b'C. Lee Giles'

research

∙ 01/28/2023

ACL-Fig: A Dataset for Scientific Figure Classification

Most existing large-scale academic search engines are built to retrieve ...

0 Zeba Karishma, et al. ∙

research

∙ 01/21/2022

SciBERTSUM: Extractive Summarization for Scientific Documents

The summarization literature focuses on the summarization of news articl...

0 Athar Sefid, et al. ∙

research

∙ 12/23/2021

A Synthetic Prediction Market for Estimating Confidence in Published Work

Explainably estimating confidence in published scholarly work offers opp...

0 Sarah Rajtmajer, et al. ∙

research

∙ 10/22/2021

SciCap: Generating Captions for Scientific Figures

Researchers use figures to communicate rich, complex information in scie...

0 Ting-Yao Hsu, et al. ∙

research

∙ 05/20/2021

Document Domain Randomization for Deep Learning Document Layout Extraction

We present document domain randomization (DDR), the first successful tra...

0 Meng Ling, et al. ∙

research

∙ 04/08/2021

Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models

In recent years, significant effort has been invested verifying the repr...

0 Jian Wu, et al. ∙

research

∙ 04/07/2021

Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units

Automated mathematical reasoning is a challenging problem that requires ...

0 Ankur Mali, et al. ∙

research

∙ 01/05/2021

Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets

We present a synthetic prediction market whose agent purchase logic is d...

0 Nishanth Nakshatri, et al. ∙

research

∙ 12/09/2020

Automating Document Classification with Distant Supervision to Increase the Efficiency of Systematic Reviews

Objective: Systematic reviews of scholarly documents often provide compl...

13 Xiaoxiao Li, et al. ∙

research

∙ 12/07/2020

Modeling Updates of Scholarly Webpages Using Archived Data

The vastness of the web imposes a prohibitive cost on building large-sca...

0 Yasith Jayawardana, et al. ∙

research

∙ 07/27/2020

Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks

Subject categories of scholarly papers generally refer to the knowledge ...

0 Bharath Kandimalla, et al. ∙

research

∙ 05/05/2020

CODA-19: Reliably Annotating Research Aspects on 10,000+ CORD-19 Abstracts Using a Non-Expert Crowd

This paper introduces CODA-19, a human-annotated dataset that codes the ...

0 Ting-Hao Kenneth Huang, et al. ∙

research

∙ 05/05/2020

CODA-19: Reliably Annotating Research Aspects on 10,000+ CORD-19 Abstracts Using Non-Expert Crowd

This paper introduces CODA-19, a human-annotated dataset that denotes th...

0 Ting-Hao Kenneth Huang, et al. ∙

research

∙ 04/23/2020

Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies

Organisations disclose their privacy practices by posting privacy polici...

0 Mukund Srinath, et al. ∙

research

∙ 02/10/2020

Reducing the Computational Burden of Deep Learning with Recursive Local Representation Alignment

Training deep neural networks on large-scale datasets requires significa...

1 Alexander Ororbia, et al. ∙

research

∙ 12/09/2019

Query Auto Completion for Math Formula Search

Query Auto Completion (QAC) is among the most appealing features of a we...

0 Shaurya Rohatgi, et al. ∙

research

∙ 11/27/2019

Automatic Generation of Headlines for Online Math Questions

Mathematical equations are an important part of dissemination and commun...

0 Ke Yuan, et al. ∙

research

∙ 11/12/2019

Connecting First and Second Order Recurrent Networks with Deterministic Finite Automata

We propose an approach that connects recurrent networks with different o...

15 Qinglong Wang, et al. ∙

research

∙ 10/15/2019

Shapley Homology: Topological Analysis of Sample Influence for Neural Networks

Data samples collected for training machine learning models are typicall...

11 Kaixuan Zhang, et al. ∙

research

∙ 09/07/2019

The Neural State Pushdown Automata

In order to learn complex grammars, recurrent neural networks (RNNs) req...

1 Ankur Mali, et al. ∙

research

∙ 06/20/2019

Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets

Automatically extracted metadata from scholarly documents in PDF formats...

0 Athar Sefid, et al. ∙

research

∙ 05/25/2019

Lifelong Neural Predictive Coding: Sparsity Yields Less Forgetting when Learning Cumulatively

In lifelong learning systems, especially those based on artificial neura...

7 Alexander Ororbia, et al. ∙

research

∙ 11/14/2018

Verification of Recurrent Neural Networks Through Rule Extraction

The verification problem for neural networks is verifying whether a neur...

8 Qinglong Wang, et al. ∙

research

∙ 10/17/2018

Online Learning of Recurrent Neural Architectures by Locally Aligning Distributed Representations

Temporal models based on recurrent neural networks have proven to be qui...

8 Alexander Ororbia, et al. ∙

research

∙ 10/17/2018

Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations

Temporal models based on recurrent neural networks have proven to be qui...

0 Alexander Ororbia, et al. ∙

research

∙ 09/09/2018

TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade

We study the problem of extracting text instance contour information fro...

0 Dafang He, et al. ∙

research

∙ 09/09/2018

A Neural Temporal Model for Human Motion Prediction

We propose novel neural temporal models for short-term motion prediction...

2 Anand Gopalakrishnan, et al. ∙

research

∙ 04/23/2018

Guided Attention for Large Scale Scene Text Verification

Many tasks are related to determining if a particular text string exists...

0 Dafang He, et al. ∙

research

∙ 03/15/2018

Learned Iterative Decoding for Lossy Image Compression Systems

For lossy image compression systems, we develop an algorithm called iter...

0 Alexander G. Ororbia, et al. ∙

research

∙ 03/05/2018

Conducting Credit Assignment by Aligning Local Representations

The use of back-propagation and its variants to train deep networks is o...

0 Alexander G. Ororbia, et al. ∙

research

∙ 01/19/2018

Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations

Strict partial order is a mathematical structure commonly seen in relati...

0 Chen Liang, et al. ∙

research

∙ 01/16/2018

A Comparison of Rule Extraction for Different Recurrent Neural Network Models and Grammatical Complexity

It has been shown that rules can be extracted from highly non-linear, re...

0 Qinglong Wang, et al. ∙

research

∙ 01/04/2018

Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media

Daily engagement in life experiences is increasingly interwoven with mob...

0 Agnese Chiatti, et al. ∙

research

∙ 11/30/2017

Learning to Adapt by Minimizing Discrepancy

We explore whether useful temporal neural generative models can be learn...

0 Alexander G. Ororbia II, et al. ∙

research

∙ 09/27/2017

Scaling Author Name Disambiguation with CNF Blocking

An author name disambiguation (AND) algorithm identifies a unique author...

0 Kunho Kim, et al. ∙

research

∙ 06/07/2017

Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network

We present an end-to-end, multimodal, fully convolutional network for ex...

0 Xiao Yang, et al. ∙

research

∙ 05/17/2017

Learning a Hierarchical Latent-Variable Model of 3D Shapes

We propose the Variational Shape Learner (VSL), a hierarchical latent-va...

0 Shikun Liu, et al. ∙

research

∙ 11/22/2016

Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading

Physical library collections are valuable and long standing resources fo...

0 Xiao Yang, et al. ∙

research

∙ 01/26/2016

Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization

Many previous proposals for adversarial training of deep neural nets hav...

0 Alexander G. Ororbia II, et al. ∙

research

∙ 01/16/2013

Bayesian Classification and Feature Selection from Finite Data Sets

Feature selection aims to select the smallest subset of features for a s...

0 Frans Coetzee, et al. ∙

research

∙ 12/12/2012

Modelling Information Incorporation in Markets, with Application to Detecting and Explaining Events

We develop a model of how information flows into a market, and derive al...

0 David M. Pennock, et al. ∙

C. Lee Giles

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