​​Animating Pictures with Eulerian Motion Fields

New method for single image animation. Authors promised to release code soon!

Website: https://eulerian.cs.washington.edu
Paper: https://eulerian.cs.washington.edu/animating_pictures_2020.pdf
ArXiV: https://arxiv.org/abs/2011.15128
YouTube: https://www.youtube.com/watch?v=4zKliOMilGY

#DL #animation #WashingtonUni

​​Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes

This technology allows to move camera a bit on any video, slow down time or do both. Great application for video producers and motion designers.

Website: https://www.cs.cornell.edu/~zl548/NSFF/
ArXiV: https://arxiv.org/abs/2011.13084
YouTube: https://youtu.be/qsMIH7gYRCc

#Nerf #videointerpolation #DL

Trying Out New Ampere GPUs and MIG

Our hands on experience with new Ampere GPUs - 3090 and A100 (with multi instance GPU)

https://habr.com/ru/post/531436/

Please like / share / repost!

#hardware
#deep_learning

👩‍🎓Online lectures on Special Topics in AI: Deep Learning

Fresh free and open playlist on special topics in #DL from University of Wisconsin-Madison. Topics covering reliable deep learning, generalization, learning with less supervision, lifelong learning, deep generative models and more.

Overview Lecture: https://www.youtube.com/watch?v=6LSErxKe634&list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
YouTube Playlist: https://www.youtube.com/playlist?list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
Syllabus: https://pages.cs.wisc.edu/~sharonli/courses/cs839_fall2020/schedule.html

#wheretostart #lectures #YouTube

Opinion: Remote jobs are to stay even after pandemic

As Packy McCormick (author of Notboring blog) writes in his recent post: we are never going back. Pandemic has catalized the global switch to remote jobs and acceptance of it (including consideration of stock options for remote workers).

Given that we as @opendatascience community are able to admit that those are only opinions and the future is more complex and unpredictable, we are posting a list of remote job aggregators so every reader can explore those opportunities if needed.

Blog entry: https://notboring.substack.com/p/were-never-going-back
Audio version: https://open.spotify.com/show/6k1YLBvORRMyosKy3x1xIl?si=_Z7mdecqTSSYrwhHexwGEA

Telegram bots:

@sixnomads_bot — a bot that connects you with relevant remote and full-time jobs that fit your tech stack, desired time zone and salary
@remotejobss — a channel that posts new remote opportunities daily
@datasciencejobseeker — a chat for jobs in data science
@remotejobpositions — a channel with interesting remote jobs for developers.

Here are some websites that might be a good alternative for you:

remoteok.io — a colorful job board with remote jobs in tech companies (from the creators of Nomadlist)
weworkremotely.com — another job board with new remote opportunities updated every day.
remote.co — a hub to learn new tips on working remotely and find your new remote job.

#hr #career #job #remote

​​Tool for restoration of pixelated images

Tool uses De Bruijn sequence to restore the original information

Github: https://github.com/beurtschipper/Depix

#pixelization #github

​​MPG: A Multi-ingredient Pizza Image Generator with Conditional StyleGANs

Work on conditional image generation

ArXiV: https://arxiv.org/abs/2012.02821

#GAN #DL #food2vec

Bayesian Image Reconstruction using Deep Generative Models

Really impressive results reported, but there is no code yet.

ArXiV: https://arxiv.org/abs/2012.04567

#DL #BayesianLearning #Reconstruction

Yandex Team Talk at NeurIPS. Talk will be most interesting for those who are working on critical aspects of successful data collection and labeling.

Moderation team will focus on:
- Remoteness. A discussion about effectiveness and efficiency of remote work on crowdsourcing platforms.
- Fairness. How the working environment (e.g., a crowdsourcing platform) may help provide executors flexibility in choosing/switching tasks and working hours.
- Mechanisms. Discussion on bilateral mechanisms that not only provide flexibility to the performers, but also guarantee the quality of the result and the efficiency of the process to the customers.

Toloka's workshop info: https://clck.ru/SNwi3

#NeurIPS2020 #labeling #Yandex

​​Supporting content decision makers with machine learning

#Netflix shared a post providing information about how they research and prepare data for new title production.

Link: https://netflixtechblog.com/supporting-content-decision-makers-with-machine-learning-995b7b76006f

#NLU #NLP #recommendation #embeddings

🔥Everything You Always Wanted To Know About GitHub (But Were Afraid To Ask)

ClickHouse team provided extensive statistics on GitHub, including but not limited to distribution of repositories by star count, top repositories by stars, affinity list, top labels etc.

All the data is available for download with instructions for ClickHouse import

Link: https://gh.clickhouse.tech/explorer/

#GitHub #ClickHouse #Yandex #statistics #EDA #engineerketing

Do you need any more proofs that GitHub is the best social network ever?

If you know someone who might like our channel @opendatascience, please invite them or share this post

GML Newsletter - Issue #5: Was 2020 a good year for graph research?

My new newsletter is out! 🔥 Talking about my predictions for 2020, NeurIPS recordings, ICLR submissions and a few links that you probably have seen already, my friends!

Hey, fellow researchers, engineers and students.

We can recommend you another great frequently updated channel, covering Machine and Deep Learning: @ai_machinelearning_big_data

YolactEdge: Real-time Instance Segmentation on the Edge

Github: https://github.com/haotian-liu/yolact_edge

Demo: https://www.youtube.com/watch?v=GBCK9SrcCLM

Paper: https://arxiv.org/abs/2012.12259

@ai_machinelearning_big_data

​​QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement Learning

Paper extends the Deep Quality-Value (DQV) family of al-
gorithms to multi-agent  reinforcement learning and outperforms #SOTA

ArXiV: https://arxiv.org/abs/2012.12062

#DQV #RL #Starcraft

​​Solving Mixed Integer Programs Using Neural Networks

Article on speeding up Mixed Integer Programs with ML. Mixed Integer Programs are usually NP-hard problems:

- Problems solved with linear programming
- Production planning (pipeline optimization)
- Scheduling / Dispatching

Or any problems where integers represent various decisions (including some of the graph problems).

ArXiV: https://arxiv.org/abs/2012.13349
Wikipedia on Mixed Integer Programming: https://en.wikipedia.org/wiki/Integer_programming

#NPhard #MILP #DeepMind #productionml #linearprogramming #optimizationproblem