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Machine Learning: Overviews


Top Artificial Intelligence Books to Read in 2021

#artificialintelligence

A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. In this mind-expanding book, scientific pioneer Marvin Minsky continues his groundbreaking research, offering a fascinating new model for how our minds work. He argues persuasively that emotions, intuitions, and feelings are not distinct things, but different ways of thinking. Introduction to Artificial Intelligence presents an introduction to the science of reasoning processes in computers, and the research approaches and results of the past two decades.


Digital Scent Technology And AI Machines Can Smell Now. So What! - News Break

#artificialintelligence

When I mention AI (Artificial Intelligence) machines can smell now, my friends exclaim with the statement of "So What"! The best way is to explain to them the importance of smell in our lives. This article introduces considerable research to olfactory development in computer science and engineering at a high level and points out recent developments in the industry. I also touch on potential use cases and business value propositions. Let me give you a high-level background to digital scent technology as part of the technical literature review that I conducted reflecting olfactory progress in AI.


The Essential Guide to Transformers, the Key to Modern SOTA AI - KDnuggets

#artificialintelligence

Are you overwhelmed by the vast array of X-formers? X-formers are the name being given to the wide array of Transformer variants that have been implemented or proposed. You likely know Transformers from their recent spate of success stories in natural language processing, computer vision, and other areas of artificial intelligence, but are familiar with all of the X-formers? More importantly, do you know the differences, and why you might use one over another? A Survey of Transformers, by Tianyang Lin, Yuxin Wang, Xiangyang Liu, and Xipeng Qiu, has been written to help interested readers in this regard.


A survey of machine learning techniques in adversarial image forensics

#artificialintelligence

Image forensic plays a crucial role in both criminal investigations (e.g., dissemination of fake images to spread racial hate or false narratives about specific ethnicity groups or political campaigns) and civil litigation (e.g., defamation). Increasingly, machine learning approaches are also utilized in image forensics. However, there are also a number of limitations and vulnerabilities associated with machine learning-based approaches (e.g., how to detect adversarial (image) examples), and there are associated real-world consequences (e.g., inadmissible evidence, or wrongful conviction). Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios.


Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models

#artificialintelligence

The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human–computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.


Under the Hood of Modern Machine and Deep Learning

#artificialintelligence

In this chapter, we investigate whether unique, optimal decision boundaries can be found. In order to do so, we first have to revisit several fundamental mathematical principles. Regularization is a mathematical tool, which allows us to find unique solutions even for highly ill-posed problems. In order to use this trick, we review norms and how they can be used to steer regression problems. Rosenblatt's Perceptron and Multi-Layer Perceptrons which are also called Artificial Neural Networks inherently suffer from this ill-posedness.


Four Deep Learning Papers to Read in June 2021

#artificialintelligence

Welcome to the June edition of the ‚Machine-Learning-Collage' series, where I provide an overview of the different Deep Learning research streams. So what is a ML collage? Simply put, I draft one-slide visual summaries of one of my favourite recent papers. At the end of the month all of the resulting visual collages are collected in a summary blog post. Thereby, I hope to give you a visual and intuitive deep dive into some of the coolest trends. May has been quite the month including the virtual ICLR 2021 conference, ICML review decisions as well as the NeurIPS deadlines.


AI still writes lousy poetry

ZDNet

Her eyes, twin pools of mystic light, Forever in her radiance white--, She sought the bosom of the Night. Away it came, that mystic sight! A survey of recent literature in the machine learning category of artificial intelligence shows steady progress in the development of techniques for automatically generating poetry. The output remains fairly mediocre, but it is getting good enough that some human readers will give the poems respectable marks in controlled evaluations. And some people will even be fooled into ascribing human authorship to machine poetry.


Under the Hood of Modern Machine and Deep Learning

#artificialintelligence

In this chapter, we investigate whether unique, optimal decision boundaries can be found. In order to do so, we first have to revisit several fundamental mathematical principles. Regularization is a mathematical tool, which allows us to find unique solutions even for highly ill-posed problems. In order to use this trick, we review norms and how they can be used to steer regression problems. Rosenblatt's Perceptron and Multi-Layer Perceptrons which are also called Artificial Neural Networks inherently suffer from this ill-posedness.


Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

#artificialintelligence

With the advances of data-driven machine learning research, a wide variety of prediction problems have been tackled. It has become critical to explore how machine learning and specifically deep learning methods can be exploited to analyse healthcare data. A major limitation of existing methods has been the focus on grid-like data; however, the structure of physiological recordings are often irregular and unordered which makes it difficult to conceptualise them as a matrix. As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be either temporal associations or anatomical junctions. In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.