Search Results for author: Siyu Huo

Found 7 papers, 2 papers with code

Natural Language Sentence Generation from API Specifications

no code implementations1 Jun 2022 Siyu Huo, Kushal Mukherjee, Jayachandu Bandlamudi, Vatche Isahagian, Vinod Muthusamy, Yara Rizk

APIs are everywhere; they provide access to automation solutions that could help businesses automate some of their tasks.

Chatbot Intent Recognition +1

Efficient Global String Kernel with Random Features: Beyond Counting Substructures

no code implementations25 Nov 2019 Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu Aggarwal

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

no code implementations WS 2019 Siyu Huo, Tengfei Ma, Jie Chen, Maria Chang, Lingfei Wu, Michael Witbrock

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.

Natural Language Understanding Semantic Parsing +3

P2L: Predicting Transfer Learning for Images and Semantic Relations

no code implementations20 Aug 2019 Bishwaranjan Bhattacharjee, John R. Kender, Matthew Hill, Parijat Dube, Siyu Huo, Michael R. Glass, Brian Belgodere, Sharath Pankanti, Noel Codella, Patrick Watson

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.

Transfer Learning

Automatic Labeling of Data for Transfer Learning

no code implementations24 Mar 2019 Parijat Dube, Bishwaranjan Bhattacharjee, Siyu Huo, Patrick Watson, John Kender, Brian Belgodere

Transfer learning uses trained weights from a source model as the initial weightsfor the training of a target dataset.

Transfer Learning

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