EVA1: wav2vec 2.0

Introduction The main prerequisite to review and criticize an article is to have completely understood the content as well as the intent of the author(s) with publishing the content. Following are criteria set by IJCAI (International Joint Conference on Artificial Intelligence): Originality, Significance, Relevance, Technical quality, Clarity and Quality of writing, Scholarship (scientific context). When criticizing, the strengths and weaknesses found in the publication need to be presented correctly, as well as highlighting the knowledge of the authors and their contribution to the field of interest. Furthermore, any gaps and contradictions found in the article should be underlined. All standpoints of the reviewer must be supported by facts pertinent to that area of knowledge. Noteworthy to mention is that it is the duty of the author(s) to provide the audience with an interpretation and analysis that demonstrate the value of the publication.  Originality (10) The paper under rev...

Introduction One of the crucial points before criticizing a publication is to fully understand its content, which otherwise entails unjust reflections. The language of choice for criticizing is another critical element, being discussed in another dedicated blogpost. The interesting point about language is that it's the sole element of knowledge transfer, the so-called Transfer Learning . Reinvention of knowledge is a consequence of a non-existing Transfer Learning, as was the case before invention of languages.  In neural networks, Transfer Learning refers to pre-training of a model in a supervised fashion, where training data are labeled. The pre-trained model may then be used for solving other tasks, as training a new model from scratch requires a vast amount of computational resources.  In speech recognition, labeled data refers to audio files accompanied by transcriptions, which work as ground truth. Once transcriptions are available, the training of neural networks is str...

So, how is a paper supposed to be written? Is there a standard for a good writing style? If yes, where does this come from? And if No, what is then the basis for the writing style of papers? More questions like these could be asked, leading the author to a never-ending loop. However, the most obvious, and probably the first, question should be "Who is the audience?".  Furthermore, the author may still fall in the trap of bad writing style by poor choice of words, truncated or long sentences, to mention a few, all of which undermine the author's message. As a result, the attention of the reader varies throughout the paper depending on the comprehension of the sentences. As the audience sets the path for the writing style, the author must assure to follow it correctly to keep the attention high throughout the paper. As an example, the background and generality of a paper is more important for a less technical audience, and vice versa.   Considering the introduction above, t...

Coming from a background in physics -- specifically, designing and developing radiation detectors -- and having worked in the industry for several years, the exposure to vast amount of data has been inevitable.  The most exciting part of physics is the mysteries it solves, more often than not, by complicated equations, which are these days more tangible thanks to the internet and the collective efficacy of enthusiastic people who invest time and put in the effort to break down complicated things  to  each and every recipient's comprehension.  The explicit nature of physics aids in solving problems once the proper equations are at hand, whereas in data science inputs and outputs are not (necessarily) explicitly correlated and one needs to rely on computers to find the appropriate model that describes the data. Fortunately, and/or unfortunately, the modern society is heavily relied on data and, in turn, on professional data analysts. Therefore, to cope with this r...