1 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Enhancing Motion in Text-to-Video Generation with Decomposed Encoding and Conditioning
Penghui Ruan, Pichao Wang, Divya Saxena +2
Despite advancements in Text-to-Video (T2V) generation, producing videos with realistic motion remains challenging. Current models often yield static or minimally dynamic outputs,…
Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Network
Jialun Zheng, Divya Saxena, Jiannong Cao +2
Inductive spatial temporal prediction can generalize historical data to predict unseen data, crucial for highly dynamic scenarios (e.g., traffic systems, stock markets). However, e…
Overcoming Growth-Induced Forgetting in Task-Agnostic Continual Learning
Yuqing Zhao, Jiannong Cao, Divya Saxena +4
In continual learning (CL), model growth enhances adaptability to new data. However, when model growth is applied improperly, especially in task-agnostic CL, where the entire grown…
NASPrecision: Neural Architecture Search-Driven Multi-Stage Learning for Surface Roughness Prediction in Ultra-Precision Machining
Penghui Ruan, Divya Saxena, Jiannong Cao +3
Accurate surface roughness prediction is critical for ensuring high product quality, especially in areas like manufacturing and aerospace, where the smallest imperfections can comp…
FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning
Changlin Song, Divya Saxena, Jiannong Cao +1
Federated Learning (FL) is a novel approach that allows for collaborative machine learning while preserving data privacy by leveraging models trained on decentralized devices. Howe…