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Yeonwoo Cha

2 accepted papers

2026

FlowBind: Efficient Any-to-Any Generation with Bidirectional Flows

ICLR 2026poster

Any-to-any generation seeks to translate between arbitrary subsets of modalities, enabling flexible cross-modal synthesis. Despite recent success, existing flow-based approaches are challenged by its inefficiency, as they require large-scale datasets often with restrictive pairing constraints, incu…

Cited by 0SourcecodeScholar
2024

Simulation-Free Training of Neural ODEs on Paired Data

NeurIPS 2024poster

In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data. Despite the analogy of NODEs as continuous-depth residual networks, their application in typical supervised learning tasks has…