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34 papers · 1 filter
SuperPADL: Scaling Language-Directed Physics-Based Control with Progressive Supervised Distillation
Jordan Juravsky, Yunrong Guo, Sanja Fidler +1
Physically-simulated models for human motion can generate high-quality responsive character animations, often in real-time. Natural language serves as a flexible interface for cont…
Graph Neural Network Approach to Semantic Type Detection in Tables
Ehsan Hoseinzade, Ke Wang
This study addresses the challenge of detecting semantic column types in relational tables, a key task in many real-world applications. While language models like BERT have improve…
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
Congyu Fang, Adam Dziedzic, Lin Zhang +5
Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healt…
FairSample: Training Fair and Accurate Graph Convolutional Neural Networks Efficiently
Zicun Cong, Shi Baoxu, Shan Li +3
Fairness in Graph Convolutional Neural Networks (GCNs) becomes a more and more important concern as GCNs are adopted in many crucial applications. Societal biases against sensitive…
Fuel Consumption Prediction for a Passenger Ferry using Machine Learning and In-service Data: A Comparative Study
Pedram Agand, Allison Kennedy, Trevor Harris +3
As the importance of eco-friendly transportation increases, providing an efficient approach for marine vessel operation is essential. Methods for status monitoring with considerati…
PADL: Language-Directed Physics-Based Character Control
Jordan Juravsky, Yunrong Guo, Sanja Fidler +1
Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be use…