Tree data occurs in many forms, such as computer programs, chemical molecules, or natural language. Unfortunately, the non-vectorial and discrete nature of trees makes it challenging to construct functions with tree-formed output, complicating tasks such as optimization or time series prediction. Autoencoders address this challenge by mapping trees to a vectorial latent space, where tasks are easier to solve, and then mapping the solution back to a tree structure. However, existing autoencoding approaches for tree data fail to take the specific grammatical structure of tree domains into account and rely on deep learning, thus requiring large training datasets and long training times. In this paper, we propose tree echo state autoencoders (TES-AE), which are guided by a tree grammar and can be trained within seconds by virtue of reservoir computing. In our evaluation on three datasets, we demonstrate that our proposed approach is not only much faster than a state-of-the-art deep learning autoencoding approach (D-VAE) but also has less autoencoding error if little data and time is given.

With a few lines of code, DeepPurpose generates drug candidates based on aggregating five pretrained state-of-the-art models while offering flexibility for users to train their own models with 15 drug/target encodings and 50 novel architectures. We demonstrated DeepPurpose using case studies, including repurposing for COVID-19 where promising candidates under trials are ranked high in our results. Drug repurposing is about investigating existing drugs for new therapeutic purposes which can potentially speed up drug development 1 . With a large number of existing drugs, it is important to quickly and accurately identify promising candidates for new indications. Especially in facing COVID-19 pandemic today, drug repurposing become particularly relevant as a potentially much faster way to discover effective and safe drugs for treating COVID-19. Deep learning has recently demonstrated its superior performance than classic methods to assist computational drug discovery 2, 3, thanks to its expressive power in extracting, processing and extrapolating patterns in molecular data.

Recent advancements in Neural Architecture Search(NAS) resulted in finding new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like image classification, object detection, or semantic segmentation without substantial human supervision. In this paper, we focus on exploring NAS for a dense prediction task that is image denoising. Due to a costly training procedure, most NAS solutions for image enhancement rely on reinforcement learning or evolutionary algorithm exploration, which usually take weeks (or even months) to train. Therefore, we introduce a new efficient implementation of various superkernel techniques that enable fast (6-8 RTX2080 GPU hours) single-shot training of models for dense predictions. We demonstrate the effectiveness of our method on the SIDD+ benchmark for image denoising.

Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the \textit{teacher}) into another model (the \textit{student}), where typically, the teacher has a greater capacity (e.g., more parameters or higher bit-widths). To our knowledge, existing methods overlook the fact that although the student absorbs extra knowledge from the teacher, both models share the same input data -- and this data is the only medium by which the teacher's knowledge can be demonstrated. Due to the difference in model capacities, the student may not benefit fully from the same data points on which the teacher is trained. On the other hand, a human teacher may demonstrate a piece of knowledge with individualized examples adapted to a particular student, for instance, in terms of her cultural background and interests. Inspired by this behavior, we design data augmentation agents with distinct roles to facilitate knowledge distillation. Our data augmentation agents generate distinct training data for the teacher and student, respectively. We find empirically that specially tailored data points enable the teacher's knowledge to be demonstrated more effectively to the student. We compare our approach with existing KD methods on training popular neural architectures and demonstrate that role-wise data augmentation improves the effectiveness of KD over strong prior approaches. The code for reproducing our results can be found at https://github.com/bigaidream-projects/role-kd

Neural networks have become a powerful tool in pattern recognition and part of their success is due to generalization from using large datasets. However, unlike other domains, time series classification datasets are often small. In order to address this problem, we propose a novel time series data augmentation called guided warping. While many data augmentation methods are based on random transformations, guided warping exploits the element alignment properties of Dynamic Time Warping (DTW) and shapeDTW, a high-level DTW method based on shape descriptors, to deterministically warp sample patterns. In this way, the time series are mixed by warping the features of a sample pattern to match the time steps of a reference pattern. Furthermore, we introduce a discriminative teacher in order to serve as a directed reference for the guided warping. We evaluate the method on all 85 datasets in the 2015 UCR Time Series Archive with a deep convolutional neural network (CNN) and a recurrent neural network (RNN). The code with an easy to use implementation can be found at https://github.com/uchidalab/time_series_augmentation .

You can also read a translated version of this file in Chinese 简体中文版 or in Korean 한국어 or in Japanese 日本語. Recognize and manipulate faces from Python or from the command line with the world's simplest face recognition library. Built using dlib's state-of-the-art face recognition built with deep learning. The model has an accuracy of 99.38% on the Labeled Faces in the Wild benchmark. This also provides a simple face_recognition command line tool that lets you do face recognition on a folder of images from the command line!

What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today. I'm sure I'm not the only one who's accidentally spent hours on YouTube when I had more important things to do! Just how do they convince you to do that?

According to MIT's Technology Review, Pitfall and Montezuma's Revenge require the AI to experiment more than usual in order to figure out how to get a better score. Meanwhile, Solaris and Skiing are difficult for the AI because there aren't as many indications as success -- the AI doesn't know if it's making the right moves for long stretches of time. DeepMind built upon its older AI agents so that Agent57 could make better decisions regarding exploration and score exploitation, as well as to optimize the trade-off between short-term and long term performance in games like Skiing. Technology Review notes that while these results are impressive, AI still has a long way to go. These systems can only figure out one game at a time, which it says is at odds with the skills of humans: "True versatility, which comes so easily to a human infant, is still far beyond AIs' reach."

We introduce physics informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given law of physics described by general nonlinear partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct classes of algorithms, namely continuous time and discrete time models. The resulting neural networks form a new class of data-efficient universal function approximators that naturally encode any underlying physical laws as prior information. In the first part, we demonstrate how these networks can be used to infer solutions to partial differential equations, and obtain physics-informed surrogate models that are fully differentiable with respect to all input coordinates and free parameters. In the second part, we focus on the problem of data-driven discovery of partial differential equations.

Artificial intelligence has the potential to greatly simplify our lives – but not everyone is a data scientist and not all data scientists are experts in machine learning. Enter AutoAI – a novel approach of designing, training and optimizing machine learning models automatically. With AutoAI, anyone could soon build machine learning pipelines from raw data directly, without writing complex code and performing tedious tuning and optimization, to then automate complicated, labor-intensive tasks. Several IBM papers selected for the AAAI-20 conference in New York demonstrate the value of AutoAI and different approaches to it in great detail. Most AutoAI research currently focuses on three areas: automatically determining the best models for each step of the desired machine learning and data science pipeline (model selection), automatically finding the best architecture of a deep learning-based AI model, and automatically finding the best hyperparameters (parameters for the model training process) for AI models and algorithms.