Apple Workshop on Privacy-Preserving Machine Learning: Private Federated Learning (PFL) framework
AuthorsMona Chitnis (Apple), Filip Granqvist (Apple)
Apple Workshop on Privacy-Preserving Machine Learning: Private Federated Learning (PFL) framework
AuthorsMona Chitnis (Apple), Filip Granqvist (Apple)
Taming Text-to-Sounding Video Generation via Advanced Modality Condition and Interaction
July 7, 2026research area Computer Visionconference ECCV
This study focuses on Text-to-Sounding-Video (T2SV) generation, which aims to generate a video with synchronized audio from text, with both modalities aligned to the text conditions. Despite progress in joint audio-video training, two critical challenges remain: (1) text conditioning is a bottleneck—shared captions (TV=TA) trigger modal interference, while a gap persists between dense training captions and concise inference user prompts, and (2)…
DynaMiCS: Fine-Tuning LLMs with Performance Constraints Using Dynamic Mixtures
July 7, 2026research area Methods and Algorithms, research area Speech and Natural Language Processing
Multi-domain fine-tuning of large language models requires improving performance on target domains while preserving performance on constrained domains, such as general knowledge, instruction following, or safety evaluations. Existing data mixing strategies rely on fixed heuristics or adaptive rules that cannot explicitly enforce preservation of such capabilities. We propose DynaMiCS, a dynamic mixture optimizer that casts multi-domain fine-tuning…