pwc-archive/papers-with-abstracts huggingface dataset:
Research Report on Automatic Synthesis of Local Search Neighborhood Operators
Authors: Mateusz Ślażyński
Date: 2019-09-18 00:00:00

Constraint Programming (CP) and Local Search (LS) are different paradigms for dealing with combinatorial search and optimization problems. Their complementary features motivated researchers to create hybrid CP/LS solutions, maintaining both the modeling capabilities of CP and the computational advantages of the heuristic-based LS approach. Research presented in this report is focused on developing a novel method to infer an efficient LS neighborhood operator based on the problem structure, as modeled in the CP paradigm. We consider a limited formal language that we call a Neighborhood Definition Language, used to specify the neighborhood operators in a fine-grained and declarative manner. Together with Logic Programming runtime called Noodle, it allows to automatically synthesize complex operators using a Grammar Evolution algorithm.
NatCSNN: A Convolutional Spiking Neural Network for recognition of objects extracted from natural images
Authors: Pedro Machado, Georgina Cosma, T. M. McGinnity
Date: 2019-09-18 00:00:00

Biological image processing is performed by complex neural networks composed of thousands of neurons interconnected via thousands of synapses, some of which are excitatory and others inhibitory. Spiking neural models are distinguished from classical neurons by being biological plausible and exhibiting the same dynamics as those observed in biological neurons. This paper proposes a Natural Convolutional Neural Network (NatCSNN) which is a 3-layer bio-inspired Convolutional Spiking Neural Network (CSNN), for classifying objects extracted from natural images. A two-stage training algorithm is proposed using unsupervised Spike Timing Dependent Plasticity (STDP) learning (phase 1) and ReSuMe supervised learning (phase 2). The NatCSNN was trained and tested on the CIFAR-10 dataset and achieved an average testing accuracy of 84.7% which is an improvement over the 2-layer neural networks previously applied to this dataset.
Long-Term Progress and Behavior Complexification in Competitive Co-Evolution
Authors: Luca Simione, Stefano Nolfi
Date: 2019-09-18 00:00:00
Tasks: Evolutionary Algorithms

The possibility to use competitive evolutionary algorithms to generate long-term progress is normally prevented by the convergence on limit cycle dynamics in which the evolving agents keep progressing against their current competitors by periodically rediscovering solutions adopted previously over and over again. This leads to local but not to global progress, i.e. progress against all possible competitors. We propose a new competitive algorithm that produces long-term global progress by identifying and by filtering out opportunistic variations, i.e. variations leading to progress against current competitors and retrogression against other competitors. The efficacy of the method is validated on the co-evolution of predator and prey robots, a classic problem that has been used in other related researches. The accumulation of global progress over many generations leads to effective solutions that involve the production of rather articulated behaviors. The complexity of the behavior displayed by the evolving robots increases across generations although progresses in performance are not always accompanied by behavior complexification.
Bifurcation Spiking Neural Network
Authors: Shao-Qun Zhang, Zhao-Yu Zhang, Zhi-Hua Zhou
Date: 2019-09-18 00:00:00
Tasks: Time Series, Time Series Analysis

Spiking neural networks (SNNs) has attracted much attention due to its great potential of modeling time-dependent signals. The firing rate of spiking neurons is decided by control rate which is fixed manually in advance, and thus, whether the firing rate is adequate for modeling actual time series relies on fortune. Though it is demanded to have an adaptive control rate, it is a non-trivial task because the control rate and the connection weights learned during the training process are usually entangled. In this paper, we show that the firing rate is related to the eigenvalue of the spike generation function. Inspired by this insight, by enabling the spike generation function to have adaptable eigenvalues rather than parametric control rates, we develop the Bifurcation Spiking Neural Network (BSNN), which has an adaptive firing rate and is insensitive to the setting of control rates. Experiments validate the effectiveness of BSNN on a broad range of tasks, showing that BSNN achieves superior performance to existing SNNs and is robust to the setting of control rates.
Generation mechanism of cell assembly to store information about hand recognition
Authors: Takahiro Homma
Date: 2019-09-18 00:00:00

A specific memory is stored in a cell assembly that is activated during fear learning in mice; however, research regarding cell assemblies associated with procedural and habit learning processes is lacking. In modeling studies, simulations of the learning process for hand regard, which is a type of procedural learning, resulted in the formation of cell assemblies. However, the mechanisms through which the cell assemblies form and the information stored in these cell assemblies remain unknown. In this paper, the relationship between hand movements and weight changes during the simulated learning process for hand regard was used to elucidate the mechanism through which inhibitory weights are generated, which plays an important role in the formation of cell assemblies. During the early training phase, trial and error attempts to bring the hand into the field of view caused the generation of inhibitory weights, and the cell assemblies self-organized from these inhibitory weights. The information stored in the cell assemblies was estimated by examining the contributions of the cell assemblies outputs to hand movements. During sustained hand regard, the outputs from these cell assemblies moved the hand into the field of view, using hand-related inputs almost exclusively. Therefore, infants are likely able to select the inputs associated with their hand (that is, distinguish between their hand and others), based on the information stored in the cell assembly, and move their hands into the field of view during sustained hand regard.
Recognition of Handwritten Digit using Convolutional Neural Network in Python with Tensorflow and Comparison of Performance for Various Hidden Layers
Authors: Fathma Siddique, Shadman Sakib, Md. Abu Bakr Siddique
Date: 2019-09-12 00:00:00
Tasks: Deep Learning, Face Recognition, Handwritten Digit Recognition, Sentence, Sentence Classification, speech-recognition, Speech Recognition, Text Categorization

In recent times, with the increase of Artificial Neural Network (ANN), deep learning has brought a dramatic twist in the field of machine learning by making it more artificially intelligent. Deep learning is remarkably used in vast ranges of fields because of its diverse range of applications such as surveillance, health, medicine, sports, robotics, drones, etc. In deep learning, Convolutional Neural Network (CNN) is at the center of spectacular advances that mixes Artificial Neural Network (ANN) and up to date deep learning strategies. It has been used broadly in pattern recognition, sentence classification, speech recognition, face recognition, text categorization, document analysis, scene, and handwritten digit recognition. The goal of this paper is to observe the variation of accuracies of CNN to classify handwritten digits using various numbers of hidden layers and epochs and to make the comparison between the accuracies. For this performance evaluation of CNN, we performed our experiment using Modified National Institute of Standards and Technology (MNIST) dataset. Further, the network is trained using stochastic gradient descent and the backpropagation algorithm.
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
Authors: Yixin Nie, Songhe Wang, Mohit Bansal
Date: 2019-09-17 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Fact Verification, Information Retrieval, Reading Comprehension, Representation Learning, Retrieval, Semantic Retrieval, Sentence

Machine Reading at Scale (MRS) is a challenging task in which a system is given an input query and is asked to produce a precise output by "reading" information from a large knowledge base. The task has gained popularity with its natural combination of information retrieval (IR) and machine comprehension (MC). Advancements in representation learning have led to separated progress in both IR and MC; however, very few studies have examined the relationship and combined design of retrieval and comprehension at different levels of granularity, for development of MRS systems. In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task. The system is evaluated on both fact verification and open-domain multihop QA, achieving state-of-the-art results on the leaderboard test sets of both FEVER and HOTPOTQA. To further demonstrate the importance of semantic retrieval, we present ablation and analysis studies to quantify the contribution of neural retrieval modules at both paragraph-level and sentence-level, and illustrate that intermediate semantic retrieval modules are vital for not only effectively filtering upstream information and thus saving downstream computation, but also for shaping upstream data distribution and providing better data for downstream modeling. Code/data made publicly available at: https://github.com/easonnie/semanticRetrievalMRS
Enriching BERT with Knowledge Graph Embeddings for Document Classification
Authors: Malte Ostendorff, Peter Bourgonje, Maria Berger, Julian Moreno-Schneider, Georg Rehm, Bela Gipp
Date: 2019-09-18 00:00:00
Proceeding: KONVENS / GermEval 2019 2019 9
Tasks: Classification, Descriptive, Document Classification, General Classification, Knowledge Graph Embeddings, Language Modeling, Language Modelling

In this paper, we focus on the classification of books using short descriptive texts (cover blurbs) and additional metadata. Building upon BERT, a deep neural language model, we demonstrate how to combine text representations with metadata and knowledge graph embeddings, which encode author information. Compared to the standard BERT approach we achieve considerably better results for the classification task. For a more coarse-grained classification using eight labels we achieve an F1- score of 87.20, while a detailed classification using 343 labels yields an F1-score of 64.70. We make the source code and trained models of our experiments publicly available
A Data-Center FPGA Acceleration Platform for Convolutional Neural Networks
Authors: Xiaoyu Yu, Yuwei Wang, Jie Miao, Ephrem Wu, Heng Zhang, Yu Meng, Bo Zhang, Biao Min, Dewei Chen, Jianlin Gao
Date: 2019-09-17 00:00:00
Tasks: GPU

Intensive computation is entering data centers with multiple workloads of deep learning. To balance the compute efficiency, performance, and total cost of ownership (TCO), the use of a field-programmable gate array (FPGA) with reconfigurable logic provides an acceptable acceleration capacity and is compatible with diverse computation-sensitive tasks in the cloud. In this paper, we develop an FPGA acceleration platform that leverages a unified framework architecture for general-purpose convolutional neural network (CNN) inference acceleration at a data center. To overcome the computation bound, 4,096 DSPs are assembled and shaped as supertile units (SUs) for different types of convolution, which provide up to 4.2 TOP/s 16-bit fixed-point performance at 500 MHz. The interleaved-task-dispatching method is proposed to map the computation across the SUs, and the memory bound is solved by a dispatching-assembling buffering model and broadcast caches. For various non-convolution operators, a filter processing unit is designed for general-purpose filter-like/pointwise operators. In the experiment, the performances of CNN models running on server-class CPUs, a GPU, and an FPGA are compared. The results show that our design achieves the best FPGA peak performance and a throughput at the same level as that of the state-of-the-art GPU in data centers, with more than 50 times lower latency.
Optimizing Through Learned Errors for Accurate Sports Field Registration
Authors: Wei Jiang, Juan Camilo Gamboa Higuera, Baptiste Angles, Weiwei Sun, Mehrsan Javan, Kwang Moo Yi
Date: 2019-09-17 00:00:00

We propose an optimization-based framework to register sports field templates onto broadcast videos. For accurate registration we go beyond the prevalent feed-forward paradigm. Instead, we propose to train a deep network that regresses the registration error, and then register images by finding the registration parameters that minimize the regressed error. We demonstrate the effectiveness of our method by applying it to real-world sports broadcast videos, outperforming the state of the art. We further apply our method on a synthetic toy example and demonstrate that our method brings significant gains even when the problem is simplified and unlimited training data is available.
Masking Salient Object Detection, a Mask Region-based Convolutional Neural Network Analysis for Segmentation of Salient Objects
Authors: Bruno A. Krinski, Daniel V. Ruiz, Guilherme Z. Machado, Eduardo Todt
Date: 2019-09-17 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

In this paper, we propose a broad comparison between Fully Convolutional Networks (FCNs) and Mask Region-based Convolutional Neural Networks (Mask-RCNNs) applied in the Salient Object Detection (SOD) context. Studies in the SOD literature usually explore architectures based in FCNs to detect salient regions and objects in visual scenes. However, besides the promising results achieved, FCNs showed issues in some challenging scenarios. Fairly recently studies in the SOD literature proposed the use of a Mask-RCNN approach to overcome such issues. However, there is no extensive comparison between the two networks in the SOD literature endorsing the effectiveness of Mask-RCNNs over FCN when segmenting salient objects. Aiming to effectively show the superiority of Mask-RCNNs over FCNs in the SOD context, we compare two variations of Mask-RCNNs with two variations of FCNs in eight datasets widely used in the literature and in four metrics. Our findings show that in this context Mask-RCNNs achieved an improvement on the F-measure up to 47% over FCNs.
Masked-RPCA: Sparse and Low-rank Decomposition Under Overlaying Model and Application to Moving Object Detection
Authors: Amirhossein Khalilian-Gourtani, Shervin Minaee, Yao Wang
Date: 2019-09-17 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Foreground detection in a given video sequence is a pivotal step in many computer vision applications such as video surveillance system. Robust Principal Component Analysis (RPCA) performs low-rank and sparse decomposition and accomplishes such a task when the background is stationary and the foreground is dynamic and relatively small. A fundamental issue with RPCA is the assumption that the low-rank and sparse components are added at each element, whereas in reality, the moving foreground is overlaid on the background. We propose the representation via masked decomposition (i.e. an overlaying model) where each element either belongs to the low-rank or the sparse component, decided by a mask. We propose the Masked-RPCA algorithm to recover the mask and the low-rank components simultaneously, utilizing linearizing and alternating direction techniques. We further extend our formulation to be robust to dynamic changes in the background and enforce spatial connectivity in the foreground component. Our study shows significant improvement of the detected mask compared to post-processing on the sparse component obtained by other frameworks.
Learn to Estimate Labels Uncertainty for Quality Assurance
Authors: Agnieszka Tomczack, Nassir Navab, Shadi Albarqouni
Date: 2019-09-17 00:00:00
Tasks: Bayesian Inference, Deep Learning

Deep Learning sets the state-of-the-art in many challenging tasks showing outstanding performance in a broad range of applications. Despite its success, it still lacks robustness hindering its adoption in medical applications. Modeling uncertainty, through Bayesian Inference and Monte-Carlo dropout, has been successfully introduced for better understanding the underlying deep learning models. Yet, another important source of uncertainty, coming from the inter-observer variability, has not been thoroughly addressed in the literature. In this paper, we introduce labels uncertainty which better suits medical applications and show that modeling such uncertainty together with epistemic uncertainty is of high interest for quality control and referral systems.
Ensemble Knowledge Distillation for Learning Improved and Efficient Networks
Authors: Umar Asif, Jianbin Tang, Stefan Harrer
Date: 2019-09-17 00:00:00
Tasks: Ensemble Learning, General Classification, Knowledge Distillation

Ensemble models comprising of deep Convolutional Neural Networks (CNN) have shown significant improvements in model generalization but at the cost of large computation and memory requirements. In this paper, we present a framework for learning compact CNN models with improved classification performance and model generalization. For this, we propose a CNN architecture of a compact student model with parallel branches which are trained using ground truth labels and information from high capacity teacher networks in an ensemble learning fashion. Our framework provides two main benefits: i) Distilling knowledge from different teachers into the student network promotes heterogeneity in feature learning at different branches of the student network and enables the network to learn diverse solutions to the target problem. ii) Coupling the branches of the student network through ensembling encourages collaboration and improves the quality of the final predictions by reducing variance in the network outputs. Experiments on the well established CIFAR-10 and CIFAR-100 datasets show that our Ensemble Knowledge Distillation (EKD) improves classification accuracy and model generalization especially in situations with limited training data. Experiments also show that our EKD based compact networks outperform in terms of mean accuracy on the test datasets compared to state-of-the-art knowledge distillation based methods.
Spherical View Synthesis for Self-Supervised 360 Depth Estimation
Authors: Nikolaos Zioulis, Antonis Karakottas, Dimitrios Zarpalas, Federico Alvarez, Petros Daras
Date: 2019-09-17 00:00:00
Tasks: 3D Depth Estimation, Depth Estimation

Learning based approaches for depth perception are limited by the availability of clean training data. This has led to the utilization of view synthesis as an indirect objective for learning depth estimation using efficient data acquisition procedures. Nonetheless, most research focuses on pinhole based monocular vision, with scarce works presenting results for omnidirectional input. In this work, we explore spherical view synthesis for learning monocular 360 depth in a self-supervised manner and demonstrate its feasibility. Under a purely geometrically derived formulation we present results for horizontal and vertical baselines, as well as for the trinocular case. Further, we show how to better exploit the expressiveness of traditional CNNs when applied to the equirectangular domain in an efficient manner. Finally, given the availability of ground truth depth data, our work is uniquely positioned to compare view synthesis against direct supervision in a consistent and fair manner. The results indicate that alternative research directions might be better suited to enable higher quality depth perception. Our data, models and code are publicly available at https://vcl3d.github.io/SphericalViewSynthesis/.
Identity-Aware Deep Face Hallucination via Adversarial Face Verification
Authors: Hadi Kazemi, Fariborz Taherkhani, Nasser M. Nasrabadi
Date: 2019-09-17 00:00:00
Tasks: Face Hallucination, Face Verification, Generative Adversarial Network, Hallucination

In this paper, we address the problem of face hallucination by proposing a novel multi-scale generative adversarial network (GAN) architecture optimized for face verification. First, we propose a multi-scale generator architecture for face hallucination with a high up-scaling ratio factor, which has multiple intermediate outputs at different resolutions. The intermediate outputs have the growing goal of synthesizing small to large images. Second, we incorporate a face verifier with the original GAN discriminator and propose a novel discriminator which learns to discriminate different identities while distinguishing fake generated HR face images from their ground truth images. In particular, the learned generator cares for not only the visual quality of hallucinated face images but also preserving the discriminative features in the hallucination process. In addition, to capture perceptually relevant differences we employ a perceptual similarity loss, instead of similarity in pixel space. We perform a quantitative and qualitative evaluation of our framework on the LFW and CelebA datasets. The experimental results show the advantages of our proposed method against the state-of-the-art methods on the 8x downsampled testing dataset.
NEMO: Future Object Localization Using Noisy Ego Priors
Authors: Srikanth Malla, Isht Dwivedi, Behzad Dariush, Chiho Choi
Date: 2019-09-17 00:00:00
Tasks: motion prediction, Object, Object Localization

Predicting the future trajectory of agents from visual observations is an important problem for realization of safe and effective navigation of autonomous systems in dynamic environments. This paper focuses on two important aspects of future trajectory forecast which are particularly relevant for mobile platforms: 1) modeling uncertainty of the predictions, particularly from egocentric views, where uncertainty in the interactive reactions and behaviors of other agents must consider the uncertainty in the ego-motion, and 2) modeling multi-modality nature of the problem, which are particularly prevalent at junctions in urban traffic scenes. To address these problems in a unified approach, we propose NEMO (Noisy Ego MOtion priors for future object localization) for future forecast of agents in the egocentric view. In the proposed approach, a predictive distribution of future forecast is jointly modeled with the uncertainty of predictions. For this, we divide the problem into two tasks: future ego-motion prediction and future object localization. We first model the multi-modal distribution of future ego-motion with uncertainty estimates. The resulting distribution of ego-behavior is used to sample multiple modes of future ego-motion. Then, each modality is used as a prior to understand the interactions between the ego-vehicle and target agent. We predict the multi-modal future locations of the target from individual modes of the ego-vehicle while modeling the uncertainty of the target's behavior. To this end, we extensively evaluate the proposed framework using the publicly available benchmark dataset (HEV-I) supplemented with odometry data from an Inertial Measurement Unit (IMU).