Advances, challenges and opportunities in creating data for trustworthy AI
As artificial intelligence (AI) transitions from research to deployment, creating the appropriate
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …
datasets and data pipelines to develop and evaluate AI models is increasingly the biggest …
[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Diffumask: Synthesizing images with pixel-level annotations for semantic segmentation using diffusion models
Collecting and annotating images with pixel-wise labels is time-consuming and laborious. In
contrast, synthetic data can be freely available using a generative model (eg, DALL-E …
contrast, synthetic data can be freely available using a generative model (eg, DALL-E …
Pervasive label errors in test sets destabilize machine learning benchmarks
We identify label errors in the test sets of 10 of the most commonly-used computer vision,
natural language, and audio datasets, and subsequently study the potential for these label …
natural language, and audio datasets, and subsequently study the potential for these label …
Challenges in deploying machine learning: a survey of case studies
In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …
to a field capable of solving real-world business problems. However, the deployment of …
Fsd50k: an open dataset of human-labeled sound events
Most existing datasets for sound event recognition (SER) are relatively small and/or domain-
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …
specific, with the exception of AudioSet, based on over 2 M tracks from YouTube videos and …
Dataperf: Benchmarks for data-centric ai development
Abstract Machine learning research has long focused on models rather than datasets, and
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …
prominent datasets are used for common ML tasks without regard to the breadth, difficulty …
Dos and don'ts of machine learning in computer security
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …
massive datasets, machine learning algorithms have led to major breakthroughs in many …
Learning from disagreement: A survey
Abstract Many tasks in Natural Language Processing (NLP) and Computer Vision (CV) offer
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …
evidence that humans disagree, from objective tasks such as part-of-speech tagging to more …
Are we done with imagenet?
Yes, and no. We ask whether recent progress on the ImageNet classification benchmark
continues to represent meaningful generalization, or whether the community has started to …
continues to represent meaningful generalization, or whether the community has started to …