Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection

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Demographic fairness or equity is a crucial aspect of machine learning models, particularly in critical domains such as healthcare, where errors can have severe consequences. A fair model avoids making distinctions between groups with different sensitive o ...

By 'social learning,' in this article we refer to mechanisms for opinion formation and decision making over graphs and the study of how agents' decisions evolve dynamically through interactions with neighbors and the environment. The study of social learni ...

In medical image segmentation, supervised deep networks' success comes at the cost of requiring abundant labeled data. While asking domain experts to annotate only one or a few of the cohort's images is feasible, annotating all available images is impracti ...

Despite the high number of investments for data-based models in the expansion of Industry 4.0, too little effort has been made to ensure the maintenance of those models. In a data-streaming environment, data-based models are subject to concept drifts. A co ...

Superagers are defined as older adults who have youthful memory performance comparable to that of middle-aged adults. Classifying superagers based on the brain connectome using machine learning modeling can provide important insights on the physiology unde ...

Effective caching is crucial for performance of modern-day computing systems. A key optimization problem arising in caching – which item to evict to make room for a new item – cannot be optimally solved without knowing the future. There are many classical ...

Removal of noise from fluorescence microscopy images is an important first step in many biological analysis pipelines. Current state-of-the-art supervised methods employ convolutional neural networks that are trained with clean (ground-truth) images. Recen ...

For an autonomous car, getting surprised is the worst thing that can happen. To prevent that, plenty of studies are trying to forecast traffic participants’ actions especially in an urban scene using recurrent networks. Recurrent networks are used for temp ...

In this account, we demonstrate how statistical learning approaches can be leveraged across a range of different quantum chemical areas to transform the scaling, nature, and complexity of the problems that we are tackling. Selected examples illustrate the ...

We propose Deep Feature Factorization (DFF), a method capable of localizing similar semantic concepts within an image or a set of images. We use DFF to gain insight into a deep convolutional neural network's learned features, where we detect hierarchical c ...

Most recent approaches to monocular 3D pose estimation rely on Deep Learning. They either train a Convolutional Neural Network to directly regress from image to 3D pose, which ignores the dependencies between human joints, or model these dependencies via a ...

The way in which single neurons transform input into output spike trains has fundamental consequences for network coding. Theories and modeling studies based on standard Integrate-and-Fire models implicitly assume that, in response to increasingly strong i ...

Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection

Chattez avec Graph Search

Optimizing fairness and utility in healthcare machine learning models

Socially Intelligent Networks: A framework for decision making over graphs

Self-Supervised Generative Style Transfer for One-Shot Medical Image Segmentation

Data-based model maintenance in the era of industry 4.0: A methodology

Predicting Superagers by Machine Learning Classification Based on the Functional Brain Connectome Using Resting-State Functional Magnetic Resonance Imaging

Robust Learning-Augmented Caching: An Experimental Study

Removing Structured Noise With Self-Supervised Blind-Spot Networks

Feed-forwards meet recurrent networks in vehicle trajectory prediction

Quantum Chemistry Meets Machine Learning

Deep Feature Factorization for Concept Discovery

Structured Prediction of 3D Human Pose with Deep Neural Networks

Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons