1 code implementation • 13 May 2024 • Kyungeun Lee, Ye Seul Sim, Hye-Seung Cho, Moonjung Eo, Suhee Yoon, Sanghyu Yoon, Woohyung Lim
The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets.
no code implementations • 22 Mar 2024 • Jaeill Kim, Wonseok Lee, Moonjung Eo, Wonjong Rhee
Consequently, RFR achieves dual objectives in backward and forward compatibility: minimizing feature extractor modifications and enhancing novel task performance, respectively.
no code implementations • 21 Sep 2023 • Moonjung Eo, Suhyun Kang, Wonjong Rhee
In this study, we develop a \textit{Differentiable Framework~(DF)} that can express filter selection, rank selection, and budget constraint into a single analytical formulation.
1 code implementation • CVPR 2023 • Suhyun Kang, Duhun Hwang, Moonjung Eo, Taesup Kim, Wonjong Rhee
In this study, we propose Geometry-Adaptive Preconditioned gradient descent (GAP) that can overcome the limitations in MAML; GAP can efficiently meta-learn a preconditioner that is dependent on task-specific parameters, and its preconditioner can be shown to be a Riemannian metric.
no code implementations • 30 Nov 2021 • Moonjung Eo, Suhyun Kang, Wonjong Rhee
The resulting BSR (Beam-search and Stable Rank) algorithm requires only a single hyperparameter to be tuned for the desired compression ratio.
no code implementations • 28 Aug 2020 • Kyungeun Lee, Moonjung Eo, Euna Jung, Yoonjin Yoon, Wonjong Rhee
2) We briefly explain a wide range of DNN techniques from the earliest networks, including Restricted Boltzmann Machines, to the most recent, including graph-based and meta-learning networks.