20 citations · 42 across the 4 of their papers we have counts for
8 papers
Differentiable Product Quantization for End-to-End Embedding Compression
Ting Chen, Lala Li, Yizhou Sun
Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings. Despite their effectiveness, the number of parame…
Few-Shot Representation Learning for Out-Of-Vocabulary Words
Ziniu Hu, Ting Chen, Kai-Wei Chang +1
Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately…
Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification
Ting Chen, Song Bian, Yizhou Sun
Graph Neural Nets (GNNs) have received increasing attentions, partially due to their superior performance in many node and graph classification tasks. However, there is a lack of u…
Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations
Ting Chen, Martin Renqiang Min, Yizhou Sun
Conventional embedding methods directly associate each symbol with a continuous embedding vector, which is equivalent to applying a linear transformation based on a "one-hot" encod…
HeteroMed: Heterogeneous Information Network for Medical Diagnosis
Anahita Hosseini, Ting Chen, Wenjun Wu +2
With the recent availability of Electronic Health Records (EHR) and great opportunities they offer for advancing medical informatics, there has been growing interest in mining EHR…
Learning K-way D-dimensional Discrete Code For Compact Embedding Representations
Ting Chen, Martin Renqiang Min, Yizhou Sun
Embedding methods such as word embedding have become pillars for many applications containing discrete structures. Conventional embedding methods directly associate each symbol wit…