Deep Learning
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks Alexander Immer
Further, we emphasize the significance of principled regularization of the network parameters and prediction.
Learning Topology-Agnostic EEG Representations with Geometry-Aware Modeling
Large-scale pre-training has shown great potential to enhance models on downstream tasks in vision and language. Developing similar techniques for scalp electroencephalogram (EEG) is suitable since unlabelled data is plentiful.
SODAI: Multi-Modal Maritime Object Detection Dataset With RGB and Hyperspectral Image Sensors
Notwithstanding astonishing advances in computer vision technologies, detecting ships and floating matters in these images is challenging due to factors such as object distance.
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution Shifts
Specifically, we pose and answer the following questions: Q1. How do the learned spatial and temporal representations vary based on different VSSL pretrain-ing methodologies? How robust are these representations to different distribution shifts?
Author Contributions
A.1 Deriving the Optimum of the KL-Constrained Reward Maximization Objective In this appendix, we will derive Eq. 4. Analogously to Eq. 3, we optimize the following objective: max
