Website about Chinese food: information design promoting culture identify by website

Information design for the web and interactive multimedia integrates content with visual indicators. Legibility and clear communication of information and direction are important to the success of graphical user interface design. This thesis is a systematic introduction about Chinese fish dishes. The information is built into a website. The objective of the thesis is to increase the efficiency of transmitting information to users, making it more easily understandable and less time-consuming to be comprehended. Users can find information on classic Chinese fish dishes conveniently from the website. The project includes five parts, they are: Home page, Introduction, Restaurant, Kitchen and Fishing. It provides some interactive features: one Flash multimedia online learning course and one Flash fishing game. Users can learn about various Chinese fish dishes in the Restaurant part; can go through the cooking fish process in a virtual environment by using cooking equipment and different ingredients under instruction. Also, users can play a fun fishing game harpooning four different kinds of fish commonly used in Chinese recipes

Nested Hierarchical Dirichlet Processes

We develop a nested hierarchical Dirichlet process (nHDP) for hierarchical topic modeling. The nHDP is a generalization of the nested Chinese restaurant process (nCRP) that allows each word to follow its own path to a topic node according to a document-specific distribution on a shared tree. This alleviates the rigid, single-path formulation of the nCRP, allowing a document to more easily express thematic borrowings as a random effect. We derive a stochastic variational inference algorithm for the model, in addition to a greedy subtree selection method for each document, which allows for efficient inference using massive collections of text documents. We demonstrate our algorithm on 1.8 million documents from The New York Times and 3.3 million documents from Wikipedia.Comment: To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, Special Issue on Bayesian Nonparametric

Spartan Daily, February 15, 2018

Volume 150, Issue 10https://scholarworks.sjsu.edu/spartan_daily_2018/1009/thumbnail.jp

Spartan Daily, October 17, 2013

Volume 141, Issue 22https://scholarworks.sjsu.edu/spartandaily/1441/thumbnail.jp

Dirichlet belief networks for topic structure learning

Recently, considerable research effort has been devoted to developing deep architectures for topic models to learn topic structures. Although several deep models have been proposed to learn better topic proportions of documents, how to leverage the benefits of deep structures for learning word distributions of topics has not yet been rigorously studied. Here we propose a new multi-layer generative process on word distributions of topics, where each layer consists of a set of topics and each topic is drawn from a mixture of the topics of the layer above. As the topics in all layers can be directly interpreted by words, the proposed model is able to discover interpretable topic hierarchies. As a self-contained module, our model can be flexibly adapted to different kinds of topic models to improve their modelling accuracy and interpretability. Extensive experiments on text corpora demonstrate the advantages of the proposed model.Comment: accepted in NIPS 201

Spartan Daily, March 14, 2019

Volume 152, Issue 22https://scholarworks.sjsu.edu/spartan_daily_2019/1021/thumbnail.jp