24 citations · 26 across the 7 of their papers we have counts for
7 papers
Neural Variance-aware Dueling Bandits with Deep Representation and Shallow Exploration
Youngmin Oh, Jinje Park, Taejin Paik
We introduce the first variance-aware algorithms for contextual dueling bandits that leverage shallow exploration strategies with neural networks for nonlinear utility approximatio…
M3: Mamba-assisted Multi-Circuit Optimization via MBRL with Effective Scheduling
Youngmin Oh, Jinje Park, Seunggeun Kim +3
Recent advancements in reinforcement learning (RL) for analog circuit optimization have demonstrated significant potential for improving sample efficiency and generalization across…
Deep learning based ECG segmentation for delineation of diverse arrhythmias
Chankyu Joung, Mijin Kim, Taejin Paik +9
Accurate delineation of key waveforms in an ECG is a critical step in extracting relevant features to support the diagnosis and treatment of heart conditions. Although deep learnin…
Invariant Representations of Embedded Simplicial Complexes
Taejin Paik
Analyzing embedded simplicial complexes, such as triangular meshes and graphs, is an important problem in many fields. We propose a new approach for analyzing embedded simplicial c…
Circular Coordinates for Density-Robust Analysis
Taejin Paik, Jaemin Park
Dimensionality reduction is a crucial technique in data analysis, as it allows for the efficient visualization and understanding of high-dimensional datasets. The circular coordina…
Expectation of topological invariants
Taejin Paik, Otto van Koert
In this paper, we study the expectation values of topological invariants of the Vietoris-Rips complex and Čech complex for a finite set of sample points on a Riemannian manifold. W…
Atlas flow : compatible local structures on the manifold
Taejin Paik, Jaemin Park, Jung Ho Park
In this paper, we focus on the intersections of a manifold's local structures to analyze the global structure of a manifold. We obtain local regions on data manifolds such as the l…