Some Applications of Machine Learning to Astronomy. Eduardo Bezerra 20/fev/2018
|
|
|
- Baldwin Newman
- 5 years ago
- Views:
Transcription
1 Some Applications of Machine Learning to Astronomy Eduardo Bezerra 20/fev/2018
2 Overview 2 Introduction Definition Neural Nets Applications do Astronomy Ads: Machine Learning Course
3 3 Introduction
4 Machine Learning (to play Checkers) 4 Coined the term Machine Learning ( Field of study that gives computers the ability to learn without being explicitly programmed. ) it will learn to play a better game of checkers than can be played by the person who wrote the program. search tree alpha-beta pruning scoring functions 1959 Arthur Samuel
5 Machine Learning - definition 5 A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E Tom Mitchell
6 Machine Learning definition (cont.) 6 Suppose some computer program watches which objects you mark as galaxy/star, and based on that learns how to tell these two object apart. In such context: T à star/galaxy separation E à several labeled examples (images or catalog) P à purity/completeness
7 ML Tribes 7 There are several ML tribes! Symbolists (rule-based systems) Evolutionists (evolutionary computation, GAs) Analogists (SVMs, k-nn, ) Bayesians (Bayes update rule) Connectionists (ANNs) Pedro Domingos
8 8 Artificial Neural Nets (ANNs)
9 Artificial Neuron A model inspired in the real one (biological neuron). 9
10 Artificial Neuron - input 10
11 Artificial Neuron parameters 11
12 Artificial Neuron pre-activation 12
13 Artificial Neuron activation function Nonlinearities are necessary so that the network can learn complex representations of the data. "The composition of linear transformations is also a linear transformation" 13
14 Artificial Neural Net Feedforward Neural Network 14
15 LeNet Yan Lecun
16 LSTM Neural Nets 16 if you can understand the paper, you are better than many people in ML. It took 10 years until people understand what they were talking about. Jeff Hinton Sepp Hochreiter Citações: 2012: 75; out/2016: 1027; nov/2017: 2846 Juergen Schmidhuber
17 Deep Learning explosion now Also known as: Revenge of the Sith Neural Nets!
18 Example: AlexNet 8 layers
19 Example: AlexNet 8 layers Credits: Mathew Zeiler (Clarifai)
20 Example: GoogleNet 22 layers
21 Example: ResNet 152 layers
22 Deep Nets 22
23 Neural Nets Speech Recognition
24 Neural Nets Language Translation
25 Neural Nets Computer Vision + Text Processing Long-term Recurrent Convolutional Networks for Visual Recognition and Description, 2016.
26 Deep Learning: success factors 26 Big Data (e.g, MNIST ~ 70k; ImageNet ~ 10 6 ) Hardware improvements Crowdsourcing What was wrong in the 80 s is that we didn t have enough data and we didn t have enough computer power Geoffrey Hinton
27 27 Applications to Astronomy
28 Application: Star-Galaxy Separation 28 SGSP: telling apart stars and galaxies in photometric catalogs. The huge number of galaxies and stars in typical surveys requires that such separation be automated. This is an instance of a classification task in ML.
29 Application: Star-Galaxy Separation 29 we present a comparative analysis of several ML methods targeted at solving the SGSP at faint magnitudes. COSMOS survey was used. ML methods: ANNs, k-nn, SVM, Random Forests and Naive Bayes
30 Application: Galaxy Morphology 30 Studies exist that train some classification algorithm to assign T types to images for which measured parameters are available. Such parameters can be purely morphological, or include other information such (e.g., color).
31 Application: Stellar Clusters Detection 31 Problem: In a given field, segregate the field and cluster stars. Bezerra, E.; Lima, L.; Krone-Martins, A., A formulation of stellar cluster membership assignment as a distance geometry problem, Proceedings of the Workshop on Distance Geometry and Applications, 2013.
32 Application: Image Denoising 32 Denoising autoencoders Credits: Hugo Larochelle
33 Aplication: Event Detection 33 LSST is expected to see millions of transients per night that need to be detected in real time to allow for follow-up observations. The time dimensional is paramount here! Recurrent Neural Nets (e.g., LSTMs) should be appropriate in this case.
34 Aplication: Event Detection (cont.) 34 Timestamped datasets are expected to become increasingly important with the advent of LSST. For example, LSST is expected to produce a list of 1000 new supernovae each night for 10 years (LSST FAQ). There are several challenges, though: handling multiple observations of the same object handling heteroskedasticity (i.e., variability itself can change), robust classification of large streams of data in real time, the volume and storage of time domain information. In all of these, give ML methods a try!
35 References
36 Ads: Machine Learning Course 36 An introductory course, comprising two parts 1 st part: basic ML concepts and methods 2 nd part: Artificial Neural Nets (aka Deep Learning, if you will!) 12 lectures, starting in may/2018 Available to members to the INCT e-universe. For more details, see the LIneA news.
37 Some Applications of Machine Learning to Astronomy THANKS! Eduardo Bezerra
Convolutional Neural Networks II. Slides from Dr. Vlad Morariu
Convolutional Neural Networks II Slides from Dr. Vlad Morariu 1 Optimization Example of optimization progress while training a neural network. (Loss over mini-batches goes down over time.) 2 Learning rate
CSC321 Lecture 16: ResNets and Attention
CSC321 Lecture 16: ResNets and Attention Roger Grosse Roger Grosse CSC321 Lecture 16: ResNets and Attention 1 / 24 Overview Two topics for today: Topic 1: Deep Residual Networks (ResNets) This is the state-of-the
Lecture 17: Neural Networks and Deep Learning
UVA CS 6316 / CS 4501-004 Machine Learning Fall 2016 Lecture 17: Neural Networks and Deep Learning Jack Lanchantin Dr. Yanjun Qi 1 Neurons 1-Layer Neural Network Multi-layer Neural Network Loss Functions
Tasks ADAS. Self Driving. Non-machine Learning. Traditional MLP. Machine-Learning based method. Supervised CNN. Methods. Deep-Learning based
UNDERSTANDING CNN ADAS Tasks Self Driving Localizati on Perception Planning/ Control Driver state Vehicle Diagnosis Smart factory Methods Traditional Deep-Learning based Non-machine Learning Machine-Learning
Artificial Neural Networks. Introduction to Computational Neuroscience Tambet Matiisen
Artificial Neural Networks Introduction to Computational Neuroscience Tambet Matiisen 2.04.2018 Artificial neural network NB! Inspired by biology, not based on biology! Applications Automatic speech recognition
Feature Design. Feature Design. Feature Design. & Deep Learning
Artificial Intelligence and its applications Lecture 9 & Deep Learning Professor Daniel Yeung danyeung@ieee.org Dr. Patrick Chan patrickchan@ieee.org South China University of Technology, China Appropriately
Neural Networks 2. 2 Receptive fields and dealing with image inputs
CS 446 Machine Learning Fall 2016 Oct 04, 2016 Neural Networks 2 Professor: Dan Roth Scribe: C. Cheng, C. Cervantes Overview Convolutional Neural Networks Recurrent Neural Networks 1 Introduction There
Deep Learning Basics Lecture 7: Factor Analysis. Princeton University COS 495 Instructor: Yingyu Liang
Deep Learning Basics Lecture 7: Factor Analysis Princeton University COS 495 Instructor: Yingyu Liang Supervised v.s. Unsupervised Math formulation for supervised learning Given training data x i, y i
Bayesian Networks (Part I)
10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Bayesian Networks (Part I) Graphical Model Readings: Murphy 10 10.2.1 Bishop 8.1,
Neural Networks. Single-layer neural network. CSE 446: Machine Learning Emily Fox University of Washington March 10, /9/17
3/9/7 Neural Networks Emily Fox University of Washington March 0, 207 Slides adapted from Ali Farhadi (via Carlos Guestrin and Luke Zettlemoyer) Single-layer neural network 3/9/7 Perceptron as a neural
Jakub Hajic Artificial Intelligence Seminar I
Jakub Hajic Artificial Intelligence Seminar I. 11. 11. 2014 Outline Key concepts Deep Belief Networks Convolutional Neural Networks A couple of questions Convolution Perceptron Feedforward Neural Network
Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation
Making Deep Learning Understandable for Analyzing Sequential Data about Gene Regulation Dr. Yanjun Qi Department of Computer Science University of Virginia Tutorial @ ACM BCB-2018 8/29/18 Yanjun Qi / UVA
TTIC 31230, Fundamentals of Deep Learning, Winter David McAllester. The Fundamental Equations of Deep Learning
TTIC 31230, Fundamentals of Deep Learning, Winter 2019 David McAllester The Fundamental Equations of Deep Learning 1 Early History 1943: McCullock and Pitts introduced the linear threshold neuron. 1962:
Introduction to Deep Learning
Introduction to Deep Learning Some slides and images are taken from: David Wolfe Corne Wikipedia Geoffrey A. Hinton https://www.macs.hw.ac.uk/~dwcorne/teaching/introdl.ppt Feedforward networks for function
Neural networks and optimization
Neural networks and optimization Nicolas Le Roux Criteo 18/05/15 Nicolas Le Roux (Criteo) Neural networks and optimization 18/05/15 1 / 85 1 Introduction 2 Deep networks 3 Optimization 4 Convolutional
From perceptrons to word embeddings. Simon Šuster University of Groningen
From perceptrons to word embeddings Simon Šuster University of Groningen Outline A basic computational unit Weighting some input to produce an output: classification Perceptron Classify tweets Written
RAGAV VENKATESAN VIJETHA GATUPALLI BAOXIN LI NEURAL DATASET GENERALITY
RAGAV VENKATESAN VIJETHA GATUPALLI BAOXIN LI NEURAL DATASET GENERALITY SIFT HOG ALL ABOUT THE FEATURES DAISY GABOR AlexNet GoogleNet CONVOLUTIONAL NEURAL NETWORKS VGG-19 ResNet FEATURES COMES FROM DATA
Machine Learning for Physicists Lecture 1
Machine Learning for Physicists Lecture 1 Summer 2017 University of Erlangen-Nuremberg Florian Marquardt (Image generated by a net with 20 hidden layers) OUTPUT INPUT (Picture: Wikimedia Commons) OUTPUT
Artificial Neural Networks D B M G. Data Base and Data Mining Group of Politecnico di Torino. Elena Baralis. Politecnico di Torino
Artificial Neural Networks Data Base and Data Mining Group of Politecnico di Torino Elena Baralis Politecnico di Torino Artificial Neural Networks Inspired to the structure of the human brain Neurons as
Non-Linearity. CS 188: Artificial Intelligence. Non-Linear Separators. Non-Linear Separators. Deep Learning I
Non-Linearity CS 188: Artificial Intelligence Deep Learning I Instructors: Pieter Abbeel & Anca Dragan --- University of California, Berkeley [These slides were created by Dan Klein, Pieter Abbeel, Anca
Deep Learning (CNNs)
10-601 Introduction to Machine Learning Machine Learning Department School of Computer Science Carnegie Mellon University Deep Learning (CNNs) Deep Learning Readings: Murphy 28 Bishop - - HTF - - Mitchell
SGD and Deep Learning
SGD and Deep Learning Subgradients Lets make the gradient cheating more formal. Recall that the gradient is the slope of the tangent. f(w 1 )+rf(w 1 ) (w w 1 ) Non differentiable case? w 1 Subgradients
Deep Learning for Gravitational Wave Analysis Results with LIGO Data
Link to these slides: http://tiny.cc/nips arxiv:1711.03121 Deep Learning for Gravitational Wave Analysis Results with LIGO Data Daniel George & E. A. Huerta NCSA Gravity Group - http://gravity.ncsa.illinois.edu/
Grundlagen der Künstlichen Intelligenz
Grundlagen der Künstlichen Intelligenz Neural networks Daniel Hennes 21.01.2018 (WS 2017/18) University Stuttgart - IPVS - Machine Learning & Robotics 1 Today Logistic regression Neural networks Perceptron
Deep learning on 3D geometries. Hope Yao Design Informatics Lab Department of Mechanical and Aerospace Engineering
Deep learning on 3D geometries Hope Yao Design Informatics Lab Department of Mechanical and Aerospace Engineering Overview Background Methods Numerical Result Future improvements Conclusion Background
Machine Learning Applications in Astronomy
Machine Learning Applications in Astronomy Umaa Rebbapragada, Ph.D. Machine Learning and Instrument Autonomy Group Big Data Task Force November 1, 2017 Research described in this presentation was carried
Introduction to Convolutional Neural Networks (CNNs)
Introduction to Convolutional Neural Networks (CNNs) nojunk@snu.ac.kr http://mipal.snu.ac.kr Department of Transdisciplinary Studies Seoul National University, Korea Jan. 2016 Many slides are from Fei-Fei
Machine Learning Lecture 12
Machine Learning Lecture 12 Neural Networks 30.11.2017 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Course Outline Fundamentals Bayes Decision Theory Probability
Deep Learning. What Is Deep Learning? The Rise of Deep Learning. Long History (in Hind Sight)
CSCE 636 Neural Networks Instructor: Yoonsuck Choe Deep Learning What Is Deep Learning? Learning higher level abstractions/representations from data. Motivation: how the brain represents sensory information
Binary Convolutional Neural Network on RRAM
Binary Convolutional Neural Network on RRAM Tianqi Tang, Lixue Xia, Boxun Li, Yu Wang, Huazhong Yang Dept. of E.E, Tsinghua National Laboratory for Information Science and Technology (TNList) Tsinghua
Vote. Vote on timing for night section: Option 1 (what we have now) Option 2. Lecture, 6:10-7:50 25 minute dinner break Tutorial, 8:15-9
Vote Vote on timing for night section: Option 1 (what we have now) Lecture, 6:10-7:50 25 minute dinner break Tutorial, 8:15-9 Option 2 Lecture, 6:10-7 10 minute break Lecture, 7:10-8 10 minute break Tutorial,
Deep Learning. Hung-yi Lee 李宏毅
Deep Learning Hung-yi Lee 李宏毅 Deep learning attracts lots of attention. I believe you have seen lots of exciting results before. Deep learning trends at Google. Source: SIGMOD 206/Jeff Dean 958: Perceptron
Machine Learning Lecture 10
Machine Learning Lecture 10 Neural Networks 26.11.2018 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Today s Topic Deep Learning 2 Course Outline Fundamentals Bayes
arxiv: v1 [astro-ph.im] 20 Jan 2017
![arxiv: v1 [astro-ph.im] 20 Jan 2017](/thumbs/95/122594303.jpg "arxiv: v1 [astro-ph.im] 20 Jan 2017")
IAU Symposium 325 on Astroinformatics Proceedings IAU Symposium No. xxx, xxx A.C. Editor, B.D. Editor & C.E. Editor, eds. c xxx International Astronomical Union DOI: 00.0000/X000000000000000X Deep learning
Deep Learning. What Is Deep Learning? The Rise of Deep Learning. Long History (in Hind Sight)
CSCE 636 Neural Networks Instructor: Yoonsuck Choe Deep Learning What Is Deep Learning? Learning higher level abstractions/representations from data. Motivation: how the brain represents sensory information
Convolutional Neural Networks. Srikumar Ramalingam
Convolutional Neural Networks Srikumar Ramalingam Reference Many of the slides are prepared using the following resources: neuralnetworksanddeeplearning.com (mainly Chapter 6) http://cs231n.github.io/convolutional-networks/
CS 343: Artificial Intelligence
CS 343: Artificial Intelligence Deep Learning Prof. Scott Niekum The University of Texas at Austin [These slides based on those of Dan Klein, Pieter Abbeel, Anca Dragan for CS188 Intro to AI at UC Berkeley.
ARTIFICIAL INTELLIGENCE. Artificial Neural Networks
INFOB2KI 2017-2018 Utrecht University The Netherlands ARTIFICIAL INTELLIGENCE Artificial Neural Networks Lecturer: Silja Renooij These slides are part of the INFOB2KI Course Notes available from www.cs.uu.nl/docs/vakken/b2ki/schema.html
Lecture 35: Optimization and Neural Nets
Lecture 35: Optimization and Neural Nets CS 4670/5670 Sean Bell DeepDream [Google, Inceptionism: Going Deeper into Neural Networks, blog 2015] Aside: CNN vs ConvNet Note: There are many papers that use
Machine Learning for Signal Processing Neural Networks Continue. Instructor: Bhiksha Raj Slides by Najim Dehak 1 Dec 2016
Machine Learning for Signal Processing Neural Networks Continue Instructor: Bhiksha Raj Slides by Najim Dehak 1 Dec 2016 1 So what are neural networks?? Voice signal N.Net Transcription Image N.Net Text
CSE446: Neural Networks Spring Many slides are adapted from Carlos Guestrin and Luke Zettlemoyer
CSE446: Neural Networks Spring 2017 Many slides are adapted from Carlos Guestrin and Luke Zettlemoyer Human Neurons Switching time ~ 0.001 second Number of neurons 10 10 Connections per neuron 10 4-5 Scene
Regression Adjustment with Artificial Neural Networks
Regression Adjustment with Artificial Neural Networks Age of Big Data: data comes in a rate and in a variety of types that exceed our ability to analyse it Texts, image, speech, video Real motivation:
CS 188: Artificial Intelligence Spring Announcements
CS 188: Artificial Intelligence Spring 2010 Lecture 24: Perceptrons and More! 4/22/2010 Pieter Abbeel UC Berkeley Slides adapted from Dan Klein Announcements W7 due tonight [this is your last written for
Neural Networks: Introduction
Neural Networks: Introduction Machine Learning Fall 2017 Based on slides and material from Geoffrey Hinton, Richard Socher, Dan Roth, Yoav Goldberg, Shai Shalev-Shwartz and Shai Ben-David, and others 1
Introduction to Deep Learning
Introduction to Deep Learning A. G. Schwing & S. Fidler University of Toronto, 2014 A. G. Schwing & S. Fidler (UofT) CSC420: Intro to Image Understanding 2014 1 / 35 Outline 1 Universality of Neural Networks
Machine Learning. Boris
Machine Learning Boris Nadion boris@astrails.com @borisnadion @borisnadion boris@astrails.com astrails http://astrails.com awesome web and mobile apps since 2005 terms AI (artificial intelligence)
Course 395: Machine Learning - Lectures
Course 395: Machine Learning - Lectures Lecture 1-2: Concept Learning (M. Pantic) Lecture 3-4: Decision Trees & CBC Intro (M. Pantic & S. Petridis) Lecture 5-6: Evaluating Hypotheses (S. Petridis) Lecture
Deep Learning: a gentle introduction
Deep Learning: a gentle introduction Jamal Atif jamal.atif@dauphine.fr PSL, Université Paris-Dauphine, LAMSADE February 8, 206 Jamal Atif (Université Paris-Dauphine) Deep Learning February 8, 206 / Why
Machine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) For this week, Reading Chapter 4: Neural Networks (Mitchell, 1997) See Canvas For subsequent weeks: Scaling Learning Algorithms toward
ECE 5984: Introduction to Machine Learning
ECE 5984: Introduction to Machine Learning Topics: Ensemble Methods: Bagging, Boosting Readings: Murphy 16.4; Hastie 16 Dhruv Batra Virginia Tech Administrativia HW3 Due: April 14, 11:55pm You will implement
Machine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) Human Brain Neurons Input-Output Transformation Input Spikes Output Spike Spike (= a brief pulse) (Excitatory Post-Synaptic Potential)
TUTORIAL PART 1 Unsupervised Learning
TUTORIAL PART 1 Unsupervised Learning Marc'Aurelio Ranzato Department of Computer Science Univ. of Toronto ranzato@cs.toronto.edu Co-organizers: Honglak Lee, Yoshua Bengio, Geoff Hinton, Yann LeCun, Andrew
Knowledge Extraction from DBNs for Images
Knowledge Extraction from DBNs for Images Son N. Tran and Artur d Avila Garcez Department of Computer Science City University London Contents 1 Introduction 2 Knowledge Extraction from DBNs 3 Experimental
Machine learning comes from Bayesian decision theory in statistics. There we want to minimize the expected value of the loss function.
Bayesian learning: Machine learning comes from Bayesian decision theory in statistics. There we want to minimize the expected value of the loss function. Let y be the true label and y be the predicted
<Special Topics in VLSI> Learning for Deep Neural Networks (Back-propagation)
Learning for Deep Neural Networks (Back-propagation) Outline Summary of Previous Standford Lecture Universal Approximation Theorem Inference vs Training Gradient Descent Back-Propagation
Neural Networks for Machine Learning. Lecture 2a An overview of the main types of neural network architecture
Neural Networks for Machine Learning Lecture 2a An overview of the main types of neural network architecture Geoffrey Hinton with Nitish Srivastava Kevin Swersky Feed-forward neural networks These are
Introduction to Deep Neural Networks
Introduction to Deep Neural Networks Presenter: Chunyuan Li Pattern Classification and Recognition (ECE 681.01) Duke University April, 2016 Outline 1 Background and Preliminaries Why DNNs? Model: Logistic
UNSUPERVISED LEARNING
UNSUPERVISED LEARNING Topics Layer-wise (unsupervised) pre-training Restricted Boltzmann Machines Auto-encoders LAYER-WISE (UNSUPERVISED) PRE-TRAINING Breakthrough in 2006 Layer-wise (unsupervised) pre-training
Deep learning attracts lots of attention.
Deep Learning Deep learning attracts lots of attention. I believe you have seen lots of exciting results before. Deep learning trends at Google. Source: SIGMOD/Jeff Dean Ups and downs of Deep Learning
Supervised Deep Learning
Supervised Deep Learning Joana Frontera-Pons Grigorios Tsagkatakis Dictionary Learning on Manifolds workshop Nice, September 2017 Supervised Learning Data Labels Model Prediction Spiral Exploiting prior
Machine Learning. Neural Networks. (slides from Domingos, Pardo, others)
Machine Learning Neural Networks (slides from Domingos, Pardo, others) For this week, Reading Chapter 4: Neural Networks (Mitchell, 1997) See Canvas For subsequent weeks: Scaling Learning Algorithms toward
Sequence Modeling with Neural Networks
Sequence Modeling with Neural Networks Harini Suresh y 0 y 1 y 2 s 0 s 1 s 2... x 0 x 1 x 2 hat is a sequence? This morning I took the dog for a walk. sentence medical signals speech waveform Successes
CS 229 Project Final Report: Reinforcement Learning for Neural Network Architecture Category : Theory & Reinforcement Learning
CS 229 Project Final Report: Reinforcement Learning for Neural Network Architecture Category : Theory & Reinforcement Learning Lei Lei Ruoxuan Xiong December 16, 2017 1 Introduction Deep Neural Network
Modularity Matters: Learning Invariant Relational Reasoning Tasks
Summer Review 3 Modularity Matters: Learning Invariant Relational Reasoning Tasks Jason Jo 1, Vikas Verma 2, Yoshua Bengio 1 1: MILA, Universit de Montral 2: Department of Computer Science Aalto University,
Convolutional neural networks
11-1: Convolutional neural networks Prof. J.C. Kao, UCLA Convolutional neural networks Motivation Biological inspiration Convolution operation Convolutional layer Padding and stride CNN architecture 11-2:
Handwritten Indic Character Recognition using Capsule Networks
Handwritten Indic Character Recognition using Capsule Networks Bodhisatwa Mandal,Suvam Dubey, Swarnendu Ghosh, RiteshSarkhel, Nibaran Das Dept. of CSE, Jadavpur University, Kolkata, 700032, WB, India.
Artificial Neural Networks. Q550: Models in Cognitive Science Lecture 5
Artificial Neural Networks Q550: Models in Cognitive Science Lecture 5 "Intelligence is 10 million rules." --Doug Lenat The human brain has about 100 billion neurons. With an estimated average of one thousand
Introduction to Machine Learning Midterm Exam
10-701 Introduction to Machine Learning Midterm Exam Instructors: Eric Xing, Ziv Bar-Joseph 17 November, 2015 There are 11 questions, for a total of 100 points. This exam is open book, open notes, but
Caesar s Taxi Prediction Services
1 Caesar s Taxi Prediction Services Predicting NYC Taxi Fares, Trip Distance, and Activity Paul Jolly, Boxiao Pan, Varun Nambiar Abstract In this paper, we propose three models each predicting either taxi
Neural Architectures for Image, Language, and Speech Processing
Neural Architectures for Image, Language, and Speech Processing Karl Stratos June 26, 2018 1 / 31 Overview Feedforward Networks Need for Specialized Architectures Convolutional Neural Networks (CNNs) Recurrent
arxiv: v3 [cs.lg] 14 Jan 2018
![arxiv: v3 [cs.lg] 14 Jan 2018](/thumbs/92/109503708.jpg "arxiv: v3 [cs.lg] 14 Jan 2018")
A Gentle Tutorial of Recurrent Neural Network with Error Backpropagation Gang Chen Department of Computer Science and Engineering, SUNY at Buffalo arxiv:1610.02583v3 [cs.lg] 14 Jan 2018 1 abstract We describe
Restricted Boltzmann Machines
Restricted Boltzmann Machines http://deeplearning4.org/rbm-mnist-tutorial.html Slides from Hugo Larochelle, Geoffrey Hinton, and Yoshua Bengio CSC321: Intro to Machine Learning and Neural Networks, Winter
What Do Neural Networks Do? MLP Lecture 3 Multi-layer networks 1
What Do Neural Networks Do? MLP Lecture 3 Multi-layer networks 1 Multi-layer networks Steve Renals Machine Learning Practical MLP Lecture 3 7 October 2015 MLP Lecture 3 Multi-layer networks 2 What Do Single
Announcements. CS 188: Artificial Intelligence Spring Classification. Today. Classification overview. Case-Based Reasoning
CS 188: Artificial Intelligence Spring 21 Lecture 22: Nearest Neighbors, Kernels 4/18/211 Pieter Abbeel UC Berkeley Slides adapted from Dan Klein Announcements On-going: contest (optional and FUN!) Remaining
Neural Turing Machine. Author: Alex Graves, Greg Wayne, Ivo Danihelka Presented By: Tinghui Wang (Steve)
Neural Turing Machine Author: Alex Graves, Greg Wayne, Ivo Danihelka Presented By: Tinghui Wang (Steve) Introduction Neural Turning Machine: Couple a Neural Network with external memory resources The combined
A Little History of Machine Learning
機器學習現在 過去 未來 A Little History of Machine Learning Chia-Ping Chen National Sun Yat-sen University @NPTU, December 2016 Outline ubiquitous machine intelligence challenge and reaction AI brief deep learning
Interpreting Deep Classifiers
Ruprecht-Karls-University Heidelberg Faculty of Mathematics and Computer Science Seminar: Explainable Machine Learning Interpreting Deep Classifiers by Visual Distillation of Dark Knowledge Author: Daniela
Deep Learning Sequence to Sequence models: Attention Models. 17 March 2018
Deep Learning Sequence to Sequence models: Attention Models 17 March 2018 1 Sequence-to-sequence modelling Problem: E.g. A sequence X 1 X N goes in A different sequence Y 1 Y M comes out Speech recognition:
PV021: Neural networks. Tomáš Brázdil
1 PV021: Neural networks Tomáš Brázdil 2 Course organization Course materials: Main: The lecture Neural Networks and Deep Learning by Michael Nielsen http://neuralnetworksanddeeplearning.com/ (Extremely
Denoising Autoencoders
Denoising Autoencoders Oliver Worm, Daniel Leinfelder 20.11.2013 Oliver Worm, Daniel Leinfelder Denoising Autoencoders 20.11.2013 1 / 11 Introduction Poor initialisation can lead to local minima 1986 -
(Feed-Forward) Neural Networks Dr. Hajira Jabeen, Prof. Jens Lehmann
(Feed-Forward) Neural Networks 2016-12-06 Dr. Hajira Jabeen, Prof. Jens Lehmann Outline In the previous lectures we have learned about tensors and factorization methods. RESCAL is a bilinear model for
Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions
2018 IEEE International Workshop on Machine Learning for Signal Processing (MLSP 18) Recurrent Neural Networks with Flexible Gates using Kernel Activation Functions Authors: S. Scardapane, S. Van Vaerenbergh,
Deep Learning for Natural Language Processing. Sidharth Mudgal April 4, 2017
Deep Learning for Natural Language Processing Sidharth Mudgal April 4, 2017 Table of contents 1. Intro 2. Word Vectors 3. Word2Vec 4. Char Level Word Embeddings 5. Application: Entity Matching 6. Conclusion
CS 188: Artificial Intelligence. Outline
CS 188: Artificial Intelligence Lecture 21: Perceptrons Pieter Abbeel UC Berkeley Many slides adapted from Dan Klein. Outline Generative vs. Discriminative Binary Linear Classifiers Perceptron Multi-class
Introduction to Deep Learning
Introduction to Deep Learning A. G. Schwing & S. Fidler University of Toronto, 2015 A. G. Schwing & S. Fidler (UofT) CSC420: Intro to Image Understanding 2015 1 / 39 Outline 1 Universality of Neural Networks
Neural Networks (Part 1) Goals for the lecture
Neural Networks (Part ) Mark Craven and David Page Computer Sciences 760 Spring 208 www.biostat.wisc.edu/~craven/cs760/ Some of the slides in these lectures have been adapted/borrowed from materials developed
Outline Challenges of Massive Data Combining approaches Application: Event Detection for Astronomical Data Conclusion. Abstract
Abstract The analysis of extremely large, complex datasets is becoming an increasingly important task in the analysis of scientific data. This trend is especially prevalent in astronomy, as large-scale
Artificial Neural Networks
Introduction ANN in Action Final Observations Application: Poverty Detection Artificial Neural Networks Alvaro J. Riascos Villegas University of los Andes and Quantil July 6 2018 Artificial Neural Networks
Convolutional Neural Network Architecture
Convolutional Neural Network Architecture Zhisheng Zhong Feburary 2nd, 2018 Zhisheng Zhong Convolutional Neural Network Architecture Feburary 2nd, 2018 1 / 55 Outline 1 Introduction of Convolution Motivation
Machine Learning for Computer Vision 8. Neural Networks and Deep Learning. Vladimir Golkov Technical University of Munich Computer Vision Group
Machine Learning for Computer Vision 8. Neural Networks and Deep Learning Vladimir Golkov Technical University of Munich Computer Vision Group INTRODUCTION Nonlinear Coordinate Transformation http://cs.stanford.edu/people/karpathy/convnetjs/
Understanding How ConvNets See
Understanding How ConvNets See Slides from Andrej Karpathy Springerberg et al, Striving for Simplicity: The All Convolutional Net (ICLR 2015 workshops) CSC321: Intro to Machine Learning and Neural Networks,
How to do backpropagation in a brain
How to do backpropagation in a brain Geoffrey Hinton Canadian Institute for Advanced Research & University of Toronto & Google Inc. Prelude I will start with three slides explaining a popular type of deep
Lecture 7 Convolutional Neural Networks
Lecture 7 Convolutional Neural Networks CMSC 35246: Deep Learning Shubhendu Trivedi & Risi Kondor University of Chicago April 17, 2017 We saw before: ŷ x 1 x 2 x 3 x 4 A series of matrix multiplications:
Latent Dirichlet Allocation Introduction/Overview
Latent Dirichlet Allocation Introduction/Overview David Meyer 03.10.2016 David Meyer http://www.1-4-5.net/~dmm/ml/lda_intro.pdf 03.10.2016 Agenda What is Topic Modeling? Parametric vs. Non-Parametric Models
Recurrent Neural Networks. COMP-550 Oct 5, 2017
Recurrent Neural Networks COMP-550 Oct 5, 2017 Outline Introduction to neural networks and deep learning Feedforward neural networks Recurrent neural networks 2 Classification Review y = f( x) output label
Neural Networks. David Rosenberg. July 26, New York University. David Rosenberg (New York University) DS-GA 1003 July 26, / 35
Neural Networks David Rosenberg New York University July 26, 2017 David Rosenberg (New York University) DS-GA 1003 July 26, 2017 1 / 35 Neural Networks Overview Objectives What are neural networks? How
Final Examination CS 540-2: Introduction to Artificial Intelligence
Final Examination CS 540-2: Introduction to Artificial Intelligence May 7, 2017 LAST NAME: SOLUTIONS FIRST NAME: Problem Score Max Score 1 14 2 10 3 6 4 10 5 11 6 9 7 8 9 10 8 12 12 8 Total 100 1 of 11
Brief Introduction of Machine Learning Techniques for Content Analysis
1 Brief Introduction of Machine Learning Techniques for Content Analysis Wei-Ta Chu 2008/11/20 Outline 2 Overview Gaussian Mixture Model (GMM) Hidden Markov Model (HMM) Support Vector Machine (SVM) Overview
Machine Learning Lecture 5
Machine Learning Lecture 5 Linear Discriminant Functions 26.10.2017 Bastian Leibe RWTH Aachen http://www.vision.rwth-aachen.de leibe@vision.rwth-aachen.de Course Outline Fundamentals Bayes Decision Theory