Finite Time Singularities in Fluids
Explorez les singularités en temps fini dans les fluides, en examinant les résultats récents et classiques, les preuves assistées par ordinateur et les applications de l'apprentissage automatique.
Polynomial Time Cryptanalytic Extraction of Neural Network Models
Explore polynomial-time techniques for extracting neural network parameters, improving upon previous methods and demonstrating practical efficiency on a CIFAR10 classifier.
Understanding the Visual World Through Object Intrinsics
Explore neuro-symbolic approaches to scene and object understanding, focusing on intrinsic structures and their role in visual data interpretation and generalization.
Heuristics for the Arithmetic of Elliptic Curves and Abelian Varieties
Explore probabilistic models for elliptic curve arithmetic, examining theoretical evidence and predictions for higher-dimensional abelian varieties.
Modeling and Analyzing Cell-Cell Communication from Single-Cell Data
Explore CellChat, an integrated method for inferring and analyzing cell-cell communication from single-cell sequencing data using systems biology and machine learning approaches.
Federated Learning for Nonparametric Function Estimation - Framework and Optimality
Explore federated learning for nonparametric regression, focusing on statistical optimality, privacy preservation, and the balance between accuracy and data protection in distributed settings.
Geometric Deep Graph Learning: Exploring Opportunities in Different Geometric Spaces
Explore geometric deep graph learning in non-Euclidean spaces. Discover properties of hyperbolic and hyperspherical spaces for enhanced graph representation and analysis across various applications.
Higher Symmetries in Quantum Field Theory from String Theory
Explore higher symmetries in quantum field theory through string theory on singular special holonomy spaces. Uncover their origins and implications in compact spaces and F-theory duals.
Modelling, Data Analytics, and AI for Investigating Heterogeneous Substances
Explore advanced techniques for studying heterogeneous materials, integrating physics-based modeling, data analytics, and AI to unlock new insights and accelerate research in material science.
Optimization: The Philosophical Background of Artificial Intelligence
Explore optimization's role in AI, computational models, and its impact on modern science. Gain insights into efficient optimization schemes related to human activity.
The Important Roles of Modeling in the Development of Climate Science
Explore the evolution of climate science through modeling, from simple energy balance models to sophisticated Earth System Models, and their role in understanding and predicting climate change.
Classic Problems with Modern Techniques - Prize-collecting Framework
Explore state-of-the-art approximation algorithms for classic network design problems using modern techniques, from clustering to recursive methodologies.
When and Why Do Efficient Algorithms Exist for Constraint Satisfaction and Beyond
Explore the mathematical structures behind efficient algorithms for constraint satisfaction problems and beyond, focusing on polymorphisms and their role in computational complexity.
Educability: Defining and Simulating Human Capabilities - ICBS 2024
Explore "educability" as a defining human capability, synthesizing learning from experience and others to explain humanity's achievements and guide future technology development.
Lensless Imaging: Overview, Opportunities, and Challenges
Explore lensless imaging advancements, principles, and applications in 3D reconstruction, discussing challenges and future opportunities in camera design and computational algorithms.
Building World Model Intelligence: The Crucial Role of Perception
Explore multimodal sensing's role in building comprehensive world models. Discover advancements in wireless perception, AI model adaptability, and integrated sensing for transformative applications.
Small but Mighty: Subset Selection and Knowledge Distillation
Explore cost-efficient AI training methods, focusing on subset selection and knowledge distillation techniques to address challenges in large language model development.
Locally Purified Density Operators for Noisy Quantum Circuits
Explore efficient representation of mixed states in noisy quantum circuits using locally purified density operators, addressing challenges and insights in open quantum systems simulation.
Comparative Lattice Analysis of SU(2) Dark Glueball
Explore SU(2) glueballs as dark matter using lattice simulations. Compare traditional and machine learning methods for mass calculation and scattering cross-section analysis.
Prediction of Multi-Class Peptides by T-Cell Receptor Sequences with Deep Learning
Explore deep learning techniques for predicting multi-class peptides using T-cell receptor sequences, enhancing immune system understanding and potential disease treatments.
Computational Quantum Mechanics in Phase Space - An Attempt to Break the Curse of Dimensionality
Explore computational quantum mechanics in phase space, focusing on breaking the curse of dimensionality through grid-based and particle-based methods for high-dimensional Wigner quantum dynamics simulations.