HKBU-RIKEN AIP Joint Workshop on Artificial Intelligence and Machine Learning - Opening Remarks

Introduction of RIKEN-AIP and Imperfect Information Learning Team

Digital Pathology for Non-Alcoholic Fatty Liver Disease Diagnosis

The Bayesian Learning Rule

Imbalanced Data Learning and Its Applications

Statistical Test for AI-driven Hypotheses

Tensor Representation in Data Science

Efficient and Robust Machine Learning with Tensor Networks

User Trust in Recommender Systems

An Information Geometric and Optimal Transport Framework for Gaussian Processes

Unravelling Genomic Code Using Deep Language Models

Development of AI and Increasing Complexity of Its Threat Models

Space-efficient MoE Approximation via Wasserstein Barycenter and Residual Restoration

Generalizing Importance Weighting to a Universal Solver for Distribution Shift Problems

Learning the Appearance, Dynamics, and Physics of Animatable Virtual Avatars

A Direct Approach to Estimating the Bayes Error in Binary Classification

Exploring Trustworthy Foundation Models under Imperfect Data

Deep Ridgelet Transform: Harmonic Analysis for Deep Neural Network

Building NeRF in the Wild and Protecting Its Copyright

Mapping High-resolution Land Cover Products

A Closer Look at Vision-Language Models

Robust Contrastive Learning and Its Applications

Large Language Model for Coding and Social Safety

Towards Interpretable and Robust Multimodal Machine Learning for Healthcare

Understanding Convergence and Generalization in Federated Learning through Feature Learning Theory

Efficient Multilayer Graph Analytics and Search

AIMWeL: Bridging Age of Information and Semi-Asynchronous Federated Learning and Closing Remarks

Computers and Music

Artificial Intelligence (AI): Past, Present and Future

Data Valuation in Federated Learning