Perceptrons And
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Malware Classification with BERT
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 5-25-2021 Malware Classification with BERT Joel Lawrence Alvares Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Artificial Intelligence and Robotics Commons, and the Information Security Commons Malware Classification with Word Embeddings Generated by BERT and Word2Vec Malware Classification with BERT Presented to Department of Computer Science San José State University In Partial Fulfillment of the Requirements for the Degree By Joel Alvares May 2021 Malware Classification with Word Embeddings Generated by BERT and Word2Vec The Designated Project Committee Approves the Project Titled Malware Classification with BERT by Joel Lawrence Alvares APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE San Jose State University May 2021 Prof. Fabio Di Troia Department of Computer Science Prof. William Andreopoulos Department of Computer Science Prof. Katerina Potika Department of Computer Science 1 Malware Classification with Word Embeddings Generated by BERT and Word2Vec ABSTRACT Malware Classification is used to distinguish unique types of malware from each other. This project aims to carry out malware classification using word embeddings which are used in Natural Language Processing (NLP) to identify and evaluate the relationship between words of a sentence. Word embeddings generated by BERT and Word2Vec for malware samples to carry out multi-class classification. BERT is a transformer based pre- trained natural language processing (NLP) model which can be used for a wide range of tasks such as question answering, paraphrase generation and next sentence prediction. However, the attention mechanism of a pre-trained BERT model can also be used in malware classification by capturing information about relation between each opcode and every other opcode belonging to a malware family. -
Backpropagation and Deep Learning in the Brain
Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the -
Implementing Machine Learning & Neural Network Chip
Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Implementing Machine Learning & Neural Network Chip Architectures USING NETWORK-ON-CHIP INTERCONNECT IP TY GARIBAY Chief Technology Officer [email protected] Title: Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP Primary author: Ty Garibay, Chief Technology Officer, ArterisIP, [email protected] Secondary author: Kurt Shuler, Vice President, Marketing, ArterisIP, [email protected] Abstract: A short tutorial and overview of machine learning and neural network chip architectures, with emphasis on how network-on-chip interconnect IP implements these architectures. Time to read: 10 minutes Time to present: 30 minutes Copyright © 2017 Arteris www.arteris.com 1 Implementing Machine Learning & Neural Network Chip Architectures Using Network-on-Chip Interconnect IP What is Machine Learning? “ Field of study that gives computers the ability to learn without being explicitly programmed.” Arthur Samuel, IBM, 1959 • Machine learning is a subset of Artificial Intelligence Copyright © 2017 Arteris 2 • Machine learning is a subset of artificial intelligence, and explicitly relies upon experiential learning rather than programming to make decisions. • The advantage to machine learning for tasks like automated driving or speech translation is that complex tasks like these are nearly impossible to explicitly program using rule-based if…then…else statements because of the large solution space. • However, the “answers” given by a machine learning algorithm have a probability associated with them and can be non-deterministic (meaning you can get different “answers” given the same inputs during different runs) • Neural networks have become the most common way to implement machine learning. -
Training Autoencoders by Alternating Minimization
Under review as a conference paper at ICLR 2018 TRAINING AUTOENCODERS BY ALTERNATING MINI- MIZATION Anonymous authors Paper under double-blind review ABSTRACT We present DANTE, a novel method for training neural networks, in particular autoencoders, using the alternating minimization principle. DANTE provides a distinct perspective in lieu of traditional gradient-based backpropagation techniques commonly used to train deep networks. It utilizes an adaptation of quasi-convex optimization techniques to cast autoencoder training as a bi-quasi-convex optimiza- tion problem. We show that for autoencoder configurations with both differentiable (e.g. sigmoid) and non-differentiable (e.g. ReLU) activation functions, we can perform the alternations very effectively. DANTE effortlessly extends to networks with multiple hidden layers and varying network configurations. In experiments on standard datasets, autoencoders trained using the proposed method were found to be very promising and competitive to traditional backpropagation techniques, both in terms of quality of solution, as well as training speed. 1 INTRODUCTION For much of the recent march of deep learning, gradient-based backpropagation methods, e.g. Stochastic Gradient Descent (SGD) and its variants, have been the mainstay of practitioners. The use of these methods, especially on vast amounts of data, has led to unprecedented progress in several areas of artificial intelligence. On one hand, the intense focus on these techniques has led to an intimate understanding of hardware requirements and code optimizations needed to execute these routines on large datasets in a scalable manner. Today, myriad off-the-shelf and highly optimized packages exist that can churn reasonably large datasets on GPU architectures with relatively mild human involvement and little bootstrap effort. -
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 -
Fun with Hyperplanes: Perceptrons, Svms, and Friends
Perceptrons, SVMs, and Friends: Some Discriminative Models for Classification Parallel to AIMA 18.1, 18.2, 18.6.3, 18.9 The Automatic Classification Problem Assign object/event or sequence of objects/events to one of a given finite set of categories. • Fraud detection for credit card transactions, telephone calls, etc. • Worm detection in network packets • Spam filtering in email • Recommending articles, books, movies, music • Medical diagnosis • Speech recognition • OCR of handwritten letters • Recognition of specific astronomical images • Recognition of specific DNA sequences • Financial investment Machine Learning methods provide one set of approaches to this problem CIS 391 - Intro to AI 2 Universal Machine Learning Diagram Feature Things to Magic Vector Classification be Classifier Represent- Decision classified Box ation CIS 391 - Intro to AI 3 Example: handwritten digit recognition Machine learning algorithms that Automatically cluster these images Use a training set of labeled images to learn to classify new images Discover how to account for variability in writing style CIS 391 - Intro to AI 4 A machine learning algorithm development pipeline: minimization Problem statement Given training vectors x1,…,xN and targets t1,…,tN, find… Mathematical description of a cost function Mathematical description of how to minimize/maximize the cost function Implementation r(i,k) = s(i,k) – maxj{s(i,j)+a(i,j)} … CIS 391 - Intro to AI 5 Universal Machine Learning Diagram Today: Perceptron, SVM and Friends Feature Things to Magic Vector -
Artificial Neural Networks Part 2/3 – Perceptron
Artificial Neural Networks Part 2/3 – Perceptron Slides modified from Neural Network Design by Hagan, Demuth and Beale Berrin Yanikoglu Perceptron • A single artificial neuron that computes its weighted input and uses a threshold activation function. • It effectively separates the input space into two categories by the hyperplane: wTx + b = 0 Decision Boundary The weight vector is orthogonal to the decision boundary The weight vector points in the direction of the vector which should produce an output of 1 • so that the vectors with the positive output are on the right side of the decision boundary – if w pointed in the opposite direction, the dot products of all input vectors would have the opposite sign – would result in same classification but with opposite labels The bias determines the position of the boundary • solve for wTp+b = 0 using one point on the decision boundary to find b. Two-Input Case + a = hardlim(n) = [1 2]p + -2 w1, 1 = 1 w1, 2 = 2 - Decision Boundary: all points p for which wTp + b =0 If we have the weights and not the bias, we can take a point on the decision boundary, p=[2 0]T, and solving for [1 2]p + b = 0, we see that b=-2. p w wT.p = ||w||||p||Cosθ θ Decision Boundary proj. of p onto w proj. of p onto w T T w p + b = 0 w p = -bœb = ||p||Cosθ 1 1 = wT.p/||w|| • All points on the decision boundary have the same inner product (= -b) with the weight vector • Therefore they have the same projection onto the weight vector; so they must lie on a line orthogonal to the weight vector ADVANCED An Illustrative Example Boolean OR ⎧ 0 ⎫ ⎧ 0 ⎫ ⎧ 1 ⎫ ⎧ 1 ⎫ ⎨p1 = , t1 = 0 ⎬ ⎨p2 = , t2 = 1 ⎬ ⎨p3 = , t3 = 1⎬ ⎨p4 = , t4 = 1⎬ ⎩ 0 ⎭ ⎩ 1 ⎭ ⎩ 0 ⎭ ⎩ 1 ⎭ Given the above input-output pairs (p,t), can you find (manually) the weights of a perceptron to do the job? Boolean OR Solution 1) Pick an admissable decision boundary 2) Weight vector should be orthogonal to the decision boundary. -
CS 189 Introduction to Machine Learning Spring 2021 Jonathan Shewchuk HW6
CS 189 Introduction to Machine Learning Spring 2021 Jonathan Shewchuk HW6 Due: Wednesday, April 21 at 11:59 pm Deliverables: 1. Submit your predictions for the test sets to Kaggle as early as possible. Include your Kaggle scores in your write-up (see below). The Kaggle competition for this assignment can be found at • https://www.kaggle.com/c/spring21-cs189-hw6-cifar10 2. The written portion: • Submit a PDF of your homework, with an appendix listing all your code, to the Gradescope assignment titled “Homework 6 Write-Up”. Please see section 3.3 for an easy way to gather all your code for the submission (you are not required to use it, but we would strongly recommend using it). • In addition, please include, as your solutions to each coding problem, the specific subset of code relevant to that part of the problem. Whenever we say “include code”, that means you can either include a screenshot of your code, or typeset your code in your submission (using markdown or LATEX). • You may typeset your homework in LaTeX or Word (submit PDF format, not .doc/.docx format) or submit neatly handwritten and scanned solutions. Please start each question on a new page. • If there are graphs, include those graphs in the correct sections. Do not put them in an appendix. We need each solution to be self-contained on pages of its own. • In your write-up, please state with whom you worked on the homework. • In your write-up, please copy the following statement and sign your signature next to it. -
Can Temporal-Difference and Q-Learning Learn Representation? a Mean-Field Analysis
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Analysis Yufeng Zhang Qi Cai Northwestern University Northwestern University Evanston, IL 60208 Evanston, IL 60208 [email protected] [email protected] Zhuoran Yang Yongxin Chen Zhaoran Wang Princeton University Georgia Institute of Technology Northwestern University Princeton, NJ 08544 Atlanta, GA 30332 Evanston, IL 60208 [email protected] [email protected] [email protected] Abstract Temporal-difference and Q-learning play a key role in deep reinforcement learning, where they are empowered by expressive nonlinear function approximators such as neural networks. At the core of their empirical successes is the learned feature representation, which embeds rich observations, e.g., images and texts, into the latent space that encodes semantic structures. Meanwhile, the evolution of such a feature representation is crucial to the convergence of temporal-difference and Q-learning. In particular, temporal-difference learning converges when the function approxi- mator is linear in a feature representation, which is fixed throughout learning, and possibly diverges otherwise. We aim to answer the following questions: When the function approximator is a neural network, how does the associated feature representation evolve? If it converges, does it converge to the optimal one? We prove that, utilizing an overparameterized two-layer neural network, temporal- difference and Q-learning globally minimize the mean-squared projected Bellman error at a sublinear rate. Moreover, the associated feature representation converges to the optimal one, generalizing the previous analysis of [21] in the neural tan- gent kernel regime, where the associated feature representation stabilizes at the initial one. The key to our analysis is a mean-field perspective, which connects the evolution of a finite-dimensional parameter to its limiting counterpart over an infinite-dimensional Wasserstein space. -
Introduction to Machine Learning
Introduction to Machine Learning Perceptron Barnabás Póczos Contents History of Artificial Neural Networks Definitions: Perceptron, Multi-Layer Perceptron Perceptron algorithm 2 Short History of Artificial Neural Networks 3 Short History Progression (1943-1960) • First mathematical model of neurons ▪ Pitts & McCulloch (1943) • Beginning of artificial neural networks • Perceptron, Rosenblatt (1958) ▪ A single neuron for classification ▪ Perceptron learning rule ▪ Perceptron convergence theorem Degression (1960-1980) • Perceptron can’t even learn the XOR function • We don’t know how to train MLP • 1963 Backpropagation… but not much attention… Bryson, A.E.; W.F. Denham; S.E. Dreyfus. Optimal programming problems with inequality constraints. I: Necessary conditions for extremal solutions. AIAA J. 1, 11 (1963) 2544-2550 4 Short History Progression (1980-) • 1986 Backpropagation reinvented: ▪ Rumelhart, Hinton, Williams: Learning representations by back-propagating errors. Nature, 323, 533—536, 1986 • Successful applications: ▪ Character recognition, autonomous cars,… • Open questions: Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? • Hopfield nets (1982), Boltzmann machines,… 5 Short History Degression (1993-) • SVM: Vapnik and his co-workers developed the Support Vector Machine (1993). It is a shallow architecture. • SVM and Graphical models almost kill the ANN research. • Training deeper networks consistently yields poor results. • Exception: deep convolutional neural networks, Yann LeCun 1998. (discriminative model) 6 Short History Progression (2006-) Deep Belief Networks (DBN) • Hinton, G. E, Osindero, S., and Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18:1527-1554. • Generative graphical model • Based on restrictive Boltzmann machines • Can be trained efficiently Deep Autoencoder based networks Bengio, Y., Lamblin, P., Popovici, P., Larochelle, H. -
Neural Networks Shuai Li John Hopcroft Center, Shanghai Jiao Tong University
Lecture 6: Neural Networks Shuai Li John Hopcroft Center, Shanghai Jiao Tong University https://shuaili8.github.io https://shuaili8.github.io/Teaching/VE445/index.html 1 Outline • Perceptron • Activation functions • Multilayer perceptron networks • Training: backpropagation • Examples • Overfitting • Applications 2 Brief history of artificial neural nets • The First wave • 1943 McCulloch and Pitts proposed the McCulloch-Pitts neuron model • 1958 Rosenblatt introduced the simple single layer networks now called Perceptrons • 1969 Minsky and Papert’s book Perceptrons demonstrated the limitation of single layer perceptrons, and almost the whole field went into hibernation • The Second wave • 1986 The Back-Propagation learning algorithm for Multi-Layer Perceptrons was rediscovered and the whole field took off again • The Third wave • 2006 Deep (neural networks) Learning gains popularity • 2012 made significant break-through in many applications 3 Biological neuron structure • The neuron receives signals from their dendrites, and send its own signal to the axon terminal 4 Biological neural communication • Electrical potential across cell membrane exhibits spikes called action potentials • Spike originates in cell body, travels down axon, and causes synaptic terminals to release neurotransmitters • Chemical diffuses across synapse to dendrites of other neurons • Neurotransmitters can be excitatory or inhibitory • If net input of neuro transmitters to a neuron from other neurons is excitatory and exceeds some threshold, it fires an action potential 5 Perceptron • Inspired by the biological neuron among humans and animals, researchers build a simple model called Perceptron • It receives signals 푥푖’s, multiplies them with different weights 푤푖, and outputs the sum of the weighted signals after an activation function, step function 6 Neuron vs. -
Artificial Neuron Using Mos2/Graphene Threshold Switching Memristor
University of Central Florida STARS Electronic Theses and Dissertations, 2004-2019 2018 Artificial Neuron using MoS2/Graphene Threshold Switching Memristor Hirokjyoti Kalita University of Central Florida Part of the Electrical and Electronics Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Masters Thesis (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. STARS Citation Kalita, Hirokjyoti, "Artificial Neuron using MoS2/Graphene Threshold Switching Memristor" (2018). Electronic Theses and Dissertations, 2004-2019. 5988. https://stars.library.ucf.edu/etd/5988 ARTIFICIAL NEURON USING MoS2/GRAPHENE THRESHOLD SWITCHING MEMRISTOR by HIROKJYOTI KALITA B.Tech. SRM University, 2015 A thesis submitted in partial fulfilment of the requirements for the degree of Master of Science in the Department of Electrical and Computer Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2018 Major Professor: Tania Roy © 2018 Hirokjyoti Kalita ii ABSTRACT With the ever-increasing demand for low power electronics, neuromorphic computing has garnered huge interest in recent times. Implementing neuromorphic computing in hardware will be a severe boost for applications involving complex processes such as pattern recognition. Artificial neurons form a critical part in neuromorphic circuits, and have been realized with complex complementary metal–oxide–semiconductor (CMOS) circuitry in the past. Recently, insulator-to-metal-transition (IMT) materials have been used to realize artificial neurons.