pwc-archive/papers-with-abstracts huggingface dataset:
Progressive Image Deraining Networks: A Better and Simpler Baseline
Authors: Dongwei Ren, WangMeng Zuo, QinGhua Hu, Pengfei Zhu, Deyu Meng
Date: 2019-01-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Super-Resolution, Rain Removal, Single Image Deraining, SSIM

Along with the deraining performance improvement of deep networks, their structures and learning become more and more complicated and diverse, making it difficult to analyze the contribution of various network modules when developing new deraining networks. To handle this issue, this paper provides a better and simpler baseline deraining network by considering network architecture, input and output, and loss functions. Specifically, by repeatedly unfolding a shallow ResNet, progressive ResNet (PRN) is proposed to take advantage of recursive computation. A recurrent layer is further introduced to exploit the dependencies of deep features across stages, forming our progressive recurrent network (PReNet). Furthermore, intra-stage recursive computation of ResNet can be adopted in PRN and PReNet to notably reduce network parameters with graceful degradation in deraining performance. For network input and output, we take both stage-wise result and original rainy image as input to each ResNet and finally output the prediction of {residual image}. As for loss functions, single MSE or negative SSIM losses are sufficient to train PRN and PReNet. Experiments show that PRN and PReNet perform favorably on both synthetic and real rainy images. Considering its simplicity, efficiency and effectiveness, our models are expected to serve as a suitable baseline in future deraining research. The source codes are available at https://github.com/csdwren/PReNet.
Implicit Dimension Identification in User-Generated Text with LSTM Networks
Authors: Victor Makarenkov, Ido Guy, Niva Hazon, Tamar Meisels, Bracha Shapira, Lior Rokach
Date: 2019-01-26 00:00:00
Tasks: Articles, Community Question Answering, Question Answering

In the process of online storytelling, individual users create and consume
highly diverse content that contains a great deal of implicit beliefs and not
plainly expressed narrative. It is hard to manually detect these implicit
beliefs, intentions and moral foundations of the writers. We study and
investigate two different tasks, each of which reflect the difficulty of
detecting an implicit user's knowledge, intent or belief that may be based on
writer's moral foundation: 1) political perspective detection in news articles
2) identification of informational vs. conversational questions in community
question answering (CQA) archives and. In both tasks we first describe new
interesting annotated datasets and make the datasets publicly available.
Second, we compare various classification algorithms, and show the differences
in their performance on both tasks. Third, in political perspective detection
task we utilize a narrative representation language of local press to identify
perspective differences between presumably neutral American and British press.
Modelling Bounded Rationality in Multi-Agent Interactions by Generalized Recursive Reasoning
Authors: Ying Wen, Yaodong Yang, Rui Luo, Jun Wang
Date: 2019-01-26 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning, Reinforcement Learning

Though limited in real-world decision making, most multi-agent reinforcement learning (MARL) models assume perfectly rational agents -- a property hardly met due to individual's cognitive limitation and/or the tractability of the decision problem. In this paper, we introduce generalized recursive reasoning (GR2) as a novel framework to model agents with different \emph{hierarchical} levels of rationality; our framework enables agents to exhibit varying levels of "thinking" ability thereby allowing higher-level agents to best respond to various less sophisticated learners. We contribute both theoretically and empirically. On the theory side, we devise the hierarchical framework of GR2 through probabilistic graphical models and prove the existence of a perfect Bayesian equilibrium. Within the GR2, we propose a practical actor-critic solver, and demonstrate its convergent property to a stationary point in two-player games through Lyapunov analysis. On the empirical side, we validate our findings on a variety of MARL benchmarks. Precisely, we first illustrate the hierarchical thinking process on the Keynes Beauty Contest, and then demonstrate significant improvements compared to state-of-the-art opponent modeling baselines on the normal-form games and the cooperative navigation benchmark.
Intrinsically Sparse Long Short-Term Memory Networks
Authors: Shiwei Liu, Decebal Constantin Mocanu, Mykola Pechenizkiy
Date: 2019-01-26 00:00:00
Tasks: Model Compression, Sentiment Analysis

Long Short-Term Memory (LSTM) has achieved state-of-the-art performances on a
wide range of tasks. Its outstanding performance is guaranteed by the long-term
memory ability which matches the sequential data perfectly and the gating
structure controlling the information flow. However, LSTMs are prone to be
memory-bandwidth limited in realistic applications and need an unbearable
period of training and inference time as the model size is ever-increasing. To
tackle this problem, various efficient model compression methods have been
proposed. Most of them need a big and expensive pre-trained model which is a
nightmare for resource-limited devices where the memory budget is strictly
limited. To remedy this situation, in this paper, we incorporate the Sparse
Evolutionary Training (SET) procedure into LSTM, proposing a novel model dubbed
SET-LSTM. Rather than starting with a fully-connected architecture, SET-LSTM
has a sparse topology and dramatically fewer parameters in both phases,
training and inference. Considering the specific architecture of LSTMs, we
replace the LSTM cells and embedding layers with sparse structures and further
on, use an evolutionary strategy to adapt the sparse connectivity to the data.
Additionally, we find that SET-LSTM can provide many different good
combinations of sparse connectivity to substitute the overparameterized
optimization problem of dense neural networks. Evaluated on four sentiment
analysis classification datasets, the results demonstrate that our proposed
model is able to achieve usually better performance than its fully connected
counterpart while having less than 4\% of its parameters.
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning
Authors: Ying Wen, Yaodong Yang, Rui Luo, Jun Wang, Wei Pan
Date: 2019-01-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans are capable of attributing latent mental contents such as beliefs or
intentions to others. The social skill is critical in daily life for reasoning
about the potential consequences of others' behaviors so as to plan ahead. It
is known that humans use such reasoning ability recursively by considering what
others believe about their own beliefs. In this paper, we start from level-$1$
recursion and introduce a probabilistic recursive reasoning (PR2) framework for
multi-agent reinforcement learning. Our hypothesis is that it is beneficial for
each agent to account for how the opponents would react to its future
behaviors. Under the PR2 framework, we adopt variational Bayes methods to
approximate the opponents' conditional policies, to which each agent finds the
best response and then improve their own policies. We develop
decentralized-training-decentralized-execution algorithms, namely PR2-Q and
PR2-Actor-Critic, that are proved to converge in the self-play scenarios when
there exists one Nash equilibrium. Our methods are tested on both the matrix
game and the differential game, which have a non-trivial equilibrium where
common gradient-based methods fail to converge. Our experiments show that it is
critical to reason about how the opponents believe about what the agent
believes. We expect our work to contribute a new idea of modeling the opponents
to the multi-agent reinforcement learning community.
Kernel-Guided Training of Implicit Generative Models with Stability Guarantees
Authors: Arash Mehrjou, Wittawat Jitkrittum, Krikamol Muandet, Bernhard Schölkopf
Date: 2019-01-26 00:00:00

Modern implicit generative models such as generative adversarial networks (GANs) are generally known to suffer from issues such as instability, uninterpretability, and difficulty in assessing their performance. If we see these implicit models as dynamical systems, some of these issues are caused by being unable to control their behavior in a meaningful way during the course of training. In this work, we propose a theoretically grounded method to guide the training trajectories of GANs by augmenting the GAN loss function with a kernel-based regularization term that controls local and global discrepancies between the model and true distributions. This control signal allows us to inject prior knowledge into the model. We provide theoretical guarantees on the stability of the resulting dynamical system and demonstrate different aspects of it via a wide range of experiments.
ACNN: a Full Resolution DCNN for Medical Image Segmentation
Authors: Xiao-Yun Zhou, Jian-Qing Zheng, Peichao Li, Guang-Zhong Yang
Date: 2019-01-26 00:00:00
Tasks: Computed Tomography (CT), Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

Deep Convolutional Neural Networks (DCNNs) are used extensively in medical image segmentation and hence 3D navigation for robot-assisted Minimally Invasive Surgeries (MISs). However, current DCNNs usually use down sampling layers for increasing the receptive field and gaining abstract semantic information. These down sampling layers decrease the spatial dimension of feature maps, which can be detrimental to image segmentation. Atrous convolution is an alternative for the down sampling layer. It increases the receptive field whilst maintains the spatial dimension of feature maps. In this paper, a method for effective atrous rate setting is proposed to achieve the largest and fully-covered receptive field with a minimum number of atrous convolutional layers. Furthermore, a new and full resolution DCNN - Atrous Convolutional Neural Network (ACNN), which incorporates cascaded atrous II-blocks, residual learning and Instance Normalization (IN) is proposed. Application results of the proposed ACNN to Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) image segmentation demonstrate that the proposed ACNN can achieve higher segmentation Intersection over Unions (IoUs) than U-Net and Deeplabv3+, but with reduced trainable parameters.
Deep Convolutional Encoder-Decoders with Aggregated Multi-Resolution Skip Connections for Skin Lesion Segmentation
Authors: Ahmed H. Shahin, Karim Amer, Mustafa A. Elattar
Date: 2019-01-26 00:00:00
Tasks: Decoder, Image Segmentation, Lesion Segmentation, Scene Parsing, Segmentation, Semantic Segmentation, Skin Lesion Segmentation

The prevalence of skin melanoma is rapidly increasing as well as the recorded
death cases of its patients. Automatic image segmentation tools play an
important role in providing standardized computer-assisted analysis for skin
melanoma patients. Current state-of-the-art segmentation methods are based on
fully convolutional neural networks, which utilize an encoder-decoder approach.
However, these methods produce coarse segmentation masks due to the loss of
location information during the encoding layers. Inspired by Pyramid Scene
Parsing Network (PSP-Net), we propose an encoder-decoder model that utilizes
pyramid pooling modules in the deep skip connections which aggregate the global
context and compensate for the lost spatial information. We trained and
validated our approach using ISIC 2018: Skin Lesion Analysis Towards Melanoma
Detection grand challenge dataset. Our approach showed a validation accuracy
with a Jaccard index of 0.837, which outperforms U-Net. We believe that with
this reported reliable accuracy, this method can be introduced for clinical
practice.
Scene Text Synthesis for Efficient and Effective Deep Network Training
Authors: Changgong Zhang, Fangneng Zhan, Hongyuan Zhu, Shijian Lu
Date: 2019-01-26 00:00:00
Tasks: Image Generation, Scene Text Detection, Scene Text Recognition, Text Detection

A large amount of annotated training images is critical for training accurate and robust deep network models but the collection of a large amount of annotated training images is often time-consuming and costly. Image synthesis alleviates this constraint by generating annotated training images automatically by machines which has attracted increasing interest in the recent deep learning research. We develop an innovative image synthesis technique that composes annotated training images by realistically embedding foreground objects of interest (OOI) into background images. The proposed technique consists of two key components that in principle boost the usefulness of the synthesized images in deep network training. The first is context-aware semantic coherence which ensures that the OOI are placed around semantically coherent regions within the background image. The second is harmonious appearance adaptation which ensures that the embedded OOI are agreeable to the surrounding background from both geometry alignment and appearance realism. The proposed technique has been evaluated over two related but very different computer vision challenges, namely, scene text detection and scene text recognition. Experiments over a number of public datasets demonstrate the effectiveness of our proposed image synthesis technique - the use of our synthesized images in deep network training is capable of achieving similar or even better scene text detection and scene text recognition performance as compared with using real images.
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
Authors: Yonatan Geifman, Ran El-Yaniv
Date: 2019-01-26 00:00:00
Tasks: Classification, General Classification, Prediction, regression

We consider the problem of selective prediction (also known as reject option) in deep neural networks, and introduce SelectiveNet, a deep neural architecture with an integrated reject option. Existing rejection mechanisms are based mostly on a threshold over the prediction confidence of a pre-trained network. In contrast, SelectiveNet is trained to optimize both classification (or regression) and rejection simultaneously, end-to-end. The result is a deep neural network that is optimized over the covered domain. In our experiments, we show a consistently improved risk-coverage trade-off over several well-known classification and regression datasets, thus reaching new state-of-the-art results for deep selective classification.
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm
Authors: Ricards Marcinkevics, Steven Kelk, Carlo Galuzzi, Berthold Stegemann
Date: 2019-01-26 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Medical Diagnosis, speech-recognition, Speech Recognition, Time Series, Time Series Analysis

The classification of time series data is a well-studied problem with
numerous practical applications, such as medical diagnosis and speech
recognition. A popular and effective approach is to classify new time series in
the same way as their nearest neighbours, whereby proximity is defined using
Dynamic Time Warping (DTW) distance, a measure analogous to sequence alignment
in bioinformatics. However, practitioners are not only interested in accurate
classification, they are also interested in why a time series is classified a
certain way. To this end, we introduce here the problem of finding a minimum
length subsequence of a time series, the removal of which changes the outcome
of the classification under the nearest neighbour algorithm with DTW distance.
Informally, such a subsequence is expected to be relevant for the
classification and can be helpful for practitioners in interpreting the
outcome. We describe a simple but optimized implementation for detecting these
subsequences and define an accompanying measure to quantify the relevance of
every time point in the time series for the classification. In tests on
electrocardiogram data we show that the algorithm allows discovery of important
subsequences and can be helpful in detecting abnormalities in cardiac rhythms
distinguishing sick from healthy patients.
Action Robust Reinforcement Learning and Applications in Continuous Control
Authors: Chen Tessler, Yonathan Efroni, Shie Mannor
Date: 2019-01-26 00:00:00
Tasks: continuous-control, Continuous Control, Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A policy is said to be robust if it maximizes the reward while considering a bad, or even adversarial, model. In this work we formalize two new criteria of robustness to action uncertainty. Specifically, we consider two scenarios in which the agent attempts to perform an action $a$, and (i) with probability $\alpha$, an alternative adversarial action $\bar a$ is taken, or (ii) an adversary adds a perturbation to the selected action in the case of continuous action space. We show that our criteria are related to common forms of uncertainty in robotics domains, such as the occurrence of abrupt forces, and suggest algorithms in the tabular case. Building on the suggested algorithms, we generalize our approach to deep reinforcement learning (DRL) and provide extensive experiments in the various MuJoCo domains. Our experiments show that not only does our approach produce robust policies, but it also improves the performance in the absence of perturbations. This generalization indicates that action-robustness can be thought of as implicit regularization in RL problems.
Sparse evolutionary Deep Learning with over one million artificial neurons on commodity hardware
Authors: Shiwei Liu, Decebal Constantin Mocanu, Amarsagar Reddy Ramapuram Matavalam, Yulong Pei, Mykola Pechenizkiy
Date: 2019-01-26 00:00:00
Tasks: GPU

Artificial Neural Networks (ANNs) have emerged as hot topics in the research community. Despite the success of ANNs, it is challenging to train and deploy modern ANNs on commodity hardware due to the ever-increasing model size and the unprecedented growth in the data volumes. Particularly for microarray data, the very-high dimensionality and the small number of samples make it difficult for machine learning techniques to handle. Furthermore, specialized hardware such as Graphics Processing Unit (GPU) is expensive. Sparse neural networks are the leading approaches to address these challenges. However, off-the-shelf sparsity inducing techniques either operate from a pre-trained model or enforce the sparse structure via binary masks. The training efficiency of sparse neural networks cannot be obtained practically. In this paper, we introduce a technique allowing us to train truly sparse neural networks with fixed parameter count throughout training. Our experimental results demonstrate that our method can be applied directly to handle high dimensional data, while achieving higher accuracy than the traditional two phases approaches. Moreover, we have been able to create truly sparse MultiLayer Perceptrons (MLPs) models with over one million neurons and to train them on a typical laptop without GPU, this being way beyond what is possible with any state-of-the-art techniques.
A general model for plane-based clustering with loss function
Authors: Zhen Wang, Yuan-Hai Shao, Lan Bai, Chun-Na Li, Li-Ming Liu
Date: 2019-01-26 00:00:00
Tasks: Clustering

In this paper, we propose a general model for plane-based clustering. The
general model contains many existing plane-based clustering methods, e.g.,
k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector
clustering (TWSVC) and its extensions. Under this general model, one may obtain
an appropriate clustering method for specific purpose. The general model is a
procedure corresponding to an optimization problem, where the optimization
problem minimizes the total loss of the samples. Thereinto, the loss of a
sample derives from both within-cluster and between-cluster. In theory, the
termination conditions are discussed, and we prove that the general model
terminates in a finite number of steps at a local or weak local optimal point.
Furthermore, based on this general model, we propose a plane-based clustering
method by introducing a new loss function to capture the data distribution
precisely. Experimental results on artificial and public available datasets
verify the effectiveness of the proposed method.
GCN-GAN: A Non-linear Temporal Link Prediction Model for Weighted Dynamic Networks
Authors: Kai Lei, Meng Qin, Bo Bai, Gong Zhang, Min Yang
Date: 2019-01-26 00:00:00
Tasks: Generative Adversarial Network, Link Prediction, Prediction

In this paper, we generally formulate the dynamics prediction problem of
various network systems (e.g., the prediction of mobility, traffic and
topology) as the temporal link prediction task. Different from conventional
techniques of temporal link prediction that ignore the potential non-linear
characteristics and the informative link weights in the dynamic network, we
introduce a novel non-linear model GCN-GAN to tackle the challenging temporal
link prediction task of weighted dynamic networks. The proposed model leverages
the benefits of the graph convolutional network (GCN), long short-term memory
(LSTM) as well as the generative adversarial network (GAN). Thus, the dynamics,
topology structure and evolutionary patterns of weighted dynamic networks can
be fully exploited to improve the temporal link prediction performance.
Concretely, we first utilize GCN to explore the local topological
characteristics of each single snapshot and then employ LSTM to characterize
the evolving features of the dynamic networks. Moreover, GAN is used to enhance
the ability of the model to generate the next weighted network snapshot, which
can effectively tackle the sparsity and the wide-value-range problem of edge
weights in real-life dynamic networks. To verify the model's effectiveness, we
conduct extensive experiments on four datasets of different network systems and
application scenarios. The experimental results demonstrate that our model
achieves impressive results compared to the state-of-the-art competitors.
Human Pose Estimation using Motion Priors and Ensemble Models
Authors: Norimichi Ukita
Date: 2019-01-26 00:00:00
Tasks: 2D Human Pose Estimation, 3D Human Pose Tracking, Activity Recognition, Gesture Recognition, Human Activity Recognition, Pose Estimation, Pose Tracking, Video Summarization

Human pose estimation in images and videos is one of key technologies for
realizing a variety of human activity recognition tasks (e.g., human-computer
interaction, gesture recognition, surveillance, and video summarization). This
paper presents two types of human pose estimation methodologies; 1) 3D human
pose tracking using motion priors and 2) 2D human pose estimation with ensemble
modeling.
Escaping Saddle Points with Adaptive Gradient Methods
Authors: Matthew Staib, Sashank J. Reddi, Satyen Kale, Sanjiv Kumar, Suvrit Sra
Date: 2019-01-26 00:00:00

Adaptive methods such as Adam and RMSProp are widely used in deep learning but are not well understood. In this paper, we seek a crisp, clean and precise characterization of their behavior in nonconvex settings. To this end, we first provide a novel view of adaptive methods as preconditioned SGD, where the preconditioner is estimated in an online manner. By studying the preconditioner on its own, we elucidate its purpose: it rescales the stochastic gradient noise to be isotropic near stationary points, which helps escape saddle points. Furthermore, we show that adaptive methods can efficiently estimate the aforementioned preconditioner. By gluing together these two components, we provide the first (to our knowledge) second-order convergence result for any adaptive method. The key insight from our analysis is that, compared to SGD, adaptive methods escape saddle points faster, and can converge faster overall to second-order stationary points.