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Bayesian Semi-supervised Learning with Graph Gaussian Processes
Yin Cheng Ng, Nicolò Colombo, Ricardo Silva
Neural Information Processing Systems http://nips.cc/
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/
Scalable Coordinated Exploration in Concurrent Reinforcement Learning
Maria Dimakopoulou, Ian Osband, Benjamin Van Roy
Neural Information Processing Systems http://nips.cc/
Towards
The Goldilocks phase is reminiscent of "intelligence from starvation" in Darwinian evolution, where resource limitations drivediscoveryofmore efficient solutions.
Unsupervised Learning of Object Landmarks through Conditional Image Generation
Tomas Jakab, Ankush Gupta, Hakan Bilen, Andrea Vedaldi
Neural Information Processing Systems http://nips.cc/
Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards
Alexander Trott, Stephan Zheng, Caiming Xiong, Richard Socher
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.