In this paper, we address the "black-box" problem in predictive process analytics by building interpretable models that are capable to inform both what and why is a prediction. Predictive process analytics is a newly emerged discipline dedicated to providing business process intelligence in modern organisations. It uses event logs, which capture process execution traces in the form of multi-dimensional sequence data, as the key input to train predictive models. These predictive models, often built upon deep learning techniques, can be used to make predictions about the future states of business process execution. We apply attention mechanism to achieve model interpretability. We propose i) two types of attentions: event attention to capture the impact of specific process events on a prediction, and attribute attention to reveal which attribute(s) of an event influenced the prediction; and ii) two attention mechanisms: shared attention mechanism and specialised attention mechanism to reflect different design decisions in when to construct attribute attention on individual input features (specialised) or using the concatenated feature tensor of all input feature vectors (shared). These lead to two distinct attention-based models, and both are interpretable models that incorporate interpretability directly into the structure of a process predictive model. We conduct experimental evaluation of the proposed models using real-life dataset, and comparative analysis between the models for accuracy and interpretability, and draw insights from the evaluation and analysis results.

The Linked Open Data practice has led to a significant growth of structured data on the Web in the last decade. Such structured data describe real-world entities in a machine-readable way, and have created an unprecedented opportunity for research in the field of Natural Language Processing. However, there is a lack of studies on how such data can be used, for what kind of tasks, and to what extent they can be useful for these tasks. This work focuses on the e-commerce domain to explore methods of utilising such structured data to create language resources that may be used for product classification and linking. We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pre-training of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords. Our evaluation on an extensive set of benchmarks shows word embeddings to be the most reliable and consistent method to improve the accuracy on both tasks (with up to 6.9 percentage points in macro-average F1 on some datasets). The other two methods however, are not as useful. Our analysis shows that this could be due to a number of reasons, including the biased domain representation in the structured data and lack of vocabulary coverage. We share our datasets and discuss how our lessons learned could be taken forward to inform future research in this direction.

In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristics such as digit class, width, and style on MNIST. Furthermore, through topographic organization over time (i.e. temporal coherence), we demonstrate how predefined latent space transformation operators can be encouraged for observed transformed input sequences -- a primitive form of unsupervised learned equivariance. We demonstrate that this model successfully learns sets of approximately equivariant features (i.e. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Equivariance is verified quantitatively by measuring the approximate commutativity of the inference network and the sequence transformations. Finally, we demonstrate approximate equivariance to complex transformations, expanding upon the capabilities of existing group equivariant neural networks.

In particular, our ecosystem has the satisfying the set of idiosyncratic requirements that this ecosystem following set of requirements: has for AutoML solutions. Our framework was piloted and deployed in numerous applications and performed at the level of - AutoML system should be able to work with different types the experienced data scientists while building high-quality ML of data collected from hundreds of different information models significantly faster than these data scientists. We also compare systems and often changes more rapidly than these systems the performance of our system with various general-purpose can be fully documented using metadata and painstakingly open source AutoML solutions and show that it performs better for preprocessed by data scientists for the ML tasks using ETL most of the ecosystem and OpenML problems. We also present the tools.

Google plans to shut down its controversial Streams app, which analysed medical record information and aimed to improve monitoring of vital signs and other tests to improve care. The tech company's AI subsidiary, DeepMind, first announced in February 2016 that it was working with the National Health Service (NHS) trusts to analyse patient data. The company intended to combine machine learning with bulk medical data to develop models that could predict or diagnose acute kidney injury.

Efficient pipeline design is crucial for data scientists. When composing complex end-to-end workflows, you may choose from a wide variety of building blocks, each of them specialized for a dedicated task. Unfortunately, repeatedly converting between data formats is an error-prone and performance-degrading endeavor. To learn more on framework interoperability, check out our presentation at NVIDIA's GTC 2021 Conference. Zero-copy functionality is a crucial technique to efficiently copy data across GPU-accelerated data science frameworks: TensorFlow, PyTorch, MXNet, cuDF, CuPy, Numba, and JAX (see Figure 2).

SHL is hiring for research interns that can convert theory into practice. The selected interns would work with the AI Team on cutting edge AI/ML problems in computer vision, natural language processing and audio/speech processing. Interns along with the advancement in their career might also get a full time offer from the company based on their performances. Available to work full time for next 3 to 6 months. Should have strong programming skills preferably in Python.

It achieves orders of magnitude faster multi-agent RL training with 2000 environments and 1000 agents in a simple Tag environment. WarpDrive provides lightweight tools and workflow objects to build your own fast RL workflows. Check out the code, this blog, and the white paper for more details! The name WarpDrive is inspired by the science fiction concept of a fictional superluminal spacecraft propulsion system. Moreover, at the time of writing, a "warp" is a group of 32 threads that are executing at the same time in (certain) GPUs.

One possible definition of reinforcement learning (RL) is a computational approach to learning how to maximize the total sum of rewards when interacting with an environment. While a definition is useful, this tutorial aims to illustrate what reinforcement learning is through images, code, and video examples and along the way introduce reinforcement learning terms like agents and environments. As a previous post noted, machine learning (ML), a sub-field of AI, uses neural networks or other types of mathematical models to learn how to interpret complex patterns. Two areas of ML that have recently become very popular due to their high level of maturity are supervised learning (SL), in which neural networks learn to make predictions based on large amounts of data, and reinforcement learning (RL), where the networks learn to make good action decisions in a trial-and-error fashion, using a simulator. RL is the tech behind mind-boggling successes such as DeepMind's AlphaGo Zero and the StarCraft II AI (AlphaStar) or OpenAI's DOTA 2 AI ("OpenAI Five").

Note: The parameters in the neuron are initialized randomly. Get the function value 0.55…, the neural network at this time is an untrained network. The calculation of the gradient is complicated, especially for deep neural networks. Back Propagation Algorithm is an algorithm specifically designed to calculate the gradient of a neural network. Due to its complexity, it will not be described here.