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Found 14 papers, 9 papers with code
AILuminate: Introducing v1.0 of the AI Risk and Reliability Benchmark from MLCommons
This work represents a crucial step toward establishing global standards for AI risk and reliability evaluation while acknowledging the need for continued development in areas such as multiturn interactions, multimodal understanding, coverage of additional languages, and emerging hazard categories.
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
Croissant: A Metadata Format for ML-Ready Datasets
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point.
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science.
Generative Fractional Diffusion Models
To ensure tractable inference and learning, we employ a recently popularized Markov approximation of fBM (MA-fBM) and derive its reverse-time model, resulting in generative fractional diffusion models (GFDM).
Localized Data Work as a Precondition for Data-Centric ML: A Case Study of Full Lifecycle Crop Disease Identification in Ghana
The Ghana Cashew Disease Identification with Artificial Intelligence (CADI AI) project demonstrates the importance of sound data work as a precondition for the delivery of useful, localized datacentric solutions for public good tasks such as agricultural productivity and food security.
Machine Learning for Health symposium 2022 -- Extended Abstract track
A collection of the extended abstracts that were presented at the 2nd Machine Learning for Health symposium (ML4H 2022), which was held both virtually and in person on November 28, 2022, in New Orleans, Louisiana, USA.
Data Models for Dataset Drift Controls in Machine Learning With Optical Images
This limits our ability to study and understand the relationship between data generation and downstream machine learning model performance in a physically accurate manner.
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021
A collection of the accepted abstracts for the Machine Learning for Health (ML4H) symposium 2021.
More Than Meets The Eye: Semi-supervised Learning Under Non-IID Data
In this work, we demonstrate the limits of semantic data set matching.
Post-Hoc Domain Adaptation via Guided Data Homogenization
Addressing shifts in data distributions is an important prerequisite for the deployment of deep learning models to real-world settings.
MixMOOD: A systematic approach to class distribution mismatch in semi-supervised learning using deep dataset dissimilarity measures
In this work, we propose MixMOOD - a systematic approach to mitigate effect of class distribution mismatch in semi-supervised deep learning (SSDL) with MixMatch.
Interval Neural Networks as Instability Detectors for Image Reconstructions
This work investigates the detection of instabilities that may occur when utilizing deep learning models for image reconstruction tasks.
Interval Neural Networks: Uncertainty Scores
We propose a fast, non-Bayesian method for producing uncertainty scores in the output of pre-trained deep neural networks (DNNs) using a data-driven interval propagating network.
