Xiaoxiao Guo

University of Michigan, IBM Research
Publications46
h-index16
Citations1,915
Highly Influential Citations265
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Action-Conditional Video Prediction using Deep Networks in Atari Games
TLDR
We propose and evaluate two deep neural network architectures that consist of encoding, action-conditional transformation, and decoding layers based on convolutional neural networks and recurrent neural networks. Expand
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R3: Reinforced Ranker-Reader for Open-Domain Question Answering
TLDR
We propose a new pipeline for open-domain QA with a Ranker component that learns to rank retrieved passages in terms of likelihood of extracting the ground-truth answer to a given question. Expand
  • 138
  • 32
DeepCas: An End-to-end Predictor of Information Cascades
TLDR
We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods. Expand
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Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
TLDR
A popular recent approach to answering open-domain questions is to first search for question-related passages and then apply reading comprehension models to extract answers. Expand
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Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning
TLDR
The combination of modern Reinforcement Learning and Deep Learning approaches holds the promise of making significant progress on challenging applications requiring both rich perception and policy-selection. Expand
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R3: Reinforced Reader-Ranker for Open-Domain Question Answering
TLDR
We propose a new pipeline for open-domain QA with a Ranker component, which learns to rank retrieved passages in terms of likelihood of generating the ground-truth answer to a given question. Expand
  • 53
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Convolutional Neural Networks for Steady Flow Approximation
TLDR
We propose a general and flexible approximation model for real-time prediction of non-uniform steady laminar flow in a 2D or 3D domain based on convolutional neural networks (CNNs). Expand
  • 150
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One-Shot Relational Learning for Knowledge Graphs
TLDR
We propose a one-shot relational learning framework that utilizes the knowledge distilled by embedding models and learns a matching metric by considering both the learned embeddings and one-hop graph structures. Expand
  • 46
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Eigenoption Discovery through the Deep Successor Representation
TLDR
We propose an algorithm that discovers eigenoptions while learning non-linear state representations from raw pixels. Expand
  • 52
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Extracting Multiple-Relations in One-Pass with Pre-Trained Transformers
TLDR
We propose the first system that simultaneously extracts multiple relations with one-time encoding of an input paragraph by encoding the paragraph only once (one-pass). Expand
  • 41
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