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cs.LG2024
ADOPT: Modified Adam Can Converge with Any with the Optimal Rate
Shohei Taniguchi, Keno Harada, Gouki Minegishi +7
Adam is one of the most popular optimization algorithms in deep learning. However, it is known that Adam does not converge in theory unless choosing a hyperparameter, i.e., ,…
cs.LG2024
Enhancing Unimodal Latent Representations in Multimodal VAEs through Iterative Amortized Inference
Yuta Oshima, Masahiro Suzuki, Yutaka Matsuo
Multimodal variational autoencoders (VAEs) aim to capture shared latent representations by integrating information from different data modalities. A significant challenge is accura…
cs.CV2024
SSM Meets Video Diffusion Models: Efficient Long-Term Video Generation with Structured State Spaces
Yuta Oshima, Shohei Taniguchi, Masahiro Suzuki +1
Given the remarkable achievements in image generation through diffusion models, the research community has shown increasing interest in extending these models to video generation.…