Deep learning-based modeling of dynamical systems driven by partial differential equations (PDEs) has become quite popular in recent years. However, most of the existing deep learning-based methods either assume strong physics prior, or depend on specific initial and boundary conditions, or require data in dense regular grid making them inapt for modeling unknown PDEs from sparsely-observed data. This paper presents a deep learning-based collocation method for modeling dynamical systems driven by unknown PDEs when data sites are sparsely distributed. The proposed method is spatial dimension-independent, geometrically flexible, learns from sparsely-available data and the learned model does not depend on any specific initial and boundary conditions. We demonstrate our method in the forecasting task for two-dimensional wave equation and Burgers-Fisher equation in multiple geometries with different boundary conditions.

Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans. Existing approaches provide results under strong assumptions concerning unimodality of future states, or, at best, assuming specific probability distributions that often poorly fit to real-life conditions. In this work we introduce a robust and flexible probabilistic framework that allows to model future predictions with virtually no constrains regarding the modality or underlying probability distribution. To achieve this goal, we leverage a hypernetwork architecture and train a continuous normalizing flow model. The resulting method dubbed RegFlow achieves state-of-the-art results on several benchmark datasets, outperforming competing approaches by a significant margin.

In this work, we study the trade-off between differential privacy and adversarial robustness under L2-perturbations in the context of learning halfspaces. We prove nearly tight bounds on the sample complexity of robust private learning of halfspaces for a large regime of parameters. A highlight of our results is that robust and private learning is harder than robust or private learning alone. We complement our theoretical analysis with experimental results on the MNIST and USPS datasets, for a learning algorithm that is both differentially private and adversarially robust.

The idea of convolutions was first introduced by Kunihiko Fukushima in this paper. The neocognitron introduced 2 types of layers, convolutional layers and downsampling layers. Then next key advancement was by Yann LeCun et al. when they used back-propagation to learn the coefficients of the convolutional kernel from images. This made learning automatic and not laboriously handcrafted. According to Wikipedia, this approach became a foundation for modern computer vision.

In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning algorithms. Stickers pasted on stop signs that cause computer vision systems to mistake them for speed limits; glasses that fool facial recognition systems, turtles that get classified as rifles -- these are just some of the many adversarial examples that have made the headlines in the past few years. There's increasing concern about the cybersecurity implications of adversarial examples, especially as machine learning systems continue to become an important component of many applications we use. AI researchers and security experts are engaging in various efforts to educate the public about adversarial attacks and create more robust machine learning systems. Among these efforts is adversarial.js,

Doctors are often deluged by signals from charts, test results, and other metrics to keep track of. It can be difficult to integrate and monitor all of these data for multiple patients while making real-time treatment decisions, especially when data is documented inconsistently across hospitals. In a new pair of papers, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) explore ways for computers to help doctors make better medical decisions. One team created a machine-learning approach called "ICU Intervene" that takes large amounts of intensive-care-unit (ICU) data, from vitals and labs to notes and demographics, to determine what kinds of treatments are needed for different symptoms. The system uses "deep learning" to make real-time predictions, learning from past ICU cases to make suggestions for critical care, while also explaining the reasoning behind these decisions.

As AI and deep learning uses skyrocket, organizations are finding they are running these systems on similar resource as they do with high-performance computing (HPC) systems – and wondering if this is the path to peak efficiency. Ostensibly AI and HPC architectures have a lot in common, as AI has evolved into even more data-intensive machine learning (ML) and deep learning (DL) domains (Figure 1). Workloads often require multiple GPU systems as a cluster, and share those systems in a coordinated way among multiple data scientists. Secondly, both AI and HPC workloads require shared access to data at a high level of performance and communicate over a fast RDMA-enabled network. Especially in scientific research, the classic HPC systems nowadays tend to have GPUs added to the compute nodes to have the same cluster suitable for classic HPC and new AI/DL workloads.

Say Hi! to an extraordinary feature of GPT3 that can generate code, tweets, provide answers and suggestions to normal queries, poetry, and blogs. If you're desperate with the beta waitlist, you can in the meantime download the prior version, GPT-2, which is easy to run using a Docker installation. Source code is posted in the same GitHub repository, in Python format for the TensorFlow framework. You won't get the same results as GPT-3, of course, but it's a way to start the journey. This behavior was completely novel to scientists as they developed GPT for predicting the next word in a sequence of words instead, they were surprised to know that it can generate computer code and interesting tweets.

In this notebook we will predict the best quality of the wine using PyTorch and linear regression. If you haven't checked out my previous blog on Linear Regression check this out . First of all lets import required libraries.. Now lets analyse our dataset.. its important to analyse to see what we are dealing with.. Training Dataset: The sample of data used to fit the model. The actual dataset that we use to train the model (weights and biases in the case of a Neural Network). The model sees and learns from this data.

The capabilities that underpin AI solutions – machine learning, deep learning, computer vision, and natural language processing – are not ethical or …