4 citations · 5 across the 5 of their papers we have counts for
5 papers
A Step-by-step Introduction to the Implementation of Automatic Differentiation
Yu-Hsueh Fang, He-Zhe Lin, Jie-Jyun Liu +1
Automatic differentiation is a key component in deep learning. This topic is well studied and excellent surveys such as Baydin et al. (2018) have been available to clearly describe…
Linear Classifier: An Often-Forgotten Baseline for Text Classification
Yu-Chen Lin, Si-An Chen, Jie-Jyun Liu +1
Large-scale pre-trained language models such as BERT are popular solutions for text classification. Due to the superior performance of these advanced methods, nowadays, people ofte…
Mimic-IV-ICD: A new benchmark for eXtreme MultiLabel Classification
Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Kashyap +4
Clinical notes are assigned ICD codes - sets of codes for diagnoses and procedures. In the recent years, predictive machine learning models have been built for automatic ICD coding…
On the Use of Unrealistic Predictions in Hundreds of Papers Evaluating Graph Representations
Li-Chung Lin, Cheng-Hung Liu, Chih-Ming Chen +4
Prediction using the ground truth sounds like an oxymoron in machine learning. However, such an unrealistic setting was used in hundreds, if not thousands of papers in the area of…
Parameter Selection: Why We Should Pay More Attention to It
Jie-Jyun Liu, Tsung-Han Yang, Si-An Chen +1
The importance of parameter selection in supervised learning is well known. However, due to the many parameter combinations, an incomplete or an insufficient procedure is often app…