Changkyu Choi,
Shujian Yu,
Michael Christian Kampffmeyer,
Arnt-Børre Salberg,
Nils Olav Handegard,
Robert Jenssen
:
DIB-X: Formulating Explainability Principles for a Self-Explainable Model Through Information Theoretic Learning
Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing 2024
DOI
Hyeongji Kim,
Changkyu Choi,
Michael Christian Kampffmeyer,
Terje Berge,
Pekka Parviainen,
Ketil Malde
:
ProxyDR: Deep Hyperspherical Metric Learning with Distance Ratio-Based Formulation
Lecture Notes in Computer Science (LNCS) 2024
Changkyu Choi,
Michael Kampffmeyer,
Nils Olav Handegard,
Arnt-Børre Salberg,
Robert Jenssen
:
Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data
IEEE Journal of Oceanic Engineering 2023
ARKIV /
DOI
Changkyu Choi,
Michael Kampffmeyer,
Nils Olav Handegard,
Arnt Børre Salberg,
Olav Brautaset,
Line Eikvil
m.fl.:
Semi-supervised target classification in multi-frequency echosounder data
Changkyu Choi,
Filippo Maria Bianchi,
Michael Kampffmeyer,
Robert Jenssen
:
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
Proceedings of the Northern Lights Deep Learning Workshop 2020
ARKIV /
DOI
Adín Ramírez Rivera,
Sarina Thomas,
Changkyu Choi
:
Unsupervised Learning of visual feature hierarchy by contrasting hierarchical topic assignments
2024
Changkyu Choi
:
Introduction to generative AI
2023
Changkyu Choi
:
DIB-X: Formulating Explainability Principles for a Self-explainable Model through Information Theoretic Learning
2023
Changkyu Choi
:
Deep learning landscape 2: Image tasks and CNNs
2023
Changkyu Choi
:
Deep learning landscape 1: Neural networks basic
2023
Changkyu Choi
:
Recent Deep Learning Models and their Applicability in the Marine Domain Now and in the Near Future
2023
Changkyu Choi,
Michael Christian Kampffmeyer,
Nils Olav Handegard,
Arnt-Børre Salberg,
Robert Jenssen
:
Deep Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
2023
Changkyu Choi
:
Deep learning landscape 4: Transformers and their applicability in the marine domain now and in the near future
2023
Changkyu Choi
:
Deep learning landscape 3: Explainable deep learning
2023
Changkyu Choi
:
Recent Deep Learning Models and their Applicability in the Marine Domain Now and in the Near Future
2023
Changkyu Choi,
Shujian Yu,
Michael Kampffmeyer,
Arnt-Børre Salberg,
Nils Olav Handegard,
Suaiba Amina Salahuddin
m.fl.:
Explaining Marine Acoustic Target Classification in Multi-channel Echosounder Data using Self-attention Mask, Information-Bottleneck, and Mask Prior
2022
Nils Olav Handegard,
Olav Brautaset,
Changkyu Choi,
Tomasz Furmanek,
Arne Johan Hestnes,
Espen Johnsen
m.fl.:
Developing and deploying machine learning methods for acoustic data
Changkyu Choi
:
Segmenting Multi-frequency Marine Acoustic Data in a Semi-supervised Fashion
2022
Changkyu Choi
:
Information Bottleneck-based Interpretability Method in Marine Acoustic Data
2022
Changkyu Choi
:
Deep Semi-supervised Target Classification in Multi-frequency Echosounder Data
2021
Changkyu Choi
:
Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
Changkyu Choi
:
Marine Vision: When Visual Intelligence meets Marine Science
Changkyu Choi,
Michael Kampffmeyer,
Nils Olav Handegard,
Arnt Børre Salberg,
Line Eikvil,
Robert Jenssen
:
Semi-supervised Semantic Segmentation in Multi-frequency Echosounder Data
2021
Nils Olav Handegard,
Lars Nonboe Andersen,
Olav Brautaset,
Changkyu Choi,
Inge Kristian Eliassen,
Yngve Heggelund
m.fl.:
Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics
Nils Olav Handegard,
Changkyu Choi
:
Arbeidet hans sparer forskerne for store ressurser, og åpner for et selvstyrt fiskeri
Changkyu Choi
:
Semisupervised target classification in multifrequency echosounder data
2020
Changkyu Choi,
Filippo Maria Bianchi,
Michael Kampffmeyer,
Robert Jenssen
:
Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
Changkyu Choi,
Michael Kampffmeyer,
Robert Jenssen
:
A Robustness Analysis of Personalized Propagation of Neural Prediction