Topic Modeling of Multimodal Data: An Autoregressive Approach

By Yin Zheng, Yujin Zhang, Hugo Larochelle. 2014

70 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Mingkui Tan
2. Jian Chen
3. Yong Guo
4. Peilin Zhao
5. Qi Chen
6. Junzhou Huang
7. Liang Wang
8. Hanning Zhou

In Computer Vision and Pattern Recognition

1. Mingkui Tan
2. Yong Guo
3. Jian Chen
4. Peilin Zhao
5. Qi Chen
6. Junzhou Huang
7. Liang Wang
8. Shu Wu

Citations in the corpus

• 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).”
• A Comprehensive Survey on Cross-modal Retrieval (2016, methodology): “…methods Subspace learning methods CDFE [45], GMA [15], I 2 SCA [46], PFAR [47], JRL [48], SliM 2 [49], cluster-CCA [50], CCA-3V [51], LCFS [52], JFSSL [53] Topic model SupDocNADE [54], NPBUS [55], M 3 R [56] Deep learning methods RE-DNN [57], deep-SM [58], MDNN [59] Binary representation learning”
• Gated Multimodal Units for Information Fusion (2017, methodology): “An alternative approach involves an objective or loss function suited for the target task (Akata et al., 2014; Frome et al., 2013; Kiros et al., 2014b; Mao et al., 2014; Socher et al., 2013; 2014; Zheng et al., 2014).”
• Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering (2016, background): “Recently, deep learning has achieved widespread success in numerous machine learning tasks [Krizhevsky et al., 2012; Zheng et al., 2014b; Szegedy et al., 2015; Zheng et al., 2014a; He et al., 2016; Zheng et al., 2015; 2016], where learning good representations by deep neural networks (DNN) lies in P”
• A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data (2014, unknown): “We then rank the annotation words in A in decreasing order of their probability and select the top 5 words as our predicted annotation.”
• NAT: Neural Architecture Transformer for Accurate and Compact Architectures (2019, background): “Deep neural 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,…”