Our paper was accepted for Machine Learning.(Springer)

◼︎Information Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo. “Information-theoretic regularization for learning global features by sequential VAE”, Mach Learn (2021). https://doi.org/10.1007/s10994-021-06032-4 ◼︎Overview Sequential variational autoencoders (VAEs) with a global latent variable z have been studied for disentangling the global features of data, which is useful for several downstream tasks. To further assist the sequential VAEs in…

Our paper was accepted for UAI2021.

◼︎Information Akiyoshi Sannai, Masaaki Imaizumi, Makoto Kawano. “Improved Generalization Bounds of Group Invariant / Equivariant Deep Networks via Quotient Feature Spaces”, 37th Conference on Uncertainty in Artificial Intelligence (UAI 2021). ◼︎Overview Numerous invariant (or equivariant) neural networks have succeeded in handling the invariant data such as point clouds and graphs. However, a generalization theory for…