Recurrent Neural Networks

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ROB 537: Learning Based Control Week 6, Lecture 2 Recurrent Neural Networks Artificial Neural Networks • • • Your Name | Event Name 1 Assumptions • • Your Name | Event Name 2 Example: XOR Hidden layer • – Output layer Input 1 Input 2 Your Name | Event Name 3 Example: XOR with single input channel Hidden layer • – Output layer • Input – – Your Name | Event Name 4 Example: XOR with single input channel; Hidden • Intermediate output – Input 1 t1 Hidden • Output – Input 2 t2 – Your Name | Event Name 5 Example: XOR • – Your Name | Event Name 6 Example: XOR • – • Your Name | Event Name 7 Example: XOR • – • • – – – Your Name | Event Name 8 Some approaches • 9 Some approaches • – – 10 Some approaches • – – • Recurrent Neural Networks (RNNs) – Use ‘recurrent connections’ to bridge information flow across time 11 XOR problem with one input channel Hidden Intermediate output Input 1 t1 Hidden Output Input 2 t2 12 XOR problem with one input channel Hidden • Intermediate output Input 1 • t1 Hidden Output Input 2 t2 Recurrent Connection 13 XOR problem with one input channel Hidden • Intermediate output Input 1 • t1 Hidden Output Input 2 • t2 Recurrent Connection 14 Recurrent Connections Hidden • Intermediate output Input 1 t1 Hidden Output Input 2 t2 15 Recurrent Connections Hidden • Intermediate output Input 1 t1 Hidden Output Input 2 t2 16 Recurrent Connections Hidden • Intermediate output Input 1 t1 Hidden Output Input 2 t2 17 Recurrent Neural Networks Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. • 18 Recurrent Neural Networks Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. • • 19 Recurrent Neural Networks Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. • • 20 Overview of RNNs • • 21 Overview of RNNs • • • • 22 Training a RNN • • • 23 Solution • – – 24 Solution • – – Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep Learning. MIT Press, 2016. 25 Gating mechanisms • • 26 Gating mechanisms • • Input Hidden Output Input gate 27 Standard RNN (SRN) Source: Deeplearning4J Source: Deeplearning4J 28 Long short term memory (LSTM) Source: Deeplearning4J 29 Long short term memory (LSTM) • • • • LSTM (Chung et al.) 30 Gated Recurrent Unit (GRU) • • • GRU (Chung et al.) 31 GRU and LSTM • • GRU (Chung et al.) LSTM (Chung et al.) 32 Topology Limitation • • GRU (Chung et al.) LSTM (Chung et al.) 33 Gated Recurrent Unit with Memory Block (GRU-MB) • • Khadka, Chung and Tumer. GECCO 2017 34 Feedforward NN 35 Read from external memory 36 Write to memory 37 Gate Input 38 Gate what’s read from memory 39 Gate what’s written to memory 40 Topology comparison GRU-MB GRU (Chung et al.) LSTM (Chung et al.) 41 Topology comparison GRU-MB GRU (Chung et al.) LSTM (Chung et al.) 42 Topology comparison GRU-MB GRU (Chung et al.) LSTM (Chung et al.) 43 RNNs with Attention Mechanisms • • • NTM (Graves 2016) 44.

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Recurrent Neural Networks
Sequence Modeling: Recurrent and Recursive Nets Lecture slides for Chapter 10 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-27 Adapted by m.n. for CMPS 392 RNN • RNNs are a family of neural networks for processing sequential data • Recurrent networks can scale to much longer sequences than would be practical for networks without sequence-based specialization. • Most recurrent networks can also process sequences of variable length. • Based on parameter sharing q If we had separate parameters for each value of the time index, o we could not generalize to sequence lengths not seen during training, o nor share statistical strength across different sequence lengths, o and across different positions in time. (Goodfellow 2016) Example • Consider the two sentences “I went to Nepal in 2009” and “In 2009, I went to Nepal.” • How a machine learning can extract the year information? q A traditional fully connected feedforward network would have separate parameters for each input feature, q so it would need to learn all of the rules of the language separately at each position in the sentence. • By comparison, a recurrent neural network shares the same weights across several time steps. (Goodfellow 2016) RNN vs. 1D convolutional • The output of convolution is a sequence where each member of the output is a function of a small number of neighboring members of the input. • Recurrent networks share parameters in a different way. q Each member of the output is a function of the previous members of the output q Each member of the output is produced using the same update rule applied to the previous outputs.
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NVIDIA CEO Jensen Huang to Host AI Pioneers Yoshua Bengio, Geoffrey Hinton and Yann Lecun, and Others, at GTC21
NVIDIA CEO Jensen Huang to Host AI Pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun, and Others, at GTC21 Online Conference to Feature Jensen Huang Keynote and 1,300 Talks from Leaders in Data Center, Networking, Graphics and Autonomous Vehicles NVIDIA today announced that its CEO and founder Jensen Huang will host renowned AI pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun at the company’s upcoming technology conference, GTC21, running April 12-16. The event will kick off with a news-filled livestreamed keynote by Huang on April 12 at 8:30 am Pacific. Bengio, Hinton and LeCun won the 2018 ACM Turing Award, known as the Nobel Prize of computing, for breakthroughs that enabled the deep learning revolution. Their work underpins the proliferation of AI technologies now being adopted around the world, from natural language processing to autonomous machines. Bengio is a professor at the University of Montreal and head of Mila - Quebec Artificial Intelligence Institute; Hinton is a professor at the University of Toronto and a researcher at Google; and LeCun is a professor at New York University and chief AI scientist at Facebook. More than 100,000 developers, business leaders, creatives and others are expected to register for GTC, including CxOs and IT professionals focused on data center infrastructure. Registration is free and is not required to view the keynote. In addition to the three Turing winners, major speakers include: Girish Bablani, Corporate Vice President, Microsoft Azure John Bowman, Director of Data Science, Walmart
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On Recurrent and Deep Neural Networks
On Recurrent and Deep Neural Networks Razvan Pascanu Advisor: Yoshua Bengio PhD Defence Universit´ede Montr´eal,LISA lab September 2014 Pascanu On Recurrent and Deep Neural Networks 1/ 38 Studying the mechanism behind learning provides a meta-solution for solving tasks. Motivation \A computer once beat me at chess, but it was no match for me at kick boxing" | Emo Phillips Pascanu On Recurrent and Deep Neural Networks 2/ 38 Motivation \A computer once beat me at chess, but it was no match for me at kick boxing" | Emo Phillips Studying the mechanism behind learning provides a meta-solution for solving tasks. Pascanu On Recurrent and Deep Neural Networks 2/ 38 I fθ(x) = f (θ; x) ? F I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Supervised Learing I f :Θ D T F × ! Pascanu On Recurrent and Deep Neural Networks 3/ 38 ? I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Supervised Learing I f :Θ D T F × ! I fθ(x) = f (θ; x) F Pascanu On Recurrent and Deep Neural Networks 3/ 38 Supervised Learing I f :Θ D T F × ! I fθ(x) = f (θ; x) ? F I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Pascanu On Recurrent and Deep Neural Networks 3/ 38 Optimization for learning θ[k+1] θ[k] Pascanu On Recurrent and Deep Neural Networks 4/ 38 Neural networks Output neurons Last hidden layer bias = 1 Second hidden layer First hidden layer Input layer Pascanu On Recurrent and Deep Neural Networks 5/ 38 Recurrent neural networks Output neurons Output neurons Last hidden layer bias = 1 bias = 1 Recurrent Layer Second hidden layer First hidden layer Input layer Input layer (b) Recurrent
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Ian Goodfellow, Staff Research Scientist, Google Brain CVPR
MedGAN ID-CGAN CoGAN LR-GAN CGAN IcGAN b-GAN LS-GAN LAPGAN DiscoGANMPM-GAN AdaGAN AMGAN iGAN InfoGAN CatGAN IAN LSGAN Introduction to GANs SAGAN McGAN Ian Goodfellow, Staff Research Scientist, Google Brain MIX+GAN MGAN CVPR Tutorial on GANs BS-GAN FF-GAN Salt Lake City, 2018-06-22 GoGAN C-VAE-GAN C-RNN-GAN DR-GAN DCGAN MAGAN 3D-GAN CCGAN AC-GAN BiGAN GAWWN DualGAN CycleGAN Bayesian GAN GP-GAN AnoGAN EBGAN DTN Context-RNN-GAN MAD-GAN ALI f-GAN BEGAN AL-CGAN MARTA-GAN ArtGAN MalGAN Generative Modeling: Density Estimation Training Data Density Function (Goodfellow 2018) Generative Modeling: Sample Generation Training Data Sample Generator (CelebA) (Karras et al, 2017) (Goodfellow 2018) Adversarial Nets Framework D tries to make D(G(z)) near 0, D(x) tries to be G tries to make near 1 D(G(z)) near 1 Differentiable D function D x sampled from x sampled from data model Differentiable function G Input noise z (Goodfellow et al., 2014) (Goodfellow 2018) Self-Play 1959: Arthur Samuel’s checkers agent (OpenAI, 2017) (Silver et al, 2017) (Bansal et al, 2017) (Goodfellow 2018) 3.5 Years of Progress on Faces 2014 2015 2016 2017 (Brundage et al, 2018) (Goodfellow 2018) p.15 General Framework for AI & Security Threats Published as a conference paper at ICLR 2018 <2 Years of Progress on ImageNet Odena et al 2016 monarch butterfly goldfinch daisy redshank grey whale Miyato et al 2017 monarch butterfly goldfinch daisy redshank grey whale Zhang et al 2018 monarch butterfly goldfinch (Goodfellow 2018) daisy redshank grey whale Figure 7: 128x128 pixel images generated by SN-GANs trained on ILSVRC2012 dataset.
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Yoshua Bengio and Gary Marcus on the Best Way Forward for AI
Yoshua Bengio and Gary Marcus on the Best Way Forward for AI Transcript of the 23 December 2019 AI Debate, hosted at Mila Moderated and transcribed by Vincent Boucher, Montreal AI AI DEBATE : Yoshua Bengio | Gary Marcus — Organized by MONTREAL.AI and hosted at Mila, on Monday, December 23, 2019, from 6:30 PM to 8:30 PM (EST) Slides, video, readings and more can be found on the MONTREAL.AI debate webpage. Transcript of the AI Debate Opening Address | Vincent Boucher Good Evening from Mila in Montreal Ladies & Gentlemen, Welcome to the “AI Debate”. I am Vincent Boucher, Founding Chairman of Montreal.AI. Our participants tonight are Professor GARY MARCUS and Professor YOSHUA BENGIO. Professor GARY MARCUS is a Scientist, Best-Selling Author, and Entrepreneur. Professor MARCUS has published extensively in neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence and is perhaps the youngest Professor Emeritus at NYU. He is Founder and CEO of Robust.AI and the author of five books, including The Algebraic Mind. His newest book, Rebooting AI: Building Machines We Can Trust, aims to shake up the field of artificial intelligence and has been praised by Noam Chomsky, Steven Pinker and Garry Kasparov. Professor YOSHUA BENGIO is a Deep Learning Pioneer. In 2018, Professor BENGIO was the computer scientist who collected the largest number of new citations worldwide. In 2019, he received, jointly with Geoffrey Hinton and Yann LeCun, the ACM A.M. Turing Award — “the Nobel Prize of Computing”. He is the Founder and Scientific Director of Mila — the largest university-based research group in deep learning in the world.
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Hello, It's GPT-2
Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems Paweł Budzianowski1;2;3 and Ivan Vulic´2;3 1Engineering Department, Cambridge University, UK 2Language Technology Lab, Cambridge University, UK 3PolyAI Limited, London, UK [email protected], [email protected] Abstract (Young et al., 2013). On the other hand, open- domain conversational bots (Li et al., 2017; Serban Data scarcity is a long-standing and crucial et al., 2017) can leverage large amounts of freely challenge that hinders quick development of available unannotated data (Ritter et al., 2010; task-oriented dialogue systems across multiple domains: task-oriented dialogue models are Henderson et al., 2019a). Large corpora allow expected to learn grammar, syntax, dialogue for training end-to-end neural models, which typ- reasoning, decision making, and language gen- ically rely on sequence-to-sequence architectures eration from absurdly small amounts of task- (Sutskever et al., 2014). Although highly data- speciﬁc data. In this paper, we demonstrate driven, such systems are prone to producing unre- that recent progress in language modeling pre- liable and meaningless responses, which impedes training and transfer learning shows promise their deployment in the actual conversational ap- to overcome this problem. We propose a task- oriented dialogue model that operates solely plications (Li et al., 2017). on text input: it effectively bypasses ex- Due to the unresolved issues with the end-to- plicit policy and language generation modules. end architectures, the focus has been extended to Building on top of the TransferTransfo frame- retrieval-based models.
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Hierarchical Multiscale Recurrent Neural Networks
Published as a conference paper at ICLR 2017 HIERARCHICAL MULTISCALE RECURRENT NEURAL NETWORKS Junyoung Chung, Sungjin Ahn & Yoshua Bengio ∗ Département d’informatique et de recherche opérationnelle Université de Montréal {junyoung.chung,sungjin.ahn,yoshua.bengio}@umontreal.ca ABSTRACT Learning both hierarchical and temporal representation has been among the long- standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural network, that can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that the proposed model can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level language modelling and handwriting sequence generation. 1 INTRODUCTION One of the key principles of learning in deep neural networks as well as in the human brain is to obtain a hierarchical representation with increasing levels of abstraction (Bengio, 2009; LeCun et al., 2015; Schmidhuber, 2015). A stack of representation layers, learned from the data in a way to optimize the target task, make deep neural networks entertain advantages such as generalization to unseen examples (Hoffman et al., 2013), sharing learned knowledge among multiple tasks, and discovering disentangling factors of variation (Kingma & Welling, 2013).
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I2t2i: Learning Text to Image Synthesis with Textual Data Augmentation
I2T2I: LEARNING TEXT TO IMAGE SYNTHESIS WITH TEXTUAL DATA AUGMENTATION Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo Data Science Institute, Imperial College London ABSTRACT of text-to-image synthesis is still far from being solved. Translating information between text and image is a funda- GAN-CLS I2T2I mental problem in artificial intelligence that connects natural language processing and computer vision. In the past few A yellow years, performance in image caption generation has seen sig- school nificant improvement through the adoption of recurrent neural bus parked in networks (RNN). Meanwhile, text-to-image generation begun a parking to generate plausible images using datasets of specific cate- lot. gories like birds and flowers. We’ve even seen image genera- A man tion from multi-category datasets such as the Microsoft Com- swinging a baseball mon Objects in Context (MSCOCO) through the use of gen- bat over erative adversarial networks (GANs). Synthesizing objects home plate. with a complex shape, however, is still challenging. For ex- ample, animals and humans have many degrees of freedom, which means that they can take on many complex shapes. We propose a new training method called Image-Text-Image Fig. 1: Comparing text-to-image synthesis with and without (I2T2I) which integrates text-to-image and image-to-text (im- textual data augmentation. I2T2I synthesizes the human pose age captioning) synthesis to improve the performance of text- and bus color better. to-image synthesis. We demonstrate that I2T2I can generate better multi-categories images using MSCOCO than the state- Recently, both fractionally-strided convolutional and con- of-the-art.
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Defending Black Box Facial Recognition Classifiers Against Adversarial Attacks
Defending Black Box Facial Recognition Classifiers Against Adversarial Attacks Rajkumar Theagarajan and Bir Bhanu Center for Research in Intelligent Systems, University of California, Riverside, CA 92521 [email protected], [email protected] Abstract attacker has full knowledge about the classification model’s parameters and architecture, whereas in the Black box set- Defending adversarial attacks is a critical step towards ting [50] the attacker does not have this knowledge. In this reliable deployment of deep learning empowered solutions paper we focus on the Black box based adversarial attacks. for biometrics verification. Current approaches for de- Current defenses against adversarial attacks can be clas- fending Black box models use the classification accuracy sified into four approaches: 1) modifying the training data, of the Black box as a performance metric for validating 2) modifying the model, 3) using auxiliary tools, and their defense. However, classification accuracy by itself is 4) detecting and rejecting adversarial examples. Modi- not a reliable metric to determine if the resulting image is fying the training data involves augmenting the training “adversarial-free”. This is a serious problem for online dataset with adversarial examples and re-training the classi- biometrics verification applications where the ground-truth fier [22, 30, 64, 73] or performing N number of pre-selected of the incoming image is not known and hence we cannot image transformations in a random order [13, 17, 26, 52]. compute the accuracy of the classifier or know if the image Modifying the model involves pruning the architecture of is ”adversarial-free” or not. This paper proposes a novel the classifier [38, 51, 69] or adding pre/post-processing lay- framework for defending Black box systems from adversar- ers to it [9, 12, 71].
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Yoshua Bengio
Yoshua Bengio Département d’informatique et recherche opérationnelle, Université de Montréal Canada Research Chair Phone : 514-343-6804 on Statistical Learning Algorithms Fax : 514-343-5834 [email protected] www.iro.umontreal.ca/∼bengioy Titles and Distinctions • Full Professor, Université de Montréal, since 2002. Previously associate professor (1997-2002) and assistant professor (1993-1997). • Canada Research Chair on Statistical Learning Algorithms since 2000 (tier 2 : 2000-2005, tier 1 since 2006). • NSERC-Ubisoft Industrial Research Chair, since 2011. Previously NSERC- CGI Industrial Chair, 2005-2010. • Recipient of the ACFAS Urgel-Archambault 2009 prize (covering physics, ma- thematics, computer science, and engineering). • Fellow, CIFAR (Canadian Institute For Advanced Research), since 2004. Co- director of the CIFAR NCAP program since 2014. • Member of the board of the Neural Information Processing Systems (NIPS) Foundation, since 2010. • Action Editor, Journal of Machine Learning Research (JMLR), Neural Compu- tation, Foundations and Trends in Machine Learning, and Computational Intelligence. Member of the 2012 editor-in-chief nominating committee for JMLR. • Fellow, CIRANO (Centre Inter-universitaire de Recherche en Analyse des Organi- sations), since 1997. • Previously Associate Editor, Machine Learning, IEEE Trans. on Neural Net- works. • Founder (1993) and head of the Laboratoire d’Informatique des Systèmes Adaptatifs (LISA), and the Montreal Institute for Learning Algorithms (MILA), currently including 5 faculty, 40 students, 5 post-docs, 5 researchers on staff, and numerous visitors. This is probably the largest concentration of deep learning researchers in the world (around 60 researchers). • Member of the board of the Centre de Recherches Mathématiques, UdeM, 1999- 2009. Member of the Awards Committee of the Canadian Association for Computer Science (2012-2013).
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Generalized Denoising Auto-Encoders As Generative Models
Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent Departement´ d’informatique et recherche operationnelle,´ Universite´ de Montreal´ Abstract Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the cor- ruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying data- generating distribution when the data are discrete, or using other forms of corrup- tion process and reconstruction errors. Another issue is the mathematical justifi- cation which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty). 1 Introduction Auto-encoders learn an encoder function from input to representation and a decoder function back from representation to input space, such that the reconstruction (composition of encoder and de- coder) is good for training examples. Regularized auto-encoders also involve some form of regu- larization that prevents the auto-encoder from simply learning the identity function, so that recon- struction error will be low at training examples (and hopefully at test examples) but high in general.
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Exposing GAN-Synthesized Faces Using Landmark Locations
Exposing GAN-synthesized Faces Using Landmark Locations Xin Yang∗, Yuezun Li∗, Honggang Qiy, Siwei Lyu∗ ∗ Computer Science Department, University at Albany, State University of New York, USA y School of Computer and Control Engineering, University of the Chinese Academy of Sciences, China ABSTRACT Unlike previous image/video manipulation methods, realistic Generative adversary networks (GANs) have recently led to highly images are generated completely from random noise through a realistic image synthesis results. In this work, we describe a new deep neural network. Current detection methods are based on low method to expose GAN-synthesized images using the locations of level features such as color disparities [10, 13], or using the whole the facial landmark points. Our method is based on the observa- image as input to a neural network to extract holistic features [19]. tions that the facial parts configuration generated by GAN models In this work, we develop a new GAN-synthesized face detection are different from those of the real faces, due to the lack of global method based on a more semantically meaningful features, namely constraints. We perform experiments demonstrating this phenome- the locations of facial landmark points. This is because the GAN- non, and show that an SVM classifier trained using the locations of synthesized faces exhibit certain abnormality in the facial landmark facial landmark points is sufficient to achieve good classification locations. Specifically, The GAN-based face synthesis algorithm performance for GAN-synthesized faces. can generate face parts (e.g., eyes, nose, skin, and mouth, etc) with a great level of realistic details, yet it does not have an explicit KEYWORDS constraint over the locations of these parts in a face.
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