TRADER: Trace Divergence Analysis and Embedding Regulation for Debugging Recurrent Neural Networks
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Neural Networks (AI) (WBAI028-05) Lecture Notes
Herbert Jaeger Neural Networks (AI) (WBAI028-05) Lecture Notes V 1.5, May 16, 2021 (revision of Section 8) BSc program in Artificial Intelligence Rijksuniversiteit Groningen, Bernoulli Institute Contents 1 A very fast rehearsal of machine learning basics 7 1.1 Training data. .............................. 8 1.2 Training objectives. ........................... 9 1.3 The overfitting problem. ........................ 11 1.4 How to tune model flexibility ..................... 17 1.5 How to estimate the risk of a model .................. 21 2 Feedforward networks in machine learning 24 2.1 The Perceptron ............................. 24 2.2 Multi-layer perceptrons ......................... 29 2.3 A glimpse at deep learning ....................... 52 3 A short visit in the wonderland of dynamical systems 56 3.1 What is a “dynamical system”? .................... 58 3.2 The zoo of standard finite-state discrete-time dynamical systems .. 64 3.3 Attractors, Bifurcation, Chaos ..................... 78 3.4 So far, so good ... ............................ 96 4 Recurrent neural networks in deep learning 98 4.1 Supervised training of RNNs in temporal tasks ............ 99 4.2 Backpropagation through time ..................... 107 4.3 LSTM networks ............................. 112 5 Hopfield networks 118 5.1 An energy-based associative memory ................. 121 5.2 HN: formal model ............................ 124 5.3 Geometry of the HN state space .................... 126 5.4 Training a HN .............................. 127 5.5 Limitations .............................. -
Predrnn: Recurrent Neural Networks for Predictive Learning Using Spatiotemporal Lstms
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs Yunbo Wang Mingsheng Long∗ School of Software School of Software Tsinghua University Tsinghua University [email protected] [email protected] Jianmin Wang Zhifeng Gao Philip S. Yu School of Software School of Software School of Software Tsinghua University Tsinghua University Tsinghua University [email protected] [email protected] [email protected] Abstract The predictive learning of spatiotemporal sequences aims to generate future images by learning from the historical frames, where spatial appearances and temporal vari- ations are two crucial structures. This paper models these structures by presenting a predictive recurrent neural network (PredRNN). This architecture is enlightened by the idea that spatiotemporal predictive learning should memorize both spatial ap- pearances and temporal variations in a unified memory pool. Concretely, memory states are no longer constrained inside each LSTM unit. Instead, they are allowed to zigzag in two directions: across stacked RNN layers vertically and through all RNN states horizontally. The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously. PredRNN achieves the state-of-the-art prediction performance on three video prediction datasets and is a more general framework, that can be easily extended to other predictive learning tasks by integrating with other architectures. 1 Introduction -
Mxnet-R-Reference-Manual.Pdf
Package ‘mxnet’ June 24, 2020 Type Package Title MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Sys- tems Version 2.0.0 Date 2017-06-27 Author Tianqi Chen, Qiang Kou, Tong He, Anirudh Acharya <https://github.com/anirudhacharya> Maintainer Qiang Kou <[email protected]> Repository apache/incubator-mxnet Description MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix the flavours of deep learning programs together to maximize the efficiency and your productivity. License Apache License (== 2.0) URL https://github.com/apache/incubator-mxnet/tree/master/R-package BugReports https://github.com/apache/incubator-mxnet/issues Imports methods, Rcpp (>= 0.12.1), DiagrammeR (>= 0.9.0), visNetwork (>= 1.0.3), data.table, jsonlite, magrittr, stringr Suggests testthat, mlbench, knitr, rmarkdown, imager, covr Depends R (>= 3.4.4) LinkingTo Rcpp VignetteBuilder knitr 1 2 R topics documented: RoxygenNote 7.1.0 Encoding UTF-8 R topics documented: arguments . 15 as.array.MXNDArray . 15 as.matrix.MXNDArray . 16 children . 16 ctx.............................................. 16 dim.MXNDArray . 17 graph.viz . 17 im2rec . 18 internals . 19 is.mx.context . 19 is.mx.dataiter . 20 is.mx.ndarray . 20 is.mx.symbol . 21 is.serialized . 21 length.MXNDArray . 21 mx.apply . 22 mx.callback.early.stop . 22 mx.callback.log.speedometer . 23 mx.callback.log.train.metric . 23 mx.callback.save.checkpoint . 24 mx.cpu . 24 mx.ctx.default . 24 mx.exec.backward . 25 mx.exec.forward . 25 mx.exec.update.arg.arrays . 26 mx.exec.update.aux.arrays . 26 mx.exec.update.grad.arrays . -
E2 Studio Integrated Development Environment User's Manual: Getting
User’s Manual e2 studio Integrated Development Environment User’s Manual: Getting Started Guide Target Device RX, RL78, RH850 and RZ Family Rev.4.00 May 2016 www.renesas.com Notice 1. Descriptions of circuits, software and other related information in this document are provided only to illustrate the operation of semiconductor products and application examples. You are fully responsible for the incorporation of these circuits, software, and information in the design of your equipment. Renesas Electronics assumes no responsibility for any losses incurred by you or third parties arising from the use of these circuits, software, or information. 2. Renesas Electronics has used reasonable care in preparing the information included in this document, but Renesas Electronics does not warrant that such information is error free. Renesas Electronics assumes no liability whatsoever for any damages incurred by you resulting from errors in or omissions from the information included herein. 3. Renesas Electronics does not assume any liability for infringement of patents, copyrights, or other intellectual property rights of third parties by or arising from the use of Renesas Electronics products or technical information described in this document. No license, express, implied or otherwise, is granted hereby under any patents, copyrights or other intellectual property rights of Renesas Electronics or others. 4. You should not alter, modify, copy, or otherwise misappropriate any Renesas Electronics product, whether in whole or in part. Renesas Electronics assumes no responsibility for any losses incurred by you or third parties arising from such alteration, modification, copy or otherwise misappropriation of Renesas Electronics product. 5. Renesas Electronics products are classified according to the following two quality grades: “Standard” and “High Quality”. -
Jssjournal of Statistical Software MMMMMM YYYY, Volume VV, Issue II
JSSJournal of Statistical Software MMMMMM YYYY, Volume VV, Issue II. http://www.jstatsoft.org/ OSTSC: Over Sampling for Time Series Classification in R Matthew Dixon Diego Klabjan Lan Wei Stuart School of Business, Department of Industrial Department of Computer Science, Illinois Institute of Technology Engineering and Illinois Institute of Technology Management Sciences, Northwestern University Abstract The OSTSC package is a powerful oversampling approach for classifying univariant, but multinomial time series data in R. This article provides a brief overview of the over- sampling methodology implemented by the package. A tutorial of the OSTSC package is provided. We begin by providing three test cases for the user to quickly validate the functionality in the package. To demonstrate the performance impact of OSTSC,we then provide two medium size imbalanced time series datasets. Each example applies a TensorFlow implementation of a Long Short-Term Memory (LSTM) classifier - a type of a Recurrent Neural Network (RNN) classifier - to imbalanced time series. The classifier performance is compared with and without oversampling. Finally, larger versions of these two datasets are evaluated to demonstrate the scalability of the package. The examples demonstrate that the OSTSC package improves the performance of RNN classifiers applied to highly imbalanced time series data. In particular, OSTSC is observed to increase the AUC of LSTM from 0.543 to 0.784 on a high frequency trading dataset consisting of 30,000 time series observations. arXiv:1711.09545v1 [stat.CO] 27 Nov 2017 Keywords: Time Series Oversampling, Classification, Outlier Detection, R. 1. Introduction A significant number of learning problems involve the accurate classification of rare events or outliers from time series data. -
SAS® Debugging 101 Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, California
PharmaSUG 2017 - Paper TT03 SAS® Debugging 101 Kirk Paul Lafler, Software Intelligence Corporation, Spring Valley, California Abstract SAS® users are almost always surprised to discover their programs contain bugs. In fact, when asked users will emphatically stand by their programs and logic by saying they are bug free. But, the vast number of experiences along with the realities of writing code says otherwise. Bugs in software can appear anywhere; whether accidentally built into the software by developers, or introduced by programmers when writing code. No matter where the origins of bugs occur, the one thing that all SAS users know is that debugging SAS program errors and warnings can be a daunting, and humbling, task. This presentation explores the world of SAS bugs, providing essential information about the types of bugs, how bugs are created, the symptoms of bugs, and how to locate bugs. Attendees learn how to apply effective techniques to better understand, identify, and repair bugs and enable program code to work as intended. Introduction From the very beginning of computing history, program code, or the instructions used to tell a computer how and when to do something, has been plagued with errors, or bugs. Even if your own program code is determined to be error-free, there is a high probability that the operating system and/or software being used, such as the SAS software, have embedded program errors in them. As a result, program code should be written to assume the unexpected and, as much as possible, be able to handle unforeseen events using acceptable strategies and programming methodologies. -
Verification of RNN-Based Neural Agent-Environment Systems
The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) Verification of RNN-Based Neural Agent-Environment Systems Michael E. Akintunde, Andreea Kevorchian, Alessio Lomuscio, Edoardo Pirovano Department of Computing, Imperial College London, UK Abstract and realised via a previously trained recurrent neural net- work (Hausknecht and Stone 2015). As we discuss below, We introduce agent-environment systems where the agent we are not aware of other methods in the literature address- is stateful and executing a ReLU recurrent neural network. ing the formal verification of systems based on recurrent We define and study their verification problem by providing networks. The present contribution focuses on reachability equivalences of recurrent and feed-forward neural networks on bounded execution traces. We give a sound and complete analysis, that is we intend to give formal guarantees as to procedure for their verification against properties specified whether a state, or a set of states are reached in the system. in a simplified version of LTL on bounded executions. We Typically this is an unwanted state, or a bug, that we intend present an implementation and discuss the experimental re- to ensure is not reached in any possible execution. Similarly, sults obtained. to provide safety guarantees, this analysis can be used to show the system does not enter an unsafe region of the state space. 1 Introduction The rest of the paper is organised as follows. After the A key obstacle in the deployment of autonomous systems is discussion of related work below, in Section 2, we introduce the inherent difficulty of their verification. This is because the basic concepts related to neural networks that are used the behaviour of autonomous systems is hard to predict; in throughout the paper. -
Anurag Soni's Blog
ANURAG SONI 447 Sharon Rd Pittsburgh, PA, USA |[email protected] | 765-775-0116 | GitHub | Blog | LinkedIn PROFILE • MS Business Analytics student seeking position in data science and decision-making business units • 5 years of experience as Analyst/Product Mgr. for a technology consulting firm with Investment Banking clients • Skills in figuring out impactful stories with data EDUCATION Purdue University, Krannert School of Management West Lafayette, IN Master of Science in Business Analytics and Information Management May 2018 Indian Institute of Technology Guwahati Guwahati, INDIA Bachelor of Technology, Chemical Engineering May 2011 HIGHLIGHTS • Tools: PL/SQL, Python, R, Excel(VBA), SAS, MATLAB, Tableau, Gurobi • Environment: TensorFlow, Keras, Hadoop, Hive, Docker, Google Cloud, UNIX • Process: Data Mining, SDLC, Process Simulation, Optimization, Data Modeling • Skills: Client-Facing, Strong Communication, Change Management, Team Management, Mentoring EXPERIENCE Purdue BIZ Analytics LAB West Lafayette, IN Associate Analyst(Co-Op) July 2017 – Mar 2018 • Demand Forecasting of supermarket sales: Built a production ready machine-learning solution using - Python deep learning libraries with results expecting to save significant inventory costs • Delivery Rebate Forecast for retailers: Built a forecasting model that supports reporting of weekly expected rebate numbers in an annual tiered delivery contract. • Decision Support System for Music Producer: Implemented dashboard application that uses Spotify datasets to partially cluster songs by their popularity and revenue capability • Production implementation of ML solution: Created an automated machine learning pipeline of an object- detection solution built using TensorFlow on Google Cloud using Docker and Kubernetes technology • Hate Speech Recognition using NLP: Implemented a text mining model that identifies hate speech characteristics in the text. -
Agile Playbook V2.1—What’S New?
AGILE P L AY B O OK TABLE OF CONTENTS INTRODUCTION ..........................................................................................................4 Who should use this playbook? ................................................................................6 How should you use this playbook? .........................................................................6 Agile Playbook v2.1—What’s new? ...........................................................................6 How and where can you contribute to this playbook?.............................................7 MEET YOUR GUIDES ...................................................................................................8 AN AGILE DELIVERY MODEL ....................................................................................10 GETTING STARTED.....................................................................................................12 THE PLAYS ...................................................................................................................14 Delivery ......................................................................................................................15 Play: Start with Scrum ...........................................................................................15 Play: Seeing success but need more fexibility? Move on to Scrumban ............17 Play: If you are ready to kick of the training wheels, try Kanban .......................18 Value ......................................................................................................................19 -
Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK
1 From Start-ups to Scale-ups: Opportunities and Open Problems for Static and Dynamic Program Analysis Mark Harman∗, Peter O’Hearn∗ ∗Facebook London and University College London, UK Abstract—This paper1 describes some of the challenges and research questions that target the most productive intersection opportunities when deploying static and dynamic analysis at we have yet witnessed: that between exciting, intellectually scale, drawing on the authors’ experience with the Infer and challenging science, and real-world deployment impact. Sapienz Technologies at Facebook, each of which started life as a research-led start-up that was subsequently deployed at scale, Many industrialists have perhaps tended to regard it unlikely impacting billions of people worldwide. that much academic work will prove relevant to their most The paper identifies open problems that have yet to receive pressing industrial concerns. On the other hand, it is not significant attention from the scientific community, yet which uncommon for academic and scientific researchers to believe have potential for profound real world impact, formulating these that most of the problems faced by industrialists are either as research questions that, we believe, are ripe for exploration and that would make excellent topics for research projects. boring, tedious or scientifically uninteresting. This sociological phenomenon has led to a great deal of miscommunication between the academic and industrial sectors. I. INTRODUCTION We hope that we can make a small contribution by focusing on the intersection of challenging and interesting scientific How do we transition research on static and dynamic problems with pressing industrial deployment needs. Our aim analysis techniques from the testing and verification research is to move the debate beyond relatively unhelpful observations communities to industrial practice? Many have asked this we have typically encountered in, for example, conference question, and others related to it. -
Deep Learning Architectures for Sequence Processing
Speech and Language Processing. Daniel Jurafsky & James H. Martin. Copyright © 2021. All rights reserved. Draft of September 21, 2021. CHAPTER Deep Learning Architectures 9 for Sequence Processing Time will explain. Jane Austen, Persuasion Language is an inherently temporal phenomenon. Spoken language is a sequence of acoustic events over time, and we comprehend and produce both spoken and written language as a continuous input stream. The temporal nature of language is reflected in the metaphors we use; we talk of the flow of conversations, news feeds, and twitter streams, all of which emphasize that language is a sequence that unfolds in time. This temporal nature is reflected in some of the algorithms we use to process lan- guage. For example, the Viterbi algorithm applied to HMM part-of-speech tagging, proceeds through the input a word at a time, carrying forward information gleaned along the way. Yet other machine learning approaches, like those we’ve studied for sentiment analysis or other text classification tasks don’t have this temporal nature – they assume simultaneous access to all aspects of their input. The feedforward networks of Chapter 7 also assumed simultaneous access, al- though they also had a simple model for time. Recall that we applied feedforward networks to language modeling by having them look only at a fixed-size window of words, and then sliding this window over the input, making independent predictions along the way. Fig. 9.1, reproduced from Chapter 7, shows a neural language model with window size 3 predicting what word follows the input for all the. Subsequent words are predicted by sliding the window forward a word at a time. -
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges
Deep Learning for Source Code Modeling and Generation: Models, Applications and Challenges TRIET H. M. LE, The University of Adelaide HAO CHEN, The University of Adelaide MUHAMMAD ALI BABAR, The University of Adelaide Deep Learning (DL) techniques for Natural Language Processing have been evolving remarkably fast. Recently, the DL advances in language modeling, machine translation and paragraph understanding are so prominent that the potential of DL in Software Engineering cannot be overlooked, especially in the field of program learning. To facilitate further research and applications of DL in this field, we provide a comprehensive review to categorize and investigate existing DL methods for source code modeling and generation. To address the limitations of the traditional source code models, we formulate common program learning tasks under an encoder-decoder framework. After that, we introduce recent DL mechanisms suitable to solve such problems. Then, we present the state-of-the-art practices and discuss their challenges with some recommendations for practitioners and researchers as well. CCS Concepts: • General and reference → Surveys and overviews; • Computing methodologies → Neural networks; Natural language processing; • Software and its engineering → Software notations and tools; Source code generation; Additional Key Words and Phrases: Deep learning, Big Code, Source code modeling, Source code generation 1 INTRODUCTION Deep Learning (DL) has recently emerged as an important branch of Machine Learning (ML) because of its incredible performance in Computer Vision and Natural Language Processing (NLP) [75]. In the field of NLP, it has been shown that DL models can greatly improve the performance of many classic NLP tasks such as semantic role labeling [96], named entity recognition [209], machine translation [256], and question answering [174].