9 citations · 23 across the 16 of their papers we have counts for
18 papers
Rebalancing Token Importance in Language Models with TF-IDF Weighted Cross-Entropy Loss
Zhijian Li, Stefan Larson, Kevin Leach
Large language models are typically trained under uniform token weighting, which allows frequent and low-information tokens to dominate learning and can increase the tendency to me…
Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap
Stefan Larson, Attila Nagy, Sam Desai +8
RVL-CDIP is a popular dataset for benchmarking document classifiers. However, the dataset contains ample amounts of label errors as well as non-trivial amounts of test-train overla…
Label Errors in the Tobacco3482 Dataset
Gordon Lim, Stefan Larson, Kevin Leach
Tobacco3482 is a widely used document classification benchmark dataset. However, our manual inspection of the entire dataset uncovers widespread ontological issues, especially larg…
Towards Fair Pay and Equal Work: Imposing View Time Limits in Crowdsourced Image Classification
Gordon Lim, Stefan Larson, Yu Huang +1
Crowdsourcing is a common approach to rapidly annotate large volumes of data in machine learning applications. Typically, crowd workers are compensated with a flat rate based on an…
Robust Testing for Deep Learning using Human Label Noise
Gordon Lim, Stefan Larson, Kevin Leach
In deep learning (DL) systems, label noise in training datasets often degrades model performance, as models may learn incorrect patterns from mislabeled data. The area of Learning…
Document Classification using File Names
Zhijian Li, Stefan Larson, Kevin Leach
Rapid document classification is critical in several time-sensitive applications like digital forensics and large-scale media classification. Traditional approaches that rely on he…