Paper log autumn 2019
- Oct 2, Gustav: H. Zhou et al. "DeepTAM: Deep Tracking and Mapping", ECCV'18 [PDF] [webpage] [with supplementary]
- Oct 9, Karl: van den Berg et al. "Sylvester Normalizing Flows for Variational Inference"[PDF] [GIT] Background on Normalizing Flow [PDF]
- Oct 23: Felix presents: T. Probst et al. "Unsupervised Learning of Consensus Maximization for 3D Vision Problems", CVPR19 [PDF] [code] [supp] [ref 8/9]
- Oct 30: Oliver presents: C. Finn et al., "Unsupervised Learning for Physical Interaction through Video Prediction", NIPS'16 [proceedings] [website].
- Nov 13, Oliver presents: M. Bansal et al., "ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst", RSS'19 [PDF] [website]
Paper log spring 2019
- March 6: Mikael presents W. Brendel and M. Bethge, Approximating CNNs with Bag-of-local features models works surprisingly well on ImageNet, ICLR 2019. [PDF] [reviews]
- March 27: Amanda presents: S. Mukherjee, H. Asani, E. Lin, S. Kannan, "ClusterGAN: Latent Space Clustering in Generative Adversarial Networks. ArXiV 2018 [PDF] [Github]
- April 17: Joakim presents: A. Graves, G. Wayne, I. Danihelka. Neural Turing Machines, ArXiv 2014 [PDF]. Further reading: Nature 2016 [PDF], NIPS 2016 Talk: [YouTube]
- May 8: Gustav presents: A. Kar, C. Häne, and J. Malik, "Learning a Multi-View Stereo Machine", NIPS'17 [Paper+reviews]
- May 15: Oliver presents: J. Tremblay et al., "Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization", CVPR18 [PDF] [talk]
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Last updated: 2020-01-22