Machine Learning, MOTION PLANNING

Learning Equality Constraints for Motion Planning on Manifolds

Constrained robot motion planning is a widely used technique to solve complex robot tasks. We consider the problem of learning representations of constraints from demonstrations with a deep neural network, which we call Equality Constraint Manifold Neural Network (ECoMaNN)… The key idea is to learn a level-set function of the constraint suitable for integration into a constrained sampling-based motion planner. Learning proceeds by aligning subspaces in the network with subspaces of the data. We combine both learned constraints and analytically […]

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Beginner, Career, Interviews, Listicle Leave a comment

6 Key Points you Should Focus on for your Next Data Science Interview

Overview Preparing for your next data science interview? You need to ensure you’re covering your basics Here are 6 key points we’ve taken from our data science interview experience that you should focus on   Introduction You’ve finally done it! You have landed an interview for a data science role. Now, a day before your interview, you’re not sure what to study. The day is almost here but there is so much to cover! Sound familiar? Interviews can be daunting […]

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Machine Learning

Foreseeing Brain Graph Evolution Over Time Using Deep Adversarial Network Normalizer

Foreseeing the brain evolution as a complex highly inter-connected system, widely modeled as a graph, is crucial for mapping dynamic interactions between different anatomical regions of interest (ROIs) in health and disease. Interestingly, brain graph evolution models remain almost absent in the literature… Here we design an adversarial brain network normalizer for representing each brain network as a transformation of a fixed centered population-driven connectional template. Such graph normalization with respect to a fixed reference paves the way for reliably […]

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Machine Learning

Information-Theoretic Visual Explanation for Black-Box Classifiers

In this work, we attempt to explain the prediction of any black-box classifier from an information-theoretic perspective. For this purpose, we propose two attribution maps: an information gain (IG) map and a point-wise mutual information (PMI) map… IG map provides a class-independent answer to “How informative is each pixel? “, and PMI map offers a class-specific explanation by answering “How much does each pixel support a specific class?” In this manner, we propose (i) a theory-backed attribution method. The attribution […]

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Advanced, Algorithm, Technique Leave a comment

How to Rank Entities with Multi-Criteria Decision Making Methods(MCDM)

Ranking with MCDM You can’t rest on your #1 ranking-because the guy at #2 isn’t resting. He’s still improving his site — Ryan Jones We all come across different multi-criteria decision-making problems in our day to day life. Example — Shopping: Which one product should I buy out of X candidate products? Ranking problems are the most interesting problems a data To finish reading, please visit source site

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Machine Learning

Residual Embedding Similarity-Based Network Selection for Predicting Brain Network Evolution Trajectory from a Single Observation

While existing predictive frameworks are able to handle Euclidean structured data (i.e, brain images), they might fail to generalize to geometric non-Euclidean data such as brain networks. Besides, these are rooted the sample selection step in using Euclidean or learned similarity measure between vectorized training and testing brain networks… Such sample connectomic representation might include irrelevant and redundant features that could mislead the training sample selection step. Undoubtedly, this fails to exploit and preserve the topology of the brain connectome. […]

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HYPERPARAMETER OPTIMIZATION, Machine Learning

Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI

Background and Objective: Accurate and reliable segmentation of the prostate gland in MR images can support the clinical assessment of prostate cancer, as well as the planning and monitoring of focal and loco-regional therapeutic interventions. Despite the availability of multi-planar MR scans due to standardized protocols, the majority of segmentation approaches presented in the literature consider the axial scans only… Methods: We propose an anisotropic 3D multi-stream CNN architecture, which processes additional scan directions to produce a higher-resolution isotropic prostate […]

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Machine Learning

ANNdotNET — deep learning tool on .NET Platform

ANNdotNET is an open source project for deep learning written in C# with ability to create, train, evaluate and export deep learning models. The project consists of the Graphical User Interface module capable to visually prepare data, fine tune hyper-parameters, design network architecture, evaluate and test trained models… The ANNdotNET introduces the Visual Network Designer, (VND) for visually design almost any sequential deep learning network. Beside VND, ANNdotNET implements Machine Learning Engine, (MLE) based on CNTK – deep learning framework, […]

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AUTONOMOUS DRIVING, Machine Learning

Robust and efficient post-processing for video object detection

Object recognition in video is an important task for plenty of applications, including autonomous driving perception, surveillance tasks, wearable devices or IoT networks. Object recognition using video data is more challenging than using still images due to blur, occlusions or rare object poses… Specific video detectors with high computational cost or standard image detectors together with a fast post-processing algorithm achieve the current state-of-the-art. This work introduces a novel post-processing pipeline that overcomes some of the limitations of previous post-processing […]

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DEEP ATTENTION, Machine Learning

Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks

Traditional image-based survival prediction models rely on discriminative patch labeling which make those methods not scalable to extend to large datasets. Recent studies have shown Multiple Instance Learning (MIL) framework is useful for histopathological images when no annotations are available in classification task… Different to the current image-based survival models that limit to key patches or clusters derived from Whole Slide Images (WSIs), we propose Deep Attention Multiple Instance Survival Learning (DeepAttnMISL) by introducing both siamese MI-FCN and attention-based MIL […]

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