pwc-archive/papers-with-abstracts huggingface dataset:
Finding the Optimal Network Depth in Classification Tasks
Authors: Bartosz Wójcik, Maciej Wołczyk, Klaudia Bałazy, Jacek Tabor
Date: 2020-04-17 00:00:00
Tasks: Classification, General Classification

We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads. By allowing the model to determine the importance of each head and rewarding the choice of a single shallow classifier, we are able to detect and remove unneeded components of the network. This operation, which can be seen as finding the optimal depth of the model, significantly reduces the number of parameters and accelerates inference across different hardware processing units, which is not the case for many standard pruning methods. We show the performance of our method on multiple network architectures and datasets, analyze its optimization properties, and conduct ablation studies.
Deep Echo State Networks for Short-Term Traffic Forecasting: Performance Comparison and Statistical Assessment
Authors: Javier Del Ser, Ibai Lana, Eric L. Manibardo, Izaskun Oregi, Eneko Osaba, Jesus L. Lobo, Miren Nekane Bilbao, Eleni I. Vlahogianni
Date: 2020-04-17 00:00:00

In short-term traffic forecasting, the goal is to accurately predict future values of a traffic parameter of interest occurring shortly after the prediction is queried. The activity reported in this long-standing research field has been lately dominated by different Deep Learning approaches, yielding overly complex forecasting models that in general achieve accuracy gains of questionable practical utility. In this work we elaborate on the performance of Deep Echo State Networks for this particular task. The efficient learning algorithm and simpler parametric configuration of these alternative modeling approaches make them emerge as a competitive traffic forecasting method for real ITS applications deployed in devices and systems with stringently limited computational resources. An extensive comparison benchmark is designed with real traffic data captured over the city of Madrid (Spain), amounting to more than 130 automatic Traffic Readers (ATRs) and several shallow learning, ensembles and Deep Learning models. Results from this comparison benchmark and the analysis of the statistical significance of the reported performance gaps are decisive: Deep Echo State Networks achieve more accurate traffic forecasts than the rest of considered modeling counterparts.
Too Many Claims to Fact-Check: Prioritizing Political Claims Based on Check-Worthiness
Authors: Yavuz Selim Kartal, Busra Guvenen, Mucahid Kutlu
Date: 2020-04-17 00:00:00
Tasks: Misinformation, Word Embeddings

The massive amount of misinformation spreading on the Internet on a daily basis has enormous negative impacts on societies. Therefore, we need automated systems helping fact-checkers in the combat against misinformation. In this paper, we propose a model prioritizing the claims based on their check-worthiness. We use BERT model with additional features including domain-specific controversial topics, word embeddings, and others. In our experiments, we show that our proposed model outperforms all state-of-the-art models in both test collections of CLEF Check That! Lab in 2018 and 2019. We also conduct a qualitative analysis to shed light-detecting check-worthy claims. We suggest requesting rationales behind judgments are needed to understand subjective nature of the task and problematic labels.
Detailed 2D-3D Joint Representation for Human-Object Interaction
Authors: Yong-Lu Li, Xinpeng Liu, Han Lu, Shiyi Wang, Junqi Liu, Jiefeng Li, Cewu Lu
Date: 2020-04-17 00:00:00
Proceeding: CVPR 2020 6
Tasks: Action Understanding, Human-Object Interaction Detection, Micro-Action Recognition, Object, Representation Learning

Human-Object Interaction (HOI) detection lies at the core of action understanding. Besides 2D information such as human/object appearance and locations, 3D pose is also usually utilized in HOI learning since its view-independence. However, rough 3D body joints just carry sparse body information and are not sufficient to understand complex interactions. Thus, we need detailed 3D body shape to go further. Meanwhile, the interacted object in 3D is also not fully studied in HOI learning. In light of these, we propose a detailed 2D-3D joint representation learning method. First, we utilize the single-view human body capture method to obtain detailed 3D body, face and hand shapes. Next, we estimate the 3D object location and size with reference to the 2D human-object spatial configuration and object category priors. Finally, a joint learning framework and cross-modal consistency tasks are proposed to learn the joint HOI representation. To better evaluate the 2D ambiguity processing capacity of models, we propose a new benchmark named Ambiguous-HOI consisting of hard ambiguous images. Extensive experiments in large-scale HOI benchmark and Ambiguous-HOI show impressive effectiveness of our method. Code and data are available at https://github.com/DirtyHarryLYL/DJ-RN.
Space-Time Domain Tensor Neural Networks: An Application on Human Pose Classification
Authors: Konstantinos Makantasis, Athanasios Voulodimos, Anastasios Doulamis, Nikolaos Bakalos, Nikolaos Doulamis
Date: 2020-04-17 00:00:00
Tasks: General Classification

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this study, we propose a spatially and temporally aware tensor-based neural network for human pose classification using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental evaluation of the proposed model indicates that it can achieve state-of-the-art performance.
Continuous Representation of Molecules Using Graph Variational Autoencoder
Authors: Mohammadamin Tavakoli, Pierre Baldi
Date: 2020-04-17 00:00:00
Tasks: Decoder, Drug Design, Property Prediction

In order to continuously represent molecules, we propose a generative model in the form of a VAE which is operating on the 2D-graph structure of molecules. A side predictor is employed to prune the latent space and help the decoder in generating meaningful adjacency tensor of molecules. Other than the potential applicability in drug design and property prediction, we show the superior performance of this technique in comparison to other similar methods based on the SMILES representation of the molecules with RNN based encoder and decoder.
Accelerating Physics-Informed Neural Network Training with Prior Dictionaries
Authors: Wei Peng, Weien Zhou, Jun Zhang, Wen Yao
Date: 2020-04-17 00:00:00

Physics-Informed Neural Networks (PINNs) can be regarded as general-purpose PDE solvers, but it might be slow to train PINNs on particular problems, and there is no theoretical guarantee of corresponding error bounds. In this manuscript, we propose a variant called Prior Dictionary based Physics-Informed Neural Networks (PD-PINNs). Equipped with task-dependent dictionaries, PD-PINNs enjoy enhanced representation power on the tasks, which helps to capture features provided by dictionaries so that the proposed neural networks can achieve faster convergence in the process of training. In various numerical simulations, compared with existing PINN methods, combining prior dictionaries can significantly enhance convergence speed. In terms of theory, we obtain the error bounds applicable to PINNs and PD-PINNs for solving elliptic partial differential equations of second order. It is proved that under certain mild conditions, the prediction error made by neural networks can be bounded by expected loss of PDEs and boundary conditions.
NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset
Authors: Zhiwei Gao, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki
Date: 2020-04-17 00:00:00

Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019, it has affected over 200 countries and billions of people worldwide. This has affected the social life of people owing to enforcements, such as "social distancing" and "stay at home." This has resulted in an increasing interaction through social media. Given that social media can bring us valuable information about COVID-19 at a global scale, it is important to share the data and encourage social media studies against COVID-19 or other infectious diseases. Therefore, we have released a multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English and Japanese from Twitter and those in Chinese from Weibo. The data cover microblogs from January 20, 2020, to March 24, 2020. This paper also provides a quantitative as well as qualitative analysis of these datasets by creating daily word clouds as an example of text-mining analysis. The dataset is now available on Github. This dataset can be analyzed in a multitude of ways and is expected to help in efficient communication of precautions related to COVID-19.
Intention as Commitment toward Time
Authors: Marc van Zee, Dragan Doder, Leendert van der Torre, Mehdi Dastani, Thomas Icard, Eric Pacuit
Date: 2020-04-17 00:00:00

In this paper we address the interplay among intention, time, and belief in dynamic environments. The first contribution is a logic for reasoning about intention, time and belief, in which assumptions of intentions are represented by preconditions of intended actions. Intentions and beliefs are coherent as long as these assumptions are not violated, i.e. as long as intended actions can be performed such that their preconditions hold as well. The second contribution is the formalization of what-if scenarios: what happens with intentions and beliefs if a new (possibly conflicting) intention is adopted, or a new fact is learned? An agent is committed to its intended actions as long as its belief-intention database is coherent. We conceptualize intention as commitment toward time and we develop AGM-based postulates for the iterated revision of belief-intention databases, and we prove a Katsuno-Mendelzon-style representation theorem.
Modeling Extent-of-Texture Information for Ground Terrain Recognition
Authors: Shuvozit Ghose, Pinaki Nath Chowdhury, Partha Pratim Roy, Umapada Pal
Date: 2020-04-17 00:00:00
Tasks: image-classification, Image Classification

Ground Terrain Recognition is a difficult task as the context information varies significantly over the regions of a ground terrain image. In this paper, we propose a novel approach towards ground-terrain recognition via modeling the Extent-of-Texture information to establish a balance between the order-less texture component and ordered-spatial information locally. At first, the proposed method uses a CNN backbone feature extractor network to capture meaningful information of a ground terrain image, and model the extent of texture and shape information locally. Then, the order-less texture information and ordered shape information are encoded in a patch-wise manner, which is utilized by intra-domain message passing module to make every patch aware of each other for rich feature learning. Next, the Extent-of-Texture (EoT) Guided Inter-domain Message Passing module combines the extent of texture and shape information with the encoded texture and shape information in a patch-wise fashion for sharing knowledge to balance out the order-less texture information with ordered shape information. Further, Bilinear model generates a pairwise correlation between the order-less texture information and ordered shape information. Finally, the ground-terrain image classification is performed by a fully connected layer. The experimental results indicate superior performance of the proposed model over existing state-of-the-art techniques on publicly available datasets like DTD, MINC and GTOS-mobile.
GEVO: GPU Code Optimization using Evolutionary Computation
Authors: Jhe-Yu Liou, Xiaodong Wang, Stephanie Forrest, Carole-Jean Wu
Date: 2020-04-17 00:00:00
Tasks: BIG-bench Machine Learning, GPU, Handwriting Recognition, image-classification, Image Classification

GPUs are a key enabler of the revolution in machine learning and high performance computing, functioning as de facto co-processors to accelerate large-scale computation. As the programming stack and tool support have matured, GPUs have also become accessible to programmers, who may lack detailed knowledge of the underlying architecture and fail to fully leverage the GPU's computation power. GEVO (Gpu optimization using EVOlutionary computation) is a tool for automatically discovering optimization opportunities and tuning the performance of GPU kernels in the LLVM representation. GEVO uses population-based search to find edits to GPU code compiled to LLVM-IR and improves performance on desired criteria while retaining required functionality. We demonstrate that GEVO improves the execution time of the GPU programs in the Rodinia benchmark suite and the machine learning models, SVM and ResNet18, on NVIDIA Tesla P100. For the Rodinia benchmarks, GEVO improves GPU kernel runtime performance by an average of 49.48% and by as much as 412% over the fully compiler-optimized baseline. If kernel output accuracy is relaxed to tolerate up to 1% error, GEVO can find kernel variants that outperform the baseline version by an average of 51.08%. For the machine learning workloads, GEVO achieves kernel performance improvement for SVM on the MNIST handwriting recognition (3.24X) and the a9a income prediction (2.93X) datasets with no loss of model accuracy. GEVO achieves 1.79X kernel performance improvement on image classification using ResNet18/CIFAR-10, with less than 1% model accuracy reduction.
Emulation of cosmological mass maps with conditional generative adversarial networks
Authors: Nathanaël Perraudin, Sandro Marcon, Aurelien Lucchi, Tomasz Kacprzak
Date: 2020-04-17 00:00:00
Tasks: MS-SSIM, SSIM

Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can create a computational bottleneck for cosmological analyses. Modern deep generative models, such as Generative Adversarial Networks (GAN), have demonstrated their potential to achieve this goal. Most existing GAN approaches produce simulations for a fixed value of the cosmological parameters, which limits their practical applicability. We propose a novel conditional GAN model that is able to generate mass maps for any pair of matter density $\Omega_m$ and matter clustering strength $\sigma_8$, parameters which have the largest impact on the evolution of structures in the universe. Our results show that our conditional GAN can interpolate efficiently within the space of simulated cosmologies, and generate maps anywhere inside this space with good visual quality high statistical accuracy. We perform an extensive quantitative comparison of the N-body and GAN -generated maps using a range of metrics: the pixel histograms, peak counts, power spectra, bispectra, Minkowski functionals, correlation matrices of the power spectra, the Multi-Scale Structural Similarity Index (MS-SSIM) and our equivalent of the Fr\'echet Inception Distance (FID). We find a very good agreement on these metrics, with typical differences are <5% at the centre of the simulation grid, and slightly worse for cosmologies at the grid edges. The agreement for the bispectrum is slightly worse, on the <20% level. This contribution is a step towards building emulators of mass maps directly, capturing both the cosmological signal and its variability. We make the code and the data publicly available: https://renkulab.io/gitlab/nathanael.perraudin/darkmattergan
Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction
Authors: Christoph Alt, Aleksandra Gabryszak, Leonhard Hennig
Date: 2020-04-17 00:00:00
Proceeding: ACL 2020 6
Tasks: Relation, Relation Extraction, Sentence

Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models. Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation. However, the complexity of the task makes it difficult to understand how encoder architecture and supporting linguistic knowledge affect the features learned by the encoder. We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance. For example, adding contextualized word representations greatly increases performance on probing tasks with a focus on named entity and part-of-speech information, and yields better results in RE. In contrast, entity masking improves RE, but considerably lowers performance on entity type related probing tasks.
Whence the Expected Free Energy?
Authors: Beren Millidge, Alexander Tschantz, Christopher L. Buckley
Date: 2020-04-17 00:00:00

The Expected Free Energy (EFE) is a central quantity in the theory of active inference. It is the quantity that all active inference agents are mandated to minimize through action, and its decomposition into extrinsic and intrinsic value terms is key to the balance of exploration and exploitation that active inference agents evince. Despite its importance, the mathematical origins of this quantity and its relation to the Variational Free Energy (VFE) remain unclear. In this paper, we investigate the origins of the EFE in detail and show that it is not simply "the free energy in the future". We present a functional that we argue is the natural extension of the VFE, but which actively discourages exploratory behaviour, thus demonstrating that exploration does not directly follow from free energy minimization into the future. We then develop a novel objective, the Free-Energy of the Expected Future (FEEF), which possesses both the epistemic component of the EFE as well as an intuitive mathematical grounding as the divergence between predicted and desired futures.
Batch Clustering for Multilingual News Streaming
Authors: Mathis Linger, Mhamed Hajaiej
Date: 2020-04-17 00:00:00
Tasks: Articles, Clustering

Nowadays, digital news articles are widely available, published by various editors and often written in different languages. This large volume of diverse and unorganized information makes human reading very difficult or almost impossible. This leads to a need for algorithms able to arrange high amount of multilingual news into stories. To this purpose, we extend previous works on Topic Detection and Tracking, and propose a new system inspired from newsLens. We process articles per batch, looking for monolingual local topics which are then linked across time and languages. Here, we introduce a novel "replaying" strategy to link monolingual local topics into stories. Besides, we propose new fine tuned multilingual embedding using SBERT to create crosslingual stories. Our system gives monolingual state-of-the-art results on dataset of Spanish and German news and crosslingual state-of-the-art results on English, Spanish and German news.
A Cross-Stitch Architecture for Joint Registration and Segmentation in Adaptive Radiotherapy
Authors: Laurens Beljaards, Mohamed S. Elmahdy, Fons Verbeek, Marius Staring
Date: 2020-04-17 00:00:00
Proceeding: MIDL 2019 7
Tasks: Segmentation

Recently, joint registration and segmentation has been formulated in a deep learning setting, by the definition of joint loss functions. In this work, we investigate joining these tasks at the architectural level. We propose a registration network that integrates segmentation propagation between images, and a segmentation network to predict the segmentation directly. These networks are connected into a single joint architecture via so-called cross-stitch units, allowing information to be exchanged between the tasks in a learnable manner. The proposed method is evaluated in the context of adaptive image-guided radiotherapy, using daily prostate CT imaging. Two datasets from different institutes and manufacturers were involved in the study. The first dataset was used for training (12 patients) and validation (6 patients), while the second dataset was used as an independent test set (14 patients). In terms of mean surface distance, our approach achieved $1.06 \pm 0.3$ mm, $0.91 \pm 0.4$ mm, $1.27 \pm 0.4$ mm, and $1.76 \pm 0.8$ mm on the validation set and $1.82 \pm 2.4$ mm, $2.45 \pm 2.4$ mm, $2.45 \pm 5.0$ mm, and $2.57 \pm 2.3$ mm on the test set for the prostate, bladder, seminal vesicles, and rectum, respectively. The proposed multi-task network outperformed single-task networks, as well as a network only joined through the loss function, thus demonstrating the capability to leverage the individual strengths of the segmentation and registration tasks. The obtained performance as well as the inference speed make this a promising candidate for daily re-contouring in adaptive radiotherapy, potentially reducing treatment-related side effects and improving quality-of-life after treatment.
A Mean Field Games model for finite mixtures of Bernoulli and Categorical distributions
Authors: Laura Aquilanti, Simone Cacace, Fabio Camilli, Raul De Maio
Date: 2020-04-17 00:00:00
Tasks: Clustering

Finite mixture models are an important tool in the statistical analysis of data, for example in data clustering. The optimal parameters of a mixture model are usually computed by maximizing the log-likelihood functional via the Expectation-Maximization algorithm. We propose an alternative approach based on the theory of Mean Field Games, a class of differential games with an infinite number of agents. We show that the solution of a finite state space multi-population Mean Field Games system characterizes the critical points of the log-likelihood functional for a Bernoulli mixture. The approach is then generalized to mixture models of categorical distributions. Hence, the Mean Field Games approach provides a method to compute the parameters of the mixture model, and we show its application to some standard examples in cluster analysis.
Object Detection and Recognition of Swap-Bodies using Camera mounted on a Vehicle
Authors: Ebin Zacharias, Didier Stricker, Martin Teuchler, Kripasindhu Sarkar
Date: 2020-04-17 00:00:00
Tasks: Data Augmentation, object-detection, Object Detection, Optical Character Recognition, Optical Character Recognition (OCR)

Object detection and identification is a challenging area of computer vision and a fundamental requirement for autonomous cars. This project aims to jointly perform object detection of a swap-body and to find the type of swap-body by reading an ILU code using an efficient optical character recognition (OCR) method. Recent research activities have drastically improved deep learning techniques which proves to enhance the field of computer vision. Collecting enough images for training the model is a critical step towards achieving good results. The data for training were collected from different locations with maximum possible variations and the details are explained. In addition, data augmentation methods applied for training has proved to be effective in improving the performance of the trained model. Training the model achieved good results and the test results are also provided. The final model was tested with images and videos. Finally, this paper also draws attention to some of the major challenges faced during various stages of the project and the possible solutions applied.