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Found 7 papers, 2 papers with code
Natural Language Sentence Generation from API Specifications
APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks.
Efficient Global String Kernel with Random Features: Beyond Counting Substructures
In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.
Graph Enhanced Cross-Domain Text-to-SQL Generation
Semantic parsing is a fundamental problem in natural language understanding, as it involves the mapping of natural language to structured forms such as executable queries or logic-like knowledge representations.
P2L: Predicting Transfer Learning for Images and Semantic Relations
We use this measure, which we call "Predict To Learn" ("P2L"), in the two very different domains of images and semantic relations, where it predicts, from a set of "source" models, the one model most likely to produce effective transfer for training a given "target" model.
IPC: A Benchmark Data Set for Learning with Graph-Structured Data
Benchmark data sets are an indispensable ingredient of the evaluation of graph-based machine learning methods.
Ranked #2 on
Graph Classification
on IPC-lifted
Automatic Labeling of Data for Transfer Learning
Transfer learning uses trained weights from a source model as the initial weightsfor the training of a target dataset.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Automated planning is one of the foundational areas of AI.
