pwc-archive/papers-with-abstracts huggingface dataset:
Document Image Layout Analysis via Explicit Edge Embedding Network
Authors: Xingjiao Wu, Yingbin Zheng, Tianlong Ma, Hao Ye, Liang He
Date: 2021-10-01 00:00:00
Proceeding: Information Sciences 2021 10
Tasks: Data Augmentation, Document Layout Analysis

Layout analysis from a document image plays an important role in document content understanding and information extraction systems. While many existing methods focus on learning knowledge with convolutional networks directly from color channels, we argue the importance of highfrequency structures in document images, especially edge information. In this paper, we present a novel document layout analysis framework with the Explicit Edge Embedding Network. Specifically, the proposed network contains the edge embedding block and dynamic skip connection block to produce detailed features, as well as a lightweight fully convolutional subnet as the backbone for the effectiveness of the framework. The edge embedding block is designed to explicitly incorporate the edge information from the document images. The dynamic skip connection block aims to learn both color and edge representations with learnable weights. In contrast to the previous methods, we harness the model by using a synthetic document approach to overcome data scarcity. The combination of data augmentation and edge embedding is important toward a more compact representation than directly using the training images with only
color channels. We conduct experiments using the proposed
framework on three document layout analysis benchmarks
and demonstrate its superiority in terms of effectiveness and
efficiency over previous approaches.
Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All
Authors: Florian Wilhelm
Date: 2021-09-13 00:00:00
Proceeding: ACM Conference on Recommender Systems 2021 9
Tasks: All, Collaborative Filtering

Matrix factorization-based methods are among the most popular methods for collaborative filtering tasks with implicit feedback. The most effective of these methods do not apply sign constraints, such as non-negativity, to their factors. Despite their simplicity, the latent factors for users and items lack interpretability, which is becoming an increasingly important requirement. In this work, we provide a theoretical link between unconstrained and the interpretable non-negative matrix factorization in terms of the personalized ranking induced by these methods. We also introduce a novel, latent Dirichlet allocation-inspired model for recommenders and extend our theoretical link to also allow the interpretation of an unconstrained matrix factorization as an adjoint formulation of our new model. Our experiments indicate that this novel approach represents the unknown processes of implicit user-item interactions in the real world much better than unconstrained matrix factorization while being interpretable.
Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks
Authors: Yue Xu, Fei Yin, Zhaoxiang Zhang, Cheng-Lin Liu
Date: 2018-07-13 00:00:00
Proceeding: International Joint Conference on Artificial Intelligence 2018 7

Layout analysis is a fundamental process in document image analysis and understanding. It contains three key sub-processes which are page segmentation, text line segmentation and baseline detection. In this paper, we propose a multi-task layout analysis method that uses a single FCN model to solve the above three problems simultaneously. In our work, a multi-task FCN is trained to segment the document image into different regions (background, main text, comment and decoration), circle the contour of text lines and detect the centerlines of text lines by classifying pixels into different categories. By supervised learning on document images with pixel-wise labeled, the FCN can extract discriminative features and perform pixel-wise classification accurately. Based on the above results, text lines can be segmented and the baseline of each text line can be determined. After that, post-processing steps are taken to reduce noises, correct wrong segmentations and produce the final results. Experimental results on the public dataset DIVA-HisDB [Simistira et al., 2016] containing challenging medieval manuscripts demonstrate the effectiveness and superiority of the proposed method
DIVA-HisDB: A Precisely Annotated Large Dataset of Challenging Medieval Manuscripts
Authors: Fotini Simistira, Mathias Seuret, Nicole Eichenberger, Angelika Garz, Marcus Liwicki, Rolf Ingold
Date: 2016-10-23 00:00:00
Proceeding: International Conference on Frontiers in Handwriting Recognition 2016 10
Tasks: Binarization, Document Layout Analysis, Segmentation, Text-Line Extraction

This paper introduces a publicly available historical manuscript database DIVA-HisDB for the evaluation of several Document Image Analysis (DIA) tasks. The database consists of 150 annotated pages of three different medieval manuscripts with challenging layouts. Furthermore, we provide a layout analysis ground-truth which has been iterated on, reviewed, and refined by an expert in medieval studies. DIVA-HisDB and the ground truth can be used for training and evaluating DIA tasks, such as layout analysis, text line segmentation, binarization and writer identification. Layout analysis results of several representative baseline technologies are also presented in order to help researchers evaluate their methods and advance the frontiers of complex historical manuscripts analysis. An optimized state-of-the-art Convolutional AutoEncoder (CAE) performs with around 95 % accuracy, demonstrating that for this challenging layout there is much room for improvement. Finally, we show that existing text line segmentation methods fail due to interlinear and marginal text elements.
High-Performance Neural Networks for Visual Object Classification
Authors: Dan C. Cireşan, Ueli Meier, Jonathan Masci, Luca M. Gambardella, Jürgen Schmidhuber
Date: 2011-02-01 00:00:00
Tasks: Classification, GPU, Handwritten Digit Recognition, Object, Vocal Bursts Intensity Prediction

We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.
Micromodels for Efficient, Explainable, and Reusable Systems: A Case Study on Mental Health
Authors: Andrew Lee, Jonathan K. Kummerfeld, Lawrence C. An, Rada Mihalcea
Date: 2021-09-28 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Classification

Many statistical models have high accuracy on test benchmarks, but are not explainable, struggle in low-resource scenarios, cannot be reused for multiple tasks, and cannot easily integrate domain expertise. These factors limit their use, particularly in settings such as mental health, where it is difficult to annotate datasets and model outputs have significant impact. We introduce a micromodel architecture to address these challenges. Our approach allows researchers to build interpretable representations that embed domain knowledge and provide explanations throughout the model's decision process. We demonstrate the idea on multiple mental health tasks: depression classification, PTSD classification, and suicidal risk assessment. Our systems consistently produce strong results, even in low-resource scenarios, and are more interpretable than alternative methods.
Temporal Information and Event Markup Language: TIE-ML Markup Process and Schema Version 1.0
Authors: Damir Cavar, Billy Dickson, Ali Aljubailan, Soyoung Kim
Date: 2021-09-28 00:00:00
Tasks: TAG

Temporal Information and Event Markup Language (TIE-ML) is a markup strategy and annotation schema to improve the productivity and accuracy of temporal and event related annotation of corpora to facilitate machine learning based model training. For the annotation of events, temporal sequencing, and durations, it is significantly simpler by providing an extremely reduced tag set for just temporal relations and event enumeration. In comparison to other standards, as for example the Time Markup Language (TimeML), it is much easier to use by dropping sophisticated formalisms, theoretical concepts, and annotation approaches. Annotations of corpora using TimeML can be mapped to TIE-ML with a loss, and TIE-ML annotations can be fully mapped to TimeML with certain under-specification.
Evolving Evolutionary Algorithms using Multi Expression Programming
Authors: Mihai Oltean, Crina Groşan
Date: 2021-08-22 00:00:00
Tasks: Evolutionary Algorithms

Finding the optimal parameter setting (i.e. the optimal population size, the optimal mutation probability, the optimal evolutionary model etc) for an Evolutionary Algorithm (EA) is a difficult task. Instead of evolving only the parameters of the algorithm we will evolve an entire EA capable of solving a particular problem. For this purpose the Multi Expression Programming (MEP) technique is used. Each MEP chromosome will encode multiple EAs. An nongenerational EA for function optimization is evolved in this paper. Numerical experiments show the effectiveness of this approach.
A multi-stage semi-supervised improved deep embedded clustering method for bearing fault diagnosis under the situation of insufficient labeled samples
Authors: Tongda Sun, Gang Yu
Date: 2021-09-28 00:00:00
Tasks: Clustering, Deep Clustering, Diagnostic, Fault Diagnosis

Although data-driven fault diagnosis methods have been widely applied, massive labeled data are required for model training. However, a difficulty of implementing this in real industries hinders the application of these methods. Hence, an effective diagnostic approach that can work well in such situation is urgently needed.In this study, a multi-stage semi-supervised improved deep embedded clustering (MS-SSIDEC) method, which combines semi-supervised learning with improved deep embedded clustering (IDEC), is proposed to jointly explore scarce labeled data and massive unlabeled data. In the first stage, a skip-connection-based convolutional auto-encoder (SCCAE) that can automatically map the unlabeled data into a low-dimensional feature space is proposed and pre-trained to be a fault feature extractor. In the second stage, a semi-supervised improved deep embedded clustering (SSIDEC) network is proposed for clustering. It is first initialized with available labeled data and then used to simultaneously optimize the clustering label assignment and make the feature space to be more clustering-friendly. To tackle the phenomenon of overfitting, virtual adversarial training (VAT) is introduced as a regularization term in this stage. In the third stage, pseudo labels are obtained by the high-quality results of SSIDEC. The labeled dataset can be augmented by these pseudo-labeled data and then leveraged to train a bearing fault diagnosis model. Two public datasets of vibration data from rolling bearings are used to evaluate the performance of the proposed method. Experimental results indicate that the proposed method achieves a promising performance in both semi-supervised and unsupervised fault diagnosis tasks. This method provides a new approach for fault diagnosis under the situation of limited labeled samples by effectively exploring unsupervised data.
Unsolved Problems in ML Safety
Authors: Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt
Date: 2021-09-28 00:00:00

Machine learning (ML) systems are rapidly increasing in size, are acquiring new capabilities, and are increasingly deployed in high-stakes settings. As with other powerful technologies, safety for ML should be a leading research priority. In response to emerging safety challenges in ML, such as those introduced by recent large-scale models, we provide a new roadmap for ML Safety and refine the technical problems that the field needs to address. We present four problems ready for research, namely withstanding hazards ("Robustness"), identifying hazards ("Monitoring"), reducing inherent model hazards ("Alignment"), and reducing systemic hazards ("Systemic Safety"). Throughout, we clarify each problem's motivation and provide concrete research directions.
Explainable Machine Larning for liver transplantation
Authors: Pedro Cabalar, Brais Muñiz, Gilberto Pérez, Francisco Suárez
Date: 2021-09-28 00:00:00
Tasks: Decision Making

In this work, we present a flexible method for explaining, in human readable terms, the predictions made by decision trees used as decision support in liver transplantation. The decision trees have been obtained through machine learning applied on a dataset collected at the liver transplantation unit at the Coru\~na University Hospital Center and are used to predict long term (five years) survival after transplantation. The method we propose is based on the representation of the decision tree as a set of rules in a logic program (LP) that is further annotated with text messages. This logic program is then processed using the tool xclingo (based on Answer Set Programming) that allows building compound explanations depending on the annotation text and the rules effectively fired when a given input is provided. We explore two alternative LP encodings: one in which rules respect the tree structure (more convenient to reflect the learning process) and one where each rule corresponds to a (previously simplified) tree path (more readable for decision making).
Turning old models fashion again: Recycling classical CNN networks using the Lattice Transformation
Authors: Ana Paula G. S. de Almeida, Flavio de Barros Vidal
Date: 2021-09-28 00:00:00
Tasks: Classification, image-classification, Image Classification

In the early 1990s, the first signs of life of the CNN era were given: LeCun et al. proposed a CNN model trained by the backpropagation algorithm to classify low-resolution images of handwritten digits. Undoubtedly, it was a breakthrough in the field of computer vision. But with the rise of other classification methods, it fell out fashion. That was until 2012, when Krizhevsky et al. revived the interest in CNNs by exhibiting considerably higher image classification accuracy on the ImageNet challenge. Since then, the complexity of the architectures are exponentially increasing and many structures are rapidly becoming obsolete. Using multistream networks as a base and the feature infusion precept, we explore the proposed LCNN cross-fusion strategy to use the backbones of former state-of-the-art networks on image classification in order to discover if the technique is able to put these designs back in the game. In this paper, we showed that we can obtain an increase of accuracy up to 63.21% on the NORB dataset we comparing with the original structure. However, no technique is definitive. While our goal is to try to reuse previous state-of-the-art architectures with few modifications, we also expose the disadvantages of our explored strategy.
Visually Grounded Reasoning across Languages and Cultures
Authors: Fangyu Liu, Emanuele Bugliarello, Edoardo Maria Ponti, Siva Reddy, Nigel Collier, Desmond Elliott
Date: 2021-09-28 00:00:00
Proceeding: EMNLP 2021 11
Tasks: Cross-Lingual Transfer, Visual Reasoning, Zero-Shot Learning

The design of widespread vision-and-language datasets and pre-trained encoders directly adopts, or draws inspiration from, the concepts and images of ImageNet. While one can hardly overestimate how much this benchmark contributed to progress in computer vision, it is mostly derived from lexical databases and image queries in English, resulting in source material with a North American or Western European bias. Therefore, we devise a new protocol to construct an ImageNet-style hierarchy representative of more languages and cultures. In particular, we let the selection of both concepts and images be entirely driven by native speakers, rather than scraping them automatically. Specifically, we focus on a typologically diverse set of languages, namely, Indonesian, Mandarin Chinese, Swahili, Tamil, and Turkish. On top of the concepts and images obtained through this new protocol, we create a multilingual dataset for {M}ulticultur{a}l {R}easoning over {V}ision and {L}anguage (MaRVL) by eliciting statements from native speaker annotators about pairs of images. The task consists of discriminating whether each grounded statement is true or false. We establish a series of baselines using state-of-the-art models and find that their cross-lingual transfer performance lags dramatically behind supervised performance in English. These results invite us to reassess the robustness and accuracy of current state-of-the-art models beyond a narrow domain, but also open up new exciting challenges for the development of truly multilingual and multicultural systems.
A First-Occupancy Representation for Reinforcement Learning
Authors: Ted Moskovitz, Spencer R. Wilson, Maneesh Sahani
Date: 2021-09-28 00:00:00
Proceeding: ICLR 2022 4
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Both animals and artificial agents benefit from state representations that support rapid transfer of learning across tasks and which enable them to efficiently traverse their environments to reach rewarding states. The successor representation (SR), which measures the expected cumulative, discounted state occupancy under a fixed policy, enables efficient transfer to different reward structures in an otherwise constant Markovian environment and has been hypothesized to underlie aspects of biological behavior and neural activity. However, in the real world, rewards may move or only be available for consumption once, may shift location, or agents may simply aim to reach goal states as rapidly as possible without the constraint of artificially imposed task horizons. In such cases, the most behaviorally-relevant representation would carry information about when the agent was likely to first reach states of interest, rather than how often it should expect to visit them over a potentially infinite time span. To reflect such demands, we introduce the first-occupancy representation (FR), which measures the expected temporal discount to the first time a state is accessed. We demonstrate that the FR facilitates exploration, the selection of efficient paths to desired states, allows the agent, under certain conditions, to plan provably optimal trajectories defined by a sequence of subgoals, and induces similar behavior to animals avoiding threatening stimuli.
Actionable Entities Recognition Benchmark for Interactive Fiction
Authors: Alexey Tikhonov, Ivan P. Yamshchikov
Date: 2021-09-28 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper presents a new natural language processing task - Actionable Entities Recognition (AER) - recognition of entities that protagonists could interact with for further plot development. Though similar to classical Named Entity Recognition (NER), it has profound differences. In particular, it is crucial for interactive fiction, where the agent needs to detect entities that might be useful in the future. We also discuss if AER might be further helpful for the systems dealing with narrative processing since actionable entities profoundly impact the causal relationship in a story. We validate the proposed task on two previously available datasets and present a new benchmark dataset for the AER task that includes 5550 descriptions with one or more actionable entities.
Intelligent Decision Assistance Versus Automated Decision-Making: Enhancing Knowledge Work Through Explainable Artificial Intelligence
Authors: Max Schemmer, Niklas Kühl, Gerhard Satzger
Date: 2021-09-28 00:00:00
Tasks: Decision Making, Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

While recent advances in AI-based automated decision-making have shown many benefits for businesses and society, they also come at a cost. It has for long been known that a high level of automation of decisions can lead to various drawbacks, such as automation bias and deskilling. In particular, the deskilling of knowledge workers is a major issue, as they are the same people who should also train, challenge and evolve AI. To address this issue, we conceptualize a new class of DSS, namely Intelligent Decision Assistance (IDA) based on a literature review of two different research streams -- DSS and automation. IDA supports knowledge workers without influencing them through automated decision-making. Specifically, we propose to use techniques of Explainable AI (XAI) while withholding concrete AI recommendations. To test this conceptualization, we develop hypotheses on the impacts of IDA and provide first evidence for their validity based on empirical studies in the literature.
Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings
Authors: Tenzin Singhay Bhotia, Vaibhav Kumar, Tanmoy Chakraborty
Date: 2021-09-28 00:00:00
Tasks: Word Embeddings

Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases. In this paper, we extend the study of gender bias to the recently popularized hyperbolic word embeddings. We propose gyrocosine bias, a novel measure for quantifying gender bias in hyperbolic word representations and observe a significant presence of gender bias. To address this problem, we propose Poincar\'e Gender Debias (PGD), a novel debiasing procedure for hyperbolic word representations. Experiments on a suit of evaluation tests show that PGD effectively reduces bias while adding a minimal semantic offset.
StereoSpike: Depth Learning with a Spiking Neural Network
Authors: Ulysse Rançon, Javier Cuadrado-Anibarro, Benoit R. Cottereau, Timothée Masquelier
Date: 2021-09-28 00:00:00
Tasks: Autonomous Vehicles, Decoder, Depth Estimation

Depth estimation is an important computer vision task, useful in particular for navigation in autonomous vehicles, or for object manipulation in robotics. Here we solved it using an end-to-end neuromorphic approach, combining two event-based cameras and a Spiking Neural Network (SNN) with a slightly modified U-Net-like encoder-decoder architecture, that we named StereoSpike. More specifically, we used the Multi Vehicle Stereo Event Camera Dataset (MVSEC). It provides a depth ground-truth, which was used to train StereoSpike in a supervised manner, using surrogate gradient descent. We propose a novel readout paradigm to obtain a dense analog prediction -- the depth of each pixel -- from the spikes of the decoder. We demonstrate that this architecture generalizes very well, even better than its non-spiking counterparts, leading to state-of-the-art test accuracy. To the best of our knowledge, it is the first time that such a large-scale regression problem is solved by a fully spiking network. Finally, we show that low firing rates (<10%) can be obtained via regularization, with a minimal cost in accuracy. This means that StereoSpike could be efficiently implemented on neuromorphic chips, opening the door for low power and real time embedded systems.