2019-12-11: Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection https://arxiv.org/abs/1912.05651v1We proposed a new approach to unsupervised out-of-distribution detection in input space as well as in latent space, using information-theoretic metrics based on the approximated posterior over the parameters of a variational autoencoder
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Despite their successes, deep neural networks still make unreliable
predictions when faced with test data drawn from a distribution different to
that of the training data, constituting a major problem for AI safety. While
this motivated a recent surge in interest in developing methods to detect such
out-of-distribution (OoD) inputs, a robust solution is still lacking. We
propose a new probabilistic, unsupervised approach to this problem based on a
Bayesian variational autoencoder model, which estimates a full posterior
distribution over the decoder parameters using stochastic gradient Markov chain
Monte Carlo, instead of fitting a point estimate. We describe how
information-theoretic measures based on this posterior can then be used to
detect OoD data both in input space as well as in the model's latent space. The
effectiveness of our approach is empirically demonstrated.
I'm excited to be talking Saturday at the @NeurIPSConf Workshop on Tackling Climate Change with Machine Learning workshop. From 8:30-9:05 AM, I'll give a talk about work we're doing at @GoogleAI in this area. From 11:15 AM-noon I'll be on a panel (cont) [climatechange.ai] @JeffDean
We're releasing "Dota 2 with Large Scale Deep Reinforcement Learning", a scientific paper analyzing our findings from our 3-year Dota project: [openai.com] One highlight we trained a new agent, Rerun, which has a 98% win rate vs the version that beat @OGEsports. [twitter] @OpenAI
Dataset of cognitive distortions[reddit]/r/datasets
Hey all, I am looking for a dataset of examples (posts, tweets, whatever) of cognitive distortions / negative thinking patterns, with no luck so far. Maybe someone has seen a study that used a similar dataset or in general something interesting around the subject (I have seen a couple of research projects, but not too many that were very promising). Also, any datasets around subjects of mental health, CBT, negative self-talk...
PyTorch implementation of Neural Painters Cool talk by @reiinakano at ML for creativity workshop. He released a new @PyTorch library and notebooks for others to easily play around with neural painter algorithms! [github.com][arxiv.org][twitter]In the same sense, using a neural painter leads to creative ways to achieve an objective whether it be recreating digits, painting faces, or maximizing a pre-trained network’s activations
2019-12-12: Line-based Camera Pose Estimation in Point Cloud of Structured Environments https://arxiv.org/abs/1912.05013v2In this paper, we have presented an image to point cloud registration method to simultaneously estimate line correspondence and camera pose in structured environments
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Accurate registration of 2D imagery with point clouds is a key technology for
image-LiDAR point cloud fusion, camera to laser scanner calibration and camera
localization. Despite continuous improvements, automatic registration of 2D and
3D data without using additional textured information still faces great
challenges. In this paper, we propose a new 2D-3D registration method to
estimate 2D-3D line feature correspondences and the camera pose in untextured
point clouds of structured environments. Specifically, we first use geometric
constraints between vanishing points and 3D parallel lines to compute all
feasible camera rotations. Then, we utilize a hypothesis testing strategy to
estimate the 2D-3D line correspondences and the translation vector. By checking
the consistency with computed correspondences, the best rotation matrix can be
found. Finally, the camera pose is further refined using non-linear
optimization with all the 2D-3D line correspondences. The experimental results
demonstrate the effectiveness of the proposed method on the synthetic and real
dataset (outdoors and indoors) with repeated structures and rapid depth
changes.
[D] Is it OK to not cite a relevant paper due to it not being open-access?[reddit]/r/MachineLearning
A recent paper has popped up on my radar that appears (from the title + abstract) to be related to the work in a paper I'm writing. However, it's published in a fairly low-tier conference that has all proceedings behind a pay-wall. I've tried to find it on the authors homepages and also tried to email them for a copy, but they haven't responded to me. Am I expected to buy the paper for 25 euros, just to see if the content is actually relevant?
I'm excited to be talking Saturday at the @NeurIPSConf Workshop on Tackling Climate Change with Machine Learning workshop. From 8:30-9:05 AM, I'll give a talk about work we're doing at @GoogleAI in this area. From 11:15 AM-noon I'll be on a panel (cont) [climatechange.ai]
![Figure 1: Histograms of LL (left) and ] BVAE-ESSθ φ (right) on FashionMNIST (in-distribution) and MNIST (out-of-distribution). The ESS score separates the data more clearly than the LL score. (Out-of-distribution detection in input space)](/q/Pj9dtbkxOH/f0.png)
![Figure 4: Histograms of LL (left) and ] BVAE-ESS (right) on SVHN (in-distribution) and CIFAR10 (out-of-distribution). The ESS score separates the data more clearly than the LL score. (SVHN vs. CIFAR10 results for OoD detection in input space)](/q/Pj9dtbkxOH/f3.png)
![Figure 1: Using a neural painter as a differentiable image parameterization [21], we are able to directly optimize brushstrokes to minimize style transfer’s content loss (left), or visualize ImageNet classes (right; pineapple, starfish, and violin categories). (Introduction)](/q/s9EVpK4nAa/f0.png)
