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 Ke Yi
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.
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
Parallel Spiking Neurons with High Efficiency and Ability to Learn Long-term Dependencies Wei Fang
To avoid the use of future inputs for step-by-step inference, the weights of the PSN can be masked, resulting in the masked PSN.
Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods
Specifically, we consider an individual calibration objective for characterizing the quantiles of the prediction model.
CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes Y ulei Qin 1 Xingyu Chen
For instance, web images retrieved with queries of " tiger cat " (a cat species) and " drumstick " (a musical instrument) are almost dominated by images of tigers and