pwc-archive/papers-with-abstracts huggingface dataset:
On Domain Transfer When Predicting Intent in Text
Authors: Petar Stojanov, Ahmed Hassan Awadallah, Paul Bennett, Saghar Hosseini
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12

In many domains, especially enterprise text analysis, there is an abundance of data which can be used for the development of new AI-powered intelligent experiences to improve people's productivity. However, there are strong-guarantees of privacy which prevent broad sampling and  labeling of personal text data to learn or evaluate models of interest. Fortunately, in some cases like enterprise email, manual annotation is possible on certain public datasets. The hope is that models trained on these public datasets would perform well on the target private datasets of interest. In this paper, we study the challenges of transferring information from one email dataset to another, for predicting user intent. In particular, we present approaches to characterizing the transfer gap in text corpora from both an intrinsic and extrinsic point-of-view, and evaluate several proposed methods in the literature for bridging this gap. We conclude with raising issues for further discussion in this arena.
Neural Contract Element Extraction Revisited
Authors: Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Ion Androutsopoulos
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: POS, TAG

We investigate contract element extraction. We show that LSTM-based encoders perform better than dilated CNNs, Transformers, and BERT in this task. We also find that domain-specific WORD2VEC embeddings outperform generic pre-trained GLOVE embeddings. Morpho-syntactic features in the form of POS tag and token shape embeddings, as well as context-aware ELMO embeddings do not improve performance. Several of these observations contradict choices or findings of previous work on contract element extraction and generic sequence labeling tasks, indicating that contract element extraction requires careful task-specific choices.
Representation Learning in Geology and GilBERT
Authors: Zikri Bayraktar, Hedi Driss, Marie Lefranc
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: Language Modeling, Language Modelling, Representation Learning, TAG

Geology lays at the foundation of the oil and gas industry and a good understanding of geology in each newly drilled well can make or break an exploration project with a price tag in the millions of dollars.  Over the past decades, each drilled well have been extensively analyzed, where geology and other petrophysical properties were interpreted by experts and rigorously documented. As this creates a valuable source of information for future drilling success, most of it is stored in PDF files in knowledge silos of companies. Recent advancements in cloud technologies and machine learning techniques are enabling the future to be open-source and access to these technical documents is providing a broad geological knowledge of the different basins in the world.  In this work, we focus on geology reports of wells drilled in the Norwegian Sea with the goal to learn numerical representations for geological descriptions in these fields and utilize these representations to find worldwide geological analogues. The automation of analog identification can improve expert interpretation, exploration success, and save a significant amount of effort and time for oil and gas companies. We will present numerical encoding approaches we took in the pursuit of capturing representations of geological knowledge from files as well as challenges faced during this work and road map towards GilBERT; Geologically informed language modeling with BERT, for the use in geology-based NLP applications in oil-and-gas (O&G) industry.
CORD: A Consolidated Receipt Dataset for Post-OCR Parsing
Authors: Seunghyun Park, Seung Shin, Bado Lee, Junyeop Lee, Jaeheung Surh, Minjoon Seo, Hwalsuk Lee
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: Optical Character Recognition (OCR), Semantic Parsing

OCR is inevitably linked to NLP since its final output is in text. Advances in document intelligence are driving the need for a unified technology that integrates OCR with various NLP tasks, especially semantic parsing. Since OCR and semantic parsing have been studied as separate tasks so far, the datasets for each task on their own are rich, while those for the integrated post-OCR parsing tasks are relatively insufficient. In this study, we publish a consolidated dataset for receipt parsing as the first step towards post-OCR parsing tasks. The dataset consists of thousands of Indonesian receipts, which contains images and box/text annotations for OCR, and multi-level semantic labels for parsing. The proposed dataset can be used to address various OCR and parsing tasks.
Document Enhancement System Using Auto-encoders
Authors: Mehrdad J. Gangeh, Sunil R. Tiyyagura, Sridhar V. Dasaratha, Hamid Motahari, Nigel P. Duffy
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: Denoising, Document Enhancement, Optical Character Recognition, Optical Character Recognition (OCR)

The conversion of scanned documents to digital forms is performed using an Optical Character Recognition (OCR) software. This work focuses on improving the quality of scanned documents in order to improve the OCR output. We create an end-to-end document enhancement pipeline which takes in a set of noisy documents and produces clean ones. Deep neural network based denoising auto-encoders are trained to improve the OCR quality. We train a blind model that works on different noise levels of scanned text documents. Results are shown for blurring and watermark noise removal from noisy scanned documents.
Semantic Structure Extraction for Spreadsheet Tables with a Multi-task Learning Architecture
Authors: Haoyu Dong, Shijie Liu, Zhouyu Fu, Shi Han, Dongmei Zhang
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: Language Modeling, Language Modelling, Multi-Task Learning

Semantic structure extraction for spreadsheets includes detecting table regions, recognizing structural components and classifying cell types. Automatic semantic structure extraction is key to automatic data transformation from various table structures into canonical schema so as to enable data analysis and knowledge discovery. However, they are challenged by the diverse table structures and the spatial-correlated semantics on cell grids. To learn spatial correlations and capture semantics on spreadsheets, we have developed a novel learning-based framework for spreadsheet semantic structure extraction. First, we propose a multi-task framework that learns table region, structural components and cell types jointly; second, we leverage the advances of the recent language model to capture semantics in each cell value; third, we build a large human-labeled dataset with broad coverage of table structures. Our evaluation shows that our proposed multi-task framework is highly effective that outperforms the results of training each task separately.
SVDocNet: Spatially Variant U-Net for Blind Document Deblurring
Authors: Bharat Mamidibathula, Prabir Kumar Biswas
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: Deblurring, Optical Character Recognition, Optical Character Recognition (OCR)

Blind document deblurring is a fundamental task in the field of document processing and restoration, having wide enhancement applications in optical character recognition systems, forensics, etc. Since this problem is highly ill-posed, supervised and unsupervised learning methods are well suited for this application. Using various techniques, extensive work has been done on natural-scene deblurring. However, these extracted features are not suitable for document images. We present SVDocNet, an end-to-end trainable U-Net based spatial recurrent neural network (RNN) for blind document deblurring where the weights of the RNNs are determined by different convolutional neural networks (CNNs). This network achieves state of the art performance in terms of both quantitative measures and qualitative results.
Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection
Authors: Christian Reisswig, Anoop R Katti, Marco Spinaci, Johannes Höhne
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: GPU, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR), Semantic Segmentation

We present an end-to-end trainable approach for optical character recognition (OCR) on printed documents. It is based on predicting a two-dimensional character grid ('chargrid') representation of a document image as a semantic segmentation task.
To identify individual character instances from the chargrid, we regard characters as objects and use object detection techniques from computer vision.
We demonstrate experimentally that our method outperforms previous state-of-the-art approaches in accuracy while being easily parallelizable on GPU (thereby being significantly faster), as well as easier to train.
CrossLang: the system of cross-lingual plagiarism detection
Authors: Oleg Bakhteev, Alexandr Ogaltsov, Andrey Khazov, Kamil Safin, Rita Kuznetsova
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12

Plagiarism and text reuse become more available with the Internet development. Therefore it is important to check scientific papers for the fact of cheating, especially in Academia. Existing systems of plagiarism detection show the good performance and have a huge source databases. Thus now it is not enough just to copy the text as is from the source document to get the original work. Therefore, another type of plagiarism become popular -- cross-lingual plagiarism. We present a CrossLang system for such kind of plagiarism detection for English-Russian language pair.
Post-OCR parsing: building simple and robust parser via BIO tagging
Authors: Wonseok Hwang, Seonghyeon Kim, Minjoon Seo, Jinyeong Yim, Seunghyun Park, Sungrae Park, Junyeop Lee, Bado Lee, Hwalsuk Lee
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Parsing textual information embedded in images is important for various down- stream tasks. However, many previously developed parsers are limited to handling the information presented in one dimensional sequence format. Here, we present Post Ocr Tagging based parser (POT), a simple and robust parser that can parse visually embedded texts by BIO-tagging the output of optical character recognition (OCR) task. Our shallow parsing approach enables building robust neural parser with less than a thousand labeled data. POT is validated on receipt and namecard parsing tasks.
From Stroke to Finite Automata: An Offline Recognition Approach
Authors: Kehinde Aruleba
Date: 2019-09-14 00:00:00
Proceeding: NeurIPS Workshop Document_Intelligen 2019 12
Tasks: valid

 A major challenge in making online education easier and more effective lies in developing automatic recognition, interpretation, and grading systems that can provide meaningful feedback to lecturers and students. Formal Languages and Automata Theory is a major module for many computer science and computing programmes worldwide. In such a module, students are taught how to design a finite state machine to recognise words in any given language. Despite the wide acceptance of this module by most universities across the globe, most students find this module difficult, boring and too abstract. Several research has been conducted on how to make this module interesting, but there still exist some gap. In this work, we propose and implement a system that assistant learners in learning this module. The system is in two units: unit one focus on offline recognition of hand-drawn finite automata diagram and the second unit focus on the tutor system. The system produced a 97% recognition rate. In future work, we intend to use formal grammars (second unit) to represent the recognised FA components, this will be used to automatically parse the output of the recognition system to determine if valid FA has been drawn.
EEG based Emotion Recognition of Image Stimuli
Authors: Anonymous
Date: 2019-09-11 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition, Marketing

Emotion is playing a great role in our daily lives. The necessity and importance of an automatic Emotion recognition system is getting increased. Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc. However, these features can potentially be changed to fake features. So to detect hidden and real features that is not controlled by the person are data measured from brain signals. There are various ways of measuring brain waves: EEG, MEG, FMRI, etc. On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work. The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli. The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements).  The signals were filtered and type of frequency band was computed and detected. The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli. Finally, the performance of the proposed approach was tested. The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively.  In this study, we also applied EEG applications in the context of neuro-marketing. The results empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service.
Recurrent neural networks learn robust representations by dynamically balancing compression and expansion
Authors: Matthew Farrell, Stefano Recanatesi, Guillaume Lajoie, Eric Shea-Brown
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Dimensionality Reduction

Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics. What determines the dimensionality of activity in neural circuits? What is the functional role of dimensionality in behavior and task learning? In this work we address these questions using recurrent neural network (RNN) models. We find that, depending on the dynamics of the initial network, RNNs learn to increase and reduce dimensionality in a way that matches task demands. These findings shed light on fundamental dynamical mechanisms by which neural networks solve tasks with robust representations that generalize to new cases.
Modelling Working Memory using Deep Recurrent Reinforcement Learning
Authors: Pravish Sainath, Pierre Bellec, Guillaume Lajoie
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Visual Reasoning

In cognitive systems, the role of a working memory is crucial for visual reasoning and decision making. Tremendous progress has been made in understanding the mechanisms of the human/animal working memory, as well as in formulating different frameworks of artificial neural networks.  In the case of humans, the visual working memory (VWM) task is a standard one in which the subjects are presented with a sequence of images, each of which needs to be identified as to whether it was already seen or not. 

Our work is a study of multiple ways to learn a working memory model using recurrent neural networks that learn to remember input images across timesteps. We train these neural networks to solve the working memory task by training them with a sequence of images in supervised and reinforcement learning settings. The supervised setting uses image sequences with their corresponding labels. The reinforcement learning setting is inspired by the popular view in neuroscience that the working memory in the prefrontal cortex is modulated by a dopaminergic mechanism. We consider the VWM task as an environment that rewards the agent when it remembers past information and penalizes it for forgetting. 
 
We quantitatively estimate the performance of these models on sequences of images from a standard image dataset (CIFAR-100). Further, we evaluate their ability to remember and recall as they are increasingly trained over episodes. Based on our analysis, we establish that a gated recurrent neural network model with long short-term memory units trained using reinforcement learning is powerful and more efficient in temporally consolidating the input spatial information. 

This work is an initial analysis as a part of our ultimate goal to use artificial neural networks to model the behavior and information processing of the working memory of the brain and to use brain imaging data captured from human subjects during the VWM cognitive task to understand various memory mechanisms of the brain.
Eligibility traces provide a data-inspired alternative to backpropagation through time
Authors: Guillaume Bellec, Franz Scherr, Elias Hajek, Darjan Salaj, Anand Subramoney, Robert Legenstein, Wolfgang Maass
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Diversity, speech-recognition, Speech Recognition

Learning in recurrent neural networks (RNNs) is most often implemented by gradient descent using backpropagation through time (BPTT), but BPTT does not model accurately how the brain learns. Instead, many experimental results on synaptic plasticity can be summarized as three-factor learning rules involving eligibility traces of the local neural activity and a third factor. We present here eligibility propagation (e-prop), a new factorization of the loss gradients in RNNs that fits the framework of three factor learning rules when derived for biophysical spiking neuron models. When tested on the TIMIT speech recognition benchmark, it is competitive with BPTT both for training artificial LSTM networks and spiking RNNs. Further analysis suggests that the diversity of learning signals and the consideration of slow internal neural dynamics are decisive to the learning efficiency of e-prop.
How well do deep neural networks trained on object recognition characterize the mouse visual system?
Authors: Santiago A. Cadena, Fabian H. Sinz, Taliah Muhammad, Emmanouil Froudarakis, Erick Cobos, Edgar Y. Walker, Jake Reimer, Matthias Bethge, Andreas Tolias, Alexander S. Ecker
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12
Tasks: Object Recognition

Recent work on modeling neural responses in the primate visual system has benefited from deep neural networks trained on large-scale object recognition, and found a hierarchical correspondence between layers of the artificial neural network and brain areas along the ventral visual stream. However, we neither know whether such task-optimized networks enable equally good models of the rodent visual system, nor if a similar hierarchical correspondence exists. Here, we address these questions in the mouse visual system by extracting features at several layers of a convolutional neural network (CNN) trained on ImageNet to predict the responses of thousands of neurons in four visual areas (V1, LM, AL, RL) to natural images. We found that the CNN features outperform classical subunit energy models, but found no evidence for an order of the areas we recorded via a correspondence to the hierarchy of CNN layers. Moreover, the same CNN but with random weights provided an equivalently useful feature space for predicting neural responses. Our results suggest that object recognition as a high-level task does not provide more discriminative features to characterize the mouse visual system than a random network. Unlike in the primate, training on ethologically relevant visually guided behaviors -- beyond static object recognition -- may be needed to unveil the functional organization of the mouse visual cortex.
Learning a Convolutional Bilinear Sparse Code for Natural Videos
Authors: Dimitrios C. Gklezakos, Rajesh P. N. Rao
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

In contrast to the monolithic deep architectures used in deep learning today for computer vision, the visual cortex processes retinal images via two functionally distinct but interconnected networks: the ventral pathway for processing object-related information and the dorsal pathway for processing motion and transformations. Inspired by this cortical division of labor and properties of the magno- and parvocellular systems, we explore an unsupervised approach to feature learning that jointly learns object features and their transformations from natural videos. We propose a new convolutional bilinear sparse coding model that (1) allows independent feature transformations and (2) is capable of processing large images. Our learning procedure leverages smooth motion in natural videos. Our results show that our model can learn groups of features and their transformations directly from natural videos in a completely unsupervised manner. The learned "dynamic filters" exhibit certain equivariance properties, resemble cortical spatiotemporal filters, and capture the statistics of transitions between video frames. Our model can be viewed as one of the first approaches to demonstrate unsupervised learning of primary "capsules" (proposed by Hinton and colleagues for supervised learning) and has strong connections to the Lie group approach to visual perception.
Does the neuronal noise in cortex help generalization?
Authors: Brian Hu, Jiaqi Shang, Ramakrishnan Iyer, Josh Siegle, Stefan Mihalas
Date: 2019-09-11 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

Neural activity is highly variable in response to repeated stimuli. We used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated natural movie presentations. A large fraction of responses are best fit by log-normal distributions or Gaussian mixtures with two components. These distributions are similar to those from units in deep neural networks with dropout. Using a separate set of electrophysiological recordings, we constructed a population coupling model as a control for state-dependent activity fluctuations and found that the model residuals also show non-Gaussian distributions. We then analyzed responses across trials from multiple sections of different movie clips and observed that the noise in cortex aligns better with in-clip versus out-of-clip stimulus variations. We argue that noise is useful for generalization when it moves along representations of different exemplars in-class, similar to the structure of cortical noise.