For this year's annual conference on Neural Information Processing Systems, NeurIPS 2020, the research paper submission has reached 38% more than last year. This means that there are a total of 1,903 papers accepted, compared to 1,428 last year. This year, the committee has accepted more than 40 research papers submitted by Google researchers. Below here, we have listed the top fifteen AI research papers, in no particular order, from Google AI Research that have been accepted at the NeurIPS 2020 conference. About: In this paper, the researchers at Google demonstrated the power of a simple combination of two common SSL methods, such as consistency regularisation and pseudo-labelling.

Facebook AI Research (FAIR) has announced the release of Opacus, a high-speed library for applying differential privacy techniques when training deep-learning models using the PyTorch framework. Opacus can achieve an order-of-magnitude speedup compared to other privacy libraries. The library was described on the FAIR blog. Opacus provides an API and implementation of a PrivacyEngine, which attaches directly to the PyTorch optimizer during training. By using hooks in the PyTorch Autograd component, Opacus can efficiently calculate per-sample gradients, a key operation for differential privacy.

This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. Teaching yourself Python machine learning can be a daunting task if you don't know where to start. Fortunately, there are plenty of good introductory books and online courses that teach you the basics. It is the advanced books, however, that teach you the skills you need to decide which algorithm better solves a problem and which direction to take when tuning hyperparameters. A while ago, I was introduced to Machine Learning Algorithms, Second Edition by Giuseppe Bonaccorso, a book that almost falls into the latter category. While the title sounds like another introductory book on machine learning algorithms, the content is anything but.

Artificial intelligence (AI) can become more efficient and reliable if it is made to mimic biological models. New approaches in AI research are hugely successful in experiments. Artificial intelligence has arrived in our everyday lives--from search engines to self-driving cars. This has to do with the enormous computing power that has become available in recent years. But new results from AI research now show that simpler, smaller neural networks can be used to solve certain tasks even better, more efficiently, and more reliably than ever before.

"Attention takes two sentences, turns them into a matrix where the words of one sentence form the columns, and the words of another sentence form the rows, and then it makes matches, identifying relevant context." Check out the graphic from the Attention is All You Need paper below. It's two sentences, in different languages (French and English), translated by a professional human translator. The attention mechanism can generate a heat map, showing what French words the model focused on to generate the translated English words in the output.

This tutorial will show you how to build a basic speech recognition network that recognizes ten different words. It's important to know that real speech and audio recognition systems are much more complex, but like MNIST for images, it should give you a basic understanding of the techniques involved. Once you've completed this tutorial, you'll have a model that tries to classify a one second audio clip as "down", "go", "left", "no", "right", "stop", "up" and "yes". Let's install TensorFlow to get started. The script will start off by downloading a portion of the Speech Commands dataset.

Customer obsession, one of the key Amazon Leadership principles that guides everything we do at Amazon, has helped Amazon Translate be recognized as an industry leading neural machine translation provider. This year, Intento ranked Amazon Translate #1 on the list of top-performing machine translation providers in its The State of Machine Translation 2020 report. We are excited to be recognized for pursuing our passion--designing the best customer experience in machine translation. Amazon Translate is a neural machine translation service that delivers fast, high-quality, and affordable language translation. Neural machine translation is a form of machine translation that uses deep learning models to deliver more accurate and more natural sounding translation than traditional statistical and rule-based translation algorithms.

It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today. Methods that are currently considered cutting-edge will have become outdated; methods that today are nascent or on the fringes will be mainstream.

An essential part for the accurate classification of electrocardiogram (ECG) signals is the extraction of informative yet general features, which are able to discriminate diseases. Cardiovascular abnormalities manifest themselves in features on different time scales: small scale morphological features, such as missing P-waves, as well as rhythmical features apparent on heart rate scales. For this reason we incorporate a variant of the complex wavelet transform, called a scatter transform, in a deep residual neural network (ResNet). The former has the advantage of being derived from theory, making it well behaved under certain transformations of the input. The latter has proven useful in ECG classification, allowing feature extraction and classification to be learned in an end-to-end manner. Through the incorporation of trainable layers in between scatter transforms, the model gains the ability to combine information from different channels, yielding more informative features for the classification task and adapting them to the specific domain. For evaluation, we submitted our model in the official phase in the PhysioNet/Computing in Cardiology Challenge 2020. Our (Team Triage) approach achieved a challenge validation score of 0.640, and full test score of 0.485, placing us 4th out of 41 in the official ranking.

Self-training algorithms, which train a model to fit pseudolabels predicted by another previously-learned model, have been very successful for learning with unlabeled data using neural networks. However, the current theoretical understanding of self-training only applies to linear models. This work provides a unified theoretical analysis of self-training with deep networks for semi-supervised learning, unsupervised domain adaptation, and unsupervised learning. At the core of our analysis is a simple but realistic "expansion" assumption, which states that a low-probability subset of the data must expand to a neighborhood with large probability relative to the subset. We also assume that neighborhoods of examples in different classes have minimal overlap. We prove that under these assumptions, the minimizers of population objectives based on self-training and input-consistency regularization will achieve high accuracy with respect to ground-truth labels. By using off-the-shelf generalization bounds, we immediately convert this result to sample complexity guarantees for neural nets that are polynomial in the margin and Lipschitzness. Our results help explain the empirical successes of recently proposed self-training algorithms which use input consistency regularization.