LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

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Atom probe tomography (APT) is a burgeoning characterization technique that provides compositional mapping of materials in three-dimensions at near-atomic scale. Since its significant expansion in the past 30 years, we estimate that one million APT dataset ...

While convolution and self-attention mechanisms have dominated architectural design in deep learning, this survey examines a fundamental yet understudied primitive: the Hadamard product. Despite its widespread implementation across various applications, th ...

The Songdo Vision dataset provides high-resolution (4K, 3840×2160 pixels) RGB images annotated with categorized axis-aligned bounding boxes (BBs) for vehicle detection from a high-altitude bird’s-eye view (BeV) perspective. Captured over Songdo Internation ...

While stability analysis is a mainstay for control science, especially computing regions of attraction of equilibrium points, until recently most stability analysis tools always required explicit knowledge of the model or a high-fidelity simulator represen ...

The uncanny ability of over-parameterised neural networks to generalise well has been explained using various ‘simplicity biases’. These theories postulate that neural networks avoid overfitting by first fitting simple, linear classifiers before learning m ...

This work investigates adversarial training in the context of margin-based linear classifiers in the high-dimensional regime where the dimension d and the number of data points n diverge with a fixed ratio α = n/d. We introduce a tractable mathematical mod ...

The rise of high-throughput first-principles calculations of materials since the turn of the millennium has gifted the fields of physics, quantum chemistry, and materials science with a mountainous and ever-growing pile of data ready to be mined for hidden ...

Coral reefs are crucial for biodiversity and provide vital resources for humankind. But despite such a central role, they are confronted to increasing threats linked to climate change, pollution, and local stressors. To ensure effective conservation, effic ...

Structured interviews often include past-behavior questions inviting applicants to recount a past work experience. While optimal responses to these questions should take the form of a story, applicants struggle to produce them extemporaneously. Asynchronou ...

Planning for diverse real-world robotic tasks necessitates to know and write all constraints. However, instances exist where these constraints are either unknown or challenging to specify accurately. A possible solution is to infer the unknown constraints ...

Recent advancements in recommender systems have focused on integrating knowledge graphs (KGs) to leverage their auxiliary information. The core idea of KG-enhanced recommenders is to incorporate rich semantic information for more accurate recommendations. ...

The rapid advancement of artificial intelligence (AI) has greatly influenced numerous research areas, leading to significant breakthroughs in human face-related technologies, particularly in face recognition and deepfake detection. While offering substanti ...

Metal additive manufacturing is a recent breakthrough technology that promises automated production of complex geometric shapes at low operating costs. However, its potential is not yet fully exploited due to the low reproducibility of quality in mass prod ...

The current generation of large language models (LLMs) has limited chemical knowledge. Recently, it has been shown that these LLMs can learn and predict chemical properties through fine-tuning. Using natural language to train machine learning models opens ...

A potential framework to estimate the volume of water stored in a porous storage reservoir from seismic data is neural networks. In this study, the man-made groundwater reservoir is modeled as a coupled poroviscoelastic–viscoelastic medium, and the underly ...

Constrained Markov decision processes (CMDPs) are a common way to model safety constraints in reinforcement learning. State-of-the-art methods for efficiently solving CMDPs are based on primal-dual algorithms. For these algorithms, all currently known regr ...

Datacenter networks are becoming increasingly flexible with the incorporation of new optical communication technologies, such as optical circuit switches, enabling self-adjusting topologies that can adapt to the traffic pattern in a demand-aware manner. In ...

With the rapid development of remote sensing image archives, asking questions about images has become an effective way of gathering specific information or performing image retrieval. However, automatically generated image-based questions tend to be simpli ...

Photovoltaic water pumping systems (PVWPS) can greatly enhance access to clean water in rural and low-income regions of sub-Saharan Africa, thereby contributing to improved health and well-being. PVWPS monitoring can significantly help for effective operat ...

We prove closed-form equations for the exact high-dimensional asymptotics of a family of first-order gradient-based methods, learning an estimator (e.g., M-estimator, shallow neural network) from observations on Gaussian data with empirical risk minimizati ...

LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields

Chattez avec Graph Search

Machine learning enhanced atom probe tomography analysis

Hadamard product in deep learning: Introduction, Advances and Challenges

Songdo Vision: Vehicle Annotations from High-Altitude BeV Drone Imagery in a Smart City

Robustly Learning Regions of Attraction From Fixed Data

Neural networks trained with SGD learn distributions of increasing complexity

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs

Representing and machine learning complex electronic-structure properties of materials

Semantic Segmentation of Benthic Classes in Reef Environments Using a Large Vision Transformer

Identifying storytelling in job interviews using deep learning

Positive-Unlabeled Constraint Learning for Inferring Nonlinear Continuous Constraints Functions From Expert Demonstrations

Higher-order knowledge-enhanced recommendation with heterogeneous hypergraph multi-attention

Enhancing Explainability and Performance in AI-Driven Face Recognition and Deepfake Detection

Unsupervised quality monitoring of metal additive manufacturing using Bayesian adaptive resonance

Assessment of fine-tuned large language models for real-world chemistry and material science applications

Monitoring of water volume in a porous reservoir using seismic data: A 3D simulation study

Truly No-Regret Learning in Constrained MDPs

Toward Self-Adjusting k-Ary Search Tree Networks

Knowledge-Aware Visual Question Generation for Remote Sensing Images

Analysis of Monitoring Strategies for Photovoltaic Water Pumping Systems in Low-Income Remote Areas

Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods