Fast convolutional neural networks on FPGAs with hls4ml. - Thea Aarrestad; Vladimir Loncar; Maurizio Pierini; Sioni Summers; Jennifer Ngadiuba; Christoffer Petersson; Hampus Linander; Yutaro Iiyama; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; Dylan S. Rankin; et al. (CoRR, 2021)

Fast convolutional neural networks on FPGAs with hls4ml. - Thea Aarrestad; Vladimir Loncar; Nicolò Ghielmetti; Maurizio Pierini; Sioni Summers; Jennifer Ngadiuba; Christoffer Petersson; Hampus Linander; Yutaro Iiyama; Giuseppe Di Guglielmo; Javier M. Duarte; Philip C. Harris; et al. (Mach. Learn. Sci. Technol., 2021)

Equivariance versus Augmentation for Spherical Images. - Jan E. Gerken; Oscar Carlsson; Hampus Linander; Fredrik Ohlsson; Christoffer Petersson; Daniel Persson (ICML, 2022)

Looking at the posterior: on the origin of uncertainty in neural-network classification. - Hampus Linander; Oleksandr Balabanov; Henry Yang; Bernhard Mehlig (CoRR, 2022)

Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. - Nicolò Ghielmetti; Vladimir Loncar; Maurizio Pierini; Marcel Roed; Sioni Summers; Thea Aarrestad; Christoffer Petersson; Hampus Linander; Jennifer Ngadiuba; Kelvin Lin; Philip C. Harris (Mach. Learn. Sci. Technol., 2022)

Bayesian Posterior Approximation With Stochastic Ensembles. - Oleksandr Balabanov; Bernhard Mehlig; Hampus Linander (CVPR, 2023)

Finite-time Lyapunov exponents of deep neural networks. - L. Storm; Hampus Linander; J. Bec; Kristian Gustavsson; Bernhard Mehlig (CoRR, 2023)

Geometric deep learning and equivariant neural networks. - Jan E. Gerken; Jimmy Aronsson; Oscar Carlsson; Hampus Linander; Fredrik Ohlsson; Christoffer Petersson; Daniel Persson (Artif. Intell. Rev., 2023)

Looking at the posterior: accuracy and uncertainty of neural-network predictions. - Hampus Linander; Oleksandr Balabanov; Henry Yang; Bernhard Mehlig (Mach. Learn. Sci. Technol., 2023)

HEAL-SWIN: A Vision Transformer on the Sphere. - Oscar Carlsson; Jan E. Gerken; Hampus Linander; Heiner Spieß; Fredrik Ohlsson; Christoffer Petersson; Daniel Persson (CVPR, 2024)

Uncertainty quantification in fine-tuned LLMs using LoRA ensembles. - Oleksandr Balabanov; Hampus Linander (CoRR, 2024)

Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks. - Longde Huang; Oleksandr Balabanov; Hampus Linander; Mats Granath; Daniel Persson; Jan E. Gerken (CoRR, 2025)

Bayesian Predictive Coding. - Alexander Tschantz; Magnus T. Koudahl; Hampus Linander; Lancelot Da Costa; Conor Heins; Jeff Beck; Christopher L. Buckley (CoRR, 2025)

PEAR: Equal Area Weather Forecasting on the Sphere. - Hampus Linander; Christoffer Petersson; Daniel Persson; Jan E. Gerken (CoRR, 2025)

AXIOM: Learning to Play Games in Minutes with Expanding Object-Centric Models. - Conor Heins; Toon Van de Maele; Alexander Tschantz; Hampus Linander; Dimitrije Markovic; Tommaso Salvatori; Corrado Pezzato; Ozan Çatal; Ran Wei; Magnus T. Koudahl; Marco Perin; Karl J. Friston; et al. (CoRR, 2025)