scholarly journals HDI-Forest: Highest Density Interval Regression Forest

2019 ◽  
Author(s):  
Lin Zhu ◽  
Jiaxing Lu ◽  
Yihong Chen
Keyword(s):  
Random Forest ◽  
Coverage Probability ◽  
Deep Neural Networks ◽  
Linear Models ◽  
State Of The Art ◽  
Estimation Method ◽  
Estimation Methods ◽  
Benchmark Datasets ◽  
Model Training ◽  
Highest Density Interval

By seeking the narrowest prediction intervals (PIs) that satisfy the specified coverage probability requirements, the recently proposed quality-based PI learning principle can extract high-quality PIs that better summarize the predictive certainty in regression tasks, and has been widely applied to solve many practical problems. Currently, the state-of-the-art quality-based PI estimation methods are based on deep neural networks or linear models. In this paper, we propose Highest Density Interval Regression Forest (HDI-Forest), a novel quality-based PI estimation method that is instead based on Random Forest. HDI-Forest does not require additional model training, and directly reuses the trees learned in a standard Random Forest model. By utilizing the special properties of Random Forest, HDI-Forest could efficiently and more directly optimize the PI quality metrics. Extensive experiments on benchmark datasets show that HDI-Forest significantly outperforms previous approaches, reducing the average PI width by over 20% while achieving the same or better coverage probability.

Lower Bound on the Accuracy of Parameter Estimation Methods for Linear Sensorimotor Synchronization Models

2015 ◽  
Vol 3 (1-2) ◽  
pp. 32-51 ◽  
Author(s):  
Nori Jacoby ◽  
Peter E. Keller ◽  
Bruno H. Repp ◽  
Merav Ahissar ◽  
Naftali Tishby
Keyword(s):  
Parameter Estimation ◽  
Lower Bound ◽  
Computational Models ◽  
Linear Models ◽  
Estimation Error ◽  
Explanatory Power ◽  
Estimation Method ◽  
Sensorimotor Synchronization ◽  
Estimation Methods ◽  
Unbiased Estimation

The mechanisms that support sensorimotor synchronization — that is, the temporal coordination of movement with an external rhythm — are often investigated using linear computational models. The main method used for estimating the parameters of this type of model was established in the seminal work of Vorberg and Schulze (2002), and is based on fitting the model to the observed auto-covariance function of asynchronies between movements and pacing events. Vorberg and Schulze also identified the problem of parameter interdependence, namely, that different sets of parameters might yield almost identical fits, and therefore the estimation method cannot determine the parameters uniquely. This problem results in a large estimation error and bias, thereby limiting the explanatory power of existing linear models of sensorimotor synchronization. We present a mathematical analysis of the parameter interdependence problem. By applying the Cramér–Rao lower bound, a general lower bound limiting the accuracy of any parameter estimation procedure, we prove that the mathematical structure of the linear models used in the literature determines that this problem cannot be resolved by any unbiased estimation method without adopting further assumptions. We then show that adding a simple and empirically justified constraint on the parameter space — assuming a relationship between the variances of the noise terms in the model — resolves the problem. In a follow-up paper in this volume, we present a novel estimation technique that uses this constraint in conjunction with matrix algebra to reliably estimate the parameters of almost all linear models used in the literature.

scholarly journals Link quality estimation based on over-sampling and weighted random forest

2021 ◽  
pp. 41-41
Author(s):  
Linlan Liu ◽  
Yi Feng ◽  
Shengrong Gao ◽  
Jian Shu
Keyword(s):  
Random Forest ◽  
Decision Trees ◽  
Estimation Method ◽  
Classification Performance ◽  
Link Quality ◽  
Estimation Methods ◽  
Quality Level ◽  
Estimation Model ◽  
Quality Estimation ◽  
Link Quality Estimation

Aiming at the imbalance problem of wireless link samples, we propose the link quality estimation method which combines the K-means synthetic minority over-sampling technique (K-means SMOTE) and weighted random forest. The method adopts the mean, variance and asymmetry metrics of the physical layer parameters as the link quality parameters. The link quality is measured by link quality level which is determined by the packet receiving rate. K-means is used to cluster link quality samples. SMOTE is employed to synthesize samples for minority link quality samples, so as to make link quality samples of different link quality levels reach balance. Based on the weighted random forest, the link quality estimation model is constructed. In the link quality estimation model, the decision trees with worse classification performance are assigned smaller weight, and the decision trees with better classification performance are assigned bigger weight. The experimental results show that the proposed link quality estimation method has better performance with samples processed by K-means SMOTE. Furthermore, it has better estimation performance than the ones of Naive Bayesian, Logistic Regression and K-nearest Neighbour estimation methods.

scholarly journals Learning Deep Relations to Promote Saliency Detection

2020 ◽  
Vol 34 (07) ◽  
pp. 10510-10517
Author(s):  
Changrui Chen ◽  
Xin Sun ◽  
Yang Hua ◽  
Junyu Dong ◽  
Hongwei Xv
Keyword(s):  
Deep Neural Networks ◽  
State Of The Art ◽  
Saliency Detection ◽  
The State ◽  
Label Free ◽  
Object Boundary ◽  
Root Cause ◽  
Benchmark Datasets ◽  
Construction Strategy ◽  
Training Pair

Though saliency detectors has made stunning progress recently. The performances of the state-of-the-art saliency detectors are not acceptable in some confusing areas, e.g., object boundary. We argue that the feature spatial independence should be one of the root cause. This paper explores the ubiquitous relations on the deep features to promote the existing saliency detectors efficiently. We establish the relation by maximizing the mutual information of the deep features of the same category via deep neural networks to break this independence. We introduce a threshold-constrained training pair construction strategy to ensure that we can accurately estimate the relations between different image parts in a self-supervised way. The relation can be utilized to further excavate the salient areas and inhibit confusing backgrounds. The experiments demonstrate that our method can significantly boost the performance of the state-of-the-art saliency detectors on various benchmark datasets. Besides, our model is label-free and extremely efficient. The inference speed is 140 FPS on a single GTX1080 GPU.

scholarly journals The FaceChannel: A Fast and Furious Deep Neural Network for Facial Expression Recognition

2020 ◽  
Vol 1 (6) ◽  
Author(s):  
Pablo Barros ◽  
Nikhil Churamani ◽  
Alessandra Sciutti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Facial Expression ◽  
Facial Expression Recognition ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Facial Features ◽  
Expression Recognition ◽  
Current State ◽  
Benchmark Datasets

AbstractCurrent state-of-the-art models for automatic facial expression recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. Given the dynamic conditions of FER, this characteristic hinders such models of been used as a general affect recognition. In this paper, we address this problem by formalizing the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We introduce an inhibitory layer that helps to shape the learning of facial features in the last layer of the network and, thus, improving performance while reducing the number of trainable parameters. To evaluate our model, we perform a series of experiments on different benchmark datasets and demonstrate how the FaceChannel achieves a comparable, if not better, performance to the current state-of-the-art in FER. Our experiments include cross-dataset analysis, to estimate how our model behaves on different affective recognition conditions. We conclude our paper with an analysis of how FaceChannel learns and adapts the learned facial features towards the different datasets.

scholarly journals Landmark-Guided Local Deep Neural Networks for Age and Gender Classification

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yungang Zhang ◽  
Tianwei Xu
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Global Network ◽  
Gender Classification ◽  
Local Networks ◽  
Age And Gender ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Model Training ◽  
And Gender

Many types of deep neural networks have been proposed to address the problem of human biometric identification, especially in the areas of face detection and recognition. Local deep neural networks have been recently used in face-based age and gender classification, despite their improvement in performance, their costs on model training is rather expensive. In this paper, we propose to construct a local deep neural network for age and gender classification. In our proposed model, local image patches are selected based on the detected facial landmarks; the selected patches are then used for the network training. A holistical edge map for an entire image is also used for training a “global” network. The age and gender classification results are obtained by combining both the outputs from both the “global” and the local networks. Our proposed model is tested on two face image benchmark datasets; competitive performance is obtained compared to the state-of-the-art methods.

scholarly journals Androgen Receptor Binding Prediction with Random Forest, Deep Neural Networks, and Graph Convolutional Neural Networks

2021 ◽  
Author(s):  
Alfonso T. García-Sosa
Keyword(s):  
Prostate Cancer ◽  
Neural Networks ◽  
Androgen Receptor ◽  
Random Forest ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Source Code ◽  
Protein Structures ◽  
Binding Prediction ◽  
Machine Learning Classifiers

Substances that can modify the androgen receptor pathway in humans and animals are entering the environment and food chain with the proven ability to disrupt hormonal systems and leading to toxicity and adverse effects on reproduction, brain development, and prostate cancer, among others. State-of-the-art databases with experimental data of human, chimp, and rat effects by chemicals have been used to build machine learning classifiers and regressors and evaluate these on independent sets. Different featurizations, algorithms, and protein structures lead to different results, with deep neural networks on user-defined physicochemically-relevant features developed for this work outperform graph convolutional, random forest, and large featurizations. The results can help provide clues on risk of substances and better experimental design for toxicity assays. Source code and data are available at https://github.com/AlfonsoTGarcia-Sosa/ML

scholarly journals Leveraging the Bhattacharyya coefficient for uncertainty quantification in deep neural networks

2021 ◽  
Author(s):  
Pieter Van Molle ◽  
Tim Verbelen ◽  
Bert Vankeirsbilck ◽  
Jonas De Vylder ◽  
Bart Diricx ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Use Case ◽  
Bhattacharyya Coefficient ◽  
Output Uncertainty ◽  
Novel Approach ◽  
Benchmark Datasets ◽  
Network Approaches

AbstractModern deep learning models achieve state-of-the-art results for many tasks in computer vision, such as image classification and segmentation. However, its adoption into high-risk applications, e.g. automated medical diagnosis systems, happens at a slow pace. One of the main reasons for this is that regular neural networks do not capture uncertainty. To assess uncertainty in classification, several techniques have been proposed casting neural network approaches in a Bayesian setting. Amongst these techniques, Monte Carlo dropout is by far the most popular. This particular technique estimates the moments of the output distribution through sampling with different dropout masks. The output uncertainty of a neural network is then approximated as the sample variance. In this paper, we highlight the limitations of such a variance-based uncertainty metric and propose an novel approach. Our approach is based on the overlap between output distributions of different classes. We show that our technique leads to a better approximation of the inter-class output confusion. We illustrate the advantages of our method using benchmark datasets. In addition, we apply our metric to skin lesion classification—a real-world use case—and show that this yields promising results.

scholarly journals Parametric Versus Semi and Nonparametric Regression Models

2021 ◽  
Vol 10 (2) ◽  
pp. 90
Author(s):  
Hamdy F. F. Mahmoud
Keyword(s):  
Nonparametric Regression ◽  
Robust Estimation ◽  
Regression Models ◽  
Linear Models ◽  
Kernel Smoothing ◽  
Estimation Method ◽  
Parametric Models ◽  
Estimation Methods ◽  
Explanatory Variables ◽  
Local Linear

There are three common types of regression models: parametric, semiparametric and nonparametric regression. The model should be used to fit the real data depends on how much information is available about the form of the relationship between the response variable and explanatory variables, and the random error distribution that is assumed. Researchers need to be familiar with each modeling approach requirements. In this paper, differences between these models, common estimation methods, robust estimation, and applications are introduced. For parametric models, there are many known methods of estimation, such as least squares and maximum likelihood methods which are extensively studied but they require strong assumptions. On the other hand, nonparametric regression models are free of assumptions regarding the form of the response-explanatory variables relationships but estimation methods, such as kernel and spline smoothing are computationally expensive and smoothing parameters need to be obtained. For kernel smoothing there two common estimators: local constant and local linear smoothing methods. In terms of bias, especially at the boundaries of the data range, local linear is better than local constant estimator.  Robust estimation methods for linear models are well studied, however the robust estimation methods in nonparametric regression methods are limited. A robust estimation method for the semiparametric and nonparametric regression models is introduced.

scholarly journals Object Detection Meets Knowledge Graphs

2017 ◽  
Author(s):  
Yuan Fang ◽  
Kingsley Kuan ◽  
Jie Lin ◽  
Cheston Tan ◽  
Vijay Chandrasekhar
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Empirical Evaluation ◽  
Background Knowledge ◽  
Detection Algorithm ◽  
Benchmark Datasets ◽  
Knowledge Graphs ◽  
Security Surveillance

Object detection in images is a crucial task in computer vision, with important applications ranging from security surveillance to autonomous vehicles. Existing state-of-the-art algorithms, including deep neural networks, only focus on utilizing features within an image itself, largely neglecting the vast amount of background knowledge about the real world. In this paper, we propose a novel framework of knowledge-aware object detection, which enables the integration of external knowledge such as knowledge graphs into any object detection algorithm. The framework employs the notion of semantic consistency to quantify and generalize knowledge, which improves object detection through a re-optimization process to achieve better consistency with background knowledge. Finally, empirical evaluation on two benchmark datasets show that our approach can significantly increase recall by up to 6.3 points without compromising mean average precision, when compared to the state-of-the-art baseline.

scholarly journals Robust Scale Adaptive Visual Tracking with Correlation Filters

2018 ◽  
Vol 8 (11) ◽  
pp. 2037 ◽  
Author(s):  
Chunbao Li ◽  
Bo Yang
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Estimation Method ◽  
Color Naming ◽  
Target Object ◽  
Correlation Filter ◽  
Correlation Filters ◽  
Object Proposals ◽  
Benchmark Datasets ◽  
Candidate Object

Visual tracking is a challenging task in computer vision due to various appearance changes of the target object. In recent years, correlation filter plays an important role in visual tracking and many state-of-the-art correlation filter based trackers are proposed in the literature. However, these trackers still have certain limitations. Most of existing trackers cannot well deal with scale variation, and they may easily drift to the background in the case of occlusion. To overcome the above problems, we propose a Correlation Filters based Scale Adaptive (CFSA) visual tracker. In the tracker, a modified EdgeBoxes generator, is proposed to generate high-quality candidate object proposals for tracking. The pool of generated candidate object proposals is adopted to estimate the position of the target object using a kernelized correlation filter based tracker with HOG and color naming features. In order to deal with changes in target scale, a scale estimation method is proposed by combining the water flow driven MBD (minimum barrier distance) algorithm with the estimated position. Furthermore, an online updating schema is adopted to reduce the interference of the surrounding background. Experimental results on two large benchmark datasets demonstrate that the CFSA tracker achieves favorable performance compared with the state-of-the-art trackers.

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