[Official Code] Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
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Updated
Oct 24, 2023 - Jupyter Notebook
[Official Code] Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Bias detection Toolkit: Chrome Extension, Python Package, SOTA research paper docs.
Analysis of nearly-coincident cloud top height (CTH) Level 2 data from MISR and MODIS on-board NASA's Terra satellite and ISS-CATS to validate passive sensor products.
My research, studying and assessing the extent to which latent bias is present in commercial facial recognition systems offered by Google Cloud, Face++, and Microsoft Azure. Created a 1600 image dataset with faces from 8 regions in South Asia. Used this dataset to assess each cloud service.
This repo contains the annotations and other artifacts of the paper titled: In What Languages are Generative Language Models the Most Formal? Analyzing Formality Distribution across Languages
Predicting diabetes risk from BRFSS health indicators with interpretable ML, threshold optimization, and bias/subgroup analysis.
Deep learning project using PyTorch to classify facial emotions and analyze dataset bias (neutral, surprise, focused, angry).
Benchmark tool aimed at evaluating biases of large language models
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