11 citations · 13 across the 7 of their papers we have counts for
4 papers · 1 filter
What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
Heejin Jung, Gyeongdeok Seo, Hoyoon Byun +2
Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal effects from observational data.…
MIDUS: Memory-Infused Depth Up-Scaling
Taero Kim, Hoyoon Byun, Youngjun Choi +2
Expanding pre-trained language models offers a practical way to increase capacity without training larger models from scratch. Depth Up-Scaling (DUS) does so by duplicating Transfo…
Perturb-and-Compare Approach for Detecting Out-of-Distribution Samples in Constrained Access Environments
Heeyoung Lee, Hoyoon Byun, Changdae Oh +2
Accessing machine learning models through remote APIs has been gaining prevalence following the recent trend of scaling up model parameters for increased performance. Even though t…
Graph Perceiver IO: A General Architecture for Graph Structured Data
Seyun Bae, Hoyoon Byun, Changdae Oh +2
Multimodal machine learning has been widely studied for the development of general intelligence. Recently, the Perceiver and Perceiver IO, show competitive results for diverse data…