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
Latest papers with no code
Activation Steering for Robust Type Prediction in CodeLLMs
We apply our approach to the task of type prediction for the gradually typed languages Python and TypeScript.
Learning Granger Causality from Instance-wise Self-attentive Hawkes Processes
In particular, we are interested in discovering instance-level causal structures in an unsupervised manner.
HoloAssist: an Egocentric Human Interaction Dataset for Interactive AI Assistants in the Real World
Building an interactive AI assistant that can perceive, reason, and collaborate with humans in the real world has been a long-standing pursuit in the AI community.
AsyncET: Asynchronous Learning for Knowledge Graph Entity Typing with Auxiliary Relations
Previously, KG embedding (KGE) methods tried to solve the KGET task by introducing an auxiliary relation, 'hasType', to model the relationship between entities and their types.
Graph Encoding and Neural Network Approaches for Volleyball Analytics: From Game Outcome to Individual Play Predictions
Our results show that the use of GNNs with our graph encoding yields a much more advanced analysis of the data, which noticeably improves prediction results overall.
Towards Semantically Enriched Embeddings for Knowledge Graph Completion
Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information.
Extreme Classification for Answer Type Prediction in Question Answering
In this paper, we propose use of extreme multi-label classification using Transformer models (XBERT) by clustering KG types using structural and semantic features based on question text.
VREN: Volleyball Rally Dataset with Expression Notation Language
The second goal is to introduce a volleyball descriptive language to fully describe the rally processes in the games and apply the language to our dataset.
Studying the explanations for the automated prediction of bug and non-bug issues using LIME and SHAP
Objective: We want to understand if machine learning models provide explanations for the classification that are reasonable to us as humans and align with our assumptions of what the models should learn.
Entity Type Prediction Leveraging Graph Walks and Entity Descriptions
Entity typing is the task of assigning or inferring the semantic type of an entity in a KG.
