The pooling operation is a cornerstone element of convolutional neural networks. These elements generate receptive fields for neurons, in which local perturbations should have minimal effect on the output activations, increasing robustness and invariance of the network. In this paper we will present an altered version of the most commonly applied method, maximum pooling, where pooling in theory is substituted by a continuous time differential equation, which generates a location sensitive pooling operation, more similar to biological receptive fields. We will present how this continuous method can be approximated numerically using discrete operations which fit ideally on a GPU. In our approach the kernel size is substituted by diffusion strength which is a continuous valued parameter, this way it can be optimized by gradient descent algorithms. We will evaluate the effect of continuous pooling on accuracy and computational need using commonly applied network architectures and datasets.

The proliferation of IoT sensors and edge devices makes it possible to use deep learning models to recognise daily activities locally using in-home monitoring technologies. Recently, federated learning systems that use edge devices as clients to collect and utilise IoT sensory data for human activity recognition have been commonly used as a new way to combine local (individual-level) and global (group-level) models. This approach provides better scalability and generalisability and also offers higher privacy compared with the traditional centralised analysis and learning models. The assumption behind federated learning, however, relies on supervised learning on clients. This requires a large volume of labelled data, which is difficult to collect in uncontrolled IoT environments such as remote in-home monitoring. In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data. Our experimental results show that using autoencoders and a long short-term memory (LSTM) classifier, the accuracy of our proposed system is comparable to that of a supervised federated learning system. Meanwhile, we demonstrate that our system is not affected by the Non-IID distribution of local data, and can even achieve better accuracy than supervised federated learning on some datasets. Additionally, we show that our proposed system can reduce the number of needed labels in the system and the size of local models without losing much accuracy, and has shorter local activity recognition time than supervised federated learning.

Tables are widely used with various structures to organize and present data. Recent attempts on table understanding mainly focus on relational tables, yet overlook to other common table structures. In this paper, we propose TUTA, a unified pre-training architecture for understanding generally structured tables. Since understanding a table needs to leverage both spatial, hierarchical, and semantic information, we adapt the self-attention strategy with several key structure-aware mechanisms. First, we propose a novel tree-based structure called a bi-dimensional coordinate tree, to describe both the spatial and hierarchical information in tables. Upon this, we extend the pre-training architecture with two core mechanisms, namely the tree-based attention and tree-based position embedding. Moreover, to capture table information in a progressive manner, we devise three pre-training objectives to enable representations at the token, cell, and table levels. TUTA pre-trains on a wide range of unlabeled tables and fine-tunes on a critical task in the field of table structure understanding, i.e. cell type classification. Experiment results show that TUTA is highly effective, achieving state-of-the-art on four well-annotated cell type classification datasets.

In this paper, we propose a novel approach to generate images (or other artworks) by using neural cellular automatas (NCAs). Rather than training NCAs based on single images one by one, we combined the idea with variational autoencoders (VAEs), and hence explored some applications, such as image restoration and style fusion. The code for model implementation is available online.

While GPT-3 is the largest language model trained today, it is also exploring its application in almost every field. OpenAI now provides a developer API to interact with GPT-3 and build applications on top of it. Once set correctly, GPT-3 can perform mathematical calculations, generate answers in programming languages, etc. If you are looking for ideas for a new startup idea, then an application called'IdeasAI' can inspire you to make something cool. 'IdeasAI' is developed by Pieter Levels and powered by GPT-3.

The roadmap aims to not only provide learners with an overall concept of … Paths include data science, machine learning, deep learning, and data …

To which most of the responses I get are," How really accurate, how complex and how miraculous the results given by neural networks are and their ability to learn always awe me." AI has been a hot topic for a considerable amount of time now. But to all the novice out there, it is a commonplace to be where we wonder how do I set off to AI avenue and what are prerequisites for you to really understand the notion of it. Not catching a proper direction might actually intimidate you before even starting. Today, we are breaking down from what actually AI is, what terminologies come with AI to how we can actually start our learning path.

Enterprise AI companies are increasingly growing in value and relevance. Global IT spending is expected to soon reach, and surpass $3.8 trillion. Enterprise AI companies are at the heart of this growth. This article will explain not only what enterprise AI companies are but also what they produce. We'll also look at how enterprise AI companies are impacting in various fields such as finance, logistics, and healthcare. Enterprise AI companies produce enterprise software. This is also known as enterprise application software or EAS for short. Generally, EAS is a large-scale software developed with the aim of supporting or solving organization-wide problems. Software developed by enterprise AI companies can perform a number of different roles. Its function varies depending on the task and sector it is designed for. In other words, EAS is software that "takes care of a majority of tasks and problems inherent to the enterprise, then it can be defined as enterprise software". Lots of enterprise AI companies use a combination of machine learning, deep learning, and data science solutions. This combination enables complex tasks such as data preparation or predictive analytics to be carried out quickly and reliably. Some enterprise AI companies are established names, backed by decades of experience. Other enterprises AI companies are relative newcomers, adopting a fresh approach to AI and problem-solving. This article and infographic will seek to highlight a combination of both. And focus on the real competitors for mergers and acquisitions as well as product development. To help you identify the best AI enterprise software for your business, we've segmented the landscape of enterprise AI solutions into categories. A lot of these enterprise companies can be classified in multiple categories, however, we have focused on their primary differentiation features. You're welcome to re-use the infographic below as long as the content remains unmodified and in full. The automotive industry is at the cutting edge of using artificial intelligence to support, imitate, and augment human action. Self-driving car companies and semi-autonomous vehicles of the future will rely heavily on AI systems from leveraging advanced reaction times, mapping, and machine-based systems.

To stay on top of AI innovation, it's time to upgrade from multicore architecture. Join this VB Live event to learn how cutting-edge computer architecture can unlock new AI capabilities, from common use cases to real-world case studies and more. AI and machine learning demand new approaches to computer architecture -- but, of course, there are more factors. Large amounts of data, the arrival of industry-standard frameworks such as TensorFlow and PyTorch, and the death of Moore's Law, are all signs that it's time for the next generation of computing systems. And it's one of the biggest transitions that the computer industry has seen since the changes demanded by the Internet and online connectivity.

You may have heard about GPT-3 this summer, the new cool kid on the AI block. GPT-3 came out of OpenAI, one of the top AI research labs in the world which was founded in late 2015 by Elon Musk, Sam Altman and others and later backed with a $1B investment from Microsoft. You've probably also heard about the ongoing AI revolution in healthcare, thanks to promising results in areas such as automated diagnosis, medical documentation and drug discovery, to name a few. Some have claimed that algorithms now outperform doctors on certain tasks and others have even announced that robots will soon receive medical degrees of their own! This can all sound far-fetched... but could this robot actually be GPT-3?