Type prediction
42 papers with code • 3 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Type prediction
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Libraries
Use these libraries to find Type prediction models and implementations
ManyTypes4TypeScript
Most implemented papers
Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their semantics.
Convolutional neural network models for cancer type prediction based on gene expression
In breast cancer, for instance, our model identified well-known markers, such as GATA3 and ESR1.
A Physical Embedding Model for Knowledge Graphs
We present a novel and scalable paradigm for the computation of knowledge graph embeddings, which we dub PYKE .
Code Prediction by Feeding Trees to Transformers
facebookresearch/code-prediction-transformer
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We provide comprehensive experimental evaluation of our proposal, along with alternative design choices, on a standard Python dataset, as well as on a Python corpus internal to Facebook.
OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
joernio/joernti-codetidal5
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OptTyper combines a continuous interpretation of logical constraints derived by classical static analysis of TypeScript code, with natural constraints obtained from a deep learning model, which learns naming conventions for types from a large codebase.
Improving Broad-Coverage Medical Entity Linking with Semantic Type Prediction and Large-Scale Datasets
To address the dearth of annotated training data for medical entity linking, we present WikiMed and PubMedDS, two large-scale medical entity linking datasets, and demonstrate that pre-training MedType on these datasets further improves entity linking performance.
HookNet: multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images
computationalpathologygroup/hooknet
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We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks.
Contrastive Code Representation Learning
parasj/contracode
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EMNLP 2021
Recent work learns contextual representations of source code by reconstructing tokens from their context.
GraphCodeBERT: Pre-training Code Representations with Data Flow
microsoft/CodeBERT
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ICLR 2021
Instead of taking syntactic-level structure of code like abstract syntax tree (AST), we use data flow in the pre-training stage, which is a semantic-level structure of code that encodes the relation of "where-the-value-comes-from" between variables.
MIMOSA: Multi-constraint Molecule Sampling for Molecule Optimization
futianfan/mimosa
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Molecule optimization is a fundamental task for accelerating drug discovery, with the goal of generating new valid molecules that maximize multiple drug properties while maintaining similarity to the input molecule.
