pwc-archive/papers-with-abstracts huggingface dataset:
A Rigorous Framework for the Mean Field Limit of Multilayer Neural Networks
Authors: Phan-Minh Nguyen, Huy Tuan Pham
Date: 2020-01-30 00:00:00

We develop a mathematically rigorous framework for multilayer neural networks in the mean field regime. As the network's widths increase, the network's learning trajectory is shown to be well captured by a meaningful and dynamically nonlinear limit (the \textit{mean field} limit), which is characterized by a system of ODEs. Our framework applies to a broad range of network architectures, learning dynamics and network initializations. Central to the framework is the new idea of a \textit{neuronal embedding}, which comprises of a non-evolving probability space that allows to embed neural networks of arbitrary widths. Using our framework, we prove several properties of large-width multilayer neural networks. Firstly we show that independent and identically distributed initializations cause strong degeneracy effects on the network's learning trajectory when the network's depth is at least four. Secondly we obtain several global convergence guarantees for feedforward multilayer networks under a number of different setups. These include two-layer and three-layer networks with independent and identically distributed initializations, and multilayer networks of arbitrary depths with a special type of correlated initializations that is motivated by the new concept of \textit{bidirectional diversity}. Unlike previous works that rely on convexity, our results admit non-convex losses and hinge on a certain universal approximation property, which is a distinctive feature of infinite-width neural networks and is shown to hold throughout the training process. Aside from being the first known results for global convergence of multilayer networks in the mean field regime, they demonstrate flexibility of our framework and incorporate several new ideas and insights that depart from the conventional convex optimization wisdom.
Grassmannian Optimization for Online Tensor Completion and Tracking with the t-SVD
Authors: Kyle Gilman, Davoud Ataee Tarzanagh, Laura Balzano
Date: 2020-01-30 00:00:00

We propose a new fast streaming algorithm for the tensor completion problem of imputing missing entries of a low-tubal-rank tensor using the tensor singular value decomposition (t-SVD) algebraic framework. We show the t-SVD is a specialization of the well-studied block-term decomposition for third-order tensors, and we present an algorithm under this model that can track changing free submodules from incomplete streaming 2-D data. The proposed algorithm uses principles from incremental gradient descent on the Grassmann manifold of subspaces to solve the tensor completion problem with linear complexity and constant memory in the number of time samples. We provide a local expected linear convergence result for our algorithm. Our empirical results are competitive in accuracy but much faster in compute time than state-of-the-art tensor completion algorithms on real applications to recover temporal chemo-sensing and MRI data under limited sampling.
NCVis: Noise Contrastive Approach for Scalable Visualization
Authors: Aleksandr Artemenkov, Maxim Panov
Date: 2020-01-30 00:00:00
Tasks: Data Visualization, Dimensionality Reduction

Modern methods for data visualization via dimensionality reduction, such as t-SNE, usually have performance issues that prohibit their application to large amounts of high-dimensional data. In this work, we propose NCVis -- a high-performance dimensionality reduction method built on a sound statistical basis of noise contrastive estimation. We show that NCVis outperforms state-of-the-art techniques in terms of speed while preserving the representation quality of other methods. In particular, the proposed approach successfully proceeds a large dataset of more than 1 million news headlines in several minutes and presents the underlying structure in a human-readable way. Moreover, it provides results consistent with classical methods like t-SNE on more straightforward datasets like images of hand-written digits. We believe that the broader usage of such software can significantly simplify the large-scale data analysis and lower the entry barrier to this area.
Analysing Affective Behavior in the First ABAW 2020 Competition
Authors: Dimitrios Kollias, Attila Schulc, Elnar Hajiyev, Stefanos Zafeiriou
Date: 2020-01-30 00:00:00
Tasks: Action Unit Detection, Arousal Estimation, Gesture Recognition

The Affective Behavior Analysis in-the-wild (ABAW) 2020 Competition is the first Competition aiming at automatic analysis of the three main behavior tasks of valence-arousal estimation, basic expression recognition and action unit detection. It is split into three Challenges, each one addressing a respective behavior task. For the Challenges, we provide a common benchmark database, Aff-Wild2, which is a large scale in-the-wild database and the first one annotated for all these three tasks. In this paper, we describe this Competition, to be held in conjunction with the IEEE Conference on Face and Gesture Recognition, May 2020, in Buenos Aires, Argentina. We present the three Challenges, with the utilized Competition corpora. We outline the evaluation metrics, present both the baseline system and the top-3 performing teams' methodologies per Challenge and finally present their obtained results. More information regarding the Competition, the leaderboard of each Challenge and details for accessing the utilized database, are provided in the Competition site: http://ibug.doc.ic.ac.uk/resources/fg-2020-competition-affective-behavior-analysis.
NAViDAd: A No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder
Authors: Helard Martinez, M. C. Farias, A. Hines
Date: 2020-01-30 00:00:00
Tasks: Descriptive

The development of models for quality prediction of both audio and video signals is a fairly mature field. But, although several multimodal models have been proposed, the area of audio-visual quality prediction is still an emerging area. In fact, despite the reasonable performance obtained by combination and parametric metrics, currently there is no reliable pixel-based audio-visual quality metric. The approach presented in this work is based on the assumption that autoencoders, fed with descriptive audio and video features, might produce a set of features that is able to describe the complex audio and video interactions. Based on this hypothesis, we propose a No-Reference Audio-Visual Quality Metric Based on a Deep Autoencoder (NAViDAd). The model visual features are natural scene statistics (NSS) and spatial-temporal measures of the video component. Meanwhile, the audio features are obtained by computing the spectrogram representation of the audio component. The model is formed by a 2-layer framework that includes a deep autoencoder layer and a classification layer. These two layers are stacked and trained to build the deep neural network model. The model is trained and tested using a large set of stimuli, containing representative audio and video artifacts. The model performed well when tested against the UnB-AV and the LiveNetflix-II databases. %Results shows that this type of approach produces quality scores that are highly correlated to subjective quality scores.
Graph Convolution Machine for Context-aware Recommender System
Authors: Jiancan Wu, Xiangnan He, Xiang Wang, Qifan Wang, Weijian Chen, Jianxun Lian, Xing Xie
Date: 2020-01-30 00:00:00
Tasks: Collaborative Filtering, Decoder, Recommendation Systems

The latest advance in recommendation shows that better user and item representations can be learned via performing graph convolutions on the user-item interaction graph. However, such finding is mostly restricted to the collaborative filtering (CF) scenario, where the interaction contexts are not available. In this work, we extend the advantages of graph convolutions to context-aware recommender system (CARS, which represents a generic type of models that can handle various side information). We propose \textit{Graph Convolution Machine} (GCM), an end-to-end framework that consists of three components: an encoder, graph convolution (GC) layers, and a decoder. The encoder projects users, items, and contexts into embedding vectors, which are passed to the GC layers that refine user and item embeddings with context-aware graph convolutions on user-item graph. The decoder digests the refined embeddings to output the prediction score by considering the interactions among user, item, and context embeddings. We conduct experiments on three real-world datasets from Yelp and Amazon, validating the effectiveness of GCM and the benefits of performing graph convolutions for CARS. Our implementations are available at \url{https://github.com/wujcan/GCM}.
Optimized Feature Space Learning for Generating Efficient Binary Codes for Image Retrieval
Authors: Abin Jose, Erik Stefan Ottlik, Christian Rohlfing, Jens-Rainer Ohm
Date: 2020-01-30 00:00:00
Tasks: Image Retrieval, Quantization, Retrieval

In this paper we propose an approach for learning low dimensional optimized feature space with minimum intra-class variance and maximum inter-class variance. We address the problem of high-dimensionality of feature vectors extracted from neural networks by taking care of the global statistics of feature space. Classical approach of Linear Discriminant Analysis (LDA) is generally used for generating an optimized low dimensional feature space for single-labeled images. Since, image retrieval involves both multi-labeled and single-labeled images, we utilize the equivalence between LDA and Canonical Correlation Analysis (CCA) to generate an optimized feature space for single-labeled images and use CCA to generate an optimized feature space for multi-labeled images. Our approach correlates the projections of feature vectors with label vectors in our CCA based network architecture. The neural network minimize a loss function which maximizes the correlation coefficients. We binarize our generated feature vectors with the popular Iterative Quantization (ITQ) approach and also propose an ensemble network to generate binary codes of desired bit length for image retrieval. Our measurement of mean average precision shows competitive results on other state-of-the-art single-labeled and multi-labeled image retrieval datasets.
Uncovering life-course patterns with causal discovery and survival analysis
Authors: Bojan Kostic, Romain Crastes dit Sourd, Stephane Hess, Joachim Scheiner, Christian Holz-Rau, Francisco C. Pereira
Date: 2020-01-30 00:00:00
Tasks: Causal Discovery, Survival Analysis

We provide a novel approach and an exploratory study for modelling life event choices and occurrence from a probabilistic perspective through causal discovery and survival analysis. Our approach is formulated as a bi-level problem. In the upper level, we build the life events graph, using causal discovery tools. In the lower level, for the pairs of life events, time-to-event modelling through survival analysis is applied to model time-dependent transition probabilities. Several life events were analysed, such as getting married, buying a new car, child birth, home relocation and divorce, together with the socio-demographic attributes for survival modelling, some of which are age, nationality, number of children, number of cars and home ownership. The data originates from a survey conducted in Dortmund, Germany, with the questionnaire containing a series of retrospective questions about residential and employment biography, travel behaviour and holiday trips, as well as socio-economic characteristic. Although survival analysis has been used in the past to analyse life-course data, this is the first time that a bi-level model has been formulated. The inclusion of a causal discovery algorithm in the upper-level allows us to first identify causal relationships between life-course events and then understand the factors that might influence transition rates between events. This is very different from more classic choice models where causal relationships are subject to expert interpretations based on model results.
Non-Determinism in TensorFlow ResNets
Authors: Miguel Morin, Matthew Willetts
Date: 2020-01-30 00:00:00
Tasks: GPU, image-classification, Image Classification

We show that the stochasticity in training ResNets for image classification on GPUs in TensorFlow is dominated by the non-determinism from GPUs, rather than by the initialisation of the weights and biases of the network or by the sequence of minibatches given. The standard deviation of test set accuracy is 0.02 with fixed seeds, compared to 0.027 with different seeds---nearly 74\% of the standard deviation of a ResNet model is non-deterministic. For test set loss the ratio of standard deviations is more than 80\%. These results call for more robust evaluation strategies of deep learning models, as a significant amount of the variation in results across runs can arise simply from GPU randomness.
ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos
Authors: Lichao Mou, Yuansheng Hua, Pu Jin, Xiao Xiang Zhu
Date: 2020-01-30 00:00:00

Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is https://lcmou.github.io/ERA_Dataset/
Tackling Air Traffic Conflicts as a Weighted CSP : Experiments with the Lumberjack Method
Authors: Thomas Chaboud, Cédric Pralet, Nicolas Schmidt
Date: 2020-01-30 00:00:00

In this paper, we present an extension to an air traffic conflicts resolution method consisting in generating a large number of trajectories for a set of aircraft, and efficiently selecting the best compatible ones. We propose a multimanoeuvre version which encapsulates different conflict-solving algorithms, in particular an original "smart brute-force" method and the well-known ToulBar2 CSP toolset. Experiments on several benchmarks show that the first one is very efficient on cases involving few aircraft (representative of what actually happens in operations), allowing us to search through a large pool of manoeuvres and trajectories; however, this method is overtaken by its complexity when the number of aircraft increases to 7 and more. Conversely, within acceptable times, the ToulBar2 toolset can handle conflicts involving more aircraft, but with fewer possible trajectories for each.
Learning to Structure Long-term Dependence for Sequential Recommendation
Authors: Renqin Cai, Qinglei Wang, Chong Wang, Xiaobing Liu
Date: 2020-01-30 00:00:00
Tasks: Sequential Recommendation

Sequential recommendation recommends items based on sequences of users' historical actions. The key challenge in it is how to effectively model the influence from distant actions to the action to be predicted, i.e., recognizing the long-term dependence structure; and it remains an underexplored problem. To better model the long-term dependence structure, we propose a GatedLongRec solution in this work. To account for the long-term dependence, GatedLongRec extracts distant actions of top-$k$ related categories to the user's ongoing intent with a top-$k$ gating network, and utilizes a long-term encoder to encode the transition patterns among these identified actions. As user intent is not directly observable, we take advantage of available side-information about the actions, i.e., the category of their associated items, to infer the intents. End-to-end training is performed to estimate the intent representation and predict the next action for sequential recommendation. Extensive experiments on two large datasets show that the proposed solution can recognize the structure of long-term dependence, thus greatly improving the sequential recommendation.
Black-Box Saliency Map Generation Using Bayesian Optimisation
Authors: Mamuku Mokuwe, Michael Burke, Anna Sergeevna Bosman
Date: 2020-01-30 00:00:00
Tasks: Bayesian Optimisation

Saliency maps are often used in computer vision to provide intuitive interpretations of what input regions a model has used to produce a specific prediction. A number of approaches to saliency map generation are available, but most require access to model parameters. This work proposes an approach for saliency map generation for black-box models, where no access to model parameters is available, using a Bayesian optimisation sampling method. The approach aims to find the global salient image region responsible for a particular (black-box) model's prediction. This is achieved by a sampling-based approach to model perturbations that seeks to localise salient regions of an image to the black-box model. Results show that the proposed approach to saliency map generation outperforms grid-based perturbation approaches, and performs similarly to gradient-based approaches which require access to model parameters.
BUT Opensat 2019 Speech Recognition System
Authors: Martin Karafiát, Murali Karthick Baskar, Igor Szöke, Hari Krishna Vydana, Karel Veselý, Jan "Honza'' Černocký
Date: 2020-01-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, speech-recognition, Speech Recognition

The paper describes the BUT Automatic Speech Recognition (ASR) systems submitted for OpenSAT evaluations under two domain categories such as low resourced languages and public safety communications. The first was challenging due to lack of training data, therefore various architectures and multilingual approaches were employed. The combination led to superior performance. The second domain was challenging due to recording in extreme conditions such as specific channel, speaker under stress and high levels of noise. Data augmentation process was inevitable to get reasonably good performance.
Adversarial Training for Aspect-Based Sentiment Analysis with BERT
Authors: Akbar Karimi, Leonardo Rossi, Andrea Prati
Date: 2020-01-30 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification

Aspect-Based Sentiment Analysis (ABSA) deals with the extraction of sentiments and their targets. Collecting labeled data for this task in order to help neural networks generalize better can be laborious and time-consuming. As an alternative, similar data to the real-world examples can be produced artificially through an adversarial process which is carried out in the embedding space. Although these examples are not real sentences, they have been shown to act as a regularization method which can make neural networks more robust. In this work, we apply adversarial training, which was put forward by Goodfellow et al. (2014), to the post-trained BERT (BERT-PT) language model proposed by Xu et al. (2019) on the two major tasks of Aspect Extraction and Aspect Sentiment Classification in sentiment analysis. After improving the results of post-trained BERT by an ablation study, we propose a novel architecture called BERT Adversarial Training (BAT) to utilize adversarial training in ABSA. The proposed model outperforms post-trained BERT in both tasks. To the best of our knowledge, this is the first study on the application of adversarial training in ABSA.
A Deeper Look into Hybrid Images
Authors: Jimut Bahan Pal
Date: 2020-01-30 00:00:00

$Hybrid$ $images$ was first introduced by Olivia et al., that produced static images with two interpretations such that the images changes as a function of viewing distance. Hybrid images are built by studying human processing of multiscale images and are motivated by masking studies in visual perception. The first introduction of hybrid images showed that two images can be blend together with a high pass filter and a low pass filter in such a way that when the blended image is viewed from a distance, the high pass filter fades away and the low pass filter becomes prominent. Our main aim here is to study and review the original paper by changing and tweaking certain parameters to see how they affect the quality of the blended image produced. We have used exhaustively different set of images and filters to see how they function and whether this can be used in a real time system or not.
Which way? Direction-Aware Attributed Graph Embedding
Authors: Zekarias T. Kefato, Nasrullah Sheikh, Alberto Montresor
Date: 2020-01-30 00:00:00
Tasks: General Classification, Graph Embedding, Link Prediction, Node Classification

Graph embedding algorithms are used to efficiently represent (encode) a graph in a low-dimensional continuous vector space that preserves the most important properties of the graph. One aspect that is often overlooked is whether the graph is directed or not. Most studies ignore the directionality, so as to learn high-quality representations optimized for node classification. On the other hand, studies that capture directionality are usually effective on link prediction but do not perform well on other tasks. This preliminary study presents a novel text-enriched, direction-aware algorithm called DIAGRAM , based on a carefully designed multi-objective model to learn embeddings that preserve the direction of edges, textual features and graph context of nodes. As a result, our algorithm does not have to trade one property for another and jointly learns high-quality representations for multiple network analysis tasks. We empirically show that DIAGRAM significantly outperforms six state-of-the-art baselines, both direction-aware and oblivious ones,on link prediction and network reconstruction experiments using two popular datasets. It also achieves a comparable performance on node classification experiments against these baselines using the same datasets.
Conditioning Autoencoder Latent Spaces for Real-Time Timbre Interpolation and Synthesis
Authors: Joseph T Colonel, Sam Keene
Date: 2020-01-30 00:00:00
Tasks: Timbre Interpolation

We compare standard autoencoder topologies' performances for timbre generation. We demonstrate how different activation functions used in the autoencoder's bottleneck distributes a training corpus's embedding. We show that the choice of sigmoid activation in the bottleneck produces a more bounded and uniformly distributed embedding than a leaky rectified linear unit activation. We propose a one-hot encoded chroma feature vector for use in both input augmentation and latent space conditioning. We measure the performance of these networks, and characterize the latent embeddings that arise from the use of this chroma conditioning vector. An open source, real-time timbre synthesis algorithm in Python is outlined and shared.