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Found 9 papers, 4 papers with code
A Survey on Semantics in Automated Data Science
Data Scientists leverage common sense reasoning and domain knowledge to understand and enrich data for building predictive models.
Federated Data Science to Break Down Silos [Vision]
Similar to Open Data initiatives, data science as a community has launched initiatives for sharing not only data but entire pipelines, derivatives, artifacts, etc.
A Scalable AutoML Approach Based on Graph Neural Networks
AutoML systems build machine learning models automatically by performing a search over valid data transformations and learners, along with hyper-parameter optimization for each learner.
Results of SemTab 2021
SemTab 2021 was the third edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 20th International Semantic Web Conference (ISWC) and the 16th Ontology Matching (OM) Workshop.
Can Machines Read Coding Manuals Yet? -- A Benchmark for Building Better Language Models for Code Understanding
Code understanding is an increasingly important application of Artificial Intelligence.
Learning to Guide a Saturation-Based Theorem Prover
In addition, to the best of our knowledge, TRAIL is the first reinforcement learning-based approach to exceed the performance of a state-of-the-art traditional theorem prover on a standard theorem proving benchmark (solving up to 17% more problems).
A Toolkit for Generating Code Knowledge Graphs
We make the toolkit to build such graphs as well as the sample extraction of the 2 billion triples graph publicly available to the community for use.
A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving
Automated theorem provers have traditionally relied on manually tuned heuristics to guide how they perform proof search.
Merging datasets through deep learning
Our approach depends on (a) creating a deep learning model that maps surface forms of an entity into a set of vectors such that alternate forms for the same entity are closest in vector space, (b) indexing these vectors using a nearest neighbors algorithm to find the forms that can be potentially joined together.
