Search Results for author:
Found 57 papers, 10 papers with code
Learning CNF Blocking for Large-scale Author Name Disambiguation
Author name disambiguation (AND) algorithms identify a unique author entity record from all similar or same publication records in scholarly or similar databases.
Acknowledgement Entity Recognition in CORD-19 Papers
We built a supplementary database by linking CORD-19 papers with acknowledgement entities extracted by AckExtract including persons and organizations and find that only up to 50–60% of named entities are actually acknowledged.
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Crafting effective captions for figures is important.
Automated Detection and Analysis of Data Practices Using A Real-World Corpus
Privacy policies are crucial for informing users about data practices, yet their length and complexity often deter users from reading them.
Stability Analysis of Various Symbolic Rule Extraction Methods from Recurrent Neural Network
We sampled the datasets from $7$ Tomita and $4$ Dyck grammars and trained them on $4$ RNN cells: LSTM, GRU, O2RNN, and MIRNN.
GPT-4 as an Effective Zero-Shot Evaluator for Scientific Figure Captions
We first constructed SCICAP-EVAL, a human evaluation dataset that contains human judgments for 3, 600 scientific figure captions, both original and machine-made, for 600 arXiv figures.
A prototype hybrid prediction market for estimating replicability of published work
We present a prototype hybrid prediction market and demonstrate the avenue it represents for meaningful human-AI collaboration.
ACL-Fig: A Dataset for Scientific Figure Classification
However, there are no large-scale retrieval services for scientific figures and tables.
A Synthetic Prediction Market for Estimating Confidence in Published Work
Explainably estimating confidence in published scholarly work offers opportunity for faster and more robust scientific progress.
SciCap: Generating Captions for Scientific Figures
Researchers use figures to communicate rich, complex information in scientific papers.
Ranked #1 on
Image Captioning
on SCICAP
Document Domain Randomization for Deep Learning Document Layout Extraction
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation.
Predicting the Reproducibility of Social and Behavioral Science Papers Using Supervised Learning Models
In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features.
Recognizing and Verifying Mathematical Equations using Multiplicative Differential Neural Units
Two particular tasks that test this type of reasoning are (1) mathematical equation verification, which requires determining whether trigonometric and linear algebraic statements are valid identities or not, and (2) equation completion, which entails filling in a blank within an expression to make it true.
Design and Analysis of a Synthetic Prediction Market using Dynamic Convex Sets
We show that under certain assumptions on the underlying geometry, the resulting synthetic prediction market can be used to arbitrarily closely approximate a binary function defined on a set of input data.
Automating Document Classification with Distant Supervision to Increase the Efficiency of Systematic Reviews
Conclusions: In sum, the automated procedure of document classification presented here could improve both the precision and efficiency of systematic reviews, as well as facilitating live reviews, where reviews are updated regularly.
Modeling Updates of Scholarly Webpages Using Archived Data
The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources.
Large Scale Subject Category Classification of Scholarly Papers with Deep Attentive Neural Networks
The results showed the importance of retraining word embedding models to maximize the vocabulary overlap and the effectiveness of the attention mechanism.
A provably stable neural network Turing Machine
We introduce a neural stack architecture, including a differentiable parametrized stack operator that approximates stack push and pop operations for suitable choices of parameters that explicitly represents a stack.
CODA-19: Using a Non-Expert Crowd to Annotate Research Aspects on 10,000+ Abstracts in the COVID-19 Open Research Dataset
This paper introduces CODA-19, a human-annotated dataset that codes the Background, Purpose, Method, Finding/Contribution, and Other sections of 10, 966 English abstracts in the COVID-19 Open Research Dataset.
Privacy at Scale: Introducing the PrivaSeer Corpus of Web Privacy Policies
Organisations disclose their privacy practices by posting privacy policies on their website.
Large-Scale Gradient-Free Deep Learning with Recursive Local Representation Alignment
Training deep neural networks on large-scale datasets requires significant hardware resources whose costs (even on cloud platforms) put them out of reach of smaller organizations, groups, and individuals.
Query Auto Completion for Math Formula Search
Query Auto Completion (QAC) is among the most appealing features of a web search engine.
Automatic Generation of Headlines for Online Math Questions
Compared to conventional summarization tasks, this task has two extra and essential constraints: 1) Detailed math questions consist of text and math equations which require a unified framework to jointly model textual and mathematical information; 2) Unlike text, math equations contain semantic and structural features, and both of them should be captured together.
Connecting First and Second Order Recurrent Networks with Deterministic Finite Automata
We propose an approach that connects recurrent networks with different orders of hidden interaction with regular grammars of different levels of complexity.
Shapley Homology: Topological Analysis of Sample Influence for Neural Networks
This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models.
The Neural State Pushdown Automata
The NSPDA is also compared to a classical analog stack neural network pushdown automaton (NNPDA) as well as a wide array of first and second-order RNNs with and without external memory, trained using different learning algorithms.
Cleaning Noisy and Heterogeneous Metadata for Record Linking Across Scholarly Big Datasets
We introduce a system designed to match scholarly document entities with noisy metadata against a reference dataset.
Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting
In lifelong learning systems based on artificial neural networks, one of the biggest obstacles is the inability to retain old knowledge as new information is encountered.
Verification of Recurrent Neural Networks Through Rule Extraction
The verification problem for neural networks is verifying whether a neural network will suffer from adversarial samples, or approximating the maximal allowed scale of adversarial perturbation that can be endured.
Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations
We compare our model and learning procedure to other back-propagation through time alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization.
A Neural Temporal Model for Human Motion Prediction
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation.
TextContourNet: a Flexible and Effective Framework for Improving Scene Text Detection Architecture with a Multi-task Cascade
We propose a novel and effective framework for this and experimentally demonstrate that: (1) A CNN that can be effectively used to extract instance-level text contour from natural images.
Distractor Generation for Multiple Choice Questions Using Learning to Rank
We investigate how machine learning models, specifically ranking models, can be used to select useful distractors for multiple choice questions.
Large Scale Scene Text Verification with Guided Attention
In this work, we propose a new framework that learns this task in an end-to-end way.
Adversarial Training for Community Question Answer Selection Based on Multi-scale Matching
We frame this task as a binary (relevant/irrelevant) classification problem, and present an adversarial training framework to alleviate label imbalance issue.
Learned Neural Iterative Decoding for Lossy Image Compression Systems
For lossy image compression systems, we develop an algorithm, iterative refinement, to improve the decoder's reconstruction compared to standard decoding techniques.
Conducting Credit Assignment by Aligning Local Representations
Using back-propagation and its variants to train deep networks is often problematic for new users.
Active Learning of Strict Partial Orders: A Case Study on Concept Prerequisite Relations
Strict partial order is a mathematical structure commonly seen in relational data.
A Comparative Study of Rule Extraction for Recurrent Neural Networks
Then we empirically evaluate different recurrent networks for their performance of DFA extraction on all Tomita grammars.
Text Extraction and Retrieval from Smartphone Screenshots: Building a Repository for Life in Media
Effective and efficient Information Extraction and Retrieval from digital screenshots is a crucial prerequisite to successful use of screen data.
Learning to Adapt by Minimizing Discrepancy
We investigate the viability of a more neurocognitively-grounded approach in the context of unsupervised generative modeling of sequences.
An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks
Rule extraction from black-box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis.
Learning to Extract Semantic Structure From Documents Using Multimodal Fully Convolutional Neural Networks
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
Multi-Scale FCN With Cascaded Instance Aware Segmentation for Arbitrary Oriented Word Spotting in the Wild
Scene text detection has attracted great attention these years.
Learning to Extract Semantic Structure from Documents Using Multimodal Fully Convolutional Neural Network
We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images.
Learning a Hierarchical Latent-Variable Model of 3D Shapes
We propose the Variational Shape Learner (VSL), a generative model that learns the underlying structure of voxelized 3D shapes in an unsupervised fashion.
Ranked #6 on
3D Object Recognition
on ModelNet40
Learning Adversary-Resistant Deep Neural Networks
Despite the superior performance of DNNs in these applications, it has been recently shown that these models are susceptible to a particular type of attack that exploits a fundamental flaw in their design.
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
Physical library collections are valuable and long standing resources for knowledge and learning.
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks
Deep neural networks have proven to be quite effective in a wide variety of machine learning tasks, ranging from improved speech recognition systems to advancing the development of autonomous vehicles.
Adversary Resistant Deep Neural Networks with an Application to Malware Detection
However, after a thorough analysis of the fundamental flaw in DNNs, we discover that the effectiveness of current defenses is limited and, more importantly, cannot provide theoretical guarantees as to their robustness against adversarial sampled-based attacks.
Unifying Adversarial Training Algorithms with Flexible Deep Data Gradient Regularization
Many previous proposals for adversarial training of deep neural nets have included di- rectly modifying the gradient, training on a mix of original and adversarial examples, using contractive penalties, and approximately optimizing constrained adversarial ob- jective functions.
Online Semi-Supervised Learning with Deep Hybrid Boltzmann Machines and Denoising Autoencoders
Two novel deep hybrid architectures, the Deep Hybrid Boltzmann Machine and the Deep Hybrid Denoising Auto-encoder, are proposed for handling semi-supervised learning problems.
A neural probabilistic model for context based citation recommendation
It is not always easy for knowledgeable researchers to give an accurate citation context for a cited paper or to find the right paper to cite given context.
