Y
Yang Li
Researcher at Google
Publications - 123
Citations - 6379
Yang Li is an academic researcher from Google. The author has contributed to research in topics: Gesture & Gesture recognition. The author has an hindex of 30, co-authored 122 publications receiving 4761 citations. Previous affiliations of Yang Li include University of California, Berkeley & University of Washington.
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Proceedings ArticleDOI
Gestures without libraries, toolkits or training: a $1 recognizer for user interface prototypes
TL;DR: This work presents a "$1 recognizer" that is easy, cheap, and usable almost anywhere in about 100 lines of code, and discusses the effect that the number of templates or training examples has on recognition, the score falloff along recognizers' N-best lists, and results for individual gestures.
Proceedings ArticleDOI
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
TL;DR: Cluster-GCN is proposed, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure and allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy.
Proceedings ArticleDOI
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
TL;DR: Cluster-GCN as discussed by the authors is a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure, where at each step, it samples a block of nodes that associate with a dense subgraph and restricts the neighborhood search within this subgraph.
Proceedings ArticleDOI
User-defined motion gestures for mobile interaction
Jaime Ruiz,Yang Li,Edward Lank +2 more
TL;DR: It is demonstrated that consensus exists among participants on parameters of movement and on mappings of motion gestures onto commands, and this consensus is used to develop a taxonomy for motion gestures and to specify an end-user inspired motion gesture set.
Proceedings ArticleDOI
Rico: A Mobile App Dataset for Building Data-Driven Design Applications
Biplab Deka,Zifeng Huang,Chad Franzen,Joshua Hibschman,Daniel Afergan,Yang Li,Jeffrey Nichols,Ranjitha Kumar +7 more
TL;DR: Rico is presented, the largest repository of mobile app designs to date, created to support five classes of data-driven applications: design search, UI layout generation, UI code generation, user interaction modeling, and user perception prediction.