pwc-archive/papers-with-abstracts huggingface dataset:
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning
Authors: Felix Leibfried, Jordi Grau-Moya
Date: 2019-09-11 00:00:00
Tasks: MuJoCo, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Cumulative entropy regularization introduces a regulatory signal to the reinforcement learning (RL) problem that encourages policies with high-entropy actions, which is equivalent to enforcing small deviations from a uniform reference marginal policy. This has been shown to improve exploration and robustness, and it tackles the value overestimation problem. It also leads to a significant performance increase in tabular and high-dimensional settings, as demonstrated via algorithms such as soft Q-learning (SQL) and soft actor-critic (SAC). Cumulative entropy regularization has been extended to optimize over the reference marginal policy instead of keeping it fixed, yielding a regularization that minimizes the mutual information between states and actions. While this has been initially proposed for Markov Decision Processes (MDPs) in tabular settings, it was recently shown that a similar principle leads to significant improvements over vanilla SQL in RL for high-dimensional domains with discrete actions and function approximators. Here, we follow the motivation of mutual-information regularization from an inference perspective and theoretically analyze the corresponding Bellman operator. Inspired by this Bellman operator, we devise a novel mutual-information regularized actor-critic learning (MIRACLE) algorithm for continuous action spaces that optimizes over the reference marginal policy. We empirically validate MIRACLE in the Mujoco robotics simulator, where we demonstrate that it can compete with contemporary RL methods. Most notably, it can improve over the model-free state-of-the-art SAC algorithm which implicitly assumes a fixed reference policy.
Adjusting Rate of Spread Factors through Derivative-Free Optimization: A New Methodology to Improve the Performance of Forest Fire Simulators
Authors: Jaime Carrasco, Cristobal Pais, Zuo-Jun Max Shen, Andres Weintraub
Date: 2019-09-11 00:00:00
Tasks: Land Cover Classification, Text-to-Image Generation

In practical applications, it is common that wildfire simulators do not correctly predict the evolution of the fire scar. Usually, this is caused due to multiple factors including inaccuracy in the input data such as land cover classification, moisture, improperly represented local winds, cumulative errors in the fire growth simulation model, high level of discontinuity/heterogeneity within the landscape, among many others. Therefore in practice, it is necessary to adjust the propagation of the fire to obtain better results, either to support suppression activities or to improve the performance of the simulator considering new default parameters for future events, best representing the current fire spread growth phenomenon. In this article, we address this problem through a new methodology using Derivative-Free Optimization (DFO) algorithms for adjusting the Rate of Spread (ROS) factors in a fire simulation growth model called Cell2Fire. To achieve this, we solve an error minimization optimization problem that captures the difference between the simulated and observed fire, which involves the evaluation of the simulator output in each iteration as part of a DFO framework, allowing us to find the best possible factors for each fuel present on the landscape. Numerical results for different objective functions are shown and discussed, including a performance comparison of alternative DFO algorithms.
Guided Learning Convolution System for DCASE 2019 Task 4
Authors: Liwei Lin, Xiangdong Wang, Hong Liu, Yueliang Qian
Date: 2019-09-11 00:00:00
Tasks: Event Detection, Sound Event Detection

In this paper, we describe in detail the system we submitted to DCASE2019 task 4: sound event detection (SED) in domestic environments. We employ a convolutional neural network (CNN) with an embedding-level attention pooling module to solve it. By considering the interference caused by the co-occurrence of multiple events in the unbalanced dataset, we utilize the disentangled feature to raise the performance of the model. To take advantage of the unlabeled data, we adopt Guided Learning for semi-supervised learning. A group of median filters with adaptive window sizes is utilized in the post-processing of output probabilities of the model. We also analyze the effect of the synthetic data on the performance of the model and finally achieve an event-based F-measure of 45.43% on the validation set and an event-based F-measure of 42.7% on the test set. The system we submitted to the challenge achieves the best performance compared to those of other participates.
Automatic Hip Fracture Identification and Functional Subclassification with Deep Learning
Authors: Justin D Krogue, Kaiyang V Cheng, Kevin M Hwang, Paul Toogood, Eric G Meinberg, Erik J Geiger, Musa Zaid, Kevin C McGill, Rina Patel, Jae Ho Sohn, Alexandra Wright, Bryan F Darger, Kevin A Padrez, Eugene Ozhinsky, Sharmila Majumdar, Valentina Pedoia
Date: 2019-09-10 00:00:00
Tasks: Deep Learning, Diagnostic, General Classification, object-detection, Object Detection, Specificity

Purpose: Hip fractures are a common cause of morbidity and mortality. Automatic identification and classification of hip fractures using deep learning may improve outcomes by reducing diagnostic errors and decreasing time to operation. Methods: Hip and pelvic radiographs from 1118 studies were reviewed and 3034 hips were labeled via bounding boxes and classified as normal, displaced femoral neck fracture, nondisplaced femoral neck fracture, intertrochanteric fracture, previous ORIF, or previous arthroplasty. A deep learning-based object detection model was trained to automate the placement of the bounding boxes. A Densely Connected Convolutional Neural Network (DenseNet) was trained on a subset of the bounding box images, and its performance evaluated on a held out test set and by comparison on a 100-image subset to two groups of human observers: fellowship-trained radiologists and orthopaedists, and senior residents in emergency medicine, radiology, and orthopaedics. Results: The binary accuracy for fracture of our model was 93.8% (95% CI, 91.3-95.8%), with sensitivity of 92.7% (95% CI, 88.7-95.6%), and specificity 95.0% (95% CI, 91.5-97.3%). Multiclass classification accuracy was 90.4% (95% CI, 87.4-92.9%). When compared to human observers, our model achieved at least expert-level classification under all conditions. Additionally, when the model was used as an aid, human performance improved, with aided resident performance approximating unaided fellowship-trained expert performance. Conclusions: Our deep learning model identified and classified hip fractures with at least expert-level accuracy, and when used as an aid improved human performance, with aided resident performance approximating that of unaided fellowship-trained attendings.
Unsupervised Image Regression for Heterogeneous Change Detection
Authors: Luigi T. Luppino, Filippo M. Bianchi, Gabriele Moser, Stian N. Anfinsen
Date: 2019-09-07 00:00:00
Tasks: Change Detection, regression

Change detection in heterogeneous multitemporal satellite images is an emerging and challenging topic in remote sensing. In particular, one of the main challenges is to tackle the problem in an unsupervised manner. In this paper we propose an unsupervised framework for bitemporal heterogeneous change detection based on the comparison of affinity matrices and image regression. First, our method quantifies the similarity of affinity matrices computed from co-located image patches in the two images. This is done to automatically identify pixels that are likely to be unchanged. With the identified pixels as pseudo-training data, we learn a transformation to map the first image to the domain of the other image, and vice versa. Four regression methods are selected to carry out the transformation: Gaussian process regression, support vector regression, random forest regression, and a recently proposed kernel regression method called homogeneous pixel transformation. To evaluate the potentials and limitations of our framework, and also the benefits and disadvantages of each regression method, we perform experiments on two real data sets. The results indicate that the comparison of the affinity matrices can already be considered a change detection method by itself. However, image regression is shown to improve the results obtained by the previous step alone and produces accurate change detection maps despite of the heterogeneity of the multitemporal input data. Notably, the random forest regression approach excels by achieving similar accuracy as the other methods, but with a significantly lower computational cost and with fast and robust tuning of hyperparameters.
3D U$^2$-Net: A 3D Universal U-Net for Multi-Domain Medical Image Segmentation
Authors: Chao Huang, Hu Han, Qingsong Yao, Shankuan Zhu, S. Kevin Zhou
Date: 2019-09-04 00:00:00
Tasks: image-classification, Image Classification, Image Segmentation, Medical Image Segmentation, Organ Segmentation, Segmentation, Semantic Segmentation

Fully convolutional neural networks like U-Net have been the state-of-the-art methods in medical image segmentation. Practically, a network is highly specialized and trained separately for each segmentation task. Instead of a collection of multiple models, it is highly desirable to learn a universal data representation for different tasks, ideally a single model with the addition of a minimal number of parameters steered to each task. Inspired by the recent success of multi-domain learning in image classification, for the first time we explore a promising universal architecture that handles multiple medical segmentation tasks and is extendable for new tasks, regardless of different organs and imaging modalities. Our 3D Universal U-Net (3D U$^2$-Net) is built upon separable convolution, assuming that {\it images from different domains have domain-specific spatial correlations which can be probed with channel-wise convolution while also share cross-channel correlations which can be modeled with pointwise convolution}. We evaluate the 3D U$^2$-Net on five organ segmentation datasets. Experimental results show that this universal network is capable of competing with traditional models in terms of segmentation accuracy, while requiring only about $1\%$ of the parameters. Additionally, we observe that the architecture can be easily and effectively adapted to a new domain without sacrificing performance in the domains used to learn the shared parameterization of the universal network. We put the code of 3D U$^2$-Net into public domain. \url{https://github.com/huangmozhilv/u2net_torch/}
BAGH -- Comparative study
Authors: B. Kamala
Date: 2019-09-04 00:00:00
Tasks: Management

Process mining is a new emerging research trend over the last decade which focuses on analyzing the processes using event log and data. The raising integration of information systems for the operation of business processes provides the basis for innovative data analysis approaches. Process mining has the strong relationship between with data mining so that it enables the bond between business intelligence approach and business process management. It focuses on end to end processes and is possible because of the growing availability of event data and new process discovery and conformance checking techniques. Process mining aims to discover, monitor and improve real processes by extracting knowledge from event logs readily available in todays information systems. The discovered process models can be used for a variety of analysis purposes. Many companies have adopted Process aware Information Systems for supporting their business processes in some form. These systems typically have their log events related to the actual business process executions. Proper analysis of Process Aware Information Systems execution logs can yield important knowledge and help organizations improve the quality of their services. This paper reviews and compares various process mining algorithms based on their input parameters, the techniques used and the output generated by them.
Face alignment by coarse-to-fine shape searching
Authors: Shizhan Zhu, Cheng Li, Chen Change Loy, Xiaoou Tang
Date: 2015-06-07 00:00:00
Proceeding: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2015 6
Tasks: Face Alignment, regression

We present a novel face alignment framework based on coarse-to-fine shape searching. Unlike the conventional cascaded regression approaches that start with an initial shape and refine the shape in a cascaded manner, our approach begins with a coarse search over a shape space that contains diverse shapes, and employs the coarse solution to constrain subsequent finer search of shapes. The unique stage-by-stage progressive and adaptive search i) prevents the final solution from being trapped in local optima due to poor initialisation, a common problem encountered by cascaded regression approaches; and ii) improves the robustness in coping with large pose variations. The framework demonstrates real-time performance and state-of-the-art results on various benchmarks including the challenging 300-W dataset.
Leveraging intra and inter-dataset variations for robust face alignment
Authors: Wenyan Wu, Shuo Yang
Date: 2017-07-21 00:00:00
Proceeding: CVPR 2017 2017 7
Tasks: Face Alignment, Robust Face Alignment

Face alignment is a critical topic in the computer vision community. Numerous efforts have been made and various benchmark datasets have been released in recent decades. However, two significant issues remain in recent datasets, e.g., Intra-Dataset Variation and Inter-Dataset Variation. Inter-Dataset Variation refers to bias on expression, head pose, etc. inside one certain dataset, while Intra-Dataset Variation refers to different bias across different datasets. To address the mentioned problems, we proposed a novel Deep Variation Leveraging Network (DVLN), which consists of two strong coupling sub-networks, e.g., Dataset-Across Network (DA-Net) and Candidate-Decision Network (CD-Net). Extensive evaluations show that our approach demonstrates real-time performance and dramatically outperforms state-of-the-art methods on the challenging 300-W dataset.
Efficient and Accurate Face Alignment by Global Regression and Cascaded Local Refinement
Authors: Jinzhan Su, Zhe Wang, Chunyuan Liao, Haibin Ling
Date: 2019-06-16 00:00:00
Proceeding: CVPR 2019 2019 6
Tasks: Face Alignment, regression

Despite great advances witnessed on facial image alignment in recent years, high accuracy high speed face alignment algorithms still have rooms to improve especially for applications where computation resources are limited. Addressing this issue, we propose a new face landmark localization algorithm by combining global regression and local refinement. In particular, for a given image, our algorithm first estimates its global facial shape through a global regression network (GRegNet) and then using cascaded local refinement networks (LRefNet) to sequentially improve the alignment result. Compared with previous face alignment algorithms, our key innovation is the sharing of low level features in GRegNet with LRefNet. Such feature sharing not only significantly improves the algorithm efficiency, but also allows full exploration of rich locality-sensitive details carried with shallow network layers and consequently boosts the localization accuracy. The advantages of our algorithm is clearly validated in our thorough experiments on four popular face alignment benchmarks, 300-W, AFLW, COFW and WFLW. On all datasets, our algorithm produces state-of-the-art alignment accuracy, while enjoys the smallest computational complexity.
Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation
Authors: Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, Xing Xie
Date: 2019-01-01 00:00:00
Proceeding: IJCAI 2019 1
Tasks: Language Modeling, Language Modelling, Recommendation Systems, Sequential Recommendation

User modeling is an essential task for online rec- ommender systems. In the past few decades, col- laborative filtering (CF) techniques have been well studied to model users’ long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users’ short term preference. A natural way to improve the rec- ommender is to combine both long-term and short- term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users’ be- haviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we im- prove the traditional RNN structure by proposing a time-aware controller and a content-aware con- troller, so that contextual information can be well considered to control the state transition. We fur- ther propose an attention-based framework to com- bine users’ long-term and short-term preferences, thus users’ representation can be generated adap- tively according to the specific context. We con- duct extensive experiments on both public and in- dustrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.
Automated EEG-based Screening of Depression Using Deep Convolutional Neural Network
Authors: U Rajendra Acharyaa, B, C, *, Shu Lih Oha, Yuki Hagiwaraa, Jen Hong Tana, Hojjat Adelid, D P Subhae
Date: 2018-04-17 00:00:00
Proceeding: Computer Methods and Programs in Biomedicine 2018 4
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG)

In recent years, advanced neurocomputing and machine learning techniques have been used for
Electroencephalogram (EEG)-based diagnosis of various neurological disorders. In this paper, a
novel computer model is presented for EEG-based screening of depression using a deep neural
network machine learning approach, known as Convolutional Neural Network (CNN). The
proposed technique does not require a semi-manually-selected set of features to be fed into a
classifier for classification. It learns automatically and adaptively from the input EEG signals to
differentiate EEGs obtained from depressive and normal subjects. The model was tested using
EEGs obtained from 15 normal and 15 depressed patients. The algorithm attained accuracies of
93.5% and 96.0% using EEG signals from the left and right hemisphere, respectively. It was
discovered in this research that the EEG signals from the right hemisphere are more distinctive
in depression than those from the left hemisphere. This discovery is consistent with recent
research and revelation that the depression is associated with a hyperactive right hemisphere.
An exciting extension of this research would be diagnosis of different stages and severity of
depression and development of a Depression Severity Index (DSI).
Community detection in networks: Structural communities versus ground truth
Authors: Darko Hric, Richard K. Darst, Santo Fortunato
Date: 2014-06-01 00:00:00
Tasks: Community Detection

Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (non-topological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.
Community detection in graphs
Authors: Santo Fortunato
Date: 2009-06-03 00:00:00
Tasks: Clustering, Community Detection, Sociology

The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i. e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e. g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.
ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms
Authors: Martin Aumüller, Erik Bernhardsson, Alexander Faithfull
Date: 2018-07-15 00:00:00
Tasks: Benchmarking

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several different ways of integrating $k$-NN algorithms, and its configuration system automatically tests a range of parameter settings for each algorithm. Algorithms are compared with respect to many different (approximate) quality measures, and adding more is easy and fast; the included plotting front-ends can visualise these as images, $\LaTeX$ plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of $k$-NN algorithms. In the short term, this overview allows users to choose the correct $k$-NN algorithm and parameters for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different approaches to $k$-NN search yield comparable quality-performance trade-offs. The system is available at http://ann-benchmarks.com .