Snuba builds in a threshold beta around 0.5, so anything greater than 0.5 + beta is a positive label, and anything less than 0.5 - beta is a negative label. All other values result in an “abstained” label. The system tries to find the beta that maximizes the F1 score on the labeled...
The best labeling function design strategy used by participants appeared to be defining small term sets correlated with positive and negative labels. Participants with the lowest F1 scores tended to design labeling functions with low coverage of negative labels. This is a common difficulty encountered ...
In previous work, we have presented an approach to successfully assemble descriptions of long-tail entities from relational HTML tables using supervised matching methods and manually labeled training data in the form of positive and negative entity matches. Manually labeling training data is a laborious...
(3) Weakly supervised (WS) is BioBERT trained on the probabilistic dataset generated by the label model. (4) Fully supervised (FS) is BioBERT trained on the original expert-labeled training set, tuned to match current published state-of-the-art performance, and using the validation set for ...
A bag with a positive label indicates that there exists at least one observation within that bag, whose label is positive. For a negatively labeled bag, on the other hand, all observations are known to have a negative label. This framework can directly be applied to CAD by defining each ...
We find decent weak-to-strong generalization and even positive PGR scaling on NLP tasks, decent generalization for small supervisor-student gaps but negative PGR scaling on chess puzzles, and both poor generalization and scaling for ChatGPT reward modeling. responding test accuracy curves appear ...
In anchor learning, an expert creates rules that serve as an imperfect (or noisy) label on which to build supervised models that can generalize beyond the specified anchors and yield the probability of a record having the label of interest. This framework has been applied to healthcare and ...
When using a probability threshold of 0.5 to classify COVID-positive and COVID-negative, the algorithm obtained an accuracy of 0.901, a positive predictive value of 0.840 and a very high negative predictive value of 0.982. The algorithm took only 1.93 seconds to process a single patient's CT...
and is collected from both movies and in-the-wild scenarios. With the dataset in hands, we then view weakly supervised violence detection as a multiple instance learning (MIL) task; that is, a video is cast as a bag, which consists of several instances (snippets), and instance-level ...
And U2PL [61] treats uncertain pixels as reliable negative samples to con- trast against corresponding positive samples. Similar to the core spirit of co-training [7,52,76], CPS [13] introduces dual models to supervise each other. Other works from the res...