Oceania
Mean-field theory of graph neural networks in graph partitioning
Tatsuro Kawamoto, Masashi Tsubaki, Tomoyuki Obuchi
A theoretical performance analysis of the graph neural network (GNN) is presented. For classification tasks, the neural network approach has the advantage in terms of flexibility that it can be employed in a data-driven manner, whereas Bayesian inference requires the assumption of a specific model. A fundamental question is then whether GNN has a high accuracy in addition to this flexibility.
KNG: The K-Norm Gradient Mechanism
Neural Information Processing Systems http://nips.cc/
Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections
Neural Information Processing Systems http://nips.cc/
Learning Versatile Filters for Efficient Convolutional Neural Networks
Yunhe Wang, Chang Xu, Chunjing XU, Chao Xu, Dacheng Tao
Neural Information Processing Systems http://nips.cc/
A Unified Approach to Count-Based Weakly-Supervised Learning
High-quality labels are often very scarce, whereas unlabeled data with inferred weak labels occurs more naturally.
Video Prediction via Selective Sampling
Jingwei Xu, Bingbing Ni, Xiaokang Yang
This module is trained in an adversarial learning manner [5]. The Selectionmodule selects high possibility candidates from proposals and combines to produce the final prediction, according to the criteria of better position matching.
Variational Memory Encoder-Decoder
Hung Le, Truyen Tran, Thin Nguyen, Svetha Venkatesh
Neural Information Processing Systems http://nips.cc/