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Neural Networks

Dialysis adequacy predictions using a machine learning method - Scientific Reports


Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to measuring dialysis adequacy by blood samples. This study used machine learning models to predict dialysis adequacy in chronic hemodialysis patients using repeatedly measured data during hemodialysis. This study included 1333 hemodialysis sessions corresponding to the monthly examination dates of 61 patients. Patient demographics and clinical parameters were continuously measured from the hemodialysis machine; 240 measurements were collected from each hemodialysis session. Machine learning models (random forest and extreme gradient boosting [XGBoost]) and deep learning models (convolutional neural network and gated recurrent unit) were compared with multivariable linear regression models. The mean absolute percentage error (MAPE), root mean square error (RMSE), and Spearman’s rank correlation coefficient (Corr) for each model using fivefold cross-validation were calculated as performance measurements. The XGBoost model had the best performance among all methods (MAPE = 2.500; RMSE = 2.906; Corr = 0.873). The deep learning models with convolutional neural network (MAPE = 2.835; RMSE = 3.125; Corr = 0.833) and gated recurrent unit (MAPE = 2.974; RMSE = 3.230; Corr = 0.824) had similar performances. The linear regression models had the lowest performance (MAPE = 3.284; RMSE = 3.586; Corr = 0.770) compared with other models. Machine learning methods can accurately infer hemodialysis adequacy using continuously measured data from hemodialysis machines.

Short Introduction to Generative Adversarial Networks (GANs)


General Adversarial Network (GAN) are a generative modelling approach using deep learning neural networks such as CNN. There are two types of modelling techniques, i) Discriminative modelling and ii) generative modelling. Discriminative models are typical one that are used for classification in machine learning. They take input as features X (image, for image classification) and predict the output Y(probability of the image) for the given features. On the other hand, generative models outputs features X (image) given a random value.

Review on COVID‐19 diagnosis models based on machine learning and deep learning approaches


COVID-19 is the disease evoked by a new breed of coronavirus called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Recently, COVID-19 has become a pandemic by infecting more than 152 million people in over 216 countries and territories. The exponential increase in the number of infections has rendered traditional diagnosis techniques inefficient. Therefore, many researchers have developed several intelligent techniques, such as deep learning (DL) and machine learning (ML), which can assist the healthcare sector in providing quick and precise COVID-19 diagnosis. Therefore, this paper provides a comprehensive review of the most recent DL and ML techniques for COVID-19 diagnosis.

DeepMind's XLearn trains AI agents to complete complex tasks


All the sessions from Transform 2021 are available on-demand now. DeepMind today detailed its latest efforts to create AI systems capable of completing a range of different, unique tasks. By designing a virtual environment called XLand, the Alphabet-backed lab says that it managed to train systems with the ability to succeed at problems and games including hide and seek, capture the flag, and finding objects, some of which they didn't encounter during training. The AI technique known as reinforcement learning has shown remarkable potential, enabling systems to learn to play games like chess, shogi, Go, and StarCraft II through a repetitive process of trial and error. But a lack of training data has been one of the major factors limiting reinforcement learning–trained systems' behavior being general enough to apply across diverse games.

Chipmaker Ampere to acquire AI startup OnSpecta


Chipmaker Ampere on Wednesday announced its plan to acquire OnSpecta, a startup whose software accelerates AI inference workloads in the cloud and the edge. The terms of the deal were not disclosed. OnSpecta, founded in 2017 and headquartered in Redwood City, Calif, has already collaborated with Ampere. The OnSpecta Deep Learning Software (DLS) has proven to accelerate Ampere-based instances running popular AI-inference workloads by 4x. Last year, Ampere started shipping its Altra processor, an Arm-based server chip for cloud computing and hyperscale data centers.

AI technologies used in Robotics


Robotics today is not the same as assembly line Robots of the industrial age because AI is impacting many areas of Robotics. At the AI labs, we have been exploring a few of these areas using the Dobot Magician Robotic Arm in London. Our work was originally inspired by this post from Google which used the Dobot Magician( build your own machine learning powered robot arm using TensorFlow ...). In essence, the demo allows you use voice commands to enable the robotic arm to pick up specific objects (ex a red domino). This demo uses multiple AI technologies.

Optimizing model accuracy and latency using Bayesian multi-objective neural architecture search - Facebook Research


We propose a method for sample-efficient optimization of the trade-offs between model accuracy and on-device prediction latency in deep neural networks. Neural architecture search (NAS) aims to provide an automated framework that identifies the optimal architecture for a deep neural network machine learning model given an evaluation criterion such as model accuracy. The continuing trend toward deploying models on end user devices such as mobile phones has led to increased interest in optimizing multiple competing objectives in order to achieve an optimal balance between predictive performance and computational complexity (e.g., total number of flops), memory footprint, and latency of the model. Existing NAS methods that rely on reinforcement learning and/or evolutionary strategies can incur prohibitively high computational costs because they require training and evaluating a large number of architectures. Many other approaches require integrating the optimization framework into the training and evaluation workflows, making it difficult to generalize to different production use-cases.

A Primer of Neural Networks


With the advancement of technology, Artificial Intelligence starts to live its golden age. We wake up everyday to new and exciting inventions that can be used for the benefit of living things. Throughout the history, human beings are influenced by the nature. We use nature to cope with the problems we encountered by mimicking it. A lot of tools and vehicles are inspired by animals and nature.

Introduction to Artificial Neural Networks


An artificial Neural Network(ANN) is an information processing element that is similar to the biological neural network. It is a combination of multiple interconnected neurons that execute information in parallel mode. It has the capability to learn by example. ANN is flexible in nature, it has the capability to change the weights of the network. ANN is like a black box trained to solve complex problems.

U of T prof's AI startup, Deep Genomics, raises US$180 million: The Globe and Mail


Deep Genomics, an artificial intelligence startup founded by the University of Toronto's Brendan Frey, has secured US$180 million from investors, including Japanese multinational Softbank and Canada Pension Plan Investments, the Globe and Mail reported. Launched in 2015, the startup uses machine learning to develop treatments for genetic diseases. According to the Globe and Mail, Deep Genomics currently has 10 drugs in pre-clinical development, four of which are set to enter human trials by mid-2023. It is also working with San Francisco Bay-area biopharmaceutical company BioMarin Pharmaceutical Inc. to identify drug candidates for rare diseases. "These are all new chemical entities that would not exist" without Deep Genomics' technology," Frey, who is CEO of Deep Genomics and a professor in U of T's Faculty of Applied Science & Engineering, told the Globe.