Learning from Multivariate Time Series (MTS) has attracted widespread attention in recent years. In particular, label shortage is a real challenge for the classification task on MTS, considering its complex dimensional and sequential data structure. Unlike self-training and positive unlabeled learning that rely on distance-based classifiers, in this paper, we propose SMATE, a novel semi-supervised model for learning the interpretable Spatio-Temporal representation from weakly labeled MTS. We validate empirically the learned representation on 30 public datasets from the UEA MTS archive. We compare it with 13 state-of-the-art baseline methods for fully supervised tasks and four baselines for semi-supervised tasks. The results show the reliability and efficiency of our proposed method.

Learning tasks with sequential data as inputs (and possibly outputs) arise in a wide variety of contexts, including computer vision, text and speech recognition, natural language processing, and time series analysis in the sciences and engineering. While recurrent gradient-based models have been successfully used in processing sequential data sets, it is well-known that training these models to process (very) long sequential inputs is extremely challenging on account of the so-called exploding and vanishing gradients problem [32]. This arises as calculating hidden state gradients entails the computation of an iterative product of gradients over a large number of steps. Consequently, this (long) product can easily grow or decay exponentially in the number of recurrent interactions. Mitigation of the exploding and vanishing gradients problem has received considerable attention in the literature. A classical approach, used in Long Short-Term Memory (LSTM) [18] and Gated Recurrent Units (GRUs) [11], relies on gating mechanisms and leverages the resulting additive structure to ensure that gradients do not vanish.

Restricted Boltzmann Machine is used to detect patterns in data, in an unsupervised way. If you haven't read the previous posts yet, you can read them by clicking the below links. RBMs are self-learning shallow neural networks that learn to reassemble data. They're significant models because they can extract meaningful features from a given input without having to identify them. Let's start with the fact that we have access to a matrix of viewer ratings for a specific number of Netflix movies, where each row represents a movie and each column represents a user's rating.

Deep Belief Networks (DBN) and Autoencoders, Let's take a look at DBNs and how they are created on top of RBMs. If you haven't read the previous posts yet, you can read them by clicking the below links. A DBN is a network that was created to overcome a problem that existed in standard artificial neural networks. Backpropagation is a phenomenon that might result in "local minima" or "vanishing gradients." DBN is designed to solve this problem by stacking numerous RBMs.

On Monday, Oct. 18, CERN openlab and Chelonia Applied Science will host a first-of-its-kind workshop called ExaHealth 2021. The half-day virtual event will begin at 1.00 p.m. CEST and will examine the potential for exascale computing and machine learning to support efforts to improve public health. The workshop is free and open to all. With recent advancements in high-performance computing (HPC) towards exascale (the capability to perform a billion billion (1018), or a quintillion, computing operations per second) and the continued development and proliferation of both machine- and deep-learning techniques in all sectors, it is imperative that we ensure these resources are capitalized upon fully in a realm that affects us all: public health. Projects supported by the European Union (such as Exscalate4COV and LIGATE, with the participation of dozens of institutions, including Chelonia Applied Science, hosted at the Innovation Office of the University of Basel) demonstrate the potential that exascale HPC and machine learning offer for the health sciences.

From 2012 to 2018, for instance, the computational cost of advanced AI applications that use deep-learning models increased by 300,000 times, causing a significant rise in electric power consumption and resource utilization. The emerging green AI, or environmentally friendly AI, on the other hand, addresses the issue by minimizing ML's computational demand and reducing its carbon footprint.

NLP may have been a little late to the AI epiphany but it is doing wonders with organisations like Google, OpenAI releasing state-of-the-art(SOTA) language models like BERT and GPT-2/3 respectively. GitHub Copilot and OpenAI codex are among a few very popular applications that are in the news. As someone who has very limited exposure to NLP, I decided to take up NLP as an area of research and the next few blogs/videos will be me sharing what I learn after dissecting some important components of NLP. Top Deep Learning models like BERT, GPT-2, or GPT-3 all share the same components but with different architectures that distinguish one model from another. In this newsletter(and notebook), we are going to focus on the basics of the first component of an NLP pipeline which is tokenization.

This project developed a Unit converter that convert weight in pounds to kilograms using matching learning in python. Here we used Keras API of Tensorflow to get Artificial Intelligence and deep learning. It is one of the best example of Neural Network in python for Beginners.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Recent advances in deep learning have rekindled interest in the imminence of machines that can think and act like humans, or artificial general intelligence. By following the path of building bigger and better neural networks, the thinking goes, we will be able to get closer and closer to creating a digital version of the human brain. But this is a myth, argues computer scientist Erik Larson, and all evidence suggests that human and machine intelligence are radically different. Larson's new book, The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do, discusses how widely publicized misconceptions about intelligence and inference have led AI research down narrow paths that are limiting innovation and scientific discoveries.

We suggested in January that it might be a good idea to familiarize yourself with quantum computing if you want to maximize your future employability in financial services. A new academic paper from JPMorgan's Future Lab for Applied Research and Engineering helps explain why. Authored by Marco Pistoia, JPMorgan's head of quantum technology and head of research, plus members of his team, the paper stresses that quantum computing will impact financial services sooner than you think. Goldman Sachs and JPMorgan have both been building teams of quantum researchers and Goldman has already used quantum methods to speed up derivatives pricing by over a thousand times. The finance industry stands to benefit from quantum computing "even in the short term," says JPMorgan.