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Found 10 papers, 5 papers with code
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images.
Masked Autoencoders are Scalable Learners of Cellular Morphology
Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research.
Deconstructing word embedding algorithms
Word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.
Learning Efficient Task-Specific Meta-Embeddings with Word Prisms
Word embeddings are trained to predict word cooccurrence statistics, which leads them to possess different lexical properties (syntactic, semantic, etc.)
Deconstructing and reconstructing word embedding algorithms
Uncontextualized word embeddings are reliable feature representations of words used to obtain high quality results for various NLP applications.
Word Embedding Algorithms as Generalized Low Rank Models and their Canonical Form
We derive that both of these algorithms attempt to produce embedding inner products that approximate pointwise mutual information (PMI) statistics in the corpus.
Clustering-Oriented Representation Learning with Attractive-Repulsive Loss
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective.
Sentiment Analysis: It's Complicated!
In datasets constructed for the purpose of Twitter sentiment analysis (TSA), these controversial examples can compose over 30{\%} of the originally annotated data.
Resolving Event Coreference with Supervised Representation Learning and Clustering-Oriented Regularization
This work provides insight and motivating results for a new general approach to solving coreference and clustering problems with representation learning.
