Scientists Use DL and Other Tools for Wuhan Novel Coronavirus Host and Infectivity Prediction
A new study suggests human-to-human transmission of the 2019-nCoV may have started as early as mid December and predicted the R0 to be about 2.2. The article here mentions multiple related studies, one uses deep learning to compare infectivity patterns of nCoV to other viruses — hopefully to help identify the host.
Detecting Hidden Signs of Anemia From the Eye
Google researchers find that a deep learning model can quantify hemoglobin using de-identified photographs of the back of the eye and common metadata (e.g. age, self-reported sex) from the UK Biobank, a population-based study. Compared to just using metadata, deep learning improved the detection of anemia (as measured using the AUC), from 74 percent to 88 percent
(Google Blog) / (Paper)
AI-Analyzed Blood Test Can Predict the Progression of Neurodegenerative Disease
Researchers at The Neuro (Montreal Neurological Institute and Hospital) of McGill University and the Ludmer Centre for Neuroinformatics and Mental Health used an AI algorithm to analyze the blood and post-mortem brain samples of patients with Alzheimer’s and Huntington’s diseases.
(GEN) / (Paper)
OpenAI Goes All-In on Facebook’s PyTorch Machine Learning Framework
In what might only be perceived as a win for Facebook, OpenAI announced that it will migrate to the social network’s PyTorch machine learning framework in future projects, eschewing Google’s long-in-the-tooth TensorFlow platform.
(Venture Beat) / (OpenAI)
Life Without a Brain: Neuroradiological and Behavioral Evidence of Neuroplasticity Necessary to Sustain Brain Function in the Face of Severe Hydrocephalus
A two-year-old rat survived a lifetime of extreme hydrocephaly affecting the size and organization of its brain. BOLD MRI was used to study the reorganization of its brain function showing global activation to visual, olfactory and tactile stimulation, particularly in the brainstem/cerebellum. The results are discussed in the context of neuroadaptation in the face of severe hydrocephaly and subsequent tissue loss, with an emphasis on what is the “bare minimum” for survival.
A Gentle Introduction to Deep Learning for Graphs
This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys.
(University of Pisa)
Towards a Human-Like Open-Domain Chatbot
Google Brain researchers present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is trained to minimize perplexity, an automatic metric that we compare against human judgement of multi-turn conversation quality.
(Google Brain) / (Synced)
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Next Generation Arxiv and the Economics of Open Access Publishing
Making science accessible for all is a wonderful and enriching proposal. Unfortunately the execution is not that easy — and nobody knows this better than the team that pioneered arXiv, the world’s largest free scientific paper repository.
Microsoft ImageBERT | Cross-modal Pretraining with Large-scale Image-Text Data
A recent paper published by Microsoft researchers proposes a new vision-language pretrained model for image-text joint embedding, ImageBERT, which achieves SOTA performance on both the MSCOCO (image retrieval task) and Flickr30k (text retrieval) datasets.
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