Machinelearning   49849

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NVIDIA/vid2vid: Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.
Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses.
machinelearning  video  cool  tools  graphics 
31 minutes ago by VoxPelli
Unsupervised Sentiment Neuron
While training the linear model with L1 regularization, we noticed it used surprisingly few of the learned units. Digging in, we realized there actually existed a single “sentiment neuron” that’s highly predictive of the sentiment value.
machinelearning  language  ai  SentimentAnalysis  linguistics  computing 
yesterday by wrrn
With talks on new techniques for and and a panel on Urban Data Science, you d…
MachineLearning  TerritoryManagement  from twitter_favs
2 days ago by rukku
The Human Diagnosis Project
The Human Diagnosis Project (also referred to as "Human Dx" or "the Project") is a worldwide effort created with and led by the global medical community to build an online system that maps the best steps to help any patient. By combining collective intelligence with machine learning, Human Dx intends to enable more accurate, affordable, and accessible care for all.
health  medicine  ai  machinelearning 
2 days ago by ssorc
How do we capture structure in relational data?
The key insight behind the DeepWalk algorithm is that random walks in graphs are a lot like sentences.

Grover and Leskovec (2016) generalize DeepWalk into the node2vec algorithm. Instead of “first-order” random walks that choose the next node based only on the current node, node2vec uses a family of “second-order” random walks that depend on both the current node and the one before it.

Under the structural hypothesis, nodes that serve similar structural functions — for example, nodes that act as a hub — are part of the same neighborhood due to their higher-order structural significance.

For instance, a user’s graph of friends on a social network can grow and shrink over time. We could apply node2vec, but there are two downsides.

It could be computationally expensive to run a new instance of node2vec every time the graph is modified.

Additionally, there is no guarantee that multiple applications of node2vec will produce similar or even comparable matrices .

Node2vec and DeepWalk produce summaries that are later analyzed with a machine learning technique. By contrast, graph convolutional networks (GCNs) present an end-to-end approach to structured learning.
graph  machinelearning  deeplearning 
3 days ago by mike

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