Search Results for author:
Found 9 papers, 5 papers with code
Mitigating stereotypical biases in text to image generative systems
In this work, we propose a method to mitigate such biases and ensure that the outcomes are fair across different groups of people.
Structure and Content-Guided Video Synthesis with Diffusion Models
Text-guided generative diffusion models unlock powerful image creation and editing tools.
Data Augmentation for Intent Classification with Off-the-shelf Large Language Models
Data augmentation is a widely employed technique to alleviate the problem of data scarcity.
Stochastic Batch Acquisition: A Simple Baseline for Deep Active Learning
We examine a simple stochastic strategy for adapting well-known single-point acquisition functions to allow batch active learning.
Can Active Learning Preemptively Mitigate Fairness Issues?
We also explore the interaction of algorithmic fairness methods such as gradient reversal (GRAD) and BALD.
Synbols: Probing Learning Algorithms with Synthetic Datasets
Progress in the field of machine learning has been fueled by the introduction of benchmark datasets pushing the limits of existing algorithms.
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images.
Bayesian active learning for production, a systematic study and a reusable library
Active learning is able to reduce the amount of labelling effort by using a machine learning model to query the user for specific inputs.
DECoVaC: Design of Experiments with Controlled Variability Components
Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance.
