Snuba: Automating Weak Supervision to Label Training Data
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Generating Multi-Agent Trajectories Using Programmatic Weak
Published as a conference paper at ICLR 2019 GENERATING MULTI-AGENT TRAJECTORIES USING PROGRAMMATIC WEAK SUPERVISION Eric Zhan Stephan Zheng∗ Caltech Salesforce [email protected] [email protected] Yisong Yue Long Sha & Patrick Lucey Caltech STATS [email protected] flsha,[email protected] ABSTRACT We study the problem of training sequential generative models for capturing co- ordinated multi-agent trajectory behavior, such as offensive basketball gameplay. When modeling such settings, it is often beneficial to design hierarchical mod- els that can capture long-term coordination using intermediate variables. Further- more, these intermediate variables should capture interesting high-level behavioral semantics in an interpretable and manipulatable way. We present a hierarchical framework that can effectively learn such sequential generative models. Our ap- proach is inspired by recent work on leveraging programmatically produced weak labels, which we extend to the spatiotemporal regime. In addition to synthetic settings, we show how to instantiate our framework to effectively model complex interactions between basketball players and generate realistic multi-agent trajec- tories of basketball gameplay over long time periods. We validate our approach using both quantitative and qualitative evaluations, including a user study compar- ison conducted with professional sports analysts.1 1 INTRODUCTION The ongoing explosion of recorded tracking data is enabling the study of fine-grained behavior in many domains: sports (Miller et al., 2014; Yue et al., 2014; Zheng et al., 2016; Le et al., 2017), video games (Ross et al., 2011), video & motion capture (Suwajanakorn et al., 2017; Taylor et al., 2017; Xue et al., 2016), navigation & driving (Ziebart et al., 2009; Zhang & Cho, 2017; Li et al., 2017), lab- oratory animal behaviors (Johnson et al., 2016; Eyjolfsdottir et al., 2017), and tele-operated robotics (Abbeel & Ng, 2004; Lin et al., 2006). -
Constrained Labeling for Weakly Supervised Learning
Constrained Labeling for Weakly Supervised Learning Chidubem Arachie1 Bert Huang2 1Department of Computer Science, Virginia Tech, Blacksburg, Virginia, USA 2Department of Computer Science, Data Intensive Studies Center, Tufts University, Medford, Massachusetts, USA Abstract sion. Weakly supervised learning involves training models using noisy labels. Using multiple sources or forms of weak supervision is common, as it provides diverse information Curation of large fully supervised datasets has be- to the model. However, each source of weak supervision has come one of the major roadblocks for machine its own bias that can be transmitted to the model. Different learning. Weak supervision provides an alterna- weak supervision signals can also conflict, overlap, or—in tive to supervised learning by training with cheap, the worst case—make dependent errors. Thus, a naive com- noisy, and possibly correlated labeling functions bination of these weak signals would hurt the quality of from varying sources. The key challenge in weakly a learned model. The key problem then is how to reliably supervised learning is combining the different combine various sources of weak signals to train an accurate weak supervision signals while navigating mislead- model. ing correlations in their errors. In this paper, we propose a simple data-free approach for combin- To solve this problem, we propose constrained label learn- ing weak supervision signals by defining a con- ing (CLL), a method that processes various weak supervi- strained space for the possible labels of the weak sion signals and combines them to produce high-quality signals and training with a random labeling within training labels. The idea behind CLL is that, given the weak this constrained space. -
Multi-Task Weak Supervision Enables Automated Abnormality Localization in Whole-Body FDG-PET/CT
Multi-task weak supervision enables automated abnormality localization in whole-body FDG-PET/CT Sabri Eyuboglu∗1, Geoffrey Angus∗1, Bhavik N. Patel2, Anuj Pareek2, Guido Davidzon2, Jared Dunnmon,∗∗1 Matthew P. Lungren ∗∗2 1 Department of Computer Science, Stanford University, Stanford, CA 94305, USA 2 Department of Radiology, Stanford University, Stanford, CA 94305, USA ∗ ∗∗ Equal contribution; Computational decision support systems could provide clinical value in whole-body FDG- PET/CT workflows. However, the lack of labeled data combined with the sheer size of each scan make it challenging to apply existing supervised machine learning systems. Leveraging recent advancements in natural language processing, we develop a weak supervision frame- work that extracts imperfect, yet highly granular regional abnormality labels from the radi- ologist reports associated with each scan. Our framework automatically labels each region in a custom ontology of anatomical regions, providing a structured profile of the pathologies in each scan. We design an attention-based, multi-task CNN architecture that can be trained using these generated labels to localize abnormalities in whole-body scans. We show that our multi-task representation is critical for strong performance on rare abnormalities for which we have little data and for accurately predicting mortality from imaging data alone (AUROC 80%), a critical challenge for palliative care teams. 1 Introduction The availability of large, labeled datasets has fueled recent progress in machine learning for med- ical imaging. Breakthrough studies in chest radiograph diagnosis, skin and mammographic lesion classification, and diabetic retinopathy detection have relied on hundreds of thousands of labeled training examples [1–4]. -
A Survey on Data Collection for Machine Learning a Big Data - AI Integration Perspective
1 A Survey on Data Collection for Machine Learning A Big Data - AI Integration Perspective Yuji Roh, Geon Heo, Steven Euijong Whang, Senior Member, IEEE Abstract—Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research. Index Terms—data collection, data acquisition, data labeling, machine learning F 1 INTRODUCTION E are living in exciting times where machine learning expertise. This problem applies to any novel application that W is having a profound influence on a wide range of benefits from machine learning. -
Visual Learning with Weak Supervision
Visual Learning with Weak Supervision Safa Cicek Committee: Prof. Stefano Soatto, Chair Prof. Lieven Vandenberghe Prof. Paulo Tabuada Prof. Guy Van den Broeck PHD Defense January 2021 UCLA ELECTRICAL AND COMPUTER ENGINEERING Table of Contents Introduction SaaS: Speed as a Supervisor for Semi-supervised Learning [1] Input and Weight Space Smoothing for Semi-supervised Learning [2] Unsupervised Domain Adaptation via Regularized Conditional Alignment [3] Disentangled Image Generation for Unsupervised Domain Adaptation [4] Spatial Class Distribution Shift in Unsupervised Domain Adaptation [5] Learning Topology from Synthetic Data for Unsupervised Depth Completion [6] Targeted Adversarial Perturbations for Monocular Depth Prediction [7] Concluding Remarks [1] Cicek, Safa, Alhussein Fawzi, and Stefano Soatto. Saas: Speed as a supervisor for semi-supervised learning. Proceedings of the European Conference on Computer Vision (ECCV). 2018. [2] Cicek, Safa, and Stefano Soatto. Input and Weight Space Smoothing for Semi-supervised Learning. Proceedings of the IEEE International Conference on Computer Vision (ICCV) Workshops. 2019. [3] Cicek, Safa, and Stefano Soatto. Unsupervised domain adaptation via regularized conditional alignment. Proceedings of the IEEE International Conference on Computer Vision (ICCV). 2019. [4] Cicek, Safa, Zhaowen Wang, Hailin Jin, Stefano Soatto, Generative Feature Disentangling for Unsupervised Domain Adaptation. Proceedings of the European Conference on Computer Vision (ECCV) Workshops. (2020). [5] Cicek, Safa, Ning Xu, Zhaowen Wang, Hailin Jin, Stefano Soatto, Spatial Class Distribution Shift in Unsupervised Domain Adaptation. Asian Conference on Computer Vision (ACCV). 2020. [6] Wong Alex, Safa Cicek, Stefano Soatto, Learning Topology from Synthetic Data for Unsupervised Depth Completion, IEEE Robotics and Automation Letters (RAL). 2021. [7] Wong Alex, Safa Cicek, Stefano Soatto, Targeted Adversarial Perturbations for Monocular Depth Prediction. -
Natural Language Processing and Weak Supervision
Natural language processing and weak supervision L´eonBottou COS 424 { 4/27/2010 Introduction Natural language processing \from scratch" { Natural language processing systems are heavily engineered. { How much engineering can we avoid by using more data ? { Work by Ronan Collobert, Jason Weston, and the NEC team. Summary { Natural language processing { Embeddings and models { Lots of unlabeled data { Task dependent hacks 2/43 COS 424 { 4/27/2010 I. Natural language processing The Goal We want to have a conversation with our computer . still a long way before HAL 9000 . Convert a piece of English into a computer-friendly data structure How to measure if the computer \understands" something? 4/43 COS 424 { 4/27/2010 Natural Language Processing Tasks Intermediate steps to reach the goal? Part-Of-Speech Tagging (POS): syntactic roles (noun, adverb...) Chunking (CHUNK): syntactic constituents (noun phrase, verb phrase...) Name Entity Recognition (NER): person/company/location... Semantic Role Labeling (SRL): semantic role [John]ARG0 [ate]REL [the apple]ARG1 [in the garden]ARGM LOC − 5/43 COS 424 { 4/27/2010 NLP Benchmarks Datasets: ? POS, CHUNK, SRL: WSJ( up to 1M labeled words) ? NER: Reuters( 200K labeled≈ words) ≈ System Accuracy System F1 Shen, 2007 97.33% Shen, 2005 95.23% Toutanova, 2003 97.24% Sha, 2003 94.29% Gimenez, 2004 97.16% Kudoh, 2001 93.91% (a) POS: As in (Toutanova, 2003) (b) CHUNK: CoNLL 2000 System F1 System F1 Ando, 2005 89.31% Koomen, 2005 77.92% Florian, 2003 88.76% Pradhan, 2005 77.30% Kudoh, 2001 88.31% Haghighi, 2005 -
Participant Guide
Participant Guide National High Adventure Sea Base, BSA Sea Base Scuba Programs Islamorada, Florida Scuba Adventure Scuba Certification Scuba Live Aboard Revised Date: 2.23.2021 Mission of the Boy Scouts of America The mission of the Boy Scouts of America is to prepare young people to make ethical and moral choices over their lifetime by instilling in them the values of the Scout Oath and Law. Scout Oath On my honor I will do my best to do my duty to God and my country and to obey the Scout Law; to help other people at all times; to keep myself physically strong, mentally awake, and morally straight. Scout Law A Scout is: Trustworthy. Loyal. Helpful. Friendly. Courteous. Kind. Obedient. Cheerful. Thrifty. Brave. Clean. Reverent. Mission Statement of Sea Base, BSA It is the mission of Sea Base to serve councils and units by providing an outstanding high adventure experience for older Boy Scouts, Varsity Scouts, Venturers, Sea Scouts and their leaders. Sea Base programs are designed to achieve the principal aims of the Boy Scouts of America: • To build character • To foster citizenship • To develop physical, mental, and emotional fitness Keys Blessing Bless the creatures of the Sea Bless this person I call me Bless the Keys, you make so grand Bless the sun that warms the land Bless the fellowship we feel As we gather for this meal Amen Page | 2 Table of Contents General Eligibility Requirements ................................................................................................................. 4 General Eligibility at a Glance -
Learning from Noisy Labels with Deep Neural Networks: a Survey Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee
UNDER REVIEW 1 Learning from Noisy Labels with Deep Neural Networks: A Survey Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, Jae-Gil Lee Abstract—Deep learning has achieved remarkable success in 100% 100% numerous domains with help from large amounts of big data. Noisy w/o. Reg. Noisy w. Reg. 75% However, the quality of data labels is a concern because of the 75% Clean w. Reg Gap lack of high-quality labels in many real-world scenarios. As noisy 50% 50% labels severely degrade the generalization performance of deep Noisy w/o. Reg. neural networks, learning from noisy labels (robust training) is 25% Accuracy Test 25% Training Accuracy Training Noisy w. Reg. becoming an important task in modern deep learning applica- Clean w. Reg tions. In this survey, we first describe the problem of learning with 0% 0% 1 30 60 90 120 1 30 60 90 120 label noise from a supervised learning perspective. Next, we pro- Epochs Epochs vide a comprehensive review of 57 state-of-the-art robust training Fig. 1. Convergence curves of training and test accuracy when training methods, all of which are categorized into five groups according WideResNet-16-8 using a standard training method on the CIFAR-100 dataset to their methodological difference, followed by a systematic com- with the symmetric noise of 40%: “Noisy w/o. Reg.” and “Noisy w. Reg.” are parison of six properties used to evaluate their superiority. Sub- the models trained on noisy data without and with regularization, respectively, sequently, we perform an in-depth analysis of noise rate estima- and “Clean w. -
Introduction to Deep Learning in Signal Processing & Communications with MATLAB
Introduction to Deep Learning in Signal Processing & Communications with MATLAB Dr. Amod Anandkumar Pallavi Kar Application Engineering Group, Mathworks India © 2019 The MathWorks, Inc.1 Different Types of Machine Learning Type of Machine Learning Categories of Algorithms • Output is a choice between classes Classification (True, False) (Red, Blue, Green) Supervised Learning • Output is a real number Regression Develop predictive (temperature, stock prices) model based on both Machine input and output data Learning Unsupervised • No output - find natural groups and Clustering Learning patterns from input data only Discover an internal representation from input data only 2 What is Deep Learning? 3 Deep learning is a type of supervised machine learning in which a model learns to perform classification tasks directly from images, text, or sound. Deep learning is usually implemented using a neural network. The term “deep” refers to the number of layers in the network—the more layers, the deeper the network. 4 Why is Deep Learning So Popular Now? Human Accuracy Source: ILSVRC Top-5 Error on ImageNet 5 Vision applications have been driving the progress in deep learning producing surprisingly accurate systems 6 Deep Learning success enabled by: • Labeled public datasets • Progress in GPU for acceleration AlexNet VGG-16 ResNet-50 ONNX Converter • World-class models and PRETRAINED PRETRAINED PRETRAINED MODEL MODEL CONVERTER MODEL MODEL TensorFlow- connected community Caffe GoogLeNet IMPORTER PRETRAINED Keras Inception-v3 MODEL IMPORTER MODELS 7 -
Learning from Minimally Labeled Data with Accelerated Convolutional Neural Networks Aysegul Dundar Purdue University
Purdue University Purdue e-Pubs Open Access Dissertations Theses and Dissertations 4-2016 Learning from minimally labeled data with accelerated convolutional neural networks Aysegul Dundar Purdue University Follow this and additional works at: https://docs.lib.purdue.edu/open_access_dissertations Part of the Computer Sciences Commons, and the Electrical and Computer Engineering Commons Recommended Citation Dundar, Aysegul, "Learning from minimally labeled data with accelerated convolutional neural networks" (2016). Open Access Dissertations. 641. https://docs.lib.purdue.edu/open_access_dissertations/641 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. Graduate School Form 30 Updated ¡¢ ¡£¢ ¡¤ ¥ PURDUE UNIVERSITY GRADUATE SCHOOL Thesis/Dissertation Acceptance This is to certify that the thesis/dissertation prepared By Aysegul Dundar Entitled Learning from Minimally Labeled Data with Accelerated Convolutional Neural Networks For the degree of Doctor of Philosophy Is approved by the final examining committee: Eugenio Culurciello Chair Anand Raghunathan Bradley S. Duerstock Edward L. Bartlett To the best of my knowledge and as understood by the student in the Thesis/Dissertation Agreement, Publication Delay, and Certification Disclaimer (Graduate School Form 32), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy of Integrity in Research” and the use of copyright material. Approved by Major Professor(s): Eugenio Culurciello Approved by: George R. Wodicka 04/22/2016 Head of the Departmental Graduate Program Date LEARNING FROM MINIMALLY LABELED DATA WITH ACCELERATED CONVOLUTIONAL NEURAL NETWORKS A Dissertation Submitted to the Faculty of Purdue University by Aysegul Dundar In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy May 2016 Purdue University West Lafayette, Indiana ii To my family: Nezaket, Cengiz and Deniz. -
Towards a Perceptually Relevant Control Space
Music sound synthesis using machine learning: Towards a perceptually relevant control space Fanny ROCHE PhD Defense 29 September 2020 Supervisor: Laurent GIRIN Co-supervisors: Thomas HUEBER Co-supervisors: Maëva GARNIER Co-supervisors: Samuel LIMIER Fanny ROCHE - PhD Defense - 29 September 2020 1 / 48 Context and Objectives Context and Objectives Fanny ROCHE - PhD Defense - 29 September 2020 2 / 48 Context and Objectives Context and Objectives Fanny ROCHE - PhD Defense - 29 September 2020 2 / 48 Context and Objectives Context and Objectives Fanny ROCHE - PhD Defense - 29 September 2020 2 / 48 Processed recordings ! e.g. sampling, wavetable, granular, ... + computationally efficient − very memory consuming Spectral modeling ! e.g. additive, subtractive, ... + close to human sound perception − numerous & very specific Physical modeling ! e.g. solving wave equations, modal synthesis, ... + physically meaningful controls − very specific sounds Context and Objectives Context and Objectives Music sound synthesis Abstract algorithms ! e.g. FM, waveshaping, ... + rich sounds − complex parameters Fanny ROCHE - PhD Defense - 29 September 2020 3 / 48 Spectral modeling ! e.g. additive, subtractive, ... + close to human sound perception − numerous & very specific Physical modeling ! e.g. solving wave equations, modal synthesis, ... + physically meaningful controls − very specific sounds Context and Objectives Context and Objectives Music sound synthesis Abstract algorithms ! e.g. FM, waveshaping, ... + rich sounds − complex parameters Processed recordings ! e.g. sampling, wavetable, granular, ... + computationally efficient − very memory consuming Fanny ROCHE - PhD Defense - 29 September 2020 3 / 48 Physical modeling ! e.g. solving wave equations, modal synthesis, ... + physically meaningful controls − very specific sounds Context and Objectives Context and Objectives Music sound synthesis Abstract algorithms ! e.g. FM, waveshaping, ... + rich sounds − complex parameters Processed recordings ! e.g. -
Exploring Semi-Supervised Variational Autoencoders for Biomedical Relation Extraction
Exploring Semi-supervised Variational Autoencoders for Biomedical Relation Extraction Yijia Zhanga,b and Zhiyong Lua* a National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, Maryland 20894, USA b School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116023, China Corresponding author: Zhiyong Lu ([email protected]) Abstract The biomedical literature provides a rich source of knowledge such as protein-protein interactions (PPIs), drug-drug interactions (DDIs) and chemical-protein interactions (CPIs). Biomedical relation extraction aims to automatically extract biomedical relations from biomedical text for various biomedical research. State-of-the-art methods for biomedical relation extraction are primarily based on supervised machine learning and therefore depend on (sufficient) labeled data. However, creating large sets of training data is prohibitively expensive and labor-intensive, especially so in biomedicine as domain knowledge is required. In contrast, there is a large amount of unlabeled biomedical text available in PubMed. Hence, computational methods capable of employing unlabeled data to reduce the burden of manual annotation are of particular interest in biomedical relation extraction. We present a novel semi-supervised approach based on variational autoencoder (VAE) for biomedical relation extraction. Our model consists of the following three parts, a classifier, an encoder and a decoder. The classifier is implemented using multi-layer convolutional neural networks (CNNs), and the encoder and decoder are implemented using both bidirectional long short-term memory networks (Bi-LSTMs) and CNNs, respectively. The semi-supervised mechanism allows our model to learn features from both the labeled and unlabeled data.