Articles About Machine Learning

Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Last Updated on September 7, 2020 Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset. There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an open-source Python library that provides an implementation of Bayesian Optimization that can be used to tune the hyperparameters of […]

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Revisiting Design Choices in Proximal Policy Optimization

Proximal Policy Optimization (PPO) is a popular deep policy gradient algorithm. In standard implementations, PPO regularizes policy updates with clipped probability ratios, and parameterizes policies with either continuous Gaussian distributions or discrete Softmax distributions… These design choices are widely accepted, and motivated by empirical performance comparisons on MuJoCo and Atari benchmarks. We revisit these practices outside the regime of current benchmarks, and expose three failure modes of standard PPO. We explain why standard design choices are problematic in these cases, […]

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Learning Graph Normalization for Graph Neural Networks

Graph Neural Networks (GNNs) have attracted considerable attention and have emerged as a new promising paradigm to process graph-structured data. GNNs are usually stacked to multiple layers and the node representations in each layer are computed through propagating and aggregating the neighboring node features with respect to the graph… By stacking to multiple layers, GNNs are able to capture the long-range dependencies among the data on the graph and thus bring performance improvements. To train a GNN with multiple layers […]

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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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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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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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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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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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KoBE: Knowledge-Based Machine Translation Evaluation

We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of the grounded entities found in the candidate vs. those found in the source… Our approach achieves the highest correlation with human judgements on 9 out of the 18 language pairs from the WMT19 […]

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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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