PDF4PRO ⚡AMP

Modern search engine that looking for books and documents around the web

Example: tourism industry

Lecture 13: Generative Models

Lecture 13: Generative Models Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 1 May 18, 2017 . Administrative Midterm grades released on Gradescope this week A3 due next Friday, 5/26. HyperQuest deadline extended to Sunday 5/21, 11:59pm Poster session is June 6. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 2 May 18, 2017 . Overview Unsupervised Learning Generative Models PixelRNN and PixelCNN. Variational Autoencoders (VAE). Generative Adversarial Networks (GAN). Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 3 May 18, 2017 . Supervised vs Unsupervised Learning Supervised Learning Data: (x, y). x is data, y is label Goal: Learn a function to map x -> y Examples: Classification, regression, object detection, semantic segmentation, image captioning, etc. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 13 - 4 May 18, 2017 . Supervised vs Unsupervised Learning Supervised Learning Data: (x, y).

Lecture 13 - 33 May 18, 2017 PixelRNN and PixelCNN Improving PixelCNN performance - Gated convolutional layers - Short-cut connections - Discretized logistic loss - Multi-scale - Training tricks - Etc… See - Van der Oord et al. NIPS 2016 - Salimans et al. 2017 (PixelCNN++) Pros: - Can explicitly compute likelihood p(x) - Explicit likelihood ...

Fullscreen Download

Tags:

  2017, Inps, Generative

Information

Domain:

Source:

Link to this page:

Please notify us if you found a problem with this document:

Spam in document Broken preview Other abuse

Transcription of Lecture 13: Generative Models

Documents from same domain

Lecture 9: CNN Architectures

cs231n.stanford.edu

Lecture 9 - 22 May 2, 2017 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners First CNN-based winner. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 23 May 2, 2017 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners ZFNet: Improved hyperparameters over AlexNet. Fei-Fei Li & Justin Johnson & Serena Yeung ...

  2017, Challenges, Scale, Visual, Recognition, Ilsvrc, Scale visual recognition challenge

Lecture 10: Recurrent Neural Networks

cs231n.stanford.edu

image -> sequence of words. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 13 May 4, 2017 Recurrent Neural Networks: Process Sequences e.g. Sentiment Classification sequence of words -> sentiment. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 14 May 4, 2017

  Image, Recurrent

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 2 ...

cs231n.stanford.edu

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 2 - April 6, 2017 Administrative: Piazza For questions about midterm, poster session, projects,

  Li amp justin johnson amp serena yeung lecture

Lecture 14: Reinforcement Learning

cs231n.stanford.edu

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 14 - May 23, 2017 Markov Decision Process 19 - Mathematical formulation of the RL problem - Markov property: Current state completely characterises the state of the

  Learning, Reinforcement, Reinforcement learning

Convolutional Neural Networks for Visual Recognition

cs231n.stanford.edu

Progressive GAN, Karras 2018. Models from Single RGB Images”, ECCV 2018 Beyond recognition: Segmentation, 2D/3D Generation. Fei-Fei Li, Ranjay Krishna, Danfei Xu Lecture 1 - 15 March 30, 2021 Scene Graphs Krishna et al., Visual Genome: Connecting Vision and Language using Crowdsourced Image Annotations, IJCV 2017

  Network, Visual, Recognition, Neural, Convolutional, Karar, Convolutional neural networks for visual recognition

Lecture 11: Detection and Segmentation

cs231n.stanford.edu

Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 11 - 1 May 10, 2017 Lecture 11: Detection and Segmentation

  Detection, Segmentation

NaveenAppiah SagarVare - Stanford University

cs231n.stanford.edu

NaveenAppiah Mechanical Engineering nappiahb@stanford.edu SagarVare Stanford ICME svare@stanford.edu ... the popular mobile game - Flappy Bird. It involves navi-gating a bird through a bunch of obstacles. Though, this ... the game emulator and learns to make good decisions over time. It is this simple learning framework and their

  Make, Games, Flappy, Naveenappiah

Attention and Transformers Lecture 11

cs231n.stanford.edu

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 ...

  Transformers, Attention, Graph, Spatial, Attention and transformers

CNNs for Face Detection and Recognition

cs231n.stanford.edu

development of object classification, localization and detec-tion techniques. 2.1. Sliding Window In the early development of face detection, researchers tended to treat it as a repetitive task of object classifica-tion, by imposing sliding windows and performing object classification with the neural networks on the window re-gion.

  Technique, Faces, Recognition, Object, Detection, For face detection and recognition

Vector, Matrix, and Tensor Derivatives

cs231n.stanford.edu

Erik Learned-Miller The purpose of this document is to help you learn to take derivatives of vectors, matrices, and higher order tensors (arrays with three dimensions or more), and to help you take ... At this point, we have reduced the original matrix equation (Equation 1) …

  Have, Learned

Related documents

A Unified Approach to Interpreting Model Predictions - NIPS

papers.nips.cc

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. 2. We then show that game theory results guaranteeing a unique solution apply to the entire class of additive feature attribution methods (Section 3) and propose SHAP values as …

  Information, System, 2017, Processing, Inps, Neural, Neural information processing systems, Nips 2017

Attention is All you Need - NIPS

papers.nips.cc

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Recurrent models typically factor computation along the symbol positions of the input and output sequences. Aligning the positions to steps in computation time, they generate a …

  Information, System, 2017, Processing, Inps, Neural, Neural information processing systems, Nips 2017

Lecture 14: Reinforcement Learning

cs231n.stanford.edu

Lecture 14 - May 23, 2017 Case Study: Playing Atari Games 42 Objective: Complete the game with the highest score State: Raw pixel inputs of the game state Action: Game controls e.g. Left, Right, Up, Down Reward: Score increase/decrease at each time step [Mnih et al. NIPS Workshop 2013; Nature 2015]

  2017, Learning, Reinforcement, Inps, Reinforcement learning

Neural Discrete Representation Learning

arxiv.org

31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. arXiv:1711.00937v2 [cs.LG] 30 May 2018 “posterior collapse” issue which has been problematic with many VAE models that have a powerful decoder, often caused by latents being ignored. Additionally, it is the first discrete latent VAE model

  Information, System, 2017, Processing, Representation, Inps, Neural, Neural information processing systems, Nips 2017

2017 NIPS Poster for web

media.nips.cc

LONG BEACH CA | DEC 4 - 9 | NIPS.CC NIPS 2017 TUTORIALS - DEC 4TH Statistical Relational Artificial Intelligence: Logic, Probability and Computation Luc De Raedt, David Poole, Kristian Kersting, Sriraam Natarajan Reinforcement Learning with People Emma Brunskill A Primer on Optimal Transport Marco Cuturi, Justin Solomon

  2017, Inps, 2017 nips, Nips 2017

Sentence-BERT: Sentence Embeddings using Siamese BERT …

arxiv.org

2017). RoBERTa (Liu et al.,2019) showed, that the performance of BERT can further improved by small adaptations to the pre-training process. We also tested XLNet (Yang et al.,2019), but it led in general to worse results than BERT. A large disadvantage of the BERT network structure is that no independent sentence embed-

  2017

CatBoost: gradient boosting with categorical features support

learningsys.org

CatBoost: gradient boosting with categorical features support Anna Veronika Dorogush, Vasily Ershov, Andrey Gulin Yandex Abstract In this paper we present CatBoost, a new open-sourced gradient boosting library

  Catboost

Related search queries

NIPS, Neural Information Processing Systems, NIPS 2017, Reinforcement Learning, 2017, Representation, 2017 NIPS, CatBoost