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Datasets

Greatest papers with code

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

20 Apr 2020mahmoodlab/CLAM

CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.

DOMAIN ADAPTATION MULTIPLE INSTANCE LEARNING WEAKLY SUPERVISED CLASSIFICATION WHOLE SLIDE IMAGES

Discriminative Topic Mining via Category-Name Guided Text Embedding

20 Aug 2019yumeng5/CatE

We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.

CLASSIFICATION DOCUMENT CLASSIFICATION LEXICAL ENTAILMENT TOPIC MODELS WEAKLY SUPERVISED CLASSIFICATION

Ontology-driven weak supervision for clinical entity classification in electronic health records

5 Aug 2020som-shahlab/trove

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.

CLASSIFICATION NAMED ENTITY RECOGNITION TEMPORAL INFORMATION EXTRACTION WEAKLY SUPERVISED CLASSIFICATION WEAKLY-SUPERVISED NAMED ENTITY RECOGNITION

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

ICLR 2021 benbo/interactive-weak-supervision

Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

WEAKLY SUPERVISED CLASSIFICATION

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

4 Feb 2021YivanZhang/lio

To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks.

WEAKLY SUPERVISED CLASSIFICATION

Lower-bounded proper losses for weakly supervised classification

4 Mar 2021yoshum/lower-bounded-proper-losses

The goal is to derive conditions under which loss functions for weak-label learning are proper and lower-bounded -- two essential requirements for the losses used in class-probability estimation.

CLASSIFICATION WEAKLY SUPERVISED CLASSIFICATION

Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models

30 Jan 2019clips/patcor

We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface.

WEAKLY SUPERVISED CLASSIFICATION