1 citations · 4 across the 7 of their papers we have counts for
9 papers
EXAONE Tabular 1.0 : Technical Report
Moonjung Eo, Min-Kook Suh, Hye-Seung Cho +4
EXAONE Tabular is a compact tabular foundation model family for classification and regression via in-context learning, producing predictions without dataset-specific gradient updat…
MultiTab: A Comprehensive Benchmark Suite for Multi-Dimensional Evaluation in Tabular Domains
Kyungeun Lee, Moonjung Eo, Hye-Seung Cho +5
Despite the widespread use of tabular data in real-world applications, most benchmarks rely on average-case metrics, which fail to reveal how model behavior varies across diverse d…
Towards a Better Evaluation of Out-of-Domain Generalization
Duhun Hwang, Suhyun Kang, Moonjung Eo +2
The objective of Domain Generalization (DG) is to devise algorithms and models capable of achieving high performance on previously unseen test distributions. In the pursuit of this…
Binning as a Pretext Task: Improving Self-Supervised Learning in Tabular Domains
Kyungeun Lee, Ye Seul Sim, Hye-Seung Cho +4
The ability of deep networks to learn superior representations hinges on leveraging the proper inductive biases, considering the inherent properties of datasets. In tabular domains…
Improving Forward Compatibility in Class Incremental Learning by Increasing Representation Rank and Feature Richness
Jaeill Kim, Wonseok Lee, Moonjung Eo +1
Class Incremental Learning (CIL) constitutes a pivotal subfield within continual learning, aimed at enabling models to progressively learn new classification tasks while retaining…
A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression
Moonjung Eo, Suhyun Kang, Wonjong Rhee
Filter pruning and low-rank decomposition are two of the foundational techniques for structured compression. Although recent efforts have explored hybrid approaches aiming to integ…
Meta-Learning with a Geometry-Adaptive Preconditioner
Suhyun Kang, Duhun Hwang, Moonjung Eo +2
Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared in…
The Geometric Occam's Razor Implicit in Deep Learning
Benoit Dherin, Michael Munn, David G. T. Barrett
In over-parameterized deep neural networks there can be many possible parameter configurations that fit the training data exactly. However, the properties of these interpolating so…
Short-term Traffic Prediction with Deep Neural Networks: A Survey
Kyungeun Lee, Moonjung Eo, Euna Jung +2
In modern transportation systems, an enormous amount of traffic data is generated every day. This has led to rapid progress in short-term traffic prediction (STTP), in which deep l…