Signature Verification Using a "Siamese" Time Delay Neural Network
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Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos
Noname manuscript No. (will be inserted by the editor) Explaining First Impressions: Modeling, Recognizing, and Explaining Apparent Personality from Videos Hugo Jair Escalante∗ · Heysem Kaya∗ · Albert Ali Salah∗ · Sergio Escalera · Ya˘gmur G¨u¸cl¨ut¨urk · Umut G¨u¸cl¨u · Xavier Bar´o · Isabelle Guyon · Julio Jacques Junior · Meysam Madadi · Stephane Ayache · Evelyne Viegas · Furkan G¨urpınar · Achmadnoer Sukma Wicaksana · Cynthia C. S. Liem · Marcel A. J. van Gerven · Rob van Lier Received: date / Accepted: date ∗ Means equal contribution by the authors. Hugo Jair Escalante INAOE, Mexico and ChaLearn, USA E-mail: [email protected] Heysem Kaya Namık Kemal University, Department of Computer Engineering, Turkey E-mail: [email protected] Albert Ali Salah Bo˘gazi¸ci University, Dept. of Computer Engineering, Turkey and Nagoya University, FCVRC, Japan E-mail: [email protected] Furkan G¨urpınar Bo˘gazi¸ciUniversity, Computational Science and Engineering, Turkey E-mail: [email protected] Sergio Escalera University of Barcelona and Computer Vision Center, Spain E-mail: [email protected] Meysam Madadi Computer Vision Center, Spain E-mail: [email protected] Ya˘gmur G¨u¸cl¨ut¨urk,Umut G¨u¸cl¨u,Marcel A. J. van Gerven and Rob van Lier Radboud University, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, the Netherlands E-mail: fy.gucluturk,u.guclu,m.vangerven,[email protected] Xavier Bar´oand Julio Jacques Junior Universitat Oberta de Catalunya and Computer Vision Center, Spain arXiv:1802.00745v3 [cs.CV] 29 Sep 2019 E-mail: fxbaro,[email protected] Isabelle Guyon UPSud/INRIA, Universit´eParis-Saclay, France and ChaLearn, USA 2 H.J. -
Generative Adversarial Networks (Gans)
Generative Adversarial Networks (GANs) The coolest idea in Machine Learning in the last twenty years - Yann Lecun Generative Adversarial Networks (GANs) 1 / 39 Overview Generative Adversarial Networks (GANs) 3D GANs Domain Adaptation Generative Adversarial Networks (GANs) 2 / 39 Introduction Generative Adversarial Networks (GANs) 3 / 39 Supervised Learning Find deterministic function f: y = f(x), x:data, y:label Generative Adversarial Networks (GANs) 4 / 39 Unsupervised Learning "Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we do not know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI." - Yann Lecun "You cannot predict what you cannot understand" - Anonymous Generative Adversarial Networks (GANs) 5 / 39 Unsupervised Learning More challenging than supervised learning. No label or curriculum. Some NN solutions: Boltzmann machine AutoEncoder Generative Adversarial Networks Generative Adversarial Networks (GANs) 6 / 39 Unsupervised Learning vs Generative Model z = f(x) vs. x = g(z) P(z|x) vs P(x|z) Generative Adversarial Networks (GANs) 7 / 39 Autoencoders Stacked Autoencoders Use data itself as label Generative Adversarial Networks (GANs) 8 / 39 Autoencoders Denosing Autoencoders Generative Adversarial Networks (GANs) 9 / 39 Variational Autoencoder Generative Adversarial Networks (GANs) 10 / 39 Variational Autoencoder Results Generative Adversarial Networks (GANs) 11 / 39 Generative Adversarial Networks Ian Goodfellow et al, "Generative Adversarial Networks", 2014. -
Arxiv:2008.08516V1 [Cs.LG] 19 Aug 2020
Automated Machine Learning - a brief review at the end of the early years Hugo Jair Escalante Computer Science Department Instituto Nacional de Astrof´ısica, Optica´ y Electronica,´ Tonanzintla, Puebla, 72840, Mexico E-mail: [email protected] Abstract Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system. In the context of supervised learning, AutoML is concerned with feature extraction, pre processing, model design and post processing. Major contributions and achievements in AutoML have been taking place during the recent decade. We are there- fore in perfect timing to look back and realize what we have learned. This chapter aims to summarize the main findings in the early years of AutoML. More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented. Likewise, the main paradigms of AutoML are described and research opportunities are outlined. 1 Introduction Automated Machine Learning or AutoML is a term coined by the machine learning community to refer to methods that aim at automating the design and development of machine learning systems and appli- cations [33]. In the context of supervised learning, AutoML aims at relaxing the need of the user in the loop from all stages in the design of supervised learning systems (i.e., any system relying on models for classification, recognition, regression, forecasting, etc.). This is a tangible need at present, as data are be- arXiv:2008.08516v1 [cs.LG] 19 Aug 2020 ing generated vastly and in practically any context and scenario, however, the number of machine learning experts available to analyze such data is overseeded. -
Robust Deep Learning: a Case Study Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau
Robust deep learning: A case study Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau To cite this version: Victor Estrade, Cécile Germain, Isabelle Guyon, David Rousseau. Robust deep learning: A case study. JDSE 2017 - 2nd Junior Conference on Data Science and Engineering, Sep 2017, Orsay, France. pp.1-5, 2017. hal-01665938 HAL Id: hal-01665938 https://hal.inria.fr/hal-01665938 Submitted on 17 Dec 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Robust deep learning: A case study Victor Estrade, Cecile Germain, Isabelle Guyon, David Rousseau Laboratoire de Recherche en Informatique Abstract. We report on an experiment on robust classification. The literature proposes adversarial and generative learning, as well as feature construction with auto-encoders. In both cases, the context is domain- knowledge-free performance. As a consequence, the robustness quality relies on the representativity of the training dataset wrt the possible perturbations. When domain-specific a priori knowledge is available, as in our case, a specific flavor of DNN called Tangent Propagation is an effective and less data-intensive alternative. Keywords: Domain adaptation, Deep Neural Networks, High Energy Physics 1 Motivation This paper addresses the calibration of a classifier in presence of systematic errors, with an example in High Energy Physics. -
Mlcheck– Property-Driven Testing of Machine Learning Models
MLCheck– Property-Driven Testing of Machine Learning Models Arnab Sharma Caglar Demir Department of Computer Science Data Science Group University of Oldenburg Paderborn University Oldenburg, Germany Paderborn, Germany [email protected] [email protected] Axel-Cyrille Ngonga Ngomo Heike Wehrheim Data Science Group Department of Computer Science Paderborn University University of Oldenburg Paderborn, Germany Oldenburg, Germany [email protected] [email protected] ABSTRACT 1 INTRODUCTION In recent years, we observe an increasing amount of software with The importance of quality assurance for applications developed machine learning components being deployed. This poses the ques- using machine learning (ML) increases steadily as they are being tion of quality assurance for such components: how can we validate deployed in a growing number of domains and sites. Supervised ML whether specified requirements are fulfilled by a machine learned algorithms “learn” their behaviour as generalizations of training software? Current testing and verification approaches either focus data using sophisticated statistical or mathematical methods. Still, on a single requirement (e.g., fairness) or specialize on a single type developers need to make sure that their software—whether learned of machine learning model (e.g., neural networks). or programmed—satisfies certain specified requirements. Currently, In this paper, we propose property-driven testing of machine two orthogonal approaches can be followed to achieve this goal: (A) learning models. Our approach MLCheck encompasses (1) a lan- employing an ML algorithm guaranteeing some requirement per guage for property specification, and (2) a technique for system- design, or (B) validating the requirement on the model generated atic test case generation. -
Dynamic Factor Graphs for Time Series Modeling
Dynamic Factor Graphs for Time Series Modeling Piotr Mirowski and Yann LeCun Courant Institute of Mathematical Sciences, New York University, 719 Broadway, New York, NY 10003 USA {mirowski,yann}@cs.nyu.edu http://cs.nyu.edu/∼mirowski/ Abstract. This article presents a method for training Dynamic Fac- tor Graphs (DFG) with continuous latent state variables. A DFG in- cludes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden vari- ables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Be- cause the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors’ parameters. These alternated in- ference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also suc- cessfully reconstruct missing motion capture data. Key words: factor graphs, time series, dynamic Bayesian networks, re- current networks, expectation-maximization 1 Introduction 1.1 Background Time series collected from real-world phenomena are often an incomplete picture of a complex underlying dynamical process with a high-dimensional state that cannot be directly observed. For example, human motion capture data gives the positions of a few markers that are the reflection of a large number of joint angles with complex kinematic and dynamical constraints. -
Energy-Based Generative Adversarial Network
Published as a conference paper at ICLR 2017 ENERGY-BASED GENERATIVE ADVERSARIAL NET- WORKS Junbo Zhao, Michael Mathieu and Yann LeCun Department of Computer Science, New York University Facebook Artificial Intelligence Research fjakezhao, mathieu, [email protected] ABSTRACT We introduce the “Energy-based Generative Adversarial Network” model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discrimi- nator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the dis- criminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images. 1I NTRODUCTION 1.1E NERGY-BASED MODEL The essence of the energy-based model (LeCun et al., 2006) is to build a function that maps each point of an input space to a single scalar, which is called “energy”. The learning phase is a data- driven process that shapes the energy surface in such a way that the desired configurations get as- signed low energies, while the incorrect ones are given high energies. -
Discriminative Recurrent Sparse Auto-Encoders
Discriminative Recurrent Sparse Auto-Encoders Jason Tyler Rolfe & Yann LeCun Courant Institute of Mathematical Sciences, New York University 719 Broadway, 12th Floor New York, NY 10003 frolfe, [email protected] Abstract We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of itera- tions, and connected to two linear decoders that reconstruct the input and predict its supervised classification. Training via backpropagation-through-time initially minimizes an unsupervised sparse reconstruction error; the loss function is then augmented with a discriminative term on the supervised classification. The depth implicit in the temporally-unrolled form allows the system to exhibit far more representational power, while keeping the number of trainable parameters fixed. From an initially unstructured network the hidden units differentiate into categorical-units, each of which represents an input prototype with a well-defined class; and part-units representing deformations of these prototypes. The learned organization of the recurrent encoder is hierarchical: part-units are driven di- rectly by the input, whereas the activity of categorical-units builds up over time through interactions with the part-units. Even using a small number of hidden units per layer, discriminative recurrent sparse auto-encoders achieve excellent performance on MNIST. 1 Introduction Deep networks complement the hierarchical structure in natural data (Bengio, 2009). By breaking complex calculations into many steps, deep networks can gradually build up complicated decision boundaries or input transformations, facilitate the reuse of common substructure, and explicitly com- pare alternative interpretations of ambiguous input (Lee, Ekanadham, & Ng, 2008; Zeiler, Taylor, arXiv:1301.3775v4 [cs.LG] 19 Mar 2013 & Fergus, 2011). -
Perceptron 1 History of Artificial Neural Networks
Introduction to Machine Learning (Lecture Notes) Perceptron Lecturer: Barnabas Poczos Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. 1 History of Artificial Neural Networks The history of artificial neural networks is like a roller-coaster ride. There were times when it was popular(up), and there were times when it wasn't. We are now in one of its very big time. • Progression (1943-1960) { First Mathematical model of neurons ∗ Pitts & McCulloch (1943) [MP43] { Beginning of artificial neural networks { Perceptron, Rosenblatt (1958) [R58] ∗ A single neuron for classification ∗ Perceptron learning rule ∗ Perceptron convergence theorem [N62] • Degression (1960-1980) { Perceptron can't even learn the XOR function [MP69] { We don't know how to train MLP { 1963 Backpropagation (Bryson et al.) ∗ But not much attention • Progression (1980-) { 1986 Backpropagation reinvented: ∗ Learning representations by back-propagation errors. Rumilhart et al. Nature [RHW88] { Successful applications in ∗ Character recognition, autonomous cars, ..., etc. { But there were still some open questions in ∗ Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? { Hopfield nets (1982) [H82], Boltzmann machines [AHS85], ..., etc. • Degression (1993-) 1 2 Perceptron: Barnabas Poczos { SVM: Support Vector Machine is developed by Vapnik et al.. [CV95] However, SVM is a shallow architecture. { Graphical models are becoming more and more popular { Great success of SVM and graphical models almost kills the ANN (Artificial Neural Network) research. { Training deeper networks consistently yields poor results. { However, Yann LeCun (1998) developed deep convolutional neural networks (a discriminative model). -
Path Towards Better Deep Learning Models and Implications for Hardware
Path towards better deep learning models and implications for hardware Natalia Vassilieva Product Manager, ML Cerebras Systems AI has massive potential, but is compute-limited today. Cerebras Systems © 2020 “Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools.” Yann LeCun, 1.1 Deep Learning Hardware: Past, Present and Future. 2019 IEEE International Solid-State Circuits Conference (ISSCC 2019), 12-19 “Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools.” “Hardware capabilities and software tools both motivate and limit the type of ideas that AI researchers will imagine and will allow themselves to pursue. The tools at our disposal fashion our thoughts more than we care to admit.” Yann LeCun, 1.1 Deep Learning Hardware: Past, Present and Future. 2019 IEEE International Solid-State Circuits Conference (ISSCC 2019), 12-19 “Historically, progress in neural networks and deep learning research has been greatly influenced by the available hardware and software tools.” “Hardware capabilities and software tools both motivate and limit the type of ideas that AI researchers will imagine and will allow themselves to pursue. The tools at our disposal fashion our thoughts more than we care to admit.” “Is DL-specific hardware really necessary? The answer is a resounding yes. One interesting property of DL systems is that the larger we make them, the better they seem to work.” -
What's Wrong with Deep Learning?
Y LeCun What's Wrong With Deep Learning? Yann LeCun Facebook AI Research & Center for Data Science, NYU [email protected] http://yann.lecun.com Plan Y LeCun Low-Level More Post- Classifier Features Features Processor The motivation for ConvNets and Deep Learning: end-to-end learning Integrating feature extractor, classifier, contextual post-processor A bit of archeology: ideas that have been around for a while Kernels with stride, non-shared local connections, metric learning... “fully convolutional” training What's missing from deep learning? 1. Theory 2. Reasoning, structured prediction 3. Memory, short-term/working/episodic memory 4. Unsupervised learning that actually works Deep Learning = Learning Hierarchical Representations Y LeCun Traditional Pattern Recognition: Fixed/Handcrafted Feature Extractor Feature Trainable Extractor Classifier Mainstream Modern Pattern Recognition: Unsupervised mid-level features Feature Mid-Level Trainable Extractor Features Classifier Deep Learning: Representations are hierarchical and trained Low-Level Mid-Level High-Level Trainable Features Features Features Classifier Early Hierarchical Feature Models for Vision Y LeCun [Hubel & Wiesel 1962]: simple cells detect local features complex cells “pool” the outputs of simple cells within a retinotopic neighborhood. “Simple cells” “Complex cells” pooling Multiple subsampling convolutions Cognitron & Neocognitron [Fukushima 1974-1982] The Mammalian Visual Cortex is Hierarchical Y LeCun The ventral (recognition) pathway in the visual cortex has multiple stages -
Unsupervised Learning with Regularized Autoencoders
Unsupervised Learning with Regularized Autoencoders by Junbo Zhao A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science New York University September 2019 Professor Yann LeCun c Junbo Zhao (Jake) All Rights Reserved, 2019 Dedication To my family. iii Acknowledgements To pursue a PhD has been the best decision I made in my life. First and foremost, I must thank my advisor Yann LeCun. To work with Yann, after flying half a globe from China, was indeed an unbelievable luxury. Over the years, Yann has had a huge influence on me. Some of these go far beyond research|the dedication, visionary thinking, problem-solving methodology and many others. I thank Kyunghyun Cho. We have never stopped communications during my PhD. Every time we chat, Kyunghyun is able to give away awesome advice, whether it is feedback on the research projects, startup and life. I am in his debt. I also must thank Sasha Rush and Kaiming He for the incredible collaboration experience. I greatly thank Zhilin Yang from CMU, Saizheng Zhang from MILA, Yoon Kim and Sam Wiseman from Harvard NLP for the nonstopping collaboration and friendship throughout all these years. I am grateful for having been around the NYU CILVR lab and the Facebook AI Research crowd. In particular, I want to thank Michael Mathieu, Pablo Sprechmann, Ross Goroshin and Xiang Zhang for teaching me deep learning when I was a newbie in the area. I thank Mikael Henaff, Martin Arjovsky, Cinjon Resnick, Emily Denton, Will Whitney, Sainbayar Sukhbaatar, Ilya Kostrikov, Jason Lee, Ilya Kulikov, Roberta Raileanu, Alex Nowak, Sean Welleck, Ethan Perez, Qi Liu and Jiatao Gu for all the fun, joyful and fruitful discussions.