Asia
Efficient characterization of electrically evoked responses for neural interfaces
Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E.J. Chichilnisky
Neural Information Processing Systems http://nips.cc/
Masked Image Modeling Supplementary Material Anonymous Author(s) Affiliation Address email 1 More Training Details 1
We use the same setting for different sizes RevCol models on MIM pre-training. The hyper-parameters generally follow [4, 2]. Table 3 shows the detail training settings after MIM pre-training. We also show training settings on ImageNet-1K after ImageNet-22K fine-tuning. For semantic segmentation, we evaluate different backbones on ADE20K dataset.
Multi-task Learning for Aggregated Data using Gaussian Processes
Fariba Yousefi, Michael T. Smith, Mauricio Álvarez
For example, census data are usually sampled or collected as aggregated at different administrative divisions, e.g.
Why Is My Classifier Discriminatory?
Irene Chen, Fredrik D. Johansson, David Sontag
Recent attempts to achieve fairness in predictive models focus on the balance between fairness and accuracy.
Graphcode: Learning from multiparameter persistent homology using graph neural networks
Graphcodes handle datasets that are filtered along two real-valued scale parameters.
Variational Denoising Network: Toward Blind Noise Modeling and Removal
Zongsheng Yue, Hongwei Yong, Qian Zhao, Deyu Meng, Lei Zhang
On one hand, as other data-driven deep learning methods, our method, namely variational denoising network (VDN), can perform denoising efficiently due to its explicit form of posterior expression. On the other hand, VDN inherits the advantages of traditional model-driven approaches, especially the good generalization capability of generative models.