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cs.LG2024★ 1 cited
Layer-Adaptive State Pruning for Deep State Space Models
Minseon Gwak, Seongrok Moon, Joohwan Ko +1
Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computation…
stat.ML2024
Demystifying SGD with Doubly Stochastic Gradients
Kyurae Kim, Joohwan Ko, Yi-An Ma +1
Optimization objectives in the form of a sum of intractable expectations are rising in importance (e.g., diffusion models, variational autoencoders, and many more), a setting also…
stat.ML2024★ 1 cited
Provably Scalable Black-Box Variational Inference with Structured Variational Families
Joohwan Ko, Kyurae Kim, Woo Chang Kim +1
Variational families with full-rank covariance approximations are known not to work well in black-box variational inference (BBVI), both empirically and theoretically. In fact, rec…