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Found 9 papers, 2 papers with code
The Importance of Prompt Tuning for Automated Neuron Explanations
Recent advances have greatly increased the capabilities of large language models (LLMs), but our understanding of the models and their safety has not progressed as fast.
Robust Open-Set Spoken Language Identification and the CU MultiLang Dataset
Most state-of-the-art spoken language identification models are closed-set; in other words, they can only output a language label from the set of classes they were trained on.
ContriMix: Scalable stain color augmentation for domain generalization without domain labels in digital pathology
Secondly, dependency on domain labels prevents the use of pathology images without domain labels to improve model performance.
Boundary Attention Mapping (BAM): Fine-grained saliency maps for segmentation of Burn Injuries
We built a saliency mapping method, Boundary Attention Mapping (BAM), that utilises this trained CNN for the purpose of accurately localizing and segmenting the burn regions from skin burn images.
Synthetic DOmain-Targeted Augmentation (S-DOTA) Improves Model Generalization in Digital Pathology
Machine learning algorithms have the potential to improve patient outcomes in digital pathology.
Modernizing Open-Set Speech Language Identification
While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem.
A Neural Pairwise Ranking Model for Readability Assessment
Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research.
Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study
We used TPOT and auto-sklearn as AutoML tools to generate classifiers to triage the posts.
Lensless computational imaging through deep learning
Deep learning has been proven to yield reliably generalizable answers to numerous classification and decision tasks.
