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Deliberative Explanations: visualizing network insecurities
The explanation consists of a list of insecurities, each composed of 1) an image region (more generally, a set of input variables), and 2) an ambiguity formed by the pair of classes responsible for the network uncertainty about the region.
Riemannian stochastic optimization methods avoid strict saddle points Anonymous Author(s) Affiliation Address email
Riemannian setting is a highly challenging affair.
MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis
Kundan Kumar, Rithesh Kumar, Thibault de Boissiere, Lucas Gestin, Wei Zhen Teoh, Jose Sotelo, Alexandre de Brébisson, Yoshua Bengio, Aaron C. Courville
Forexample, the Griffin-Lim (Griffin&Lim, 1984)algorithm allows one to efficiently decode an STFT sequence back to the temporal signal at thecost of introducing strong, robotic artifacts as noted in Wang et al. (2017).
Variational Learning on Aggregate Outputs with Gaussian Processes
Ho Chung Law, Dino Sejdinovic, Ewan Cameron, Tim Lucas, Seth Flaxman, Katherine Battle, Kenji Fukumizu
Neural Information Processing Systems http://nips.cc/
Discovery of Latent 3D Keypoints via End-to-end Geometric Reasoning
Supasorn Suwajanakorn, Noah Snavely, Jonathan J. Tompson, Mohammad Norouzi
Neural Information Processing Systems http://nips.cc/
Chartalist: Labeled Graph Datasets for UTXO and Account-based Blockchains
However, analyzing blockchain data requires domain expertise and computational resources, which pose a significant barrier and hinder advancement in this field.
