ZHAW: EVA Artificial Intelligence Seminar

 I chose the paper Generative Adversarial Networks[1] from Ian Goodfellow, which I am going to evaluate how good the paper is. I am going to use the IJCAI review template to asses the quality of this paper. Relevance – Is the paper fully within the scope of the conference? Will the questions and results of the paper be of interest to researchers in the field? This paper has demonstrated the viability of the adversarial modeling framework, suggesting that these research directions could prove useful. It opens up quite a few paths, as a lot of problems have a limited data set, so now it would be possible to enrich synthetically those data sets. Significance – Is this a significant advance in the state of the art? Is this a paper that people are likely to read and cite? Does the paper address an important problem? Does it open new research directions? Is it a paper that is likely to have a lasting impact? This is a significant advance in the state of the art and shows a novel idea. As it

I chose the paper Generative Adversarial Networks[1] from Ian Goodfellow, which I am going to review what the main points are and how it works. Intorduction Generative Adversarial Networks, or GANs for short, are an approach to generative modeling using deep learning methods. Generative modeling is an unsupervised learning task in machine learning that involves automatically discovering and learning the regularities or patterns in input data in such a way that the model can be used to generate or output new examples that plausibly could have been drawn from the original dataset.[1,2,3,4] How does it work? GANs consists of two networks, a Generator G(x), and a Discriminator D(x). They both play an adversarial game where the generator tries to fool the discriminator by generating data similar to those in the training set. The Discriminator tries not to be fooled by identifying fake data from real data. They both work simultaneously to learn and train complex data like audio, video or ima

 Blog Post 2: Writing Style This second post is describing, what makes a writing output a good (or bad) piece of text, especially regarding the organization of the paper, wording without looking at the content.  I chose the paper Generative Adversarial Networks from Ian J. Goodfellow (https://arxiv.org/pdf/1406.2661.pdf), as I am very interested in this topic, my master thesis is about GANs and this paper is already familiar with me. It propose a new framework for estimating generative models via an adversarial process, in which the researchers simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. This paper is often cited and is also the starting point for anyone who learns about GANs, even though that paper is from 2014! Paper Title The title is short and sweet, at it is called "Generative Adversarial Nets". Everybody who

 Hello dear reader! My name is Antonio. I have a bachelor's degree in computer science. Since I found my love for statistics and machine learning during my studies, I decided to do a master's degree in data science.  This seminar is particularly interesting because I have to read papers again and again. Furthermore, I have to write and present a larger paper myself. Moreover, these skills are very much in demand, because nowadays you not only have to be able to program, but you also need to present your findings and models. During this course i hope to learn how to write good papers and how to present the results to a professional audience.