pwc-archive/papers-with-abstracts huggingface dataset:
Optimising AI Training Deployments using Graph Compilers and Containers
Authors: Nina Mujkanovic, Karthee Sivalingam, Alfio Lazzaro
Date: 2020-08-26 00:00:00
Tasks: speech-recognition, Speech Recognition

Artificial Intelligence (AI) applications based on Deep Neural Networks (DNN) or Deep Learning (DL) have become popular due to their success in solving problems likeimage analysis and speech recognition. Training a DNN is computationally intensive and High Performance Computing(HPC) has been a key driver in AI growth. Virtualisation and container technology have led to the convergence of cloud and HPC infrastructure. These infrastructures with diverse hardware increase the complexity of deploying and optimising AI training workloads. AI training deployments in HPC or cloud can be optimised with target-specific libraries, graph compilers, andby improving data movement or IO. Graph compilers aim to optimise the execution of a DNN graph by generating an optimised code for a target hardware/backend. As part of SODALITE (a Horizon 2020 project), MODAK tool is developed to optimise application deployment in software defined infrastructures. Using input from the data scientist and performance modelling, MODAK maps optimal application parameters to a target infrastructure and builds an optimised container. In this paper, we introduce MODAK and review container technologies and graph compilers for AI. We illustrate optimisation of AI training deployments using graph compilers and Singularity containers. Evaluation using MNIST-CNN and ResNet50 training workloads shows that custom built optimised containers outperform the official images from DockerHub. We also found that the performance of graph compilers depends on the target hardware and the complexity of the neural network.
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology
Authors: Maxime W. Lafarge, Josien P. W. Pluim, Mitko Veta
Date: 2020-08-26 00:00:00
Tasks: Representation Learning

Unsupervised learning enables modeling complex images without the need for annotations. The representation learned by such models can facilitate any subsequent analysis of large image datasets. However, some generative factors that cause irrelevant variations in images can potentially get entangled in such a learned representation causing the risk of negatively affecting any subsequent use. The orientation of imaged objects, for instance, is often arbitrary/irrelevant, thus it can be desired to learn a representation in which the orientation information is disentangled from all other factors. Here, we propose to extend the Variational Auto-Encoder framework by leveraging the group structure of rotation-equivariant convolutional networks to learn orientation-wise disentangled generative factors of histopathology images. This way, we enforce a novel partitioning of the latent space, such that oriented and isotropic components get separated. We evaluated this structured representation on a dataset that consists of tissue regions for which nuclear pleomorphism and mitotic activity was assessed by expert pathologists. We show that the trained models efficiently disentangle the inherent orientation information of single-cell images. In comparison to classical approaches, the resulting aggregated representation of sub-populations of cells produces higher performances in subsequent tasks.
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
Authors: Mahdi Rezaei, Mohsen Azarmi
Date: 2020-08-26 00:00:00
Tasks: Action Recognition, Anomaly Detection, Autonomous Vehicles, Human Detection, Temporal Action Localization

Social distancing is a recommended solution by the World Health Organisation (WHO) to minimise the spread of COVID-19 in public places. The majority of governments and national health authorities have set the 2-meter physical distancing as a mandatory safety measure in shopping centres, schools and other covered areas. In this research, we develop a hybrid Computer Vision and YOLOv4-based Deep Neural Network model for automated people detection in the crowd in indoor and outdoor environments using common CCTV security cameras. The proposed DNN model in combination with an adapted inverse perspective mapping (IPM) technique and SORT tracking algorithm leads to a robust people detection and social distancing monitoring. The model has been trained against two most comprehensive datasets by the time of the research the Microsoft Common Objects in Context (MS COCO) and Google Open Image datasets. The system has been evaluated against the Oxford Town Centre dataset with superior performance compared to three state-of-the-art methods. The evaluation has been conducted in challenging conditions, including occlusion, partial visibility, and under lighting variations with the mean average precision of 99.8% and the real-time speed of 24.1 fps. We also provide an online infection risk assessment scheme by statistical analysis of the Spatio-temporal data from people's moving trajectories and the rate of social distancing violations. The developed model is a generic and accurate people detection and tracking solution that can be applied in many other fields such as autonomous vehicles, human action recognition, anomaly detection, sports, crowd analysis, or any other research areas where the human detection is in the centre of attention.
DeepVOX: Discovering Features from Raw Audio for Speaker Recognition in Non-ideal Audio Signals
Authors: Anurag Chowdhury, Arun Ross
Date: 2020-08-26 00:00:00
Tasks: Speaker Recognition, Triplet

Automatic speaker recognition algorithms typically use pre-defined filterbanks, such as Mel-Frequency and Gammatone filterbanks, for characterizing speech audio. However, it has been observed that the features extracted using these filterbanks are not resilient to diverse audio degradations. In this work, we propose a deep learning-based technique to deduce the filterbank design from vast amounts of speech audio. The purpose of such a filterbank is to extract features robust to non-ideal audio conditions, such as degraded, short duration, and multi-lingual speech. To this effect, a 1D convolutional neural network is designed to learn a time-domain filterbank called DeepVOX directly from raw speech audio. Secondly, an adaptive triplet mining technique is developed to efficiently mine the data samples best suited to train the filterbank. Thirdly, a detailed ablation study of the DeepVOX filterbanks reveals the presence of both vocal source and vocal tract characteristics in the extracted features. Experimental results on VOXCeleb2, NIST SRE 2008, 2010 and 2018, and Fisher speech datasets demonstrate the efficacy of the DeepVOX features across a variety of degraded, short duration, and multi-lingual speech. The DeepVOX features also shown to improve the performance of existing speaker recognition algorithms, such as the xVector-PLDA and the iVector-PLDA.
Improving Fairness in Criminal Justice Algorithmic Risk Assessments Using Conformal Prediction Sets
Authors: Richard A. Berk, Arun Kumar Kuchibhotla
Date: 2020-08-26 00:00:00
Tasks: Conformal Prediction, Fairness, valid

Risk assessment algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs. In this paper, we adopt a framework from conformal prediction sets to remove unfairness from risk algorithms themselves and the covariates used for forecasting. From a sample of 300,000 offenders at their arraignments, we construct a confusion table and its derived measures of fairness that are effectively free any meaningful differences between Black and White offenders. We also produce fair forecasts for individual offenders coupled with valid probability guarantees that the forecasted outcome is the true outcome. We see our work as a demonstration of concept for application in a wide variety of criminal justice decisions. The procedures provided can be routinely implemented in jurisdictions with the usual criminal justice datasets used by administrators. The requisite procedures can be found in the scripting software R. However, whether stakeholders will accept our approach as a means to achieve risk assessment fairness is unknown. There also are legal issues that would need to be resolved although we offer a Pareto improvement.
Attr2Style: A Transfer Learning Approach for Inferring Fashion Styles via Apparel Attributes
Authors: Rajdeep Hazra Banerjee, Abhinav Ravi, Ujjal Kr Dutta
Date: 2020-08-26 00:00:00
Tasks: Attribute, Image Captioning, Transfer Learning

Popular fashion e-commerce platforms mostly provide details about low-level attributes of an apparel (eg, neck type, dress length, collar type) on their product detail pages. However, customers usually prefer to buy apparel based on their style information, or simply put, occasion (eg, party/ sports/ casual wear). Application of a supervised image-captioning model to generate style-based image captions is limited because obtaining ground-truth annotations in the form of style-based captions is difficult. This is because annotating style-based captions requires a certain amount of fashion domain expertise, and also adds to the costs and manual effort. On the contrary, low-level attribute based annotations are much more easily available. To address this issue, we propose a transfer-learning based image captioning model that is trained on a source dataset with sufficient attribute-based ground-truth captions, and used to predict style-based captions on a target dataset. The target dataset has only a limited amount of images with style-based ground-truth captions. The main motivation of our approach comes from the fact that most often there are correlations among the low-level attributes and the higher-level styles for an apparel. We leverage this fact and train our model in an encoder-decoder based framework using attention mechanism. In particular, the encoder of the model is first trained on the source dataset to obtain latent representations capturing the low-level attributes. The trained model is fine-tuned to generate style-based captions for the target dataset. To highlight the effectiveness of our method, we qualitatively and quantitatively demonstrate that the captions generated by our approach are close to the actual style information for the evaluated apparel. A Proof Of Concept for our model is under pilot at Myntra where it is exposed to some internal users for feedback.
Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
Authors: Tiankuang Zhou, Xing Lin, Jiamin Wu, Yitong Chen, Hao Xie, Yipeng Li, Jintao Fan, Huaqiang Wu, Lu Fang, Qionghai Dai
Date: 2020-08-26 00:00:00

Application-specific optical processors have been considered disruptive technologies for modern computing that can fundamentally accelerate the development of artificial intelligence (AI) by offering substantially improved computing performance. Recent advancements in optical neural network architectures for neural information processing have been applied to perform various machine learning tasks. However, the existing architectures have limited complexity and performance; and each of them requires its own dedicated design that cannot be reconfigured to switch between different neural network models for different applications after deployment. Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons. It allocates almost all of its computational operations optically and achieves extremely high speed of data modulation and large-scale network parameter updating by dynamically programming optical modulators and photodetectors. We demonstrated the reconfiguration of the DPU to implement various diffractive feedforward and recurrent neural networks and developed a novel adaptive training approach to circumvent the system imperfections. We applied the trained networks for high-speed classifying of handwritten digit images and human action videos over benchmark datasets, and the experimental results revealed a comparable classification accuracy to the electronic computing approaches. Furthermore, our prototype system built with off-the-shelf optoelectronic components surpasses the performance of state-of-the-art graphics processing units (GPUs) by several times on computing speed and more than an order of magnitude on system energy efficiency.
How to tune the RBF SVM hyperparameters?: An empirical evaluation of 18 search algorithms
Authors: Jacques Wainer, Pablo Fonseca
Date: 2020-08-26 00:00:00
Tasks: Bayesian Optimization

SVM with an RBF kernel is usually one of the best classification algorithms for most data sets, but it is important to tune the two hyperparameters $C$ and $\gamma$ to the data itself. In general, the selection of the hyperparameters is a non-convex optimization problem and thus many algorithms have been proposed to solve it, among them: grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, Nelder Mead, and others. There have also been proposals to decouple the selection of $\gamma$ and $C$. We empirically compare 18 of these proposed search algorithms (with different parameterizations for a total of 47 combinations) on 115 real-life binary data sets. We find (among other things) that trees of Parzen estimators and particle swarm optimization select better hyperparameters with only a slight increase in computation time with respect to a grid search with the same number of evaluations. We also find that spending too much computational effort searching the hyperparameters will not likely result in better performance for future data and that there are no significant differences among the different procedures to select the best set of hyperparameters when more than one is found by the search algorithms.
Simplifying Architecture Search for Graph Neural Network
Authors: Huan Zhao, Lanning Wei, Quanming Yao
Date: 2020-08-26 00:00:00
Tasks: Graph Neural Network, Neural Architecture Search

Recent years have witnessed the popularity of Graph Neural Networks (GNN) in various scenarios. To obtain optimal data-specific GNN architectures, researchers turn to neural architecture search (NAS) methods, which have made impressive progress in discovering effective architectures in convolutional neural networks. Two preliminary works, GraphNAS and Auto-GNN, have made first attempt to apply NAS methods to GNN. Despite the promising results, there are several drawbacks in expressive capability and search efficiency of GraphNAS and Auto-GNN due to the designed search space. To overcome these drawbacks, we propose the SNAG framework (Simplified Neural Architecture search for Graph neural networks), consisting of a novel search space and a reinforcement learning based search algorithm. Extensive experiments on real-world datasets demonstrate the effectiveness of the SNAG framework compared to human-designed GNNs and NAS methods, including GraphNAS and Auto-GNN.
Discrete Word Embedding for Logical Natural Language Understanding
Authors: Masataro Asai, Zilu Tang
Date: 2020-08-26 00:00:00
Tasks: Decision Making, Natural Language Understanding

We propose an unsupervised neural model for learning a discrete embedding of words. Unlike existing discrete embeddings, our binary embedding supports vector arithmetic operations similar to continuous embeddings. Our embedding represents each word as a set of propositional statements describing a transition rule in classical/STRIPS planning formalism. This makes the embedding directly compatible with symbolic, state of the art classical planning solvers.
RNN-based Pedestrian Crossing Prediction using Activity and Pose-related Features
Authors: Javier Lorenzo, Ignacio Parra, Florian Wirth, Christoph Stiller, David Fernandez Llorca, Miguel Angel Sotelo
Date: 2020-08-26 00:00:00
Tasks: Autonomous Driving

Pedestrian crossing prediction is a crucial task for autonomous driving. Numerous studies show that an early estimation of the pedestrian's intention can decrease or even avoid a high percentage of accidents. In this paper, different variations of a deep learning system are proposed to attempt to solve this problem. The proposed models are composed of two parts: a CNN-based feature extractor and an RNN module. All the models were trained and tested on the JAAD dataset. The results obtained indicate that the choice of the features extraction method, the inclusion of additional variables such as pedestrian gaze direction and discrete orientation, and the chosen RNN type have a significant impact on the final performance.
Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization
Authors: Tingyu Wang, Zhedong Zheng, Chenggang Yan, Jiyong Zhang, Yaoqi Sun, Bolun Zheng, Yi Yang
Date: 2020-08-26 00:00:00
Tasks: Drone navigation, Drone-view target localization, geo-localization, Image-Based Localization, Representation Learning

Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g., drone-view cameras and satellites. It is challenging in the large visual appearance changes caused by extreme viewpoint variations. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas. In this work, we argue that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geolocalization. Specifically, we introduce a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner. Without using extra part estimators, LPN adopts a square-ring feature partition strategy, which provides the attention according to the distance to the image center. It eases the part matching and enables the part-wise representation learning. Owing to the square-ring partition design, the proposed LPN has good scalability to rotation variations and achieves competitive results on three prevailing benchmarks, i.e., University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN can be easily embedded into other frameworks to further boost performance.
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks
Authors: Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets, Wouter Verbeke
Date: 2020-08-26 00:00:00
Tasks: Multi-Task Learning

Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite loss function formed by a constant weighted combination of the separate task losses. In practice, constant loss weights lead to poor results for two reasons: (i) the relevance of the auxiliary tasks can gradually drift throughout the learning process; (ii) for mini-batch based optimisation, the optimal task weights vary significantly from one update to the next depending on mini-batch sample composition. We introduce HydaLearn, an intelligent weighting algorithm that connects main-task gain to the individual task gradients, in order to inform dynamic loss weighting at the mini-batch level, addressing i and ii. Using HydaLearn, we report performance increases on synthetic data, as well as on two supervised learning domains.
Robust trajectory generation for robotic control on the neuromorphic research chip Loihi
Authors: Carlo Michaelis, Andrew B. Lehr, Christian Tetzlaff
Date: 2020-08-26 00:00:00

Neuromorphic hardware has several promising advantages compared to von Neumann architectures and is highly interesting for robot control. However, despite the high speed and energy efficiency of neuromorphic computing, algorithms utilizing this hardware in control scenarios are still rare. One problem is the transition from fast spiking activity on the hardware, which acts on a timescale of a few milliseconds, to a control-relevant timescale on the order of hundreds of milliseconds. Another problem is the execution of complex trajectories, which requires spiking activity to contain sufficient variability, while at the same time, for reliable performance, network dynamics must be adequately robust against noise. In this study we exploit a recently developed biologically-inspired spiking neural network model, the so-called anisotropic network. We identified and transferred the core principles of the anisotropic network to neuromorphic hardware using Intel's neuromorphic research chip Loihi and validated the system on trajectories from a motor-control task performed by a robot arm. We developed a network architecture including the anisotropic network and a pooling layer which allows fast spike read-out from the chip and performs an inherent regularization. With this, we show that the anisotropic network on Loihi reliably encodes sequential patterns of neural activity, each representing a robotic action, and that the patterns allow the generation of multidimensional trajectories on control-relevant timescales. Taken together, our study presents a new algorithm that allows the generation of complex robotic movements as a building block for robotic control using state of the art neuromorphic hardware.
Buy Me That Look: An Approach for Recommending Similar Fashion Products
Authors: Abhinav Ravi, Sandeep Repakula, Ujjal Kr Dutta, Maulik Parmar
Date: 2020-08-26 00:00:00
Tasks: Active Learning, Articles, Keypoint Detection, object-detection, Object Detection, Recommendation Systems, Triplet

Have you ever looked at an Instagram model, or a model in a fashion e-commerce web-page, and thought \textit{"Wish I could get a list of fashion items similar to the ones worn by the model!"}. This is what we address in this paper, where we propose a novel computer vision based technique called \textbf{ShopLook} to address the challenging problem of recommending similar fashion products. The proposed method has been evaluated at Myntra (www.myntra.com), a leading online fashion e-commerce platform. In particular, given a user query and the corresponding Product Display Page (PDP) against the query, the goal of our method is to recommend similar fashion products corresponding to the entire set of fashion articles worn by a model in the PDP full-shot image (the one showing the entire model from head to toe). The novelty and strength of our method lies in its capability to recommend similar articles for all the fashion items worn by the model, in addition to the primary article corresponding to the query. This is not only important to promote cross-sells for boosting revenue, but also for improving customer experience and engagement. In addition, our approach is also capable of recommending similar products for User Generated Content (UGC), eg., fashion article images uploaded by users. Formally, our proposed method consists of the following components (in the same order): i) Human keypoint detection, ii) Pose classification, iii) Article localisation and object detection, along with active learning feedback, and iv) Triplet network based image embedding model.