1 citations · 1 across the 5 of their papers we have counts for
6 papers
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.…
BaRA: Budget-constrained and Reliable Web Data Collection Agent
Soojeong Lee, Joseph Lee, Yongseong Cho +3
Large language model (LLM)-based web agents automate web navigation and data collection. However, live web data collection demands capabilities beyond task completion: agents must…
Bounded Hyperbolic Tangent: A Stable and Efficient Alternative to Pre-Layer Normalization in Large Language Models
Hoyoon Byun, Youngjun Choi, Taero Kim +2
Pre-Layer Normalization (Pre-LN) is the de facto choice for large language models (LLMs) and is crucial for stable pretraining and effective transfer learning. However, Pre-LN incu…
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…
LBC: Language-Based-Classifier for Out-Of-Variable Generalization
Kangjun Noh, Baekryun Seong, Hoyoon Byun +3
Large Language Models (LLMs) have great success in natural language processing tasks such as response generation. However, their use in tabular data has been limited due to their i…
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…