Demo Data from SIGIR 2026 / reviewers in the wild / expert
Shue Kwan Mak
dblp:93/8729
· DBLP profile ↗
3ranked-venue papers
1as first author
0since 2021 · last 2011
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Rendering · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Rendering › texture mapping › environment mapping
environment map sampling |
0.1 | 1 | 2011 | Spatiotemporal Sampling of Dynamic Environment Sequences · IEEE Trans. Vis. Comput. Graph. 2011 |
Rendering › global illumination
image-based lighting |
0.0 | 1 | 2011 | Spatiotemporal Sampling of Dynamic Environment Sequences · IEEE Trans. Vis. Comput. Graph. 2011 |
Methods — techniques the papers use, named apart from their topics
temporal coherence · 0.1stratification · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2011 | Regularizers for fault tolerant multilayer feedforward networks
Shue Kwan Mak, John Sum, Andrew Chi-Sing Leung |
Neurocomputing | 1 |
| 2011 | Spatiotemporal Sampling of Dynamic Environment SequencesabstractEnvironment sampling is a popular technique for rendering scenes with distant environment illumination. However, the temporal consistency of animations synthesized under dynamic environment sequences has not been fully studied. This paper addresses this problem and proposes a novel method, namely spatiotemporal sampling, to fully exploit both the temporal and spatial coherence of environment sequences. Our method treats an environment sequence as a spatiotemporal volume and samples the sequence by stratifying the volume adaptively. For this purpose, we first present a new metric to measure the importance of each stratified volume. A stratification algorithm is then proposed to adaptively suppress the abrupt temporal and spatial changes in the generated sampling patterns. The proposed method is able to automatically adjust the number of samples for each environment frame and produce temporally coherent sampling patterns. Comparative experiments demonstrate the capability of our method to produce smooth and consistent animations under dynamic environment sequences. Shue Kwan Mak, Tien-Tsin Wong, Andrew Chi-Sing Leung |
IEEE Trans. Vis. Comput. Graph. | 2 |
| 2010 | Generalization Error of Faulty MLPs with Weight Decay Regularizer
Andrew Chi-Sing Leung, John Sum, Shue Kwan Mak |
ICONIP (2) | 3 |