Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online.

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Sentiment Analysis and Opinion Mining

“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告

Humans often leverage spatial clues to categorize scenes in a fraction of a second. This form of intelligence is very relevant in time-critical situations (e.g., when driving a car) and valuable to transfer to automated systems. This work investigates the predictive power of solely processing spatial clues for scene understanding in 2D images and compares such an approach with the predictive power of visual appearance. To this end, we design the laboratory task of predicting the identity of two objects (e.g., “man” and “horse”) and their relationship or predicate (e.g., “riding”) given exclusively the ground truth bounding box coordinates of both objects. We also measure the performance attainable in Human Object Interaction (HOI) detection, a real-world spatial task, which includes a setting where ground truth boxes are not available at test time. An additional goal is to identify the principles necessary to effectively represent a spatial template, that is, the visual region in which two objects involved in a relationship expressed by a predicate occur. We propose a scale-, mirror-, and translation-invariant representation that captures the spatial essence of the relationship, i.e., a canonical spatial representation. Tests in two benchmarks reveal: (1) High performance is attainable by using exclusively spatial information in all tasks. (2) In HOI detection, the canonical template outperforms the rest of spatial, visual, and several state-of-the-art baselines. (3) Simple fusion of visual and spatial features substantially improves performance. (4) Our methods fare remarkably well with a small amount of data and rare categories. Our results obtained on the Visual Genome (VG) and the Humans Interacting with Common Objects - Detection (HICO-DET) datasets indicate that great predictive power can be obtained from spatial clues alone, opening up possibilities for performing fast scene understanding at a glance.

/pdf/probing-spatial-clues-canonical-spatial-templates-for-object-1mwera09y2.pdf

Probing Spatial Clues: Canonical Spatial Templates for Object Relationship Understanding

In this demo we focus on cross-modal (visual and textual) e-commerce search within the fashion domain. Particularly, we demonstrate two tasks: (1) given a query image (without any accompanying text), we retrieve textual descriptions that correspond to the visual attributes in the visual query; and (2) given a textual query that may express an interest in specific visual characteristics, we retrieve relevant images (without leveraging textual meta-data) that exhibit the required visual attributes. The first task is especially useful to manage image collections by online stores who might want to automatically organize and mine predominantly visual items according to their attributes without human input. The second task renders useful for users to find items with specific visual characteristics, in the case where there is no text available describing the target image. We use a state-of-the-art visual and textual features, as well as a state-of-the-art latent variable model to bridge between textual and visual data: bilingual latent Dirichlet allocation. Unlike traditional search engines, we demonstrate a truly cross-modal system, where we can directly bridge between visual and textual content without relying on pre-annotated meta-data.

/pdf/cross-modal-fashion-search-3a2lrg49hc.pdf

Cross-Modal Fashion Search

If information extraction wants to make its results more accurate, it will have to resort increasingly to a coherent implementation of natural language semantics. In this paper, we will focus on the extraction of semantic case roles from texts. After setting the essential theoretical framework, we will argue that it is possible to detect case roles on the basis of morphosyntactic and lexical surface phenomena. We will give a concise overview of our methodology and of a preliminary test that seems to confirm our hypotheses.

/pdf/semantic-case-role-detection-for-information-extraction-3a8mkxvu1o.pdf

Semantic case role detection for information extraction

Neural networks become extremely popular in artificial intelligence. In this paper we show how they aid in automatically translating fashion item descriptions and how they use fashion images to generate the translations. More specifically, we propose a multimodal neural machine translation model in which the decoder that generates the translation attends to visually grounded representations that capture both the semantics of the fashion words in the source language and regions in the fashion image. We introduce this novel neural architecture in the context of fashion e-commerce, where product descriptions need to be available in multiple languages. We report state-of-the-art multimodal translation results on a real-world fashion e-commerce dataset.

Multimodal Neural Machine Translation of Fashion E-commerce Descriptions

In this paper we focus on cross-modal (visual and textual) attribute recognition within the fashion domain. Particularly, we investigate two tasks: 1) given a query image, we retrieve textual descriptions that correspond to the visual attributes in the query; and 2) given a textual query that may express visual characteristics, we retrieve relevant images that exhibit the required visual attributes. To this end, we collected a dataset that consists of 53,689 images coupled with textual descriptions in natural language. The images contain fashion garments that display a great variety of visual attributes. Examples of visual attributes in fashion include colors (e.g. green, purple), shapes (e.g., v-neck, a-line, one-shoulder, floor-length), patterns (e.g., floral, striped), textures (e.g., silk, chiffon, beading, sequin), and even occasions (e.g., wedding, evening, wear-to-work). Unlike previous work, the text provides a rough and noisy description of the item in the image. We extensively analyze this dataset in the context of cross-modal attribute recognition. We investigate two latent variable models to bridge between textual and visual data: bilingual latent Dirichlet allocation and canonical correlation analysis. We use visual and textual features and report promising results1.

https://lirias.kuleuven.be/bitstream/123456789/540526/1/NIPS_Workshop_Multimodal_Machine_Learning.pdf

Marie-Francine Moens

Papers

Probing Spatial Clues: Canonical Spatial Templates for Object Relationship Understanding

Cross-Modal Fashion Search

Semantic case role detection for information extraction

Multimodal Neural Machine Translation of Fashion E-commerce Descriptions

Cross-modal attribute recognition in fashion