Model Training
Model training focuses on developing efficient and effective methods for creating accurate and robust machine learning models. Current research emphasizes improving training efficiency through techniques like low-precision computation, optimized memory management (e.g., using recomputation and memory-aware scheduling), and efficient communication strategies in distributed and federated learning settings. These advancements are crucial for scaling model training to larger datasets and more complex architectures, impacting various fields from computer vision and natural language processing to healthcare and industrial applications.
Papers
D{\epsilon}pS: Delayed {\epsilon}-Shrinking for Faster Once-For-All Training
Aditya Annavajjala, Alind Khare, Animesh Agrawal, Igor Fedorov, Hugo Latapie, Myungjin Lee, Alexey Tumanov
Bounding Boxes and Probabilistic Graphical Models: Video Anomaly Detection Simplified
Mia Siemon, Thomas B. Moeslund, Barry Norton, Kamal Nasrollahi
PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning
Xiaoqi Qiu, Yongjie Wang, Xu Guo, Zhiwei Zeng, Yue Yu, Yuhong Feng, Chunyan Miao
Video-Language Understanding: A Survey from Model Architecture, Model Training, and Data Perspectives
Thong Nguyen, Yi Bin, Junbin Xiao, Leigang Qu, Yicong Li, Jay Zhangjie Wu, Cong-Duy Nguyen, See-Kiong Ng, Luu Anh Tuan