pwc-archive/papers-with-abstracts huggingface dataset:
How do Mixture Density RNNs Predict the Future?
Authors: Kai Olav Ellefsen, Charles Patrick Martin, Jim Torresen
Date: 2019-01-23 00:00:00

Gaining a better understanding of how and what machine learning systems learn
is important to increase confidence in their decisions and catalyze further
research. In this paper, we analyze the predictions made by a specific type of
recurrent neural network, mixture density RNNs (MD-RNNs). These networks learn
to model predictions as a combination of multiple Gaussian distributions,
making them particularly interesting for problems where a sequence of inputs
may lead to several distinct future possibilities. An example is learning
internal models of an environment, where different events may or may not occur,
but where the average over different events is not meaningful. By analyzing the
predictions made by trained MD-RNNs, we find that their different Gaussian
components have two complementary roles: 1) Separately modeling different
stochastic events and 2) Separately modeling scenarios governed by different
rules. These findings increase our understanding of what is learned by
predictive MD-RNNs, and open up new research directions for further
understanding how we can benefit from their self-organizing model
decomposition.
Evolving the pulmonary nodules diagnosis from classical approaches to deep learning aided decision support: three decades development course and future prospect
Authors: Bo Liu, Wenhao Chi, Xinran Li, Peng Li, Wenhua Liang, Haiping Liu, Wei Wang, Jianxing He
Date: 2019-01-23 00:00:00

Lung cancer is the commonest cause of cancer deaths worldwide, and its mortality can be reduced significantly by performing early diagnosis and screening. Since the 1960s, driven by the pressing needs to accurately and effectively interpret the massive volume of chest images generated daily, computer-assisted diagnosis of pulmonary nodule has opened up new opportunities to relax the limitation from physicians' subjectivity, experiences and fatigue. And the fair access to the reliable and affordable computer-assisted diagnosis will fight the inequalities in incidence and mortality between populations. It has been witnessed that significant and remarkable advances have been achieved since the 1980s, and consistent endeavors have been exerted to deal with the grand challenges on how to accurately detect the pulmonary nodules with high sensitivity at low false-positives rate as well as on how to precisely differentiate between benign and malignant nodules. There is a lack of comprehensive examination of the techniques' development which is evolving the pulmonary nodules diagnosis from classical approaches to machine learning-assisted decision support. The main goal of this investigation is to provide a comprehensive state-of-the-art review of the computer-assisted nodules detection and benign-malignant classification techniques developed over 3 decades, which have evolved from the complicated ad hoc analysis pipeline of conventional approaches to the simplified seamlessly integrated deep learning techniques. This review also identifies challenges and highlights opportunities for future work in learning models, learning algorithms and enhancement schemes for bridging current state to future prospect and satisfying future demand.
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test
Authors: Ke-Wei Huang, Mengke Qiao, Xuanqi Liu, Siyuan Liu, Mingxi Dai
Date: 2019-01-23 00:00:00
Tasks: Dimensionality Reduction, Metric Learning, Representation Learning, Two-sample testing

This paper proposes a new deep-learning method to construct test statistics by computer vision and metrics learning. The application highlighted in this paper is applying computer vision on Q-Q plot to construct a new test statistic for normality test. To the best of our knowledge, there is no similar application documented in the literature. Traditionally, there are two families of approaches for verifying the probability distribution of a random variable. Researchers either subjectively assess the Q-Q plot or objectively use a mathematical formula, such as Kolmogorov-Smirnov test, to formally conduct a normality test. Graphical assessment by human beings is not rigorous whereas normality test statistics may not be accurate enough when the uniformly most powerful test does not exist. It may take tens of years for statistician to develop a new test statistic that is more powerful statistically. Our proposed method integrates four components based on deep learning: an image representation learning component of a Q-Q plot, a dimension reduction component, a metrics learning component that best quantifies the differences between two Q-Q plots for normality test, and a new normality hypothesis testing process. Our experimentation results show that the machine-learning-based test statistics can outperform several widely-used traditional normality tests. This study provides convincing evidence that the proposed method could objectively create a powerful test statistic based on Q-Q plots and this method could be modified to construct many more powerful test statistics for other applications in the future.
AADS: Augmented Autonomous Driving Simulation using Data-driven Algorithms
Authors: Wei Li, Chengwei Pan, Rong Zhang, Jiaping Ren, Yuexin Ma, Jin Fang, Feilong Yan, Qichuan Geng, Xinyu Huang, Huajun Gong, Weiwei Xu, Guoping Wang, Dinesh Manocha, Ruigang Yang
Date: 2019-01-23 00:00:00
Tasks: Autonomous Driving

Simulation systems have become an essential component in the development and validation of autonomous driving technologies. The prevailing state-of-the-art approach for simulation is to use game engines or high-fidelity computer graphics (CG) models to create driving scenarios. However, creating CG models and vehicle movements (e.g., the assets for simulation) remains a manual task that can be costly and time-consuming. In addition, the fidelity of CG images still lacks the richness and authenticity of real-world images and using these images for training leads to degraded performance. In this paper we present a novel approach to address these issues: Augmented Autonomous Driving Simulation (AADS). Our formulation augments real-world pictures with a simulated traffic flow to create photo-realistic simulation images and renderings. More specifically, we use LiDAR and cameras to scan street scenes. From the acquired trajectory data, we generate highly plausible traffic flows for cars and pedestrians and compose them into the background. The composite images can be re-synthesized with different viewpoints and sensor models. The resulting images are photo-realistic, fully annotated, and ready for end-to-end training and testing of autonomous driving systems from perception to planning. We explain our system design and validate our algorithms with a number of autonomous driving tasks from detection to segmentation and predictions. Compared to traditional approaches, our method offers unmatched scalability and realism. Scalability is particularly important for AD simulation and we believe the complexity and diversity of the real world cannot be realistically captured in a virtual environment. Our augmented approach combines the flexibility in a virtual environment (e.g., vehicle movements) with the richness of the real world to allow effective simulation of anywhere on earth.
Reinforcement Learning of Markov Decision Processes with Peak Constraints
Authors: Ather Gattami
Date: 2019-01-23 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we consider reinforcement learning of Markov Decision Processes (MDP) with peak constraints, where an agent chooses a policy to optimize an objective and at the same time satisfy additional constraints. The agent has to take actions based on the observed states, reward outputs, and constraint-outputs, without any knowledge about the dynamics, reward functions, and/or the knowledge of the constraint-functions. We introduce a game theoretic approach to construct reinforcement learning algorithms where the agent maximizes an unconstrained objective that depends on the simulated action of the minimizing opponent which acts on a finite set of actions and the output data of the constraint functions (rewards). We show that the policies obtained from maximin Q-learning converge to the optimal policies. To the best of our knowledge, this is the first time learning algorithms guarantee convergence to optimal stationary policies for the MDP problem with peak constraints for both discounted and expected average rewards.
Toward Joint Image Generation and Compression using Generative Adversarial Networks
Authors: Byeongkeun Kang, Subarna Tripathi, Truong Q. Nguyen
Date: 2019-01-23 00:00:00
Tasks: Generative Adversarial Network, Image Compression, Image Generation, Quantization

In this paper, we present a generative adversarial network framework that
generates compressed images instead of synthesizing raw RGB images and
compressing them separately. In the real world, most images and videos are
stored and transferred in a compressed format to save storage capacity and data
transfer bandwidth. However, since typical generative adversarial networks
generate raw RGB images, those generated images need to be compressed by a
post-processing stage to reduce the data size. Among image compression methods,
JPEG has been one of the most commonly used lossy compression methods for still
images. Hence, we propose a novel framework that generates JPEG compressed
images using generative adversarial networks. The novel generator consists of
the proposed locally connected layers, chroma subsampling layers, quantization
layers, residual blocks, and convolution layers. The locally connected layer is
proposed to enable block-based operations. We also discuss training strategies
for the proposed architecture including the loss function and the
transformation between its generator and its discriminator. The proposed method
is evaluated using the publicly available CIFAR-10 dataset and LSUN bedroom
dataset. The results demonstrate that the proposed method is able to generate
compressed data with competitive qualities. The proposed method is a promising
baseline method for joint image generation and compression using generative
adversarial networks.
AspeRa: Aspect-based Rating Prediction Model
Authors: Sergey I. Nikolenko, Elena Tutubalina, Valentin Malykh, Ilya Shenbin, Anton Alekseev
Date: 2019-01-23 00:00:00
Tasks: model, Prediction, Recommendation Systems

We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa)
that estimates user rating based on review texts for the items and at the same
time discovers coherent aspects of reviews that can be used to explain
predictions or profile users. The AspeRa model uses max-margin losses for joint
item and user embedding learning and a dual-headed architecture; it
significantly outperforms recently proposed state-of-the-art models such as
DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews.
With qualitative examination of the aspects and quantitative evaluation of
rating prediction models based on these aspects, we show how aspect embeddings
can be used in a recommender system.
Random Forest with Learned Representations for Semantic Segmentation
Authors: Byeongkeun Kang, Truong Q. Nguyen
Date: 2019-01-23 00:00:00
Tasks: Hand Segmentation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

In this work, we present a random forest framework that learns the weights,
shapes, and sparsities of feature representations for real-time semantic
segmentation. Typical filters (kernels) have predetermined shapes and
sparsities and learn only weights. A few feature extraction methods fix weights
and learn only shapes and sparsities. These predetermined constraints restrict
learning and extracting optimal features. To overcome this limitation, we
propose an unconstrained representation that is able to extract optimal
features by learning weights, shapes, and sparsities. We, then, present the
random forest framework that learns the flexible filters using an iterative
optimization algorithm and segments input images using the learned
representations. We demonstrate the effectiveness of the proposed method using
a hand segmentation dataset for hand-object interaction and using two semantic
segmentation datasets. The results show that the proposed method achieves
real-time semantic segmentation using limited computational and memory
resources.
Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning
Authors: Shaohui Lin, Rongrong Ji, Yuchao Li, Cheng Deng, Xuelong. Li
Date: 2019-01-23 00:00:00
Tasks: Domain Adaptation, object-detection, Object Detection, Transfer Learning

The success of convolutional neural networks (CNNs) in computer vision
applications has been accompanied by a significant increase of computation and
memory costs, which prohibits its usage on resource-limited environments such
as mobile or embedded devices. To this end, the research of CNN compression has
recently become emerging. In this paper, we propose a novel filter pruning
scheme, termed structured sparsity regularization (SSR), to simultaneously
speedup the computation and reduce the memory overhead of CNNs, which can be
well supported by various off-the-shelf deep learning libraries. Concretely,
the proposed scheme incorporates two different regularizers of structured
sparsity into the original objective function of filter pruning, which fully
coordinates the global outputs and local pruning operations to adaptively prune
filters. We further propose an Alternative Updating with Lagrange Multipliers
(AULM) scheme to efficiently solve its optimization. AULM follows the principle
of ADMM and alternates between promoting the structured sparsity of CNNs and
optimizing the recognition loss, which leads to a very efficient solver (2.5x
to the most recent work that directly solves the group sparsity-based
regularization). Moreover, by imposing the structured sparsity, the online
inference is extremely memory-light, since the number of filters and the output
feature maps are simultaneously reduced. The proposed scheme has been deployed
to a variety of state-of-the-art CNN structures including LeNet, AlexNet, VGG,
ResNet and GoogLeNet over different datasets. Quantitative results demonstrate
that the proposed scheme achieves superior performance over the
state-of-the-art methods. We further demonstrate the proposed compression
scheme for the task of transfer learning, including domain adaptation and
object detection, which also show exciting performance gains over the
state-of-the-arts.
Predicting Parkinson's Disease using Latent Information extracted from Deep Neural Networks
Authors: Ilianna Kollia, Andreas-Georgios Stafylopatis, Stefanos Kollias
Date: 2019-01-23 00:00:00
Tasks: Clustering, General Classification, Medical Diagnosis, Transfer Learning

This paper presents a new method for medical diagnosis of neurodegenerative
diseases, such as Parkinson's, by extracting and using latent information from
trained Deep convolutional, or convolutional-recurrent Neural Networks (DNNs).
In particular, our approach adopts a combination of transfer learning, k-means
clustering and k-Nearest Neighbour classification of deep neural network
learned representations to provide enriched prediction of the disease based on
MRI and/or DaT Scan data. A new loss function is introduced and used in the
training of the DNNs, so as to perform adaptation of the generated learned
representations between data from different medical environments. Results are
presented using a recently published database of Parkinson's related
information, which was generated and evaluated in a hospital environment.
Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff
Authors: Yochai Blau, Tomer Michaeli
Date: 2019-01-23 00:00:00
Tasks: SSIM

Lossy compression algorithms are typically designed and analyzed through the lens of Shannon's rate-distortion theory, where the goal is to achieve the lowest possible distortion (e.g., low MSE or high SSIM) at any given bit rate. However, in recent years, it has become increasingly accepted that "low distortion" is not a synonym for "high perceptual quality", and in fact optimization of one often comes at the expense of the other. In light of this understanding, it is natural to seek for a generalization of rate-distortion theory which takes perceptual quality into account. In this paper, we adopt the mathematical definition of perceptual quality recently proposed by Blau & Michaeli (2018), and use it to study the three-way tradeoff between rate, distortion, and perception. We show that restricting the perceptual quality to be high, generally leads to an elevation of the rate-distortion curve, thus necessitating a sacrifice in either rate or distortion. We prove several fundamental properties of this triple-tradeoff, calculate it in closed form for a Bernoulli source, and illustrate it visually on a toy MNIST example.
Self-Attentive Model for Headline Generation
Authors: Daniil Gavrilov, Pavel Kalaidin, Valentin Malykh
Date: 2019-01-23 00:00:00
Tasks: Articles, Headline Generation, model, Text Summarization

Headline generation is a special type of text summarization task. While the
amount of available training data for this task is almost unlimited, it still
remains challenging, as learning to generate headlines for news articles
implies that the model has strong reasoning about natural language. To overcome
this issue, we applied recent Universal Transformer architecture paired with
byte-pair encoding technique and achieved new state-of-the-art results on the
New York Times Annotated corpus with ROUGE-L F1-score 24.84 and ROUGE-2
F1-score 13.48. We also present the new RIA corpus and reach ROUGE-L F1-score
36.81 and ROUGE-2 F1-score 22.15 on it.
Stochastic Gradient Trees
Authors: Henry Gouk, Bernhard Pfahringer, Eibe Frank
Date: 2019-01-23 00:00:00
Tasks: Classification, General Classification, Incremental Learning, Multi-Label Classification, regression

We present an algorithm for learning decision trees using stochastic gradient information as the source of supervision. In contrast to previous approaches to gradient-based tree learning, our method operates in the incremental learning setting rather than the batch learning setting, and does not make use of soft splits or require the construction of a new tree for every update. We demonstrate how one can apply these decision trees to different problems by changing only the loss function, using classification, regression, and multi-instance learning as example applications. In the experimental evaluation, our method performs similarly to standard incremental classification trees, outperforms state of the art incremental regression trees, and achieves comparable performance with batch multi-instance learning methods.
Programmable Neural Network Trojan for Pre-Trained Feature Extractor
Authors: Yu Ji, Zixin Liu, Xing Hu, Peiqi Wang, Youhui Zhang
Date: 2019-01-23 00:00:00
Tasks: Transfer Learning

Neural network (NN) trojaning attack is an emerging and important attack
model that can broadly damage the system deployed with NN models. Existing
studies have explored the outsourced training attack scenario and transfer
learning attack scenario in some small datasets for specific domains, with
limited numbers of fixed target classes. In this paper, we propose a more
powerful trojaning attack method for both outsourced training attack and
transfer learning attack, which outperforms existing studies in the capability,
generality, and stealthiness. First, The attack is programmable that the
malicious misclassification target is not fixed and can be generated on demand
even after the victim's deployment. Second, our trojan attack is not limited in
a small domain; one trojaned model on a large-scale dataset can affect
applications of different domains that reuse its general features. Thirdly, our
trojan design is hard to be detected or eliminated even if the victims
fine-tune the whole model.
A Boost in Revealing Subtle Facial Expressions: A Consolidated Eulerian Framework
Authors: Wei Peng, Xiaopeng Hong, Yingyue Xu, Guoying Zhao
Date: 2019-01-23 00:00:00
Tasks: Micro Expression Recognition, Micro-Expression Recognition, Motion Magnification

Facial Micro-expression Recognition (MER) distinguishes the underlying
emotional states of spontaneous subtle facialexpressions. Automatic MER is
challenging because that 1) the intensity of subtle facial muscle movement is
extremely lowand 2) the duration of ME is transient.Recent works adopt motion
magnification or time interpolation to resolve these issues. Nevertheless,
existing works dividethem into two separate modules due to their non-linearity.
Though such operation eases the difficulty in implementation, itignores their
underlying connections and thus results in inevitable losses in both accuracy
and speed. Instead, in this paper, weexplore their underlying joint
formulations and propose a consolidated Eulerian framework to reveal the subtle
facial movements.It expands the temporal duration and amplifies the muscle
movements in micro-expressions simultaneously. Compared toexisting approaches,
the proposed method can not only process ME clips more efficiently but also
make subtle ME movementsmore distinguishable. Experiments on two public MER
databases indicate that our model outperforms the state-of-the-art inboth speed
and accuracy.
A deep Convolutional Neural Network for topology optimization with strong generalization ability
Authors: Yiquan Zhang, Bo Peng, Xiaoyi Zhou, Cheng Xiang, Dalei Wang
Date: 2019-01-23 00:00:00

This paper proposes a deep Convolutional Neural Network(CNN) with strong generalization ability for structural topology optimization. The architecture of the neural network is made up of encoding and decoding parts, which provide down- and up-sampling operations. In addition, a popular technique, namely U-Net, was adopted to improve the performance of the proposed neural network. The input of the neural network is a well-designed tensor with each channel includes different information for the problem, and the output is the layout of the optimal structure. To train the neural network, a large dataset is generated by a conventional topology optimization approach, i.e. SIMP. The performance of the proposed method was evaluated by comparing its efficiency and accuracy with SIMP on a series of typical optimization problems. Results show that a significant reduction in computation cost was achieved with little sacrifice on the optimality of design solutions. Furthermore, the proposed method can intelligently solve problems under boundary conditions not being included in the training dataset.
Robust Learning at Noisy Labeled Medical Images: Applied to Skin Lesion Classification
Authors: Cheng Xue, Qi Dou, Xueying Shi, Hao Chen, Pheng Ann Heng
Date: 2019-01-23 00:00:00
Tasks: General Classification, image-classification, Image Classification, Lesion Classification, Medical Image Analysis, Medical Image Classification, Skin Lesion Classification

Deep neural networks (DNNs) have achieved great success in a wide variety of
medical image analysis tasks. However, these achievements indispensably rely on
the accurately-annotated datasets. If with the noisy-labeled images, the
training procedure will immediately encounter difficulties, leading to a
suboptimal classifier. This problem is even more crucial in the medical field,
given that the annotation quality requires great expertise. In this paper, we
propose an effective iterative learning framework for noisy-labeled medical
image classification, to combat the lacking of high quality annotated medical
data. Specifically, an online uncertainty sample mining method is proposed to
eliminate the disturbance from noisy-labeled images. Next, we design a sample
re-weighting strategy to preserve the usefulness of correctly-labeled hard
samples. Our proposed method is validated on skin lesion classification task,
and achieved very promising results.