pwc-archive/papers-with-abstracts huggingface dataset:
Multiview-Consistent Semi-Supervised Learning for 3D Human Pose Estimation
Authors: Rahul Mitra, Nitesh B. Gundavarapu, Abhishek Sharma, Arjun Jain
Date: 2019-08-14 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Human Pose Estimation, Metric Learning, Pose Estimation, Pose Retrieval, Retrieval

The best performing methods for 3D human pose estimation from monocular images require large amounts of in-the-wild 2D and controlled 3D pose annotated datasets which are costly and require sophisticated systems to acquire. To reduce this annotation dependency, we propose Multiview-Consistent Semi Supervised Learning (MCSS) framework that utilizes similarity in pose information from unannotated, uncalibrated but synchronized multi-view videos of human motions as additional weak supervision signal to guide 3D human pose regression. Our framework applies hard-negative mining based on temporal relations in multi-view videos to arrive at a multi-view consistent pose embedding. When jointly trained with limited 3D pose annotations, our approach improves the baseline by 25% and state-of-the-art by 8.7%, whilst using substantially smaller networks. Lastly, but importantly, we demonstrate the advantages of the learned embedding and establish view-invariant pose retrieval benchmarks on two popular, publicly available multi-view human pose datasets, Human 3.6M and MPI-INF-3DHP, to facilitate future research.
Optimizing Ensemble Weights and Hyperparameters of Machine Learning Models for Regression Problems
Authors: Mohsen Shahhosseini, Guiping Hu, Hieu Pham
Date: 2019-08-14 00:00:00
Tasks: BIG-bench Machine Learning, regression

Aggregating multiple learners through an ensemble of models aim to make better predictions by capturing the underlying distribution of the data more accurately. Different ensembling methods, such as bagging, boosting, and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting focus more on reducing variance and bias, respectively, stacking approaches target both by finding the optimal way to combine base learners. In stacking with the weighted average, ensembles are created from weighted averages of multiple base learners. It is known that tuning hyperparameters of each base learner inside the ensemble weight optimization process can produce better performing ensembles. To this end, an optimization-based nested algorithm that considers tuning hyperparameters as well as finding the optimal weights to combine ensembles (Generalized Weighted Ensemble with Internally Tuned Hyperparameters (GEM-ITH)) is designed. Besides, Bayesian search was used to speed-up the optimizing process, and a heuristic was implemented to generate diverse and well-performing base learners. The algorithm is shown to be generalizable to real data sets through analyses with ten publicly available data sets.
Two-stage Federated Phenotyping and Patient Representation Learning
Authors: Dianbo Liu, Dmitriy Dligach, Timothy Miller
Date: 2019-08-14 00:00:00
Proceeding: WS 2019 8
Tasks: Representation Learning, Vocal Bursts Valence Prediction

A large percentage of medical information is in unstructured text format in electronic medical record systems. Manual extraction of information from clinical notes is extremely time consuming. Natural language processing has been widely used in recent years for automatic information extraction from medical texts. However, algorithms trained on data from a single healthcare provider are not generalizable and error-prone due to the heterogeneity and uniqueness of medical documents. We develop a two-stage federated natural language processing method that enables utilization of clinical notes from different hospitals or clinics without moving the data, and demonstrate its performance using obesity and comorbities phenotyping as medical task. This approach not only improves the quality of a specific clinical task but also facilitates knowledge progression in the whole healthcare system, which is an essential part of learning health system. To the best of our knowledge, this is the first application of federated machine learning in clinical NLP.
FCNHSMRA_HRS: Improve the performance of the movie hybrid recommender system using resource allocation approach
Authors: Mostafa Khalaji, Nilufar Mohammadnejad
Date: 2019-08-13 00:00:00
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

Recommender systems are systems that are capable of offering the most suitable services and products to users. Through specific methods and techniques, the recommender systems try to identify the most appropriate items, such as types of information and goods and propose the closest to the user's tastes. Collaborative filtering offering active user suggestions based on the rating of a set of users is one of the simplest and most comprehensible and successful models for finding people in the same tastes in the recommender systems. In this model, with increasing number of users and movie, the system is subject to scalability. On the other hand, it is important to improve the performance of the system when there is little information available on the ratings. In this paper, a movie hybrid recommender system based on FNHSM_HRS structure using resource allocation approach called FCNHSMRA_HRS is presented. The FNHSM_HRS structure was based on the heuristic similarity measure (NHSM), along with fuzzy clustering. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system suggestions. The proposed systems is based on collaborative filtering and, by using the heuristic similarity measure and applying the resource allocation approach, improves the performance, accuracy and precision of the system. The experimental results using MAE, Accuracy, Precision and Recall metrics based on MovieLens dataset show that the performance of the system is improved and the accuracy of recommendations in comparison of FNHSM_HRS and collaborative filtering methods that use other similarity measures for finding similarity, is increased
CUPCF: Combining Users Preferences in Collaborative Filtering for Better Recommendation
Authors: Mostafa Khalaji, Nilufar Mohammadnejad
Date: 2019-08-13 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

How to make the best decision between the opinions and tastes of your friends and acquaintances? Therefore, recommender systems are used to solve such issues. The common algorithms use a similarity measure to predict active users' tastes over a particular item. According to the cold start and data sparsity problems, these systems cannot predict and suggest particular items to users. In this paper, we introduce a new recommender system is able to find user preferences and based on it, provides the recommendations. Our proposed system called CUPCF is a combination of two similarity measures in collaborative filtering to solve the data sparsity problem and poor prediction (high prediction error rate) problems for better recommendation. The experimental results based on MovieLens dataset show that, combined with the preferences of the user's nearest neighbor, the proposed system error rate compared to a number of state-of-the-art recommendation methods improved. Furthermore, the results indicate the efficiency of CUPCF. The maximum improved error rate of the system is 15.5% and the maximum values of Accuracy, Precision and Recall of CUPCF are 0.91402, 0.91436 and 0.9974 respectively.
Learning Credible Deep Neural Networks with Rationale Regularization
Authors: Mengnan Du, Ninghao Liu, Fan Yang, Xia Hu
Date: 2019-08-13 00:00:00
Tasks: text-classification, Text Classification

Recent explainability related studies have shown that state-of-the-art DNNs do not always adopt correct evidences to make decisions. It not only hampers their generalization but also makes them less likely to be trusted by end-users. In pursuit of developing more credible DNNs, in this paper we propose CREX, which encourages DNN models to focus more on evidences that actually matter for the task at hand, and to avoid overfitting to data-dependent bias and artifacts. Specifically, CREX regularizes the training process of DNNs with rationales, i.e., a subset of features highlighted by domain experts as justifications for predictions, to enforce DNNs to generate local explanations that conform with expert rationales. Even when rationales are not available, CREX still could be useful by requiring the generated explanations to be sparse. Experimental results on two text classification datasets demonstrate the increased credibility of DNNs trained with CREX. Comprehensive analysis further shows that while CREX does not always improve prediction accuracy on the held-out test set, it significantly increases DNN accuracy on new and previously unseen data beyond test set, highlighting the advantage of the increased credibility.
Multimodal Emotion Recognition Using Deep Canonical Correlation Analysis
Authors: Wei Liu, Jie-Lin Qiu, Wei-Long Zheng, Bao-liang Lu
Date: 2019-08-13 00:00:00
Tasks: Binary Classification, Emotion Recognition, General Classification, Multimodal Emotion Recognition

Multimodal signals are more powerful than unimodal data for emotion recognition since they can represent emotions more comprehensively. In this paper, we introduce deep canonical correlation analysis (DCCA) to multimodal emotion recognition. The basic idea behind DCCA is to transform each modality separately and coordinate different modalities into a hyperspace by using specified canonical correlation analysis constraints. We evaluate the performance of DCCA on five multimodal datasets: the SEED, SEED-IV, SEED-V, DEAP, and DREAMER datasets. Our experimental results demonstrate that DCCA achieves state-of-the-art recognition accuracy rates on all five datasets: 94.58% on the SEED dataset, 87.45% on the SEED-IV dataset, 84.33% and 85.62% for two binary classification tasks and 88.51% for a four-category classification task on the DEAP dataset, 83.08% on the SEED-V dataset, and 88.99%, 90.57%, and 90.67% for three binary classification tasks on the DREAMER dataset. We also compare the noise robustness of DCCA with that of existing methods when adding various amounts of noise to the SEED-V dataset. The experimental results indicate that DCCA has greater robustness. By visualizing feature distributions with t-SNE and calculating the mutual information between different modalities before and after using DCCA, we find that the features transformed by DCCA from different modalities are more homogeneous and discriminative across emotions.
From Crystallized Adaptivity to Fluid Adaptivity in Deep Reinforcement Learning -- Insights from Biological Systems on Adaptive Flexibility
Authors: Malte Schilling, Helge Ritter, Frank W. Ohl
Date: 2019-08-13 00:00:00
Tasks: BIG-bench Machine Learning, Deep Reinforcement Learning, Incremental Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent developments in machine-learning algorithms have led to impressive performance increases in many traditional application scenarios of artificial intelligence research. In the area of deep reinforcement learning, deep learning functional architectures are combined with incremental learning schemes for sequential tasks that include interaction-based, but often delayed feedback. Despite their impressive successes, modern machine-learning approaches, including deep reinforcement learning, still perform weakly when compared to flexibly adaptive biological systems in certain naturally occurring scenarios. Such scenarios include transfers to environments different than the ones in which the training took place or environments that dynamically change, both of which are often mastered by biological systems through a capability that we here term "fluid adaptivity" to contrast it from the much slower adaptivity ("crystallized adaptivity") of the prior learning from which the behavior emerged. In this article, we derive and discuss research strategies, based on analyzes of fluid adaptivity in biological systems and its neuronal modeling, that might aid in equipping future artificially intelligent systems with capabilities of fluid adaptivity more similar to those seen in some biologically intelligent systems. A key component of this research strategy is the dynamization of the problem space itself and the implementation of this dynamization by suitably designed flexibly interacting modules.
GraphSW: a training protocol based on stage-wise training for GNN-based Recommender Model
Authors: Chang-You Tai, Meng-Ru Wu, Yun-Wei Chu, Shao-Yu Chu
Date: 2019-08-13 00:00:00

Recently, researchers utilize Knowledge Graph (KG) as side information in recommendation system to address cold start and sparsity issue and improve the recommendation performance. Existing KG-aware recommendation model use the feature of neighboring entities and structural information to update the embedding of currently located entity. Although the fruitful information is beneficial to the following task, the cost of exploring the entire graph is massive and impractical. In order to reduce the computational cost and maintain the pattern of extracting features, KG-aware recommendation model usually utilize fixed-size and random set of neighbors rather than complete information in KG. Nonetheless, there are two critical issues in these approaches: First of all, fixed-size and randomly selected neighbors restrict the view of graph. In addition, as the order of graph feature increases, the growth of parameter dimensionality of the model may lead the training process hard to converge. To solve the aforementioned limitations, we propose GraphSW, a strategy based on stage-wise training framework which would only access to a subset of the entities in KG in every stage. During the following stages, the learned embedding from previous stages is provided to the network in the next stage and the model can learn the information gradually from the KG. We apply stage-wise training on two SOTA recommendation models, RippleNet and Knowledge Graph Convolutional Networks (KGCN). Moreover, we evaluate the performance on six real world datasets, Last.FM 2011, Book-Crossing,movie, LFM-1b 2015, Amazon-book and Yelp 2018. The result of our experiments shows that proposed strategy can help both models to collect more information from the KG and improve the performance. Furthermore, it is observed that GraphSW can assist KGCN to converge effectively in high-order graph feature.
Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System
Authors: Ye Liu, Chenwei Zhang, Xiaohui Yan, Yi Chang, Philip S. Yu
Date: 2019-08-13 00:00:00
Tasks: Deep Reinforcement Learning, Question Answering, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval, Word Embeddings

In real-world question-answering (QA) systems, ill-formed questions, such as wrong words, ill word order, and noisy expressions, are common and may prevent the QA systems from understanding and answering them accurately. In order to eliminate the effect of ill-formed questions, we approach the question refinement task and propose a unified model, QREFINE, to refine the ill-formed questions to well-formed question. The basic idea is to learn a Seq2Seq model to generate a new question from the original one. To improve the quality and retrieval performance of the generated questions, we make two major improvements: 1) To better encode the semantics of ill-formed questions, we enrich the representation of questions with character embedding and the recent proposed contextual word embedding such as BERT, besides the traditional context-free word embeddings; 2) To make it capable to generate desired questions, we train the model with deep reinforcement learning techniques that considers an appropriate wording of the generation as an immediate reward and the correlation between generated question and answer as time-delayed long-term rewards. Experimental results on real-world datasets show that the proposed QREFINE method can generate refined questions with more readability but fewer mistakes than the original questions provided by users. Moreover, the refined questions also significantly improve the accuracy of answer retrieval.
Distributionally Robust Optimization: A Review
Authors: Hamed Rahimian, Sanjay Mehrotra
Date: 2019-08-13 00:00:00

The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.
SHREWD: Semantic Hierarchy-based Relational Embeddings for Weakly-supervised Deep Hashing
Authors: Heikki Arponen, Tom E. Bishop
Date: 2019-08-12 00:00:00
Tasks: Binarization, Deep Hashing, Retrieval

Using class labels to represent class similarity is a typical approach to training deep hashing systems for retrieval; samples from the same or different classes take binary 1 or 0 similarity values. This similarity does not model the full rich knowledge of semantic relations that may be present between data points. In this work we build upon the idea of using semantic hierarchies to form distance metrics between all available sample labels; for example cat to dog has a smaller distance than cat to guitar. We combine this type of semantic distance into a loss function to promote similar distances between the deep neural network embeddings. We also introduce an empirical Kullback-Leibler divergence loss term to promote binarization and uniformity of the embeddings. We test the resulting SHREWD method and demonstrate improvements in hierarchical retrieval scores using compact, binary hash codes instead of real valued ones, and show that in a weakly supervised hashing setting we are able to learn competitively without explicitly relying on class labels, but instead on similarities between labels.
Learning discriminative and transformation covariant local feature detectors.
Authors: Xu Zhang, Felix X. Yu, Svebor Karaman, Shih-Fu Chang
Date: 2017-07-01 00:00:00
Proceeding: Computer Vision and Pattern Recognition 2017 7
Tasks: Image Retrieval

Robust covariant local feature detectors are important for detecting local features that are (1) discriminative of the image content and (2) can be repeatably detected at consistent locations when the image undergoes diverse transformations.Such detectors are critical for applications such as image search and scene reconstruction. Many learningbased local feature detectors address one of these two problems while overlooking the other. In this work, we propose a novel learning-based method to simultaneously address both issues. Specifically, we extend the covariant constraint proposed by Lenc and Vedaldi [8] by defining the concepts
of “standard patch” and “canonical feature” and leverage these to train a novel robust covariant detector. We show that the introduction of these concepts greatly simplifies the learning stage of the covariant detector, and also makes the detector much more robust. Extensive experiments show that our method outperforms previous handcrafted and learning-based detectors by large margins in terms of repeatability.
Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted morlet wavelets
Authors: N. G. Nikolaou and I. A. Antoniadis
Date: 2001-12-20 00:00:00
Proceeding: 1. N. Tandon and A. Choudhury 1999 Tribology International 32, 469–480. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. 2. D. Ho and R. B. Randall 2000 Mechanical Systems and Signal Processing 14, 763–788. Optimisation of bearing diagnostic techniques using simulated and actual bearing fault signals. 3. A. Barkov and N. Barkova June 1995 Sound and Vibration 10–17. Condition assesment and life prediction of rolling element bearings}Part 1. 4. P. D. McFadden and J. D. Smith 1984 Journal of Sound and Vibration 96, 69–82. Model for the Vibration produced bya single point defect in a rolling element bearing. 5. R. L. Eshleman 1997 Vibrations 13, 11–17. Comments on rolling element bearing analysis. 6. R. B. Randall 1987 Frequency Analysis, 3rd edn. Bruel & Kjaer. 7. J. C. Li and J.Ma 1997 NDT&E International 30, 143–149. Wavelet decomposition of vibrations for detection of bearing-localised defects. 8. K. Mori, N. Kasashima, T. Yoshioka and Y. Ueno 19 2001 12

Vibration signals resulting from rolling element bearing defects, present a rich content of
physical information, the appropriate analysis of which can lead to the clear identification
of the nature of the fault. The envelope detection or demodulation methods have been
established as the dominant analysis methods for this purpose, since they can separate the
useful part of the signal from its redundant contents. This paper proposes an effective
demodulation method, based on the use of a complex shifted Morlet wavelet family. The
method is designed in a way that can fully exploit the underlying physical concepts of the
modulation mechanism, present in the vibration response of faultybearings, using a time–
frequencyrepresentation of the signal. A keyelement of the proposed method is the
systematic introduction of selection criteria for the automated choice of the critical
parameters that characterise the Morlet wavelet familyused. Experimental results and
industrial measurements for two different types of bearing faults confirm the validity of the
overall approach
Markov Chain Monte Carlo Data Association for Multiple-Target Tracking
Authors: Songhwai Oh, Stuart Russell, Shankar Sastry
Date: 2009-03-04 00:00:00
Proceeding: IEEE Transactions on Automatic Control 2009 3

This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data
association problems arising in multiple-target tracking in a cluttered environment. When the number of
targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association
(JPDA). Although the exact computation of association probabilities in JPDA is NP-hard, we prove that
the single-scan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for
JPDA. For general multiple-target tracking problems, in which unknown numbers of targets appear and
disappear at random times, we present a multi-scan MCMCDA algorithm that approximates the optimal
Bayesian filter. It exhibits remarkable performance compared to multiple hypothesis tracking (MHT)
under extreme conditions, such as a large number of targets in a dense environment, low detection
probabilities, and high false alarm rates
Improved Information Gain Estimates for Decision Tree Induction
Authors: Sebastian Nowozin
Date: 2012-06-18 00:00:00
Tasks: Classification, General Classification, regression

Ensembles of classification and regression trees remain popular machine learning methods because they define flexible non-parametric models that predict well and are computationally efficient both during training and testing. During induction of decision trees one aims to find predicates that are maximally informative about the prediction target. To select good predicates most approaches estimate an information-theoretic scoring function, the information gain, both for classification and regression problems. We point out that the common estimation procedures are biased and show that by replacing them with improved estimators of the discrete and the differential entropy we can obtain better decision trees. In effect our modifications yield improved predictive performance and are simple to implement in any decision tree code.