Proceedings of the 23rd international conference on Machine learning

By William W. Cohen, Andrew W. Moore. 2006

182 citations recorded by the source corpus.

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Citations in the corpus

The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces (2018, methodology): “[Gu et al., 2016; Kalweit and Boedecker, 2017]), but the impact of this choice has not been extensively or systematically studied.”
A Survey of Topic Modeling in Text Mining (2015, background): “…often reflect the underlying topics that united to form the documents, such as hierarchical probabilistic models are easily generalized to other kinds of data; topic models have been used to analyze things rather than words such as images, biological data, and survey information and data [1].”
A Degeneracy Framework for Graph Similarity (2018, methodology): “cian kernel was introduced to effectively compare structure at different scales [Kondor and Pan, 2016], while some neural network architectures were also designed to address the same problem [Dai et al., 2016].”
BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series (2019, methodology): “Then we calculate the two metrics, AUC (Area Under ROC Curve) and AP (Average Precision) [Davis and Goadrich, 2006].”
Inverse Reinforcement Learning (2010, methodology): “One way to favor a reward function from the set of solutions is to choose those which are penalizing any single step deviation from π by high costs.”
Sequence-to-point learning with neural networks for nonintrusive load monitoring (2016, methodology): “We call this type of method a sequence-to-point learning method, which is widely applied for modelling the distributions of speech and image (Sainath et al. 2015; van den Oord et al. 2016; van den Oord, Kalchbrenner, and Kavukcuoglu 2016).”
Universal approximations of permutation invariant/equivariant functions by deep neural networks (2019, background): “Group equivariant Convolutional Neural Networks (G-CNNs) is designed in [Cohen and Welling, 2016], as a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries.”