data silos. Sharing, transferring, and centralizing the data from silos, however, is difficult due to current privacy regulations (e.g., H ...

Smart thermostats are increasingly popular in homes and buildings as they improve occupant comfort, lower energy use in heating and cooling systems, and reduce utility bills by automatically adjusting room temperature according to measurements of their bui ...

Optical imaging through complex media, such as biological tissues or fog, is challenging due to light scattering. In the multiple scattering regime, wavefront shaping provides an effective method to retrieve information; it relies on measuring how the prop ...

We introduce universal solution manifold network (USM-Net), a novel surrogate model, based on artificial neural networks (ANNs), which applies to differential problems whose solution depends on physical and geometrical parameters. We employ a mesh-less arc ...

Training convolutional neural networks (CNNs) for very high-resolution images requires a large quantity of high-quality pixel-level annotations, which is extremely labor-intensive and time-consuming to produce. Moreover, professional photograph interpreter ...

Object-centric learning has gained significant attention over the last years as it can serve as a powerful tool to analyze complex scenes as a composition of simpler entities. Well-established tasks in computer vision, such as object detection or instance ...

A Latent Implicit 3D Shape Model for Multiple Levels of Detail

Chattez avec Graph Search

Sequential Reasoning with Socially Caused Beliefs

Dark Noise Diffusion: Noise Synthesis for Low-Light Image Denoising

Accelerating Dynamic Aperture Evaluation Using Deep Neural Networks

Optimal distributed control with stability guarantees by training a network of neural closed-loop maps

Sparse autoregressive neural networks for classical spin systems

Asymptotics of feature learning in two-layer networks after one gradient-step

Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO2 Sensor and Automated Data Segmentation

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Topologically Better Delineation of Curvilinear Structures

Layer-Wise Learning Framework for Efficient DNN Deployment in Biomedical Wearable Systems

Monitoring of water table level and volume of water in a porous storage by seismic data

A dynamic attractor network model of memory formation, reinforcement and forgetting

Nonlinear optical feature generator for machine learning

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

Privacy-preserving federated neural network training and inference

Efficacy of temporal and spatial abstraction for training accurate machine learning models: A case study in smart thermostats

Physics-based neural network for non-invasive control of coherent light in scattering media

Universal Solution Manifold Networks (USM-Nets): Non-Intrusive Mesh-Free Surrogate Models for Problems in Variable Domains

Semantic Segmentation of Remote Sensing Images With Sparse Annotations

Unsupervised Visual Entity Abstraction towards 2D and 3D Compositional Models