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Yuexin Wu
Researcher at Carnegie Mellon University
Publications - 40
Citations - 2017
Yuexin Wu is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Computer science & Search algorithm. The author has an hindex of 15, co-authored 27 publications receiving 1421 citations. Previous affiliations of Yuexin Wu include Microsoft & Tsinghua University.
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Analogical Inference for Multi-Relational Embeddings
TL;DR: This paper proposes a novel framework for optimizing the latent representations with respect to thealogical properties of the embedded entities and relations, and offers an elegant unification of several well-known methods in multi-relational embedding.
Posted Content
Encode, Review, and Decode: Reviewer Module for Caption Generation.
TL;DR: The reviewer module performs a number of review steps with attention mechanism on the encoder hidden states, and outputs a fact vector after each review step; the fact vectors are used as the input of the attention mechanism in the decoder.
Proceedings ArticleDOI
StoryGAN: A Sequential Conditional GAN for Story Visualization
Yitong Li,Zhe Gan,Yelong Shen,Jingjing Liu,Yu Cheng,Yuexin Wu,Lawrence Carin,David E. Carlson,Jianfeng Gao +8 more
TL;DR: In this article, the authors proposed a new task called Story Visualization, where given a multi-sentence paragraph, the story is visualized by generating a sequence of images, one for each sentence.
Proceedings ArticleDOI
Deep Learning for Epidemiological Predictions
TL;DR: This paper develops a deep learning framework, for the first time, to predict epidemiology profiles in the time-series perspective, and adopts Recurrent Neural Networks (RNNs) to capture the long-term correlation in the data and Convolutional Neural Networks to fuse information from data of different sources.
Journal ArticleDOI
Large Language Models Can Self-Improve
TL;DR: This work uses a pre-trained LLM to generate “high-confidence” rationale-augmented answers for unlabeled questions using Chain-of-Thought prompting and self-consistency, and conducts ablation studies and shows that ablation on reasoning is critical for self-improvement.