Works (3)

Updated: July 5th, 2023 15:32

2021 article

Revisit the Scalability of Deep Auto-Regressive Models for Graph Generation

2021 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN).

By: S. Yang n, X. Shen n & S. Lim*

TL;DR: It is concluded that the perceived “inherent” scalability limitation is a misperception; with the right design and implementation, deep auto-regressive graph generation can be applied to graphs much larger than the device memory. (via Semantic Scholar)
Sources: Web Of Science, ORCID
Added: January 10, 2022

2018 article

FALCON: A Fast Drop-In Replacement of Citation KNN for Multiple Instance Learning

CIKM'18: PROCEEDINGS OF THE 27TH ACM INTERNATIONAL CONFERENCE ON INFORMATION AND KNOWLEDGE MANAGEMENT, pp. 67–76.

By: S. Yang n & X. Shen n

author keywords: Citation KNN; Triangle Inequality; Multiple-instance Learning
TL;DR: FALCON accelerates Citation KNN by removing unnecessary distance calculations through two novel optimizations, multi-level triangle inequality-based distance filtering and heap optimization, making it a promising drop-in replacement of Citation Knn for multiple instance learning. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, ORCID
Added: February 4, 2019

2018 article

LEEM: Lean Elastic EM for Gaussian Mixture Model via Bounds-Based Filtering

2018 IEEE INTERNATIONAL CONFERENCE ON DATA MINING (ICDM), pp. 677–686.

By: S. Yang n & X. Shen n

author keywords: Gaussian Mixture Model; Acceleration; Expectation Maximization; Elastic EM
TL;DR: This work proposes several novel optimizations to further accelerate Elastic EM, which brings multi-fold speedups on six datasets of various sizes and dimensions and creates Lean Elastic EM (LEEM), which is named Elastic EM in this paper. (via Semantic Scholar)
UN Sustainable Development Goal Categories
Sources: Web Of Science, ORCID
Added: May 6, 2019

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