pwc-archive/papers-with-abstracts huggingface dataset:
Variational Tracking and Prediction with Generative Disentangled State-Space Models
Authors: Adnan Akhundov, Maximilian Soelch, Justin Bayer, Patrick van der Smagt
Date: 2019-10-14 00:00:00
Tasks: Bayesian Inference, Position, State Space Models

We address tracking and prediction of multiple moving objects in visual data streams as inference and sampling in a disentangled latent state-space model. By encoding objects separately and including explicit position information in the latent state space, we perform tracking via amortized variational Bayesian inference of the respective latent positions. Inference is implemented in a modular neural framework tailored towards our disentangled latent space. Generative and inference model are jointly learned from observations only. Comparing to related prior work, we empirically show that our Markovian state-space assumption enables faithful and much improved long-term prediction well beyond the training horizon. Further, our inference model correctly decomposes frames into objects, even in the presence of occlusions. Tracking performance is increased significantly over prior art.
Comment on "AndrODet: An adaptive Android obfuscation detector"
Authors: Alireza Mohammadinodooshan, Ulf Kargén, Nahid Shahmehri
Date: 2019-10-14 00:00:00

We have identified a methodological problem in the empirical evaluation of the string encryption detection capabilities of the AndrODet system described by Mirzaei et al. in the recent paper "AndrODet: An adaptive Android obfuscation detector". The accuracy of string encryption detection is evaluated using samples from the AMD and PraGuard malware datasets. However, the authors failed to account for the fact that many of the AMD samples are highly similar due to the fact that they come from the same malware family. This introduces a risk that a machine learning system trained on these samples could fail to learn a generalizable model for string encryption detection, and might instead learn to classify samples based on characteristics of each malware family. Our own evaluation strongly indicates that the reported high accuracy of AndrODet's string encryption detection is indeed due to this phenomenon. When we evaluated AndrODet, we found that when we ensured that samples from the same family never appeared in both training and testing data, the accuracy dropped to around 50%. Moreover, the PraGuard dataset is not suitable for evaluating a static string encryption detector such as AndrODet, since the particular obfuscation tool used to produce the dataset effectively makes it impossible to extract meaningful features of static strings in Android apps.
Unsupervised Multi-stream Highlight detection for the Game "Honor of Kings"
Authors: Li Wang, Zixun Sun, Wentao Yao, Hui Zhan, Chengwei Zhu
Date: 2019-10-14 00:00:00
Tasks: Highlight Detection

With the increasing popularity of E-sport live, Highlight Flashback has been a critical functionality of live platforms, which aggregates the overall exciting fighting scenes in a few seconds. In this paper, we introduce a novel training strategy without any additional annotation to automatically generate highlights for game video live. Considering that the existing manual edited clips contain more highlights than long game live videos, we perform pair-wise ranking constraints across clips from edited and long live videos. A multi-stream framework is also proposed to fuse spatial, temporal as well as audio features extracted from videos. To evaluate our method, we test on long game live videos with an average length of about 15 minutes. Extensive experimental results on videos demonstrate its satisfying performance on highlights generation and effectiveness by the fusion of three streams.
Q8BERT: Quantized 8Bit BERT
Authors: Ofir Zafrir, Guy Boudoukh, Peter Izsak, Moshe Wasserblat
Date: 2019-10-14 00:00:00
Tasks: Linguistic Acceptability, Natural Language Inference, Quantization, Semantic Textual Similarity, Sentiment Analysis

Recently, pre-trained Transformer based language models such as BERT and GPT, have shown great improvement in many Natural Language Processing (NLP) tasks. However, these models contain a large amount of parameters. The emergence of even larger and more accurate models such as GPT2 and Megatron, suggest a trend of large pre-trained Transformer models. However, using these large models in production environments is a complex task requiring a large amount of compute, memory and power resources. In this work we show how to perform quantization-aware training during the fine-tuning phase of BERT in order to compress BERT by $4\times$ with minimal accuracy loss. Furthermore, the produced quantized model can accelerate inference speed if it is optimized for 8bit Integer supporting hardware.
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment
Authors: Vlad Hosu, Hanhe Lin, Tamas Sziranyi, Dietmar Saupe
Date: 2019-10-14 00:00:00
Tasks: Diversity, Image Quality Assessment, No-Reference Image Quality Assessment, valid, Video Quality Assessment

Deep learning methods for image quality assessment (IQA) are limited due to the small size of existing datasets. Extensive datasets require substantial resources both for generating publishable content and annotating it accurately. We present a systematic and scalable approach to creating KonIQ-10k, the largest IQA dataset to date, consisting of 10,073 quality scored images. It is the first in-the-wild database aiming for ecological validity, concerning the authenticity of distortions, the diversity of content, and quality-related indicators. Through the use of crowdsourcing, we obtained 1.2 million reliable quality ratings from 1,459 crowd workers, paving the way for more general IQA models. We propose a novel, deep learning model (KonCept512), to show an excellent generalization beyond the test set (0.921 SROCC), to the current state-of-the-art database LIVE-in-the-Wild (0.825 SROCC). The model derives its core performance from the InceptionResNet architecture, being trained at a higher resolution than previous models (512x384). Correlation analysis shows that KonCept512 performs similar to having 9 subjective scores for each test image.
Mask-Guided Attention Network for Occluded Pedestrian Detection
Authors: Yanwei Pang, Jin Xie, Muhammad Haris Khan, Rao Muhammad Anwer, Fahad Shahbaz Khan, Ling Shao
Date: 2019-10-14 00:00:00
Proceeding: ICCV 2019 10
Tasks: Pedestrian Detection

Pedestrian detection relying on deep convolution neural networks has made significant progress. Though promising results have been achieved on standard pedestrians, the performance on heavily occluded pedestrians remains far from satisfactory. The main culprits are intra-class occlusions involving other pedestrians and inter-class occlusions caused by other objects, such as cars and bicycles. These result in a multitude of occlusion patterns. We propose an approach for occluded pedestrian detection with the following contributions. First, we introduce a novel mask-guided attention network that fits naturally into popular pedestrian detection pipelines. Our attention network emphasizes on visible pedestrian regions while suppressing the occluded ones by modulating full body features. Second, we empirically demonstrate that coarse-level segmentation annotations provide reasonable approximation to their dense pixel-wise counterparts. Experiments are performed on CityPersons and Caltech datasets. Our approach sets a new state-of-the-art on both datasets. Our approach obtains an absolute gain of 9.5% in log-average miss rate, compared to the best reported results on the heavily occluded (HO) pedestrian set of CityPersons test set. Further, on the HO pedestrian set of Caltech dataset, our method achieves an absolute gain of 5.0% in log-average miss rate, compared to the best reported results. Code and models are available at: https://github.com/Leotju/MGAN.
DCDB Wintermute: Enabling Online and Holistic Operational Data Analytics on HPC Systems
Authors: Alessio Netti, Micha Mueller, Carla Guillen, Michael Ott, Daniele Tafani, Gence Ozer, Martin Schulz
Date: 2019-10-14 00:00:00
Tasks: Management

As we approach the exascale era, the size and complexity of HPC systems continues to increase, raising concerns about their manageability and sustainability. For this reason, more and more HPC centers are experimenting with fine-grained monitoring coupled with Operational Data Analytics (ODA) to optimize efficiency and effectiveness of system operations. However, while monitoring is a common reality in HPC, there is no well-stated and comprehensive list of requirements, nor matching frameworks, to support holistic and online ODA. This leads to insular ad-hoc solutions, each addressing only specific aspects of the problem. In this paper we propose Wintermute, a novel generic framework to enable online ODA on large-scale HPC installations. Its design is based on the results of a literature survey of common operational requirements. We implement Wintermute on top of the holistic DCDB monitoring system, offering a large variety of configuration options to accommodate the varying requirements of ODA applications. Moreover, Wintermute is based on a set of logical abstractions to ease the configuration of models at a large scale and maximize code re-use. We highlight Wintermute's flexibility through a series of practical case studies, each targeting a different aspect of the management of HPC systems, and then demonstrate the small resource footprint of our implementation.
Direct Energy-resolving CT Imaging via Energy-integrating CT images using a Unified Generative Adversarial Network
Authors: Lisha Yao, Sui Li, Manman Zhu, Dong Zeng, Zhaoying Bian, Jianhua Ma
Date: 2019-10-14 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Translation

Energy-resolving computed tomography (ErCT) has the ability to acquire energy-dependent measurements simultaneously and quantitative material information with improved contrast-to-noise ratio. Meanwhile, ErCT imaging system is usually equipped with an advanced photon counting detector, which is expensive and technically complex. Therefore, clinical ErCT scanners are not yet commercially available, and they are in various stage of completion. This makes the researchers less accessible to the ErCT images. In this work, we investigate to produce ErCT images directly from existing energy-integrating CT (EiCT) images via deep neural network. Specifically, different from other networks that produce ErCT images at one specific energy, this model employs a unified generative adversarial network (uGAN) to concurrently train EiCT datasets and ErCT datasets with different energies and then performs image-to-image translation from existing EiCT images to multiple ErCT image outputs at various energy bins. In this study, the present uGAN generates ErCT images at 70keV, 90keV, 110keV, and 130keV simultaneously from EiCT images at140kVp. We evaluate the present uGAN model on a set of over 1380 CT image slices and show that the present uGAN model can produce promising ErCT estimation results compared with the ground truth qualitatively and quantitatively.
Sampling-based sublinear low-rank matrix arithmetic framework for dequantizing quantum machine learning
Authors: Nai-Hui Chia, András Gilyén, Tongyang Li, Han-Hsuan Lin, Ewin Tang, Chunhao Wang
Date: 2019-10-14 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Quantum Machine Learning, Recommendation Systems

We present an algorithmic framework for quantum-inspired classical algorithms on close-to-low-rank matrices, generalizing the series of results started by Tang's breakthrough quantum-inspired algorithm for recommendation systems [STOC'19]. Motivated by quantum linear algebra algorithms and the quantum singular value transformation (SVT) framework of Gily\'en, Su, Low, and Wiebe [STOC'19], we develop classical algorithms for SVT that run in time independent of input dimension, under suitable quantum-inspired sampling assumptions. Our results give compelling evidence that in the corresponding QRAM data structure input model, quantum SVT does not yield exponential quantum speedups. Since the quantum SVT framework generalizes essentially all known techniques for quantum linear algebra, our results, combined with sampling lemmas from previous work, suffice to generalize all recent results about dequantizing quantum machine learning algorithms. In particular, our classical SVT framework recovers and often improves the dequantization results on recommendation systems, principal component analysis, supervised clustering, support vector machines, low-rank regression, and semidefinite program solving. We also give additional dequantization results on low-rank Hamiltonian simulation and discriminant analysis. Our improvements come from identifying the key feature of the quantum-inspired input model that is at the core of all prior quantum-inspired results: $\ell^2$-norm sampling can approximate matrix products in time independent of their dimension. We reduce all our main results to this fact, making our exposition concise, self-contained, and intuitive.
What's in my Room? Object Recognition on Indoor Panoramic Images
Authors: Julia Guerrero-Viu, Clara Fernandez-Labrador, Cédric Demonceaux, Jose J. Guerrero
Date: 2019-10-14 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Object Recognition, Segmentation, Semantic Segmentation

In the last few years, there has been a growing interest in taking advantage of the 360 panoramic images potential, while managing the new challenges they imply. While several tasks have been improved thanks to the contextual information these images offer, object recognition in indoor scenes still remains a challenging problem that has not been deeply investigated. This paper provides an object recognition system that performs object detection and semantic segmentation tasks by using a deep learning model adapted to match the nature of equirectangular images. From these results, instance segmentation masks are recovered, refined and transformed into 3D bounding boxes that are placed into the 3D model of the room. Quantitative and qualitative results support that our method outperforms the state of the art by a large margin and show a complete understanding of the main objects in indoor scenes.
Component Mismatches Are a Critical Bottleneck to Fielding AI-Enabled Systems in the Public Sector
Authors: Grace A. Lewis, Stephany Bellomo, April Galyardt
Date: 2019-10-14 00:00:00
Tasks: Descriptive

The use of machine learning or artificial intelligence (ML/AI) holds substantial potential toward improving many functions and needs of the public sector. In practice however, integrating ML/AI components into public sector applications is severely limited not only by the fragility of these components and their algorithms, but also because of mismatches between components of ML-enabled systems. For example, if an ML model is trained on data that is different from data in the operational environment, field performance of the ML component will be dramatically reduced. Separate from software engineering considerations, the expertise needed to field an ML/AI component within a system frequently comes from outside software engineering. As a result, assumptions and even descriptive language used by practitioners from these different disciplines can exacerbate other challenges to integrating ML/AI components into larger systems. We are investigating classes of mismatches in ML/AI systems integration, to identify the implicit assumptions made by practitioners in different fields (data scientists, software engineers, operations staff) and find ways to communicate the appropriate information explicitly. We will discuss a few categories of mismatch, and provide examples from each class. To enable ML/AI components to be fielded in a meaningful way, we will need to understand the mismatches that exist and develop practices to mitigate the impacts of these mismatches.
More Powerful Selective Kernel Tests for Feature Selection
Authors: Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira
Date: 2019-10-14 00:00:00
Tasks: feature selection, Selection bias

Refining one's hypotheses in the light of data is a common scientific practice; however, the dependency on the data introduces selection bias and can lead to specious statistical analysis. An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate our hypotheses, and prevent information to be used again after selection. Many selective inference (a.k.a. post-selection inference) algorithms typically take this approach but will "over-condition" for sake of tractability. While this practice yields well calibrated statistic tests with controlled false positive rates (FPR), it can incur a major loss in power. In our work, we extend two recent proposals for selecting features using the Maximum Mean Discrepancy and Hilbert Schmidt Independence Criterion to condition on the minimal conditioning event. We show how recent advances in multiscale bootstrap makes conditioning on the minimal selection event possible and demonstrate our proposal over a range of synthetic and real world experiments. Our results show that our proposed test is indeed more powerful in most scenarios.
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation
Authors: Marko Järvenpää, Aki Vehtari, Pekka Marttinen
Date: 2019-10-14 00:00:00
Tasks: Bayesian Optimisation, Computational Efficiency, Experimental Design, Gaussian Processes, Uncertainty Quantification

The computational efficiency of approximate Bayesian computation (ABC) has been improved by using surrogate models such as Gaussian processes (GP). In one such promising framework the discrepancy between the simulated and observed data is modelled with a GP which is further used to form a model-based estimator for the intractable posterior. In this article we improve this approach in several ways. We develop batch-sequential Bayesian experimental design strategies to parallellise the expensive simulations. In earlier work only sequential strategies have been used. Current surrogate-based ABC methods also do not fully account the uncertainty due to the limited budget of simulations as they output only a point estimate of the ABC posterior. We propose a numerical method to fully quantify the uncertainty in, for example, ABC posterior moments. We also provide some new analysis on the GP modelling assumptions in the resulting improved framework called Bayesian ABC and discuss its connection to Bayesian quadrature (BQ) and Bayesian optimisation (BO). Experiments with toy and real-world simulation models demonstrate advantages of the proposed techniques.
SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules
Authors: Irfan Al-Hussaini, Cao Xiao, M. Brandon Westover, Jimeng Sun
Date: 2019-10-14 00:00:00
Tasks: Automatic Sleep Stage Classification, Sleep Stage Detection, Sleep Staging

Sleep staging is a crucial task for diagnosing sleep disorders. It is tedious and complex as it can take a trained expert several hours to annotate just one patient's polysomnogram (PSG) from a single night. Although deep learning models have demonstrated state-of-the-art performance in automating sleep staging, interpretability which defines other desiderata, has largely remained unexplored. In this study, we propose Sleep staging via Prototypes from Expert Rules (SLEEPER), which combines deep learning models with expert defined rules using a prototype learning framework to generate simple interpretable models. In particular, SLEEPER utilizes sleep scoring rules and expert defined features to derive prototypes which are embeddings of PSG data fragments via convolutional neural networks. The final models are simple interpretable models like a shallow decision tree defined over those phenotypes. We evaluated SLEEPER using two PSG datasets collected from sleep studies and demonstrated that SLEEPER could provide accurate sleep stage classification comparable to human experts and deep neural networks with about 85% ROC-AUC and .7 kappa.
ReActNet: Temporal Localization of Repetitive Activities in Real-World Videos
Authors: Giorgos Karvounas, Iason Oikonomidis, Antonis Argyros
Date: 2019-10-14 00:00:00
Tasks: Temporal Localization

We address the problem of temporal localization of repetitive activities in a video, i.e., the problem of identifying all segments of a video that contain some sort of repetitive or periodic motion. To do so, the proposed method represents a video by the matrix of pairwise frame distances. These distances are computed on frame representations obtained with a convolutional neural network. On top of this representation, we design, implement and evaluate ReActNet, a lightweight convolutional neural network that classifies a given frame as belonging (or not) to a repetitive video segment. An important property of the employed representation is that it can handle repetitive segments of arbitrary number and duration. Furthermore, the proposed training process requires a relatively small number of annotated videos. Our method raises several of the limiting assumptions of existing approaches regarding the contents of the video and the types of the observed repetitive activities. Experimental results on recent, publicly available datasets validate our design choices, verify the generalization potential of ReActNet and demonstrate its superior performance in comparison to the current state of the art.
Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks
Authors: Jy-yong Sohn, Dong-Jun Han, Beongjun Choi, Jaekyun Moon
Date: 2019-10-14 00:00:00
Proceeding: NeurIPS 2020 12

Recent advances in large-scale distributed learning algorithms have enabled communication-efficient training via SignSGD. Unfortunately, a major issue continues to plague distributed learning: namely, Byzantine failures may incur serious degradation in learning accuracy. This paper proposes Election Coding, a coding-theoretic framework to guarantee Byzantine-robustness for SignSGD with Majority Vote, which uses minimum worker-master communication in both directions. The suggested framework explores new information-theoretic limits of finding the majority opinion when some workers could be malicious, and paves the road to implement robust and efficient distributed learning algorithms. Under this framework, we construct two types of explicit codes, random Bernoulli codes and deterministic algebraic codes, that can tolerate Byzantine attacks with a controlled amount of computational redundancy. For the Bernoulli codes, we provide upper bounds on the error probability in estimating the majority opinion, which give useful insights into code design for tolerating Byzantine attacks. As for deterministic codes, we construct an explicit code which perfectly tolerates Byzantines, and provide tight upper/lower bounds on the minimum required computational redundancy. Finally, the Byzantine-tolerance of the suggested coding schemes is confirmed by deep learning experiments on Amazon EC2 using Python with MPI4py package.