Lecture 14: Reinforcement Learning

dimensionality reduction, feature learning, density estimation, etc. 2-d density estimation 2-d density images left and right are CC0 public domain 1-d density estimation. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 14 - May 23, 2017 Today: Reinforcement Learning 7

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  Reduction, Learning, Reinforcement, Reinforcement learning, Dimensionality, Dimensionality reduction

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graph with shared weights h 0 f W h 1 f W h 2 f W h 3 x 3 y T ... Extract spatial features from a pretrained CNN Image Captioning using spatial features 11 CNN Features: H x W x D h 0 [START] Xu et al, “Show, Attend and Tell: Neural Image Caption Generation with Visual Attention”, ICML 2015 z 0,0 z 0,1 z 0,2 z 1,0 z 1,1 z 1,2 z 2,0 z 2,1 z ...

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Vector, Matrix, and Tensor Derivatives

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Convolutional Neural Networks for Visual Recognition

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sub-areas of deep learning. However, as the eld as grown, our understanding of the methods and the-ories underlying graph representation learning has also stretched backwards through time. We can now view the popular \node embedding" methods as well-understood extensions of classic work on dimensionality reduction. We

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jmlr.csail.mit.edu

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CERTIFICATE PROGRAMME IN DATA SCIENCE & …

home.iitd.ac.in

Dimensionality Reduction: PCA, TSNE Deep feedforward neural nets Convolutional neural nets Time series data models - ARX, ARMAX, ARIMA & ARIMAX models Long short-term memory (LSTM) networks Top Business Applications of Machine Learning eCommerce Customer Support, Product Recommendation Healthcare Drug Discovery, Disease Diagnosis BFSI ...

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arxiv.org

Inspired by [18] which performs dimentionality reduction gradually with multiple blocks, we design a new architec-ture with a progressive dimensionality reduction scheme. As shown in Figure2right, we use linear projections to reduce the dimensionality of the hidden embedding after each encoder layer. By adding multiple encoder layers, the

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www.stat.columbia.edu

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