Comparison of Supervised and Unsupervised Learning Algorithms for Pattern Classification
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Self-Discriminative Learning for Unsupervised Document Embedding
Self-Discriminative Learning for Unsupervised Document Embedding Hong-You Chen∗1, Chin-Hua Hu∗1, Leila Wehbe2, Shou-De Lin1 1Department of Computer Science and Information Engineering, National Taiwan University 2Machine Learning Department, Carnegie Mellon University fb03902128, [email protected], [email protected], [email protected] Abstract ingful a document embedding as they do not con- sider inter-document relationships. Unsupervised document representation learn- Traditional document representation models ing is an important task providing pre-trained such as Bag-of-words (BoW) and TF-IDF show features for NLP applications. Unlike most competitive performance in some tasks (Wang and previous work which learn the embedding based on self-prediction of the surface of text, Manning, 2012). However, these models treat we explicitly exploit the inter-document infor- words as flat tokens which may neglect other use- mation and directly model the relations of doc- ful information such as word order and semantic uments in embedding space with a discrimi- distance. This in turn can limit the models effec- native network and a novel objective. Exten- tiveness on more complex tasks that require deeper sive experiments on both small and large pub- level of understanding. Further, BoW models suf- lic datasets show the competitiveness of the fer from high dimensionality and sparsity. This is proposed method. In evaluations on standard document classification, our model has errors likely to prevent them from being used as input that are relatively 5 to 13% lower than state-of- features for downstream NLP tasks. the-art unsupervised embedding models. The Continuous vector representations for docu- reduction in error is even more pronounced in ments are being developed. -
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 -
Q-Learning in Continuous State and Action Spaces
-Learning in Continuous Q State and Action Spaces Chris Gaskett, David Wettergreen, and Alexander Zelinsky Robotic Systems Laboratory Department of Systems Engineering Research School of Information Sciences and Engineering The Australian National University Canberra, ACT 0200 Australia [cg dsw alex]@syseng.anu.edu.au j j Abstract. -learning can be used to learn a control policy that max- imises a scalarQ reward through interaction with the environment. - learning is commonly applied to problems with discrete states and ac-Q tions. We describe a method suitable for control tasks which require con- tinuous actions, in response to continuous states. The system consists of a neural network coupled with a novel interpolator. Simulation results are presented for a non-holonomic control task. Advantage Learning, a variation of -learning, is shown enhance learning speed and reliability for this task.Q 1 Introduction Reinforcement learning systems learn by trial-and-error which actions are most valuable in which situations (states) [1]. Feedback is provided in the form of a scalar reward signal which may be delayed. The reward signal is defined in relation to the task to be achieved; reward is given when the system is successfully achieving the task. The value is updated incrementally with experience and is defined as a discounted sum of expected future reward. The learning systems choice of actions in response to states is called its policy. Reinforcement learning lies between the extremes of supervised learning, where the policy is taught by an expert, and unsupervised learning, where no feedback is given and the task is to find structure in data. -
Almost Unsupervised Text to Speech and Automatic Speech Recognition
Almost Unsupervised Text to Speech and Automatic Speech Recognition Yi Ren * 1 Xu Tan * 2 Tao Qin 2 Sheng Zhao 3 Zhou Zhao 1 Tie-Yan Liu 2 Abstract 1. Introduction Text to speech (TTS) and automatic speech recognition (ASR) are two popular tasks in speech processing and have Text to speech (TTS) and automatic speech recog- attracted a lot of attention in recent years due to advances in nition (ASR) are two dual tasks in speech pro- deep learning. Nowadays, the state-of-the-art TTS and ASR cessing and both achieve impressive performance systems are mostly based on deep neural models and are all thanks to the recent advance in deep learning data-hungry, which brings challenges on many languages and large amount of aligned speech and text data. that are scarce of paired speech and text data. Therefore, However, the lack of aligned data poses a ma- a variety of techniques for low-resource and zero-resource jor practical problem for TTS and ASR on low- ASR and TTS have been proposed recently, including un- resource languages. In this paper, by leveraging supervised ASR (Yeh et al., 2019; Chen et al., 2018a; Liu the dual nature of the two tasks, we propose an et al., 2018; Chen et al., 2018b), low-resource ASR (Chuang- almost unsupervised learning method that only suwanich, 2016; Dalmia et al., 2018; Zhou et al., 2018), TTS leverages few hundreds of paired data and extra with minimal speaker data (Chen et al., 2019; Jia et al., 2018; unpaired data for TTS and ASR. -
Arxiv:2107.04562V1 [Stat.ML] 9 Jul 2021 ¯ ¯ Θ∗ = Arg Min `(Θ) Where `(Θ) = `(Yi, Fθ(Xi)) + R(Θ)
The Bayesian Learning Rule Mohammad Emtiyaz Khan H˚avard Rue RIKEN Center for AI Project CEMSE Division, KAUST Tokyo, Japan Thuwal, Saudi Arabia [email protected] [email protected] Abstract We show that many machine-learning algorithms are specific instances of a single algorithm called the Bayesian learning rule. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms such as ridge regression, Newton's method, and Kalman filter, as well as modern deep-learning algorithms such as stochastic-gradient descent, RMSprop, and Dropout. The key idea in deriving such algorithms is to approximate the posterior using candidate distributions estimated by using natural gradients. Different candidate distributions result in different algorithms and further approximations to natural gradients give rise to variants of those algorithms. Our work not only unifies, generalizes, and improves existing algorithms, but also helps us design new ones. 1 Introduction 1.1 Learning-algorithms Machine Learning (ML) methods have been extremely successful in solving many challenging problems in fields such as computer vision, natural-language processing and artificial intelligence (AI). The main idea is to formulate those problems as prediction problems, and learn a model on existing data to predict the future outcomes. For example, to design an AI agent that can recognize objects in an image, we D can collect a dataset with N images xi 2 R and object labels yi 2 f1; 2;:::;Kg, and learn a model P fθ(x) with parameters θ 2 R to predict the label for a new image. -
Self-Supervised Learning
Self-Supervised Learning Andrew Zisserman Slides from: Carl Doersch, Ishan Misra, Andrew Owens, Carl Vondrick, Richard Zhang The ImageNet Challenge Story … 1000 categories • Training: 1000 images for each category • Testing: 100k images The ImageNet Challenge Story … strong supervision The ImageNet Challenge Story … outcomes Strong supervision: • Features from networks trained on ImageNet can be used for other visual tasks, e.g. detection, segmentation, action recognition, fine grained visual classification • To some extent, any visual task can be solved now by: 1. Construct a large-scale dataset labelled for that task 2. Specify a training loss and neural network architecture 3. Train the network and deploy • Are there alternatives to strong supervision for training? Self-Supervised learning …. Why Self-Supervision? 1. Expense of producing a new dataset for each new task 2. Some areas are supervision-starved, e.g. medical data, where it is hard to obtain annotation 3. Untapped/availability of vast numbers of unlabelled images/videos – Facebook: one billion images uploaded per day – 300 hours of video are uploaded to YouTube every minute 4. How infants may learn … Self-Supervised Learning The Scientist in the Crib: What Early Learning Tells Us About the Mind by Alison Gopnik, Andrew N. Meltzoff and Patricia K. Kuhl The Development of Embodied Cognition: Six Lessons from Babies by Linda Smith and Michael Gasser What is Self-Supervision? • A form of unsupervised learning where the data provides the supervision • In general, withhold some part of the data, and task the network with predicting it • The task defines a proxy loss, and the network is forced to learn what we really care about, e.g. -
Reinforcement Learning in Supervised Problem Domains
Technische Universität München Fakultät für Informatik Lehrstuhl VI – Echtzeitsysteme und Robotik reinforcement learning in supervised problem domains Thomas F. Rückstieß Vollständiger Abdruck der von der Fakultät für Informatik der Technischen Universität München zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) genehmigten Dissertation. Vorsitzender: Univ.-Prof. Dr. Daniel Cremers Prüfer der Dissertation 1. Univ.-Prof. Dr. Patrick van der Smagt 2. Univ.-Prof. Dr. Hans Jürgen Schmidhuber Die Dissertation wurde am 30. 06. 2015 bei der Technischen Universität München eingereicht und durch die Fakultät für Informatik am 18. 09. 2015 angenommen. Thomas Rückstieß: Reinforcement Learning in Supervised Problem Domains © 2015 email: [email protected] ABSTRACT Despite continuous advances in computing technology, today’s brute for- ce data processing approaches may not provide the necessary advantage to win the race against the ever-growing amount of data that can be wit- nessed over the last decades. In this thesis, we discuss novel methods and algorithms that are capable of directing attention to relevant details and analysing it in sequence to overcome the processing bottleneck and to keep up with this data explosion. In the first of three parts, a novel exploration technique for Policy Gradi- ent Reinforcement Learning is presented which replaces traditional ad- ditive random exploration with state-dependent exploration, exploring on a higher, more strategic level. We will show how this new exploration method converges faster and finds better global solutions than random exploration can. The second part of this thesis will introduce the concept of “data con- sumption” and discuss means to minimise it in supervised learning tasks by deriving classification as a sequential decision process and ma- king it accessible to Reinforcement Learning methods. -
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018)
Reinforcement Learning Data Science Africa 2018 Abuja, Nigeria (12 Nov - 16 Nov 2018) Chika Yinka-Banjo, PhD Ayorkor Korsah, PhD University of Lagos Ashesi University Nigeria Ghana Outline • Introduction to Machine learning • Reinforcement learning definitions • Example reinforcement learning problems • The Markov decision process • The optimal policy • Value function & Q-value function • Bellman Equation • Q-learning • Building a simple Q-learning agent (coding) • Recap • Where to go from here? Introduction to Machine learning • Artificial Intelligence (AI) is the study and design of Intelligent agents. • An Intelligent agent can perceive its environment through sensors and it can act on its environment through actuators. • E.g. Agent: Humanoid robot • Environment: Earth? • Sensors: Camera, tactile sensor etc. • Actuators: Motors, grippers etc. • Machine learning is a subfield of Artificial Intelligence Branches of AI Introduction to Machine learning • Machine learning techniques learn from data without being explicitly programmed to do so. • Machine learning models enable the agent to learn from its own experience by extracting useful information from feedback from its environment. • Three types of learning feedback: • Supervised learning • Unsupervised learning • Reinforcement learning Branches of Machine learning Supervised learning • Supervised learning: the machine learning model is trained on many labelled examples of input-output pairs. • Such that when presented with a novel input, the model can estimate accurately what the correct output should be. • Data(x, y): x is input data, y is label Supervised learning task in the form of classification • Goal: learn a function to map x -> y • Examples include; Classification, regression object detection, image captioning etc. Unsupervised learning • Unsupervised learning: here the model extract useful information from unlabeled and unstructured data. -
Infinite Variational Autoencoder for Semi-Supervised Learning
Infinite Variational Autoencoder for Semi-Supervised Learning M. Ehsan Abbasnejad Anthony Dick Anton van den Hengel The University of Adelaide {ehsan.abbasnejad, anthony.dick, anton.vandenhengel}@adelaide.edu.au Abstract with whatever labelled data is available to train a discrimi- native model for classification. This paper presents an infinite variational autoencoder We demonstrate that our infinite VAE outperforms both (VAE) whose capacity adapts to suit the input data. This the classical VAE and standard classification methods, par- is achieved using a mixture model where the mixing coef- ticularly when the number of available labelled samples is ficients are modeled by a Dirichlet process, allowing us to small. This is because the infinite VAE is able to more ac- integrate over the coefficients when performing inference. curately capture the distribution of the unlabelled data. It Critically, this then allows us to automatically vary the therefore provides a generative model that allows the dis- number of autoencoders in the mixture based on the data. criminative model, which is trained based on its output, to Experiments show the flexibility of our method, particularly be more effectively learnt using a small number of samples. for semi-supervised learning, where only a small number of The main contribution of this paper is twofold: (1) we training samples are available. provide a Bayesian non-parametric model for combining autoencoders, in particular variational autoencoders. This bridges the gap between non-parametric Bayesian meth- 1. Introduction ods and the deep neural networks; (2) we provide a semi- supervised learning approach that utilizes the infinite mix- The Variational Autoencoder (VAE) [18] is a newly in- ture of autoencoders learned by our model for prediction troduced tool for unsupervised learning of a distribution x x with from a small number of labeled examples. -
Unsupervised Speech Representation Learning Using Wavenet Autoencoders Jan Chorowski, Ron J
1 Unsupervised speech representation learning using WaveNet autoencoders Jan Chorowski, Ron J. Weiss, Samy Bengio, Aaron¨ van den Oord Abstract—We consider the task of unsupervised extraction speaker gender and identity, from phonetic content, properties of meaningful latent representations of speech by applying which are consistent with internal representations learned autoencoding neural networks to speech waveforms. The goal by speech recognizers [13], [14]. Such representations are is to learn a representation able to capture high level semantic content from the signal, e.g. phoneme identities, while being desired in several tasks, such as low resource automatic speech invariant to confounding low level details in the signal such as recognition (ASR), where only a small amount of labeled the underlying pitch contour or background noise. Since the training data is available. In such scenario, limited amounts learned representation is tuned to contain only phonetic content, of data may be sufficient to learn an acoustic model on the we resort to using a high capacity WaveNet decoder to infer representation discovered without supervision, but insufficient information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the to learn the acoustic model and a data representation in a fully kind of constraint that is applied to the latent representation. supervised manner [15], [16]. We compare three variants: a simple dimensionality reduction We focus on representations learned with autoencoders bottleneck, a Gaussian Variational Autoencoder (VAE), and a applied to raw waveforms and spectrogram features and discrete Vector Quantized VAE (VQ-VAE). We analyze the quality investigate the quality of learned representations on LibriSpeech of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately re- [17]. -
A Review of Unsupervised Artificial Neural Networks with Applications
A REVIEW OF UNSUPERVISED ARTIFICIAL NEURAL NETWORKS WITH APPLICATIONS Samson Damilola Fabiyi Department of Electronic and Electrical Engineering, University of Strathclyde 204 George Street, G1 1XW, Glasgow, United Kingdom [email protected] ABSTRACT designer) who uses his or her knowledge of the environment to Artificial Neural Networks (ANNs) are models formulated to train the network with labelled data sets [7]. Hence, the mimic the learning capability of human brains. Learning in artificial neural networks learn by receiving input and target ANNs can be categorized into supervised, reinforcement and pairs of several observations from the labelled data sets, unsupervised learning. Application of supervised ANNs is processing the input, comparing the output with the target, limited to when the supervisor’s knowledge of the environment computing the error between the output and target, and using is sufficient to supply the networks with labelled datasets. the error signal and the concept of backward propagation to Application of unsupervised ANNs becomes imperative in adjust the weights interconnecting the network’s neurons with situations where it is very difficult to get labelled datasets. This the aim of minimising the error and optimising performance [6, paper presents the various methods, and applications of 7]. Fine-tuning of the network continues until the set of weights unsupervised ANNs. In order to achieve this, several secondary that minimise the discrepancy between the output and the sources of information, including academic journals and desired output is obtained. Figure 1 shows the block diagram conference proceedings, were selected. Autoencoders, self- which conceptualizes supervised learning in ANNs. -
Training Plastic Neural Networks with Backpropagation
Differentiable plasticity: training plastic neural networks with backpropagation Thomas Miconi 1 Jeff Clune 1 Kenneth O. Stanley 1 Abstract examples. By contrast, biological agents exhibit a remark- How can we build agents that keep learning from able ability to learn quickly and efficiently from ongoing experience, quickly and efficiently, after their ini- experience: animals can learn to navigate and remember the tial training? Here we take inspiration from the location of (and quickest way to) food sources, discover and main mechanism of learning in biological brains: remember rewarding or aversive properties of novel objects synaptic plasticity, carefully tuned by evolution and situations, etc. – often from a single exposure. to produce efficient lifelong learning. We show Endowing artificial agents with lifelong learning abilities that plasticity, just like connection weights, can is essential to allowing them to master environments with be optimized by gradient descent in large (mil- changing or unpredictable features, or specific features that lions of parameters) recurrent networks with Heb- are unknowable at the time of training. For example, super- bian plastic connections. First, recurrent plastic vised learning in deep neural networks can allow a neural networks with more than two million parameters network to identify letters from a specific, fixed alphabet can be trained to memorize and reconstruct sets to which it was exposed during its training; however, au- of novel, high-dimensional (1,000+ pixels) nat- tonomous learning abilities would allow an agent to acquire ural images not seen during training. Crucially, knowledge of any alphabet, including alphabets that are traditional non-plastic recurrent networks fail to unknown to the human designer at the time of training.