Building deep learning models typically involves complex data pipelines as well as a lot of trial and error, tweaking model architecture and parameters whose performance needs to be compared. It is often difficult to keep track of all the experiments, leading at best to confusion and at worst wrong conclusions. In 4 reasons why your ML code is bad we explored how to organize ML code as DAG workflows to solve that problem. In this guide we will go through a practical case study on turning an existing pytorch script into a scalable deep learning pipeline with d6tflow. The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the code into a scalable deep learning pipeline.

Building deep learning models typically involves complex data pipelines as well as a lot of trial and error, tweaking model architecture and parameters whose performance needs to be compared. It is often difficult to keep track of all the experiments, leading at best to confusion and at worst wrong conclusions. In 4 reasons why your ML code is bad we explored how to organize ML code as DAG workflows to solve that problem. In this guide we will go through a practical case study on turning an existing pytorch script into a scalable deep learning pipeline with d6tflow. The starting point is a pytorch deep recommender model by Facebook and we will go through the 5 steps of migrating the code into a scalable deep learning pipeline.

Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads, we'll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is "real world" applications, workloads, and present day challenges. Optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today's hyper-connected world, but rather a platform that shares and delivers data to create competitive business value.

Interest in data science has risen remarkably in the last five years. And while there are many programming languages suited for data science and machine learning, Python is the most popular. Scikit-Learn is a Python module for machine learning built on top of SciPy and NumPy. David Cournapeau started it as a Google Summer of Code project. Since then, it's grown to over 20,000 commits and more than 90 releases.

This repository contains agents and environments described in the ICML'18 paper "Synthesizing Programs for Images using Reinforced Adversarial Learning". For the time being, we are providing the libmypaint-based simulator (more coming soon) and a Sonnet module for the unconditional agent as well as pre-trained model snapshots (9 agents from a single population) available from TF-Hub. WARNING: Make sure that you have cmake 3.14 or later since we rely on its capability to find numpy libraries. If your package manager doesn't provide it follow the installation instructions from here. You can check the version by running cmake --version .

TWIMLcon: AI Platforms is brought to you by the team behind the TWIML AI Podcast (a.k.a. The conference has its roots in a series of interviews on the topic of AI Platforms published back in the fall of 2018. The series--which featured interviews with ML Platforms and Infrastructure engineers and leaders from Facebook, Airbnb, LinkedIn, OpenAI, Shell and Comcast--resonated very strongly with listeners and remains one of our most popular series to this day. We're excited to convene TWIMLcon: AI Platforms and provide the broader community of folks that care about productionalizing, operationalizing and scaling ML & AI an opportunity to share, learn, and connect with one another.

A fundamental problem in geostatistical modeling is to infer the heterogeneous geological field based on limited measurements and some prior spatial statistics. Semantic inpainting, a technique for image processing using deep generative models, has been recently applied for this purpose, demonstrating its effectiveness in dealing with complex spatial patterns. However, the original semantic inpainting framework incorporates only information from direct measurements, while in geostatistics indirect measurements are often plentiful. To overcome this limitation, here we propose a physics-informed semantic inpainting framework, employing the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) and jointly incorporating the direct and indirect measurements by exploiting the underlying physical laws. Our simulation results for a high-dimensional problem with 512 dimensions show that in the new method, the physical conservation laws are satisfied and contribute in enhancing the inpainting performance compared to using only the direct measurements.

Machine learning (ML) classification is increasingly used in safety-critical systems. Protecting ML classifiers from adversarial examples is crucial. We propose that the main threat is that of an attacker perturbing a confidently classified input to produce a confident misclassification. To protect against this we devise an adversarial bound (AB) for a Gaussian process classifier, that holds for the entire input domain, bounding the potential for any future adversarial method to cause such misclassification. This is a formal guarantee of robustness, not just an empirically derived result. We investigate how to configure the classifier to maximise the bound, including the use of a sparse approximation, leading to the method producing a practical, useful and provably robust classifier, which we test using a variety of datasets.

We introduce "extreme summarization," a new single-document summarization task which aims at creating a short, one-sentence news summary answering the question "What is the article about?". We argue that extreme summarization, by nature, is not amenable to extractive strategies and requires an abstractive modeling approach. In the hope of driving research on this task further: (a) we collect a real-world, large scale dataset by harvesting online articles from the British Broadcasting Corporation (BBC); and (b) propose a novel abstractive model which is conditioned on the article's topics and based entirely on convolutional neural networks. We demonstrate experimentally that this architecture captures long-range dependencies in a document and recognizes pertinent content, outperforming an oracle extractive system and state-of-the-art abstractive approaches when evaluated automatically and by humans on the extreme summarization dataset.

--The prevalence of social media has made information sharing possible across the globe. The downside, unfortunately, is the wide spread of misinformation. Methods applied in most previous rumor classifiers give an equal weight, or attention, to words in the microblog, and do not take the context beyond microblog contents into account; therefore, the accuracy becomes plateaued. In this research, we propose an ensemble neural architecture to detect rumor on Twitter . The architecture incorporates word attention and context from the author to enhance the classification performance. In particular, the word-level attention mechanism enables the architecture to put more emphasis on important words when constructing the text representation. T o derive further context, microblog posts composed by individual authors are exploited since they can reflect style and characteristics in spreading information, which are significant cues to help classify whether the shared content is rumor or legitimate news. The experiment on the real-world Twitter dataset collected from two well-known rumor tracking websites demonstrates promising results. It is indisputable that social media has significant influences on people's lives these days.