Proceedings of The 28th International Conference on Machine Learning

By A. Clark. 2011

262 citations recorded by the source corpus.

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Citations in the corpus

Group Equivariant Convolutional Networks (2016, background): “The latter denotes moderate data augmentation with horizontal ﬂips and small translations, following Goodfellow et al. (2013) and many others.”
Value Function Transfer for Deep Multi-Agent Reinforcement Learning Based on N-Step Returns (2019, background): “Earlier work focuses on constructing coordination graphs (CGs) to represent the interactive relationship between agents and conducting learning according to the speciﬁed relationship in each state [Guestrin et al. , 2002a; Guestrin et al. , 2002b; Kok and Vlassis, 2004 ] .”
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces (2018, methodology): “However, despite the introduction of increasingly effective approaches for learning predictive models in Atari games [Bellemare et al., 2013b; Bellemare et al., 2014; Oh et al., 2015a], the application of model-based methods to the ALE is still an open problem, with many challenges to overcome…”
Adversarial Attacks on Neural Networks for Graph Data (2018, background): “Since then, there has been increasing interest in adversarial attacks [Zügner and Günnemann, 2019a; Bojchevski and Günnemann, 2019] and the first work about defenses [Zügner and Günnemann, 2019b].”
The Shapley Value in Machine Learning (2022, methodology): “, 2020b] Attribution Linear regression O(|N |) [Yuan et al., 2021] Attribution Monte Carlo sampling O(|N |) [Covert et al.”
Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks (2017, methodology): “Using dual-stream autoencoders, [20] successfully jointly processed audio-video data using an architecture with two branches, one for audio and one for video that merge in the middle of the network.”
Linear Algebra and its Applications (2010, unknown): “Like for ITR we use constant τ and have not implemented a local error estimator.”
Markovian State and Action Abstractions for MDPs via Hierarchical MCTS (2016, background): “It has been shown that a memoryless policy for a POMDP can be arbitrarily worse than an optimal policy for the POMDP, which in turn can be arbitrarily worse than an optimal policy for the underlying MDP [Singh et al., 1994].”
Learning Multi-Modal Grounded Linguistic Semantics by Playing "I Spy" (2016, unknown): “Some approaches additionally incorporate language models into the learning phase [Spranger and Steels, 2015; Krishnamurthy and Kollar, 2013; Perera and Allen, 2013; Matuszek et al., 2012].”
Multiview Spectral Clustering via Structured Low-Rank Matrix Factorization (2017, unknown): “To this end, Low-Rank Representation (LRR) [Xia et al., 2014; Liu et al., 2010; Wang et al., 2015c; Liu and Yan, 2011; Liu et al., 2013] is proposed.”