1 citations · 3 across the 3 of their papers we have counts for
5 papers
LyapuFlow: Controlling Generative Flows with Lyapunov Feedback for Inverse Problems
Minseon Gwak, Hans Hao-Hsun Hsu, Danielle C. Maddix +1
Pretrained flow models are now widely used as generative priors in science and vision, where inference-time guidance enables test-time constraints without retraining. Existing meth…
Variational Streaming Flow: Probabilistic Forecasting in Physical Time
Hans Hao-Hsun Hsu, Minseon Gwak, Soon Hoe Lim +2
Probabilistic forecasting is important for predicting complex dynamical systems because intrinsic randomness and incomplete observations can cause the same observed state to evolve…
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…
Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms
Joseph Konan, Ojas Bhargave, Shikhar Agnihotri +12
In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (V…
Speech Enhancement for Virtual Meetings on Cellular Networks
Hojeong Lee, Minseon Gwak, Kawon Lee +3
We study speech enhancement using deep learning (DL) for virtual meetings on cellular devices, where transmitted speech has background noise and transmission loss that affects spee…