Search Results for author: Ni Lao

Found 24 papers, 10 papers with code

Narrative Cartography with Knowledge Graphs

1 code implementation2 Dec 2021 Gengchen Mai, Weiming Huang, Ling Cai, Rui Zhu, Ni Lao

With the help of this tool, the retrieved data from KGs are directly materialized in a GIS format which is ready for spatial analysis and mapping.

Knowledge Graphs

A Review of Location Encoding for GeoAI: Methods and Applications

no code implementations7 Nov 2021 Gengchen Mai, Krzysztof Janowicz, Yingjie Hu, Song Gao, Bo Yan, Rui Zhu, Ling Cai, Ni Lao

A common need for artificial intelligence models in the broader geoscience is to represent and encode various types of spatial data, such as points (e. g., points of interest), polylines (e. g., trajectories), polygons (e. g., administrative regions), graphs (e. g., transportation networks), or rasters (e. g., remote sensing images), in a hidden embedding space so that they can be readily incorporated into deep learning models.

Geographic Question Answering: Challenges, Uniqueness, Classification, and Future Directions

no code implementations19 May 2021 Gengchen Mai, Krzysztof Janowicz, Rui Zhu, Ling Cai, Ni Lao

As an important part of Artificial Intelligence (AI), Question Answering (QA) aims at generating answers to questions phrased in natural language.

Open-Domain Question Answering

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

1 code implementation25 Apr 2020 Gengchen Mai, Krzysztof Janowicz, Ling Cai, Rui Zhu, Blake Regalia, Bo Yan, Meilin Shi, Ni Lao

We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines.

Information Retrieval Knowledge Graph Embedding +2

Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells

2 code implementations ICLR 2020 Gengchen Mai, Krzysztof Janowicz, Bo Yan, Rui Zhu, Ling Cai, Ni Lao

The key idea is to use neural networks to convert words in texts to vector space representations based on word positions in a sentence and their contexts, which are suitable for end-to-end training of downstream tasks.

Image Classification Representation Learning

Integrated Triaging for Fast Reading Comprehension

no code implementations28 Sep 2019 Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger

This paper introduces Integrated Triaging, a framework that prunes almost all context in early layers of a network, leaving the remaining (deep) layers to scan only a tiny fraction of the full corpus.

Machine Reading Comprehension

Neural Program Planner for Structured Predictions

no code implementations ICLR Workshop drlStructPred 2019 Jacob Biloki, Chen Liang, Ni Lao

We consider the problem of weakly supervised structured prediction (SP) with reinforcement learning (RL) – for example, given a database table and a question, perform a sequence of computation actions on the table, which generates a response and receives a binary success-failure reward.

Machine Translation Program Synthesis +3

FastFusionNet: New State-of-the-Art for DAWNBench SQuAD

2 code implementations28 Feb 2019 Felix Wu, Boyi Li, Lequn Wang, Ni Lao, John Blitzer, Kilian Q. Weinberger

In this technical report, we introduce FastFusionNet, an efficient variant of FusionNet [12].

Reading Comprehension

POIReviewQA: A Semantically Enriched POI Retrieval and Question Answering Dataset

no code implementations5 Oct 2018 Gengchen Mai, Krzysztof Janowicz, Cheng He, Sumang Liu, Ni Lao

To test a system's ability to understand the text we adopt an information retrieval evaluation by ranking all the review sentences for a question based on the likelihood that they answer this question.

Information Retrieval Question Answering +1

Memory Augmented Policy Optimization for Program Synthesis and Semantic Parsing

4 code implementations NeurIPS 2018 Chen Liang, Mohammad Norouzi, Jonathan Berant, Quoc Le, Ni Lao

We present Memory Augmented Policy Optimization (MAPO), a simple and novel way to leverage a memory buffer of promising trajectories to reduce the variance of policy gradient estimate.

Combinatorial Optimization Program Synthesis +2

LEARNING TO ORGANIZE KNOWLEDGE WITH N-GRAM MACHINES

no code implementations ICLR 2018 Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao

Existing end-to-end deep QA models (Miller et al., 2016; Weston et al., 2014) need to read the entire text after observing the question, and therefore their complexity in responding a question is linear in the text size.

Language Modelling Machine Translation +1

Learning to Organize Knowledge and Answer Questions with N-Gram Machines

no code implementations17 Nov 2017 Fan Yang, Jiazhong Nie, William W. Cohen, Ni Lao

Though deep neural networks have great success in natural language processing, they are limited at more knowledge intensive AI tasks, such as open-domain Question Answering (QA).

Open-Domain Question Answering

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)

no code implementations4 Dec 2016 Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao

In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface.

Feature Engineering Natural Language Understanding +2

Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision

2 code implementations ACL 2017 Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, Ni Lao

Harnessing the statistical power of neural networks to perform language understanding and symbolic reasoning is difficult, when it requires executing efficient discrete operations against a large knowledge-base.

Feature Engineering Structured Prediction

Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields

no code implementations28 Jun 2014 Ni Lao, Jun Zhu

We prove that the gradient of candidate features can be represented solely as a function of signals and errors, and that CFI is an efficient approximation of gradient-based evaluation methods.

Feature Selection Relational Reasoning

Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic

no code implementations12 Apr 2014 William Yang Wang, Kathryn Mazaitis, Ni Lao, Tom Mitchell, William W. Cohen

We show that the problem of constructing proofs for this logic is related to computation of personalized PageRank (PPR) on a linearized version of the proof space, and using on this connection, we develop a proveably-correct approximate grounding scheme, based on the PageRank-Nibble algorithm.

Relational Reasoning

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