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Found 5 papers, 4 papers with code
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Errors in test sets are numerous and widespread: we estimate an average of at least 3. 3% errors across the 10 datasets, where for example label errors comprise at least 6% of the ImageNet validation set.
Rapformer: Conditional Rap Lyrics Generation with Denoising Autoencoders
The ability to combine symbols to generate language is a defining characteristic of human intelligence, particularly in the context of artistic story-telling through lyrics.
Comment Ranking Diversification in Forum Discussions
We conducted a double-blind, small-scale evaluation experiment requiring subjects to select between the top 5 comments of a diversified ranking and a baseline ranking ordered by score.
Confident Learning: Estimating Uncertainty in Dataset Labels
Confident learning (CL) is an alternative approach which focuses instead on label quality by characterizing and identifying label errors in datasets, based on the principles of pruning noisy data, counting with probabilistic thresholds to estimate noise, and ranking examples to train with confidence.
Learning with Confident Examples: Rank Pruning for Robust Classification with Noisy Labels
To highlight, RP with a CNN classifier can predict if an MNIST digit is a "one"or "not" with only 0. 25% error, and 0. 46 error across all digits, even when 50% of positive examples are mislabeled and 50% of observed positive labels are mislabeled negative examples.
