57 citations · 74 across the 20 of their papers we have counts for
16 papers · 1 filter
One-Step Generation via Riemannian Wasserstein Gradient Flows
David Li, Chanhyuk Lee, Jaehoon Yoo +7
Recently, Drifting Models and Wasserstein Gradient Flows have attracted substantial attention because they move iterative distributional refinement to training and amortize it into…
Thinking with Looped Flows
Ayhan Suleymanzade, Chanhyuk Lee, Floor Eijkelboom +3
Humans and machines often solve harder problems by spending more time on computation. In deep learning, looped models implement this idea during inference by recurrently updating a…
RelAgent: LLM Agents as Data Scientists for Relational Learning
Xingyue Huang, Louis Tichelman, Jinwoo Kim +2
Relational learning is a challenging problem that has motivated a wide range of approaches, including graph-based models (e.g., graph neural networks, graph transformers), tabular…
Inverting Data Transformations via Diffusion Sampling
Jinwoo Kim, Sékou-Oumar Kaba, Jiyun Park +2
We study the problem of transformation inversion on general Lie groups: a datum is transformed by an unknown group element, and the goal is to recover an inverse transformation tha…
Sequence Modeling with Spectral Mean Flows
Jinwoo Kim, Max Beier, Petar Bevanda +2
A key question in sequence modeling with neural networks is how to represent and learn highly nonlinear and probabilistic state dynamics. Operator theory views such dynamics as lin…
Flock: A Knowledge Graph Foundation Model via Learning on Random Walks
Jinwoo Kim, Xingyue Huang, Krzysztof Olejniczak +4
We study the problem of zero-shot link prediction on knowledge graphs (KGs), which requires models to generalize to novel entities and novel relations. Knowledge graph foundation m…
Simulation-Free Training of Neural ODEs on Paired Data
Semin Kim, Jaehoon Yoo, Jinwoo Kim +3
In this work, we investigate a method for simulation-free training of Neural Ordinary Differential Equations (NODEs) for learning deterministic mappings between paired data. Despit…
Revisiting Random Walks for Learning on Graphs
Jinwoo Kim, Olga Zaghen, Ayhan Suleymanzade +2
We revisit a simple model class for machine learning on graphs, where a random walk on a graph produces a machine-readable record, and this record is processed by a deep neural net…
Learning Symmetrization for Equivariance with Orbit Distance Minimization
Tien Dat Nguyen, Jinwoo Kim, Hongseok Yang +1
We present a general framework for symmetrizing an arbitrary neural-network architecture and making it equivariant with respect to a given group. We build upon the proposals of Kim…
3D Denoisers are Good 2D Teachers: Molecular Pretraining via Denoising and Cross-Modal Distillation
Sungjun Cho, Dae-Woong Jeong, Sung Moon Ko +5
Pretraining molecular representations from large unlabeled data is essential for molecular property prediction due to the high cost of obtaining ground-truth labels. While there ex…
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance
Jinwoo Kim, Tien Dat Nguyen, Ayhan Suleymanzade +2
We present a novel framework to overcome the limitations of equivariant architectures in learning functions with group symmetries. In contrary to equivariant architectures, we use…
Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost
Sungjun Cho, Seonwoo Min, Jinwoo Kim +3
To overcome the quadratic cost of self-attention, recent works have proposed various sparse attention modules, most of which fall under one of two groups: 1) sparse attention under…
Equivariant Hypergraph Neural Networks
Jinwoo Kim, Saeyoon Oh, Sungjun Cho +1
Many problems in computer vision and machine learning can be cast as learning on hypergraphs that represent higher-order relations. Recent approaches for hypergraph learning extend…
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Tien Dat Nguyen, Seonwoo Min +4
We show that standard Transformers without graph-specific modifications can lead to promising results in graph learning both in theory and practice. Given a graph, we simply treat…
Transformers Generalize DeepSets and Can be Extended to Graphs and Hypergraphs
Jinwoo Kim, Saeyoon Oh, Seunghoon Hong
We present a generalization of Transformers to any-order permutation invariant data (sets, graphs, and hypergraphs). We begin by observing that Transformers generalize DeepSets, or…
SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Data
Jinwoo Kim, Jaehoon Yoo, Juho Lee +1
Generative modeling of set-structured data, such as point clouds, requires reasoning over local and global structures at various scales. However, adopting multi-scale frameworks fo…