DOCSLIB.ORG

BERT Is to NLP What Alexnet Is to CV: Can Pre-Trained Language Models Identify Analogies?

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
BERT Is to NLP What Alexnet Is to CV: Can Pre-Trained Language Models Identify Analogies? BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies? Asahi Ushio, Luis Espinosa-Anke, Steven Schockaert, Jose Camacho-Collados Cardiff NLP, School of Computer Science and Informatics Cardiff University, United Kingdom fUshioA,Espinosa-AnkeL,SchockaertS1,[email protected] Query: word:language Abstract Candidates: (1) paint:portrait (2) poetry:rhythm Analogies play a central role in human com- (3) note:music monsense reasoning. The ability to recognize (4) tale:story analogies such as “eye is to seeing what ear is (5) week:year to hearing”, sometimes referred to as analogi- cal proportions, shape how we structure knowl- Table 1: An example analogy task from the SAT edge and understand language. Surprisingly, dataset. The third candidate is the answer to the query. however, the task of identifying such analogies has not yet received much attention in the lan- guage model era. In this paper, we analyze et al., 2013a; Vylomova et al., 2016; Allen and the capabilities of transformer-based language Hospedales, 2019; Ethayarajh et al., 2019). The models on this unsupervised task, using bench- underlying assumption is that when “a is to b what marks obtained from educational settings, as c d b − a well as more commonly used datasets. We find is to ” the word vector differences and that off-the-shelf language models can identify d − c are expected to be similar, where we write x analogies to a certain extent, but struggle with for the embedding of a word x. While this assump- abstract and complex relations, and results are tion holds for some types of syntactic relations, highly sensitive to model architecture and hy- for semantic relations this holds to a much more perparameters. Overall the best results were limited degree than was suggested in early work obtained with GPT-2 and RoBERTa, while (Linzen, 2016; Schluter, 2018). Moreover, the most configurations using BERT were not able to commonly used benchmarks have focused on spe- outperform word embedding models. Our re- sults raise important questions for future work cific and well-defined semantic relations such as about how, and to what extent, pre-trained “capital of”, rather than the more abstract notion of language models capture knowledge about ab- relational similarity that is often needed for solving stract semantic relations.1 the kind of psychometric analogy problems that can be found in IQ tests and educational settings. 1 Introduction An example of such a problem is shown in Table1. One of the most widely discussed properties of Given the central role of analogy in human cog- word embeddings has been their surprising abil- nition, it is nonetheless important to understand the ity to model certain types of relational similari- extent to which NLP models are able to solve these ties in terms of word vector differences (Mikolov more abstract analogy problems. Besides its value as an intrinsic benchmark for lexical semantics, While the title is probably self-explanatory, this is a small note explaining it. BERT is to NLP what AlexNet is to CV is the ability to recognize analogies is indeed impor- making an analogy on what the BERT and AlexNet models tant in the contexts of human creativity (Holyoak represented for Natural Language Processing (NLP) and Com- et al., 1996), innovation (Hope et al., 2017), com- puter Vision (CV), respectively. They both brought a paradigm shift in how research was undertaken in their corresponding putational creativity (Goel, 2019) and education disciplines and this is what the analogy refers to. (Pardos and Nam, 2020). Analogies are also a 1Source code and data to reproduce our ex- prerequisite to build AI systems for the legal do- perimental results are available in the following repository: https://github.com/asahi417/ main (Ashley, 1988; Walton, 2010) and are used in analogy-language-model machine learning (Miclet et al., 2008; Hug et al., 3609 Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, pages 3609–3624 August 1–6, 2021. ©2021 Association for Computational Linguistics 2016;H ullermeier¨ , 2020) and for ontology align- on understanding pre-trained LSTM-based LMs ment (Raad and Evermann, 2015), among others. (Peters et al., 2018b), attention has now shifted to- Within NLP, however, the task of recognizing ward transformer-based models. The main aspects analogies has received relatively little attention. To that have been studied in recent years are syntax solve such problems, Turney(2005) proposed La- (Goldberg, 2019; Saphra and Lopez, 2019; Hewitt tent Relational Analysis (LRA), which was essen- and Manning, 2019; van Schijndel et al., 2019; tially designed as a relational counterpart to Latent Jawahar et al., 2019; Tenney et al., 2019b) and se- Semantic Analysis (Landauer and Dumais, 1997). mantics (Ettinger, 2019; Tenney et al., 2019a). For Somewhat surprisingly, perhaps, despite the sub- a more complete overview on analyses of the differ- stantial progress that word embeddings and lan- ent properties of transformer-based LMs, we refer guage models (LMs) have enabled in NLP, LRA to Rogers et al.(2021). still represents the current state-of-the-art in solv- Despite the rise in probing analyses for LMs ing abstract word analogy problems. When go- and the importance of analogical reasoning in hu- ing beyond a purely unsupervised setting, however, man cognition, understanding the analogical capa- GPT-3 was recently found to obtain slightly better bilities of LMs remains understudied. The most results (Brown et al., 2020). similar works have focused on capturing relational The aim of this paper is to analyze the ability of knowledge from LMs (in particular the type of pre-trained LMs to recognize analogies. Our focus information available in knowledge graphs). For is on the zero-shot setting, where LMs are used instance, Petroni et al.(2019) analyzed to what without fine-tuning. To predict whether two word extent LMs could fill manually-defined templates pairs (a; b) and (c; d) are likely to be analogical, such as “Dante was born in [MASK]”. Follow-up we need a prompt, i.e. a template that is used to con- works extended this initial approach by automat- struct the input to the LM, and a scoring function. ically generating templates and fine-tuning LMs We extensively analyze the impact of both of these on them (Bouraoui et al., 2020; Jiang et al., 2020), choices, as well as the differences between differ- showing an improved performance. In this paper, ent LMs. When the prompt and scoring function we focus on the analogical knowledge that is en- are carefully calibrated, we find that GPT-2 can out- coded in pre-trained LMs, without the extra step of perform LRA, standard word embeddings as well fine-tuning on additional data. as the published results for GPT-3 in the zero-shot setting. However, we also find that these results 2.2 Word Analogy Probing are highly sensitive to the choice of the prompt, as well as two hyperparameters in our scoring func- Word analogies have been used as a standard in- tion, with the optimal choices not being consistent trinsic evaluation task for measuring the quality of across different datasets. Moreover, using BERT word embeddings. Mikolov et al.(2013b) showed leads to considerably weaker results, underperform- that word embeddings, in particular Word2vec em- ing even standard word embeddings in all of the beddings, were able to solve analogy problems by considered configurations. These findings suggest simple vector operations (e.g. king - man + woman that while transformer-based LMs learn relational = queen). The motivation for this task dates back knowledge to a meaningful extent, more work is to the connectionism theory (Feldman and Ballard, needed to understand how such knowledge is en- 1982) in cognitive science. In particular, neural coded, and how it can be exploited. networks were thought to be able to model emer- gent concepts (Hopfield, 1982; Hinton, 1986) by 2 Related work learning distributed representations across an em- bedding space (Hinton et al., 1986), similar to the 2.1 Understanding Pre-trained LMs properties that word embeddings displayed in the Since their recent dominance in standard NLP analogy task. More recent works have proposed benchmarks (Peters et al., 2018a; Devlin et al., new mathematical theories and experiments to un- 2019; Liu et al., 2019), pre-trained language mod- derstand the analogical capabilities of word embed- els have been extensively studied. This has mainly dings, attempting to understand their linear alge- been done through probing tasks, which are aimed braic structure (Arora et al., 2016; Gittens et al., at understanding the knowledge that is implicitly 2017; Allen and Hospedales, 2019) or by explic- captured by their parameters. After the initial focus itly studying their compositional nature (Levy and 3610 Goldberg, 2014; Paperno and Baroni, 2016; Etha- 3 Word Analogies yarajh et al., 2019; Chiang et al., 2020). In this section, we describe the word analogy for- However, recent works have questioned the im- mulation that is used for our experiments (Section pressive results displayed by word embeddings 3.1). Subsequently, we provide an overview of the in this task. In many cases simple baselines ex- datasets used in our experiments (Section 3.2). query cluding the input pair (or ) were competitive 3.1 Task Description (Linzen, 2016). Simultaneously, some researchers have found that many relationships may not be We frame the analogy task in terms of analogical retrieved in the embedding space by simple linear proportions (Prade and Richard, 2017). Given a transformations (Drozd et al., 2016; Bouraoui et al., query word pair (hq; tq) and a list of candidate n 2018) and others argued that the standard evalu- answer pairs f(hi; ti)gi=1, the goal is to find the ation procedure has limitations (Schluter, 2018).
Load more

Recommended publications
  • Lecture 10: Recurrent Neural Networks
    Lecture 10: Recurrent Neural Networks Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 1 May 4, 2017 Administrative A1 grades will go out soon A2 is due today (11:59pm) Midterm is in-class on Tuesday! We will send out details on where to go soon Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 2 May 4, 2017 Extra Credit: Train Game More details on Piazza by early next week Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 3 May 4, 2017 Last Time: CNN Architectures AlexNet Figure copyright Kaiming He, 2016. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 4 May 4, 2017 Last Time: CNN Architectures Softmax FC 1000 Softmax FC 4096 FC 1000 FC 4096 FC 4096 Pool FC 4096 3x3 conv, 512 Pool 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 Pool Pool 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 Pool Pool 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 Pool Pool 3x3 conv, 128 3x3 conv, 128 3x3 conv, 128 3x3 conv, 128 Pool Pool 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 Input Input VGG16 VGG19 GoogLeNet Figure copyright Kaiming He, 2016. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 5 May 4, 2017 Last Time: CNN Architectures Softmax FC 1000 Pool 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 relu 3x3 conv, 64 3x3 conv, 64 F(x) + x 3x3 conv, 64 ..
    [Show full text]
  • Image Retrieval Algorithm Based on Convolutional Neural Network
    Advances in Intelligent Systems Research, volume 133 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016) Image Retrieval Algorithm Based on Convolutional Neural Network Hailong Liu 1, 2, Baoan Li 1, 2, *, Xueqiang Lv 1 and Yue Huang 3 1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University, Beijing 100101, China 2Computer School, Beijing Information Science and Technology University, Beijing 100101, China 3Xuanwu Hospital Capital Medical University, 100053, China *Corresponding author Abstract—With the rapid development of computer technology low, can’t meet the needs of people. In order to overcome this and the increasing of multimedia data on the Internet, how to difficulty, the researchers from the image itself, and put quickly find the desired information in the massive data becomes forward the method of image retrieval based on content. The a hot issue. Image retrieval can be used to retrieve similar images, method is to extract the visual features of the image content: and the effect of image retrieval depends on the selection of image color, texture, shape etc., the image database to be detected features to a certain extent. Based on deep learning, through self- samples for similarity matching, retrieval and sample images learning ability of a convolutional neural network to extract more are similar to the image. The main process of the method is the conducive to the high-level semantic feature of image retrieval selection and extraction of features, but there are "semantic using convolutional neural network, and then use the distance gap" [2] between low-level features and high-level semantic metric function similar image.
    [Show full text]
  • LSTM-In-LSTM for Generating Long Descriptions of Images
    Computational Visual Media DOI 10.1007/s41095-016-0059-z Vol. 2, No. 4, December 2016, 379–388 Research Article LSTM-in-LSTM for generating long descriptions of images Jun Song1, Siliang Tang1, Jun Xiao1, Fei Wu1( ), and Zhongfei (Mark) Zhang2 c The Author(s) 2016. This article is published with open access at Springerlink.com Abstract In this paper, we propose an approach by means of text (description generation) is a for generating rich fine-grained textual descriptions of fundamental task in artificial intelligence, with many images. In particular, we use an LSTM-in-LSTM (long applications. For example, generating descriptions short-term memory) architecture, which consists of an of images may help visually impaired people better inner LSTM and an outer LSTM. The inner LSTM understand the content of images and retrieve images effectively encodes the long-range implicit contextual using descriptive texts. The challenge of description interaction between visual cues (i.e., the spatially- generation lies in appropriately developing a model concurrent visual objects), while the outer LSTM that can effectively represent the visual cues in generally captures the explicit multi-modal relationship images and describe them in the domain of natural between sentences and images (i.e., the correspondence language at the same time. of sentences and images). This architecture is capable There have been significant advances in of producing a long description by predicting one description generation recently. Some efforts word at every time step conditioned on the previously rely on manually-predefined visual concepts and generated word, a hidden vector (via the outer LSTM), and a context vector of fine-grained visual cues (via sentence templates [1–3].
    [Show full text]
  • The History Began from Alexnet: a Comprehensive Survey on Deep Learning Approaches
    > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md Zahangir Alom1, Tarek M. Taha1, Chris Yakopcic1, Stefan Westberg1, Paheding Sidike2, Mst Shamima Nasrin1, Brian C Van Essen3, Abdul A S. Awwal3, and Vijayan K. Asari1 Abstract—In recent years, deep learning has garnered I. INTRODUCTION tremendous success in a variety of application domains. This new ince the 1950s, a small subset of Artificial Intelligence (AI), field of machine learning has been growing rapidly, and has been applied to most traditional application domains, as well as some S often called Machine Learning (ML), has revolutionized new areas that present more opportunities. Different methods several fields in the last few decades. Neural Networks have been proposed based on different categories of learning, (NN) are a subfield of ML, and it was this subfield that spawned including supervised, semi-supervised, and un-supervised Deep Learning (DL). Since its inception DL has been creating learning. Experimental results show state-of-the-art performance ever larger disruptions, showing outstanding success in almost using deep learning when compared to traditional machine every application domain. Fig. 1 shows, the taxonomy of AI. learning approaches in the fields of image processing, computer DL (using either deep architecture of learning or hierarchical vision, speech recognition, machine translation, art, medical learning approaches) is a class of ML developed largely from imaging, medical information processing, robotics and control, 2006 onward. Learning is a procedure consisting of estimating bio-informatics, natural language processing (NLP), cybersecurity, and many others.
    [Show full text]
  • Active Authentication Using an Autoencoder Regularized CNN-Based One-Class Classifier
    Active Authentication Using an Autoencoder Regularized CNN-based One-Class Classifier Poojan Oza and Vishal M. Patel Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA email: fpoza2, [email protected] Access Abstract— Active authentication refers to the process in Enrolled User Denied which users are unobtrusively monitored and authenticated Training Images continuously throughout their interactions with mobile devices. AA Access Generally, an active authentication problem is modelled as a Module Granted one class classification problem due to the unavailability of Trained Access data from the impostor users. Normally, the enrolled user is Denied considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose Training a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise AA and an autoencoder are used to model the pseudo-negative Module class and to regularize the network to learn meaningful feature Test Images representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and (a) (b) the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the Fig. 1: An overview of a typical AA system. (a) Data base network for one class classification. Effectiveness of the corresponding to the enrolled user are used to train an AA proposed framework is demonstrated using three publically system. (b) During testing, data corresponding to the enrolled available face-based active authentication datasets and it is user as well as unknown user may be presented to the system.
    [Show full text]
  • Key Ideas and Architectures in Deep Learning Applications That (Probably) Use DL
    Key Ideas and Architectures in Deep Learning Applications that (probably) use DL Autonomous Driving Scene understanding /Segmentation Applications that (probably) use DL WordLens Prisma Outline of today’s talk Image Recognition Fun application using CNNs ● LeNet - 1998 ● Image Style Transfer ● AlexNet - 2012 ● VGGNet - 2014 ● GoogLeNet - 2014 ● ResNet - 2015 Questions to ask about each architecture/ paper Special Layers Non-Linearity Loss function Weight-update rule Train faster? Reduce parameters Reduce Overfitting Help you visualize? LeNet5 - 1998 LeNet5 - Specs MNIST - 60,000 training, 10,000 testing Input is 32x32 image 8 layers 60,000 parameters Few hours to train on a laptop Modified LeNet Architecture - Assignment 3 Training Input Forward pass Maxp Max Conv ReLU Conv ReLU FC ReLU Softmax ool pool Backpropagation - Labels Loss update weights Modified LeNet Architecture - Assignment 3 Testing Input Forward pass Maxp Max Conv ReLU Conv ReLU FC ReLU Softmax ool pool Output Compare output with labels Modified LeNet - CONV Layer 1 Input - 28 x 28 Output - 6 feature maps - each 24 x 24 Convolution filter - 5 x 5 x 1 (convolution) + 1 (bias) How many parameters in this layer? Modified LeNet - CONV Layer 1 Input - 32 x 32 Output - 6 feature maps - each 28 x 28 Convolution filter - 5 x 5 x 1 (convolution) + 1 (bias) How many parameters in this layer? (5x5x1+1)*6 = 156 Modified LeNet - Max-pooling layer Decreases the spatial extent of the feature maps, makes it translation-invariant Input - 28 x 28 x 6 volume Maxpooling with filter size 2
    [Show full text]
  • Deep Learning in MATLAB
    Deep Learning in MATLAB 성 호 현 부장 [email protected] © 2015 The MathWorks, Inc.1 Deep Learning beats Go champion! 2 AI, Machine Learning, and Deep Learning Artificial Machine Deep Learning Intelligence Learning Neural networks with many layers that Any technique Statistical methods learn representations and tasks that enables enable machines to “directly” from data machines to “learn” tasks from data mimic human without explicitly intelligence programming 1950s 1980s 2015 FLOPS Thousand Million Quadrillion 3 What is can Deep Learning do for us? (An example) 4 Example 1: Object recognition using deep learning 5 Object recognition using deep learning Training Millions of images from 1000 (GPU) different categories Real-time object recognition using Prediction a webcam connected to a laptop 6 WhatWhat is isMachine Deep Learning? Learning? 7 Machine Learning vs Deep Learning We specify the nature of the features we want to extract… …and the type of model we want to build. Machine Learning 8 Machine Learning vs Deep Learning We need only specify the architecture of the model… Deep Learning 9 ▪ Deep learning is a type of machine learning in which a model learns to perform tasks like classification – directly from images, texts, or signals. ▪ Deep learning performs end-to-end learning, and is usually implemented using a neural network architecture. ▪ Deep learning algorithms also scale with data – traditional machine learning saturates. 10 Why is Deep Learning So Popular Now? AlexNet Human Accuracy Source: ILSVRC Top-5 Error on ImageNet 11 Two
    [Show full text]
  • 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.”
    [Show full text]
  • Model-Based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction
    MoFA: Model-based Deep Convolutional Face Autoencoder for Unsupervised Monocular Reconstruction Ayush Tewari1 Michael Zollhofer¨ 1 Hyeongwoo Kim1 Pablo Garrido1 Florian Bernard1;2 Patrick Perez´ 3 Christian Theobalt1 1Max-Planck-Institute for Informatics 2 LCSB, University of Luxembourg 3Technicolor Our model-based deep convolutional face autoencoder enables unsupervised learning of semantic pose, shape, expression, reflectance and lighting parameters. The trained encoder predicts these parameters from a single monocular image, all at once. Abstract tailed three-dimensional face reconstruction from a single arbitrary in-the-wild image, e.g., downloaded from the Inter- In this work we propose a novel model-based deep convo- net, is still an open research problem due to the high degree lutional autoencoder that addresses the highly challenging of variability of uncalibrated photos in terms of resolution problem of reconstructing a 3D human face from a single and employed imaging device. In addition, in unconstrained in-the-wild color image. To this end, we combine a convolu- photos, faces show a high variability in global pose, facial tional encoder network with an expert-designed generative expression, and are captured under diverse and difficult light- model that serves as decoder. The core innovation is the ing. Detailed 3D face reconstruction is the foundation for a differentiable parametric decoder that encapsulates image broad scope of applications, which range from robust face formation analytically based on a generative model. Our recognition, over emotion estimation, to complex image ma- decoder takes as input a code vector with exactly defined nipulation tasks. In many applications, faces should ideally semantic meaning that encodes detailed face pose, shape, ex- be reconstructed in terms of meaningful low-dimensional pression, skin reflectance and scene illumination.
    [Show full text]
  • Data Movement Is All You Need: a Case Study on Optimizing Transformers
    Data Movement Is All You Need: A Case Study on Optimizing Transformers Andrei Ivanov∗, Nikoli Dryden∗, Tal Ben-Nun, Shigang Li, Torsten Hoefler ETH Zürich [email protected] ∗ Equal contribution Abstract—Transformers have become widely used for language challenges such as artificial general intelligence [27]. Thus, modeling and sequence learning tasks, and are one of the most improving transformer performance has been in the focus of important machine learning workloads today. Training one is a numerous research and industrial groups. very compute-intensive task, often taking days or weeks, and significant attention has been given to optimizing transformers. Significant attention has been given to optimizing transform- Despite this, existing implementations do not efficiently utilize ers: local and fixed-window attention [28]–[32], more general GPUs. We find that data movement is the key bottleneck when structured sparsity [33], learned sparsity [34]–[36], and other training. Due to Amdahl’s Law and massive improvements in algorithmic techniques [19], [37] improve the performance of compute performance, training has now become memory-bound. transformers. Major hardware efforts, such as Tensor Cores Further, existing frameworks use suboptimal data layouts. Using these insights, we present a recipe for globally optimizing data and TPUs [38] have accelerated tensor operations like matrix- movement in transformers. We reduce data movement by up matrix multiplication (MMM), a core transformer operation. to 22.91% and overall achieve a 1.30× performance improve- Despite this, existing implementations do not efficiently ment over state-of-the-art frameworks when training BERT. utilize GPUs. Even optimized implementations such as Mega- Our approach is applicable more broadly to optimizing deep tron [18] report achieving only 30% of peak GPU flop/s.
    [Show full text]
  • Optimizing Alexnet on Tiny Imagenet10
    Optimizing AlexNet on Tiny ImageNet10 Talal Alqadi Ella Lucas June 19, 2020 Abstract Image classification of Tiny ImageNet data by reproducing the state of the art convolutional neural network built by Alex Krizhevsky et al. called AlexNet [2]. Tiny ImageNet is a subset of ImageNet containing 200 classes, each with 500 images. After reproducing the model, we constricted the Tiny ImageNet dataset even further into the first 10 labels to speed up the model training and testing, to see how the model can perform with less labels but same amount of data. Our goal is for AlexNet to correctly classify even more test images with our new restricted dataset, as compared to its performances with Tiny ImageNet. Using Pytorch, a pipeline was built to train and validate different inputs into the AlexNet model to single out the best model parameters, and then finally create visualizations to compare accuracies between labels. 1 Introduction As machines get stronger and as more data is available every year, deep learning has been able to accomplish huge feats. It has been able to overcome extremely complicated problems with many of these deep learning models' accuracies continuously increasing the more time passes. One such problem that deep learning had a huge hand in solving is in computer vision. The main topic of computer vision in this case, object recognition, has become pivotal by replicating human intelligence with significant applications in research, politics, defense, and industry. Convolutional Neural Networks (CNNs) are the architecture traditionally optimized for image recognition. At a high level, these networks are fed an input image and return either an output class or a probability that the input belongs to a certain class.
    [Show full text]
  • What Is Reinforcement Learning?
    Deep Learning and Reinforcement Learning Workflows in AI Valerie Leung © 2015 The MathWorks, Inc.1 Why MATLAB for Artificial Intelligence? 2 Artificial Intelligence Development of computer programs to perform tasks that normally require human intelligence 3 AI Applications Object Classification Speech Recognition Predictive Maintenance Signal Classification Automated Driving Stock Market Prediction 4 Machine Learning and Deep Learning Machine Learning Unsupervised Supervised Learning Learning [Labeled Data] [No Labeled Data] Clustering Classification Regression 5 Machine Learning and Deep Learning Machine Learning Unsupervised Supervised Learning Learning [Labeled Data] Machine learning typically involves [No Labeled Data] feature extraction Clustering Classification Regression Deep Learning Deep learning typically does not involve feature extraction 6 Deep Learning Uses a Neural Network Architecture Input Layer Output Hidden Layers (n) Layer 7 Deep Learning Datatypes Image Signal Text Numeric 8 Deep Learning Workflow Prepare Data Train Model Deploy Data access and Model design, Multiplatform preprocessing hyperparameter code generation tuning (CPU, GPU) Model exchange Ground truth labeling Edge deployment across frameworks Hardware- Enterprise accelerated training deployment 9 Why MATLAB for AI Tasks? Increased productivity with interactive tools Generate simulation data for complex models and systems Ease of deployment and scaling to various platforms Full AI workflows that cannot be easily replicated by other toolchains 10 Why MATLAB
    [Show full text]