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cs.CV2024
Scaling Image Tokenizers with Grouped Spherical Quantization
Jiangtao Wang, Zhen Qin, Yifan Zhang +4
Vision tokenizers have gained a lot of attraction due to their scalability and compactness; previous works depend on old-school GAN-based hyperparameters, biased comparisons, and a…
cs.LG2024★ 1 cited
Data Pruning in Generative Diffusion Models
Rania Briq, Jiangtao Wang, Stefan Kesselheim
Data pruning is the problem of identifying a core subset that is most beneficial to training and discarding the remainder. While pruning strategies are well studied for discriminat…
cs.LG2024
Time Transfer: On Optimal Learning Rate and Batch Size In The Infinite Data Limit
Oleg Filatov, Jan Ebert, Jiangtao Wang +1
One of the main challenges in optimal scaling of large language models (LLMs) is the prohibitive cost of hyperparameter tuning, particularly learning rate and batch size . W…