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ORCID6th NLDL 2023: Tromsø, Norway
- Proceedings of the 2023 Northern Lights Deep Learning Workshop, NLDL 2023, Tromsø, Norway, January 10-12, 2023. Septentrio Academic Publishing 2023
- Jonathan D. Thomas, Raúl Santos-Rodríguez, Mihai Anca, Robert J. Piechocki:
Multi-lingual agents through multi-headed neural networks. - Alvaro Fernandez-Quilez, Christoffer Gabrielsen Andersen, Trygve Eftestøl, Svein Reidar Kjosavik
, Ketil Oppedal:
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI. - Jingpeng Li, Atle Bjørnerud:
Automatic Postoperative Brain Tumor Segmentation with Limited Data using Transfer Learning and Triplet Attention. - Christian Keilstrup Ingwersen, Janus Nørtoft Jensen, Morten Rieger Hannemose, Anders Bjorholm Dahl:
Evaluating current state of monocular 3D pose models for golf. - Nicklas Boserup, Raghavendra Selvan:
Efficient Self-Supervision using Patch-based Contrastive Learning for Histopathology Image Segmentation. - Josefine Vilsbøll Sundgaard, Morten Rieger Hannemose, Søren Laugesen, Peter Bray, James Michael Harte, Yosuke Kamide, Chiemi Tanaka, Rasmus R. Paulsen, Anders Nymark Christensen:
Multi-modal data generation with a deep metric variational autoencoder. - Qing Li, Jiahui Geng, Steinar Evje, Chunming Rong:
Solving Nonlinear Conservation Laws of Partial Differential Equations Using Graph Neural Networks. - Syed Musharraf Ali, Tobias Deußer, Sebastian Houben, Lars Patrick Hillebrand, Tim Metzler, Rafet Sifa:
Automatic Consistency Checking of Table and Text in Financial Documents. - Ørjan Langøy Olsen, Tonje Knutsen Sørdalen, Morten Goodwin, Ketil Malde, Kristian Muri Knausgård, Kim Tallaksen Halvorsen:
A contrastive learning approach for individual re-identification in a wild fish population. - Filippo Maria Bianchi:
Simplifying Clustering with Graph Neural Networks. - Michele Guerra, Indro Spinelli, Simone Scardapane, Filippo Maria Bianchi:
Explainability in subgraphs-enhanced Graph Neural Networks. - Cosimo Persia, Ricardo Guimarães:
RIDDLE: Rule Induction with Deep Learning. - Juri Backes, Wolfgang Renz:
Improving Wind Speed Uncertainty Forecasts Using Recurrent Neural Networks. - Bjørn-Jostein Singstad, Belal Tavashi:
Using deep convolutional neural networks to predict patients age based on ECGs from an independent test cohort. - Matteo Ferrante, Tommaso Boccato, Andrea Duggento, Simeon E. Spasov, Nicola Toschi:
Contrastive learning for unsupervised medical image clustering and reconstruction. - Mathijs Boezer, Maryam Tavakol, Zahra Sajadi:
FastDTI: Drug-Target Interaction Prediction using Multimodality and Transformers. - Hyeongji Kim, Pekka Parviainen, Ketil Malde:
Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary. - Miquel Martí i Rabadán, Alessandro Pieropan, Hossein Azizpour, Atsuto Maki:
Dense FixMatch: a simple semi-supervised learning method for pixel-wise prediction tasks. - Tobias Deußer, Maren Pielka, Lisa Pucknat, Basil Jacob, Tim Dilmaghani Khameneh, Mahdis Nourimand, Bernd Kliem, Rüdiger Loitz, Christian Bauckhage, Rafet Sifa:
Contradiction Detection in Financial Reports. - Daniel J. Trosten:
Questionable Practices in Methodological Deep Learning Research. - Manuel Gil-Martín, Cristina Luna Jiménez, Fernando Fernández Martínez, Rubén San Segundo:
Signal and Visual Approaches for Parkinson's Disease Detection from Spiral Drawings. - Martijn Vermeer, David Völgyes, Tord Kriznik Sørensen, Heidrun Miller, Daniele Fantin:
Semi- and weak-supervised learning for Norwegian tree species detection. - Georgios Agrafiotis, Eftychia Makri, Ilias Kalamaras, Antonios Lalas, Konstantinos Votis, Dimitrios Tzovaras:
Nearest Unitary and Toeplitz matrix techniques for adaptation of Deep Learning models in photonic FPGA. - I-Hao Chen, Nabil Belbachir:
Using Mask R-CNN for Underwater Fish Instance Segmentation as Novel Objects: A Proof of Concept. - Duo Yang, Nora Hollenstein:
PLM-AS: Pre-trained Language Models Augmented with Scanpaths for Sentiment Classification. - Felix Neubürger, Daniel Gierse, Thomas Kopinski:
Hybrid bayesian convolutional neural network object detection architectures for tracking small markers in automotive crashtest videos. - Karl Audun Borgersen, Morten Goodwin, Jivitesh Sharma:
A comparison between Tsetlin machines and deep neural networks in the context of recommendation systems. - Lidia Luque, Jon André Ottesen, Atle Bjørnerud, Kyrre Eeg Emblem, Bradley J. MacIntosh:
Reducing Annotator's Burden: Cross-Pseudo Supervision for Brain Tumor Segmentation. - Mathias Micheelsen Lowes, Jakob L. Christensen, Bjørn Schreblowski Hansen, Morten Rieger Hannemose, Anders Bjorholm Dahl, Vedrana Andersen Dahl:
Interactive Scribble Segmentation. - Bibek Aryal, Katie E. Miles, Sergio A. Vargas Zesati, Olac Fuentes:
Boundary Aware U-Net for Glacier Segmentation. - Oskar Sjögren, Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki:
Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics. - Luca Courte, Marius Zeinhofer:
Robin Pre-Training for the Deep Ritz Method. - Flavio Petruzzellis, Ling Xuan Chen, Alberto Testolin:
Learning to solve arithmetic problems with a virtual abacus. - Mingshi Li, Zifu Wang, Matthew B. Blaschko:
Improved Imagery Throughput via Cascaded Uncertainty Pruning on U-Net++.
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