Word Embedding and Text Classification Based on Deep Learning Methods
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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. -
Chombining Recurrent Neural Networks and Adversarial Training
Combining Recurrent Neural Networks and Adversarial Training for Human Motion Modelling, Synthesis and Control † ‡ Zhiyong Wang∗ Jinxiang Chai Shihong Xia Institute of Computing Texas A&M University Institute of Computing Technology CAS Technology CAS University of Chinese Academy of Sciences ABSTRACT motions from the generator using recurrent neural networks (RNNs) This paper introduces a new generative deep learning network for and refines the generated motion using an adversarial neural network human motion synthesis and control. Our key idea is to combine re- which we call the “refiner network”. Fig 2 gives an overview of current neural networks (RNNs) and adversarial training for human our method: a motion sequence XRNN is generated with the gener- motion modeling. We first describe an efficient method for training ator G and is refined using the refiner network R. To add realism, a RNNs model from prerecorded motion data. We implement recur- we train our refiner network using an adversarial loss, similar to rent neural networks with long short-term memory (LSTM) cells Generative Adversarial Networks (GANs) [9] such that the refined because they are capable of handling nonlinear dynamics and long motion sequences Xre fine are indistinguishable from real motion cap- term temporal dependencies present in human motions. Next, we ture sequences Xreal using a discriminative network D. In addition, train a refiner network using an adversarial loss, similar to Gener- we embed contact information into the generative model to further ative Adversarial Networks (GANs), such that the refined motion improve the quality of the generated motions. sequences are indistinguishable from real motion capture data using We construct the generator G based on recurrent neural network- a discriminative network. -
Arxiv:2002.06235V1
Semantic Relatedness and Taxonomic Word Embeddings Magdalena Kacmajor John D. Kelleher Innovation Exchange ADAPT Research Centre IBM Ireland Technological University Dublin [email protected] [email protected] Filip Klubickaˇ Alfredo Maldonado ADAPT Research Centre Underwriters Laboratories Technological University Dublin [email protected] [email protected] Abstract This paper1 connects a series of papers dealing with taxonomic word embeddings. It begins by noting that there are different types of semantic relatedness and that different lexical representa- tions encode different forms of relatedness. A particularly important distinction within semantic relatedness is that of thematic versus taxonomic relatedness. Next, we present a number of ex- periments that analyse taxonomic embeddings that have been trained on a synthetic corpus that has been generated via a random walk over a taxonomy. These experiments demonstrate how the properties of the synthetic corpus, such as the percentage of rare words, are affected by the shape of the knowledge graph the corpus is generated from. Finally, we explore the interactions between the relative sizes of natural and synthetic corpora on the performance of embeddings when taxonomic and thematic embeddings are combined. 1 Introduction Deep learning has revolutionised natural language processing over the last decade. A key enabler of deep learning for natural language processing has been the development of word embeddings. One reason for this is that deep learning intrinsically involves the use of neural network models and these models only work with numeric inputs. Consequently, applying deep learning to natural language processing first requires developing a numeric representation for words. Word embeddings provide a way of creating numeric representations that have proven to have a number of advantages over traditional numeric rep- resentations of language. -
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 -
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 -
Combining Word Embeddings with Semantic Resources
AutoExtend: Combining Word Embeddings with Semantic Resources Sascha Rothe∗ LMU Munich Hinrich Schutze¨ ∗ LMU Munich We present AutoExtend, a system that combines word embeddings with semantic resources by learning embeddings for non-word objects like synsets and entities and learning word embeddings that incorporate the semantic information from the resource. The method is based on encoding and decoding the word embeddings and is flexible in that it can take any word embeddings as input and does not need an additional training corpus. The obtained embeddings live in the same vector space as the input word embeddings. A sparse tensor formalization guar- antees efficiency and parallelizability. We use WordNet, GermaNet, and Freebase as semantic resources. AutoExtend achieves state-of-the-art performance on Word-in-Context Similarity and Word Sense Disambiguation tasks. 1. Introduction Unsupervised methods for learning word embeddings are widely used in natural lan- guage processing (NLP). The only data these methods need as input are very large corpora. However, in addition to corpora, there are many other resources that are undoubtedly useful in NLP, including lexical resources like WordNet and Wiktionary and knowledge bases like Wikipedia and Freebase. We will simply refer to these as resources. In this article, we present AutoExtend, a method for enriching these valuable resources with embeddings for non-word objects they describe; for example, Auto- Extend enriches WordNet with embeddings for synsets. The word embeddings and the new non-word embeddings live in the same vector space. Many NLP applications benefit if non-word objects described by resources—such as synsets in WordNet—are also available as embeddings. -
Recurrent Neural Network for Text Classification with Multi-Task
Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence (IJCAI-16) Recurrent Neural Network for Text Classification with Multi-Task Learning Pengfei Liu Xipeng Qiu⇤ Xuanjing Huang Shanghai Key Laboratory of Intelligent Information Processing, Fudan University School of Computer Science, Fudan University 825 Zhangheng Road, Shanghai, China pfliu14,xpqiu,xjhuang @fudan.edu.cn { } Abstract are based on unsupervised objectives such as word predic- tion for training [Collobert et al., 2011; Turian et al., 2010; Neural network based methods have obtained great Mikolov et al., 2013]. This unsupervised pre-training is effec- progress on a variety of natural language process- tive to improve the final performance, but it does not directly ing tasks. However, in most previous works, the optimize the desired task. models are learned based on single-task super- vised objectives, which often suffer from insuffi- Multi-task learning utilizes the correlation between related cient training data. In this paper, we use the multi- tasks to improve classification by learning tasks in parallel. [ task learning framework to jointly learn across mul- Motivated by the success of multi-task learning Caruana, ] tiple related tasks. Based on recurrent neural net- 1997 , there are several neural network based NLP models [ ] work, we propose three different mechanisms of Collobert and Weston, 2008; Liu et al., 2015b utilize multi- sharing information to model text with task-specific task learning to jointly learn several tasks with the aim of and shared layers. The entire network is trained mutual benefit. The basic multi-task architectures of these jointly on all these tasks. Experiments on four models are to share some lower layers to determine common benchmark text classification tasks show that our features. -
Word-Like Character N-Gram Embedding
Word-like character n-gram embedding Geewook Kim and Kazuki Fukui and Hidetoshi Shimodaira Department of Systems Science, Graduate School of Informatics, Kyoto University Mathematical Statistics Team, RIKEN Center for Advanced Intelligence Project fgeewook, [email protected], [email protected] Abstract Table 1: Top-10 2-grams in Sina Weibo and 4-grams in We propose a new word embedding method Japanese Twitter (Experiment 1). Words are indicated called word-like character n-gram embed- by boldface and space characters are marked by . ding, which learns distributed representations FNE WNE (Proposed) Chinese Japanese Chinese Japanese of words by embedding word-like character n- 1 ][ wwww 自己 フォロー grams. Our method is an extension of recently 2 。␣ !!!! 。␣ ありがと proposed segmentation-free word embedding, 3 !␣ ありがと ][ wwww 4 .. りがとう 一个 !!!! which directly embeds frequent character n- 5 ]␣ ございま 微博 めっちゃ grams from a raw corpus. However, its n-gram 6 。。 うござい 什么 んだけど vocabulary tends to contain too many non- 7 ,我 とうござ 可以 うござい 8 !! ざいます 没有 line word n-grams. We solved this problem by in- 9 ␣我 がとうご 吗? 2018 troducing an idea of expected word frequency. 10 了, ください 哈哈 じゃない Compared to the previously proposed meth- ods, our method can embed more words, along tion tools are used to determine word boundaries with the words that are not included in a given in the raw corpus. However, these segmenters re- basic word dictionary. Since our method does quire rich dictionaries for accurate segmentation, not rely on word segmentation with rich word which are expensive to prepare and not always dictionaries, it is especially effective when the available. -
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.