Machine Learning


A graph-convolutional neural network model for the prediction of chemical reactivity

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Let's dig deeper into the details of the algorithm. The authors used a Weisfeiler-Lehman Network, a type of graph-convolutional neural network, as depicted in the figure below. First, for each atom-atom pair (including pairs of atoms that are not bound or are located in different reactants) the neural network predicts the likelihood of the bond order to change. The starting point is from a graph representation of reactants (A), where atoms are featurized by atomic number, formal charge, degree of connectivity, valence and aromaticity, and bonds are featurized by bond order and ring status. These atom-level features are iteratively updated (B) by incorporating information from neighbor atoms.


Testing the ability of unmanned aerial systems and machine learning to map weeds at subfield scales: a test with the weed Alopecurus myosuroides (Huds) - Lambert - - Pest Management Science - Wiley Online Library

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The core objective of plant population ecology is to understand changes in numbers of individuals/organisms across time and space.1 Achieving this depends on methods that permit plants to be mapped and monitored at informative scales.2-4 Surveys of plant populations have been undertaken using a variety of different methods such as transect sampling, quadrat sampling and with unmanned aerial systems (UAS).5-7 Each of these methods has an inherent trade‐off between the area that can be surveyed and the intensity at which the subjects in that area can be studied.8 Transect and quadrat sampling can be used for either small area, high‐intensity studies or large area, low‐intensity studies, but typically not both.9 UAS present a unique opportunity for ecological monitoring because, potentially, they can yield data across both large spatial areas and at high survey intensity.


Deep Learning for Signal Processing Applications

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Why Google believes machine learning is its future

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One of the most interesting demos at this week's Google I/O keynote featured a new version of Google's voice assistant that's due out later this year. A Google employee asked the Google Assistant to bring up her photos and then show her photos with animals. She tapped one and said, "Send it to Justin." The photo was dropped into the messaging app. From there, things got more impressive.


r/MachineLearning - [R] Machine Learning Reproducibility Challenges and DVC

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When ML models need to be regularly updated in production, a host of challenges emerges. No one tool can do it all for you - organizations using a mix of Git, Makefiles, ad hoc scripts and reference files for reproducibility.


A Gentle Introduction to Object Recognition With Deep Learning

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The model is significantly faster to train and to make predictions, yet still requires a set of candidate regions to be proposed along with each input image. Python and C (Caffe) source code for Fast R-CNN as described in the paper was made available in a GitHub repository. The model architecture was further improved for both speed of training and detection by Shaoqing Ren, et al. at Microsoft Research in the 2016 paper titled "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks." The architecture was the basis for the first-place results achieved on both the ILSVRC-2015 and MS COCO-2015 object recognition and detection competition tasks. The architecture was designed to both propose and refine region proposals as part of the training process, referred to as a Region Proposal Network, or RPN. These regions are then used in concert with a Fast R-CNN model in a single model design. These improvements both reduce the number of region proposals and accelerate the test-time operation of the model to near real-time with then state-of-the-art performance.


Why You Need To Activate Intelligence in Your Business

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AI and automation are changing the business environment across industries, delivering new opportunities through intelligent, automated solutions. Some companies are ahead of the curve, while others are stagnating in adopting the technology. Operators and enterprises are aware of the benefits of AI and automation, but the questions that always remain are, "What does it bring to my business? How will it solve my problems?" Artificial intelligence (AI) is a constellation of technologies that describes the processes of intelligent automation, like machine learning, natural language processing (NLP), cognitive computing, and deep learning.


Create deep learning models with Flowpoints

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For those who like their dessert first: here's the finished model, and here's the colab for this example. A rather empty user-interface should show up on your screen. In the sidebar, click the Library-dropdown, and select TensorFlow. Now the code for our model will use TensorFlow instead of PyTorch. Next, click on the Theme-dropdown and select "orange".


AlphaGalileo Item Display

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In a recent pilot study, researchers from the National University of Singapore (NUS) have shown that a powerful artificial intelligence (AI) platform known as CURATE.AI could potentially be used to customise training regimens for individuals to personalise learning and improve cognitive performance. Using performance data from a given person, CURATE.AI creates an individualised profile that enables cognitive training to be tailored to the individual's learning habits and competencies so as to enhance training effectiveness. Such dynamic AI-guided personalisation overcomes the current limited improvement produced by using traditional training methods which often involve repetitive behavioural exercises. The results of the study provide evidence that the CURATE.AI platform has the potential to enhance learning, and paves the way for promising applications for personalised digital therapy, including the prevention of cognitive decline. The research, led by Professor Dean Ho and Assistant Professor Christopher L. Asplund from the N.1 Institute for Health (N.1) of NUS, which was formerly the Singapore Institute for Neurotechnology (SINAPSE), was published in the journal Advanced Therapeutics on 22 May 2019.


Swedish Distillery Creates First Whisky Designed By AI

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Would you drink a whisky designed and created by artificial intelligence? This fall, this hypothetical question becomes a reality, as popular award-winning Swedish whisky distillery Mackmyra releases the first ever whisky, a single malt, designed with machine learning. Working in collaboration with Microsoft and Fourkind, a Finnish technology consultancy specializing in AI spearhead projects, the distillery has made the claim that this is the first ever machine-learning designed complex consumer product recipe. I for one, welcome the chance to try a whisky created by our robot overlords. The distillery's machine learning models running off of Microsoft's Azure Cloud Computing platform and AI cognitive services will be fed raw data related to whisky production (including malting, fermentation, distillation, and maturation), Mackmyra's historical recipes, sales numbers, and customer preferences.