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Found 13 papers, 5 papers with code
Can large language models provide useful feedback on research papers? A large-scale empirical analysis
We first quantitatively compared GPT-4's generated feedback with human peer reviewer feedback in 15 Nature family journals (3, 096 papers in total) and the ICLR machine learning conference (1, 709 papers).
ArtWhisperer: A Dataset for Characterizing Human-AI Interactions in Artistic Creations
To study this interaction, we created ArtWhisperer, an online game where users are given a target image and are tasked with iteratively finding a prompt that creates a similar-looking image as the target.
Development and Clinical Evaluation of an AI Support Tool for Improving Telemedicine Photo Quality
To address this issue, we developed TrueImage 2. 0, an artificial intelligence (AI) model for assessing patient photo quality for telemedicine and providing real-time feedback to patients for photo quality improvement.
Disparities in Dermatology AI Performance on a Diverse, Curated Clinical Image Set
To ascertain potential biases in algorithm performance in this context, we curated the Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones.
Uncalibrated Models Can Improve Human-AI Collaboration
In this paper, we present an initial exploration that suggests showing AI models as more confident than they actually are, even when the original AI is well-calibrated, can improve human-AI performance (measured as the accuracy and confidence of the human's final prediction after seeing the AI advice).
Disparities in Dermatology AI: Assessments Using Diverse Clinical Images
AI diagnostic tools may aid in early skin cancer detection; however most models have not been assessed on images of diverse skin tones or uncommon diseases.
Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions
In decision support applications of AI, the AI algorithm's output is framed as a suggestion to a human user.
Adversarial Training Helps Transfer Learning via Better Representations
Recent works empirically demonstrate that adversarial training in the source data can improve the ability of models to transfer to new domains.
Better Knowledge Retention through Metric Learning
In continual learning, new categories may be introduced over time, and an ideal learning system should perform well on both the original categories and the new categories.
TrueImage: A Machine Learning Algorithm to Improve the Quality of Telehealth Photos
These promising results suggest that our solution is feasible and can improve the quality of teledermatology care.
Blind interactive learning of modulation schemes: Multi-agent cooperation without co-design
We investigate whether learning is possible under different levels of information sharing between distributed agents which are not necessarily co-designed.
Harmless interpolation of noisy data in regression
A continuing mystery in understanding the empirical success of deep neural networks is their ability to achieve zero training error and generalize well, even when the training data is noisy and there are more parameters than data points.
Are All Training Examples Created Equal? An Empirical Study
Modern computer vision algorithms often rely on very large training datasets.
