1 citations · 1 across the 3 of their papers we have counts for
6 papers
Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin +3
Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over tim…
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Xuanlin Li, Brandon Trabucco, Dong Huk Park +4
The predominant approach for language modeling is to process sequences from left to right, but this eliminates a source of information: the order by which the sequence was generate…
Robust Learning by Self-Transition for Handling Noisy Labels
Hwanjun Song, Minseok Kim, Dongmin Park +2
Real-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize fa…
How does Early Stopping Help Generalization against Label Noise?
Hwanjun Song, Minseok Kim, Dongmin Park +1
Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels. In this paper, we claim that su…
MLAT: Metric Learning for kNN in Streaming Time Series
Dongmin Park, Susik Yoon, Hwanjun Song +1
Learning a good distance measure for distance-based classification in time series leads to significant performance improvement in many tasks. Specifically, it is critical to effect…
Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation
Dongmin Park, Seokil Hong, Bohyung Han +1
Catastrophic forgetting is a critical challenge in training deep neural networks. Although continual learning has been investigated as a countermeasure to the problem, it often suf…