Advances in Neural Information Processing Systems 29

By Onur Teymur, K. Zygalakis, Ben Calderhead. 2016

155 citations recorded by the source corpus.

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Citations in the corpus

What can the brain teach us about building artificial intelligence? (2016, methodology): “, 2015), augmented working memory (Graves et al., 2014; Grefenstette et al., 2015; Sukhbaatar et al., 2015; Weston et al., 2015), and experience replay (McClelland,”
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces (2018, methodology): “[Tamar et al., 2016; Oh et al., 2017; Weber et al., 2017]), these methods arguably discard many of the anticipated benefits of model-learning by making the model essentially a part of the parameterization of the value function or policy.”
Everything is Connected: Graph Neural Networks (2023, background): “That being said: representative convolutional GNNs include the Chebyshev network (Defferrard et al., 2016, ChebyNet), graph convolutional network (Kipf and Welling, 2017, GCN) and the simplified graph convolution (Wu et al., 2019, SGC); representative attentional GNNs include the mixture model CNN…”
Knowledge Transfer for Out-of-Knowledge-Base Entities : A Graph Neural Network Approach (2017, methodology): “into a vector [Cao et al., 2016; Defferrard et al., 2016], here we focus on the one that provides the means to encode nodes and edges into vectors, as this is more suitable for KBC.”
A survey of deep meta-learning (2020, methodology): “(2018) model this likelihood using Stein Variational Gradient Descent (SVGD) (Liu and Wang 2016).”
Reconstruction of three-dimensional porous media using generative adversarial neural networks (2017, background): “An additional stabilization measure called one-sided label smoothing, wherein the class label of 1 for real images is replaced by a new value of 1 − , has been empirically shown to improve training of GANs [37].”
Clustering-Based Relational Unsupervised Representation Learning with an Explicit Distributed Representation (2016, background): “Much of the recent work focused on constructing the embeddings of relational constructs [Niepert, 2016; Bordes et al., 2011; Yang et al., 2014; Bordes et al., 2013; Henaff et al., 2015; Niepert et al., 2016].”
Multi-stage image denoising with the wavelet transform (2022, methodology): “Besides, our WMDCNN is beneficial to low-level metrics, i.e., FSIM, SSIM and LPIPS and high-level metric, i.e., Inception-Score.”
Structure Learning for Safe Policy Improvement (2019, methodology): “Until recently, SPI algorithms used flat representations [Thomas et al., 2015; Petrik et al., 2016; Laroche et al., 2019].”