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Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi
Wedosobyintroducing apairofoperators: gather,which efficiently aggregates feature responses from a large spatial extent, and excite, which redistributes the pooled information to local features.
Outlier Detection and Robust PCA Using a Convex Measure of Innovation
Neural Information Processing Systems http://nips.cc/
A Notation N: the set of natural numbers R d: d-dimensional Euclidean space R
H . (32) We refer to ( H
Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations
Kevin Smith, Lingjie Mei, Shunyu Yao, Jiajun Wu, Elizabeth Spelke, Josh Tenenbaum, Tomer Ullman
Neural Information Processing Systems http://nips.cc/
Fast Greedy MAP Inference for Determinantal Point Process to Improve Recommendation Diversity
Laming Chen, Guoxin Zhang, Eric Zhou
In addition, our algorithm also adapts to scenarios where the repulsion is onlyrequired among nearby fewitems intheresult sequence.
A Theory of Optimistically Universal Online Learnability for General Concept Classes
Haussler et al. [1994], Ryabko [2006] researched the online learning problem with a mix of both restrictions. There are also substantial amount of papers investigating online learnability with all measurable functions but restricted data processes.
Mean Field for the Stochastic Blockmodel: Optimization Landscape and Convergence Issues
Soumendu Sundar Mukherjee, Purnamrita Sarkar, Y. X. Rachel Wang, Bowei Yan
Neural Information Processing Systems http://nips.cc/
Incremental Few-Shot Learning with Attention Attractor Networks
Mengye Ren, Renjie Liao, Ethan Fetaya, Richard Zemel
Machine learning classifiers are often trained to recognize a set of pre-defined classes.