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Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Publications associées (49)

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Graph-to-Graph Transformer for Transition-based Dependency Parsing

James Henderson, Alireza Mohammadshahi

We propose the Graph2Graph Transformer architecture for conditioning on and predicting arbitrary graphs, and apply it to the challenging task of transition-based dependency parsing. After proposing two novel Transformer models of transition-based dependenc ...

Association for Computational Linguistics2020

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Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

2019

Evaluating and Interpreting Deep Convolutional Neural Networks via Non-negative Matrix Factorization

Edo Collins

With ever greater computational resources and more accessible software, deep neural networks have become ubiquitous across industry and academia. Their remarkable ability to generalize to new samples defies the conventional view, which holds that complex, ...

EPFL2019

A comparative study on network alignment techniques

Thanh Trung Huynh, Thành Tâm Nguyên, Chi Thang Duong

Network alignment is a method to align nodes that belong to the same entity from different networks. A well-known application of network alignment is to map user accounts from different social networks that belong to the same person. As network alignment h ...

PERGAMON-ELSEVIER SCIENCE LTD2019

BookTubing Across Regions: Examining Differences based on Nonverbal and Verbal Cues

Daniel Gatica-Perez, Dinesh Babu Jayagopi

BookTubers are a rapidly growing community in YouTube who shares content related to books. Previous literature has addressed problems related to automatically analyzing opinions and mood of video logs (vlogs) as a generic category in YouTube. Unfortunately ...

ASSOC COMPUTING MACHINERY2019

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

2018

The committee machine: Computational to statistical gaps in learning a two-layers neural network

Nicolas Macris, Florent Gérard Krzakala, Lenka Zdeborová, Jean François Emmanuel Barbier

Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-student scenario in multi-layer neural networks. In this contribution, we provide ...

NEURAL INFORMATION PROCESSING SYSTEMS (NIPS)2018

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Polarimetric imaging techniques demonstrate enhanced capabilities in advanced object detection tasks with their capability to discriminate man-made objects from natural background surfaces. While spectral signatures carry information only about material pr ...

SPIE-INT SOC OPTICAL ENGINEERING2018

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A non-intrusive reduced-basis (RB) method is proposed for parametrized unsteady flows. A set of reduced basis functions are extracted from a collection of high-fidelity solutions via a proper orthogonal decomposition (POD), and the coefficients of the redu ...

2018

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017