[D] Where do you rent compute resources (GPU, FPGA, etc.)?[reddit]/r/MachineLearning
Where do you guys rent compute resources for training? What are your primary selection criteria (cost/reliability/bandwidth/ data location), for your particular use case?
Do you also own your own AI/ML gears for consistent workload, in addition to the cloud? I am asking this as I am building an exchange where people can share quality compute resources at-cost or near-cost. Would this be something that you are interested in? If not, what are the main objections?
And finally, if you are living in Melbourne (Australia) and actively learning/working with/competing in AI/ML, I'd like to take you out for Starbucks.
P.S. The project is close to release (on the technical side) so let me know if you want to poke around with the beta version. The compute (nodes with at least 2x 1080Ti & workstation CPU) is on me!
2019-09-12: 3D Ken Burns Effect from a Single Image https://arxiv.org/abs/1909.05483v1Using our synthesis model, the extreme views of the camera path are synthesized from the input image and the predicted depth map, which can be used to efficiently synthesize all intermediate views of the target video, resulting in the final 3D Ken Burns effect
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The Ken Burns effect allows animating still images with a virtual camera scan
and zoom. Adding parallax, which results in the 3D Ken Burns effect, enables
significantly more compelling results. Creating such effects manually is
time-consuming and demands sophisticated editing skills. Existing automatic
methods, however, require multiple input images from varying viewpoints. In
this paper, we introduce a framework that synthesizes the 3D Ken Burns effect
from a single image, supporting both a fully automatic mode and an interactive
mode with the user controlling the camera. Our framework first leverages a
depth prediction pipeline, which estimates scene depth that is suitable for
view synthesis tasks. To address the limitations of existing depth estimation
methods such as geometric distortions, semantic distortions, and inaccurate
depth boundaries, we develop a semantic-aware neural network for depth
prediction, couple its estimate with a segmentation-based depth adjustment
process, and employ a refinement neural network that facilitates accurate depth
predictions at object boundaries. According to this depth estimate, our
framework then maps the input image to a point cloud and synthesizes the
resulting video frames by rendering the point cloud from the corresponding
camera positions. To address disocclusions while maintaining geometrically and
temporally coherent synthesis results, we utilize context-aware color- and
depth-inpainting to fill in the missing information in the extreme views of the
camera path, thus extending the scene geometry of the point cloud. Experiments
with a wide variety of image content show that our method enables realistic
synthesis results. Our study demonstrates that our system allows users to
achieve better results while requiring little effort compared to existing
solutions for the 3D Ken Burns effect creation.
Simple statistical methods are shown to much better than fancy machine learning on a whole bunch of real-world sequence-prediction datasets. The reason: the time series used are tiny by ML standards, and all the ML methods overfit. [twitter] @togelius
[P] PyTorch implementation of 17 Deep RL algorithms[reddit]/r/MachineLearning
For anyone trying to learn or practice RL, here's a repo with working PyTorch implementations of 17 RL algorithms including DQN, DQN-HER, Double DQN, REINFORCE, DDPG, DDPG-HER, PPO, SAC, SAC Discrete, A3C, A2C etc..
How Do Developers Promote Open Source Projects?
https://arxiv.org/abs/1908.04219In this paper, we investigated the most common promotion channels used by popular GitHub projects
Abstract: Open source projects have an increasing importance on modern software
development. For this reason, these projects, as usual with commercial software
projects, should make use of promotion channels to communicate and establish
contact with users and contributors. In this article, we study the channels
used to promote a set of 100 popular GitHub projects. First, we reveal that
Twitter, user meetings, and blogs are the most common promotion channels used
by the studied projects. Second, we report a major difference between the
studied projects and a random sample of projects, regarding the use of the
investigated promotion channels. Third, we show the importance of a popular
news aggregation site (Hacker News) on the promotion of open source. We
conclude by presenting a set of practical recommendation to open source project
managers and leaders, regarding the promotion of their projects.
Thanks to causal machine learning, we are finding planets that could sustain life. Brilliant inspiring talk by Bernhard Schlkopf at #CCN2019 #explanatoryknowledge [twitter]
If you're based in South America ( only, though), consider taking part in this NLP challenge! & prizes win a ticket to @Khipu_AI! The problem has some cool properties: - Non-English languages: - Highly multiclass: ~1500 labels [mercadolibre.com] @seb_ruder
Mask-RCNN to cut objects [PROJECT][reddit]/r/MachineLearning
This is the project a friend and me did for our first online hackathon. It uses Mask-RCNN to extract easily objects from pictures. Although it was kinda strange it was really cool to work in an online hackathon, and we've learned a lot through the way.
![Fig. 2. Overview of our depth estimation pipeline. Given a high-resolution image, we start by estimating a coarse depth based on a low-resolution input image. This depth estimation network is guided by semantic information extracted using VGG-19 [?] and supervised on a computer-generated dataset with accurate ground truth depth in order to facilitate geometrically sound predictions. To avoid semantic distortions, we then adjust the depth map according to the segmentation of Mask R-CNN [?] and make sure that each salient object is mapped to a coherent plane. Lastly, we utilize a depth refinement network that, guided by the input image, upsamples the coarse depth and ensures accurate depth boundaries. (Semantic-aware Depth Estimation)](/q/OHYGsCSTts/f1.png)
