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ORCID3rd NLDL 2020: Tromsø, Norway
- Proceedings of the 2020 Northern Lights Deep Learning Workshop, NLDL 2020, Tromsø, Norway, January 19-21, 2020. Septentrio Academic Publishing 2020
- Henning Petzka, Martin Trimmel, Cristian Sminchisescu:
Notes on the Symmetries of 2-Layer ReLU-Networks. 1-6 - Alexander Hepburn, Valero Laparra
, Ryan McConville, Raúl Santos-Rodríguez:
Enforcing perceptual consistency on Generative Adversarial Networks by using the Normalised Laplacian Pyramid Distance. 1-6 - Jörn Anemüller:
Machine listening in spatial acoustic scenes with deep networks in different microphone geometries. 1-6 - Polina Kurtser, Ola Ringdahl, Nati Rotstein, Henrik Andreasson:
PointNet and geometric reasoning for detection of grape vines from single frame RGB-D data in outdoor conditions. 1-6 - Thomas Haugland Johansen, Steffen Aagaard Sørensen:
Towards detection and classification of microscopic foraminifera using transfer learning. 1-6 - Alexandra-Ioana Albu, Alina Enescu, Luigi Malagò:
Tumor Detection in Brain MRIs by Computing Dissimilarities in the Latent Space of a Variational AutoEncoder. 1-6 - Fotini Patrona, Paraskevi Nousi, Ioannis Mademlis, Anastasios Tefas, Ioannis Pitas:
Visual Object Detection For Autonomous UAV Cinematography. 1-6 - Changkyu Choi:
Short-Term Load Forecasting with Dilated Recurrent Attention Networks in Presence of Missing Data. 1-6 - Ashish Gupta, Rishabh Mehrotra:
Joint Attention Neural Model for Demand Prediction in Online Marketplaces. 1-6 - Kristoffer M. Tangrand, Bernt A. Bremdal:
Using Deep Learning Methods to Monitor Non-Observable States in a Building. 1-6 - Petru Hlihor, Riccardo Volpi, Luigi Malagò:
Evaluating the Robustness of Defense Mechanisms based on AutoEncoder Reconstructions against Carlini-Wagner Adversarial Attacks. 1-6 - Alexandra Vedeler, Narada Warakagoda:
Generative Adversarial Immitation Learning for Steering an Unmanned Surface Vehicle. 1-6 - Miguel Angel Tejedor Hernandez, Jonas Nordhaug Myhre:
Controlling Blood Glucose For Patients With Type 1 DiabetesUsing Deep Reinforcement Learning - The Influence OfChanging The Reward Function. 1-6
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