A Neural Autoregressive Approach to Attention-based Recognition

By Yin Zheng, Richard S. Zemel, Yujin Zhang, Hugo Larochelle. 2015

30 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Electrical and Electronic Engineering, Natural Language Processing

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In Cognitive Neuroscience

In Computer Vision and Pattern Recognition

In Electrical and Electronic Engineering

In Natural Language Processing

Citations in the corpus

DRAW: A Recurrent Neural Network For Image Generation (2015, background): “…structure can be better captured by a sequence of partial glimpses, or foveations, than by a single sweep through the entire image (Larochelle & Hinton, 2010; Denil et al., 2012; Tang et al., 2013; Ranzato, 2014; Zheng et al., 2014; Mnih et al., 2014; Ba et al., 2014; Ser-manet et al., 2014).”
A Neural Autoregressive Approach to Collaborative Filtering (2016, methodology): “NADE together with its variants achieved competitive results on many machine learning tasks (Larochelle & Lauly, 2012; Uria et al., 2013; Zheng et al., 2014b; Uria et al., 2014; Zheng et al., 2014a; 2015).”
Describing Multimedia Content Using Attention-Based Encoder-Decoder Networks (2015, background): “Earlier works on utilizing the attention mechanism, both content-based and location-based, for object recognition/tracking can be found in [69], [70], [71].”
Inner Attention based Recurrent Neural Networks for Answer Selection (2016, background): “Many recent works show that attention techniques can improve the performance of machine learning models (Mnih et al., 2014; Zheng et al., 2015).”
The Human Cell Atlas (2017, unknown): “Combining molecular profiles with tissue architecture will require new computational methods, drawing perhaps on advances in machine vision (Xu et al., 2015; Zheng et al., 2015).”
Seeing with Humans: Gaze-Assisted Neural Image Captioning (2016, methodology): “Even more importantly, attention mechanisms add localization capabilities to neural networks, resulting in improved performance for target localization and recognition tasks [18], [37], [38].”
Recurrent Mixture Density Network for Spatiotemporal Visual Attention (2016, background): “The second category embodies hard attentional methods that completely discard (as opposed to re-weight) information from the input [16–18, 13, 11, 12, 19–21].”
Attention for Fine-Grained Categorization (2014, background): “2011; Denil et al., 2012; Ranzato, 2014; Mnih et al., 2014), the vision-control game of “catch” (Mnih et al., 2014), expression classiﬁcation for 100 100 aligned faces (Larochelle & Hinton, 2010; Zheng et al., 2014), detection of frontal faces (Tang et al., 2013), tracking of hockey players (Baz”
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016, unknown): “tensively used, learning good representations most suitable for clustering tasks is left largely unexplored. Recently, deep learning has achieved widespread success in numerous machine learning tasks [9,11,3,36,35,37,1, 7], where learning good representations by deep neural networks (DNN) lies in th”
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search (2020, background): “…have been producing stateof-the-art results in many challenging tasks including image classification (Krizhevsky et al., 2012; Liu et al., 2020; Zheng et al., 2015; Guo et al., 2020a), semantic segmentation (Shelhamer et al., 2017; Chen et al., 2018; Huang et al., 2019), and many other areas…”
The Misgendering Machines (2018, unknown): “" [109] 88:8”
NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019, background): “…networks (DNNs) [25] have been producing state-of-the-art results in many challenging tasks including image classiﬁcation [12, 23, 43, 60, 61, 18, 11, 56], face recognition [39, 44, 59], brain signal processing [34, 35], video analysis [48, 53, 52] and many other areas [58, 57, 24, 3, 10, 9, 4].”