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Mikhail Grankin proslijedio/la je Tweet
"How to do machine learning efficiently". There's so much to love about this wonderful article. https://medium.com/hackernoon/doing-machine-learning-efficiently-8ba9d9bc679d …pic.twitter.com/n6otKKP3gJ
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Mikhail Grankin proslijedio/la je Tweet
Pop Music Transformer: Generating Music with Rhythm and Harmony. Google drive full of samples and a pretrained model in the repo. I chose two randomly - these are listed as prompted samples. h/t
@ak92501 abs: https://arxiv.org/abs/2002.00212 repo: https://github.com/YatingMusic/remi …pic.twitter.com/QtkKPpnsygPrikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mikhail Grankin proslijedio/la je Tweet
Pop Music Transformer: Generating Music with Rhythm and Harmony pdf: https://arxiv.org/pdf/2002.00212.pdf … abs: https://arxiv.org/abs/2002.00212 github: https://github.com/YatingMusic/remi …pic.twitter.com/nwdyGG6kvZ
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Mikhail Grankin proslijedio/la je Tweet
In January,
@anishathalye,@jjgort, and I ran a short class at@MIT_CSAIL on topics we think are missing in most CS programs — tools we use every day that everyone should know, like bash, git, vim, and tmux. And now the lecture notes and videos are online! https://missing.csail.mit.edu/ pic.twitter.com/xNSlLgJfd4Hvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mikhail Grankin proslijedio/la je Tweet
"We achieved an overall accuracy of 94.5%, more than 4.5% of an increase on the previous state-of-the-art"; classifying the patterns in 18,577 Scanning Electron Microscope imageshttps://twitter.com/Iain_Keaney/status/1224420379076198402 …
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Mikhail Grankin proslijedio/la je Tweet
Added ImageNet validation results for 164 pretrained
#PyTorch models on several datasets, incl ImageNet-A, ImageNetV2, and Imagenet-Sketch. No surprise, models with exposure to more data do quite well. Without extra, EfficientNets are holding their own.https://github.com/rwightman/pytorch-image-models/tree/master/results …Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mikhail Grankin proslijedio/la je Tweet
Check out BREAK - a new NLU benchmark for testing the ability of models to break down a question into the required steps for computing its answer. https://allenai.github.io/Break/ A work by Tomer Wolfson, accepted to TACL 2020.
@JonathanBerant@yoavgo@ankgup2@nlpmattgHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mikhail Grankin proslijedio/la je Tweet
Each downstream packages has tests and they are run at package build time. Those tests could be rerun (without rebuilding) whenever a new version of an upstream dependency is released to continuously tag a Known Good Set of packages that work well together. The cost can be high.
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Mikhail Grankin proslijedio/la je Tweet
Some people asked about DAIN. It's Depth-Aware Frame Interpolation. I like to try low frame-rate sources like 16fps 8mm family films. Footage is supposedly anonymous but that sure looks like Stan Lee... project: https://sites.google.com/view/wenbobao/dain … Original Footage: https://www.youtube.com/watch?v=o0tv4im6HPo …pic.twitter.com/rFGPPxBVkK
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Mikhail Grankin proslijedio/la je Tweet
This repo is full of amazing awesomeness. I don't know of anything else like it. Independent refactored carefully tested implementations of modern CNNshttps://twitter.com/wightmanr/status/1224178577593241602 …
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Mikhail Grankin proslijedio/la je Tweet
Given that data loading can be a major bottleneck in many DL projects, this sounds like an interesting project to check out: "Accelerating Pytorch with Nvidia DALI" --> "on small models it's ~4X faster than the Pytorch dataloader"https://github.com/yaysummeriscoming/DALI_pytorch_demo …
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Mikhail Grankin proslijedio/la je Tweet
I highly recommend checking out the lecture series from "Full Stack Deep Learning" on YouTube https://www.youtube.com/watch?v=5ygO8FxNB8c&t=920s …
#100DaysOfMLCodeHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mikhail Grankin proslijedio/la je Tweet
Today we announce a novel, open-source method for text generation tasks (e.g., summarization or sentence fusion), which uses edit operations instead of generating text from scratch, leading to less errors and faster model execution. Read about it below.https://goo.gle/38XfRXU
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Mikhail Grankin proslijedio/la je Tweet
If you're running xgboost, lgbm, or other forest based models in production you need to check out our new forest inference library. 40x faster predictions, cheaper than cpu and way less rack space.https://twitter.com/rapidsai/status/1222955777993797632 …
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Mikhail Grankin proslijedio/la je Tweet
Even my continued fiddling with the SHA-RNN model shows there's a _lot_ to be studied and explored. I haven't published new incremental progress but you can tie the RNN across the 4 layers to substantially decrease total params yet get nearly equivalent perplexity results.
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Mikhail Grankin proslijedio/la je Tweet
New paper: Towards a Human-like Open-Domain Chatbot. Key takeaways: 1. "Perplexity is all a chatbot needs" ;) 2. We're getting closer to a high-quality chatbot that can chat about anything Paper: https://arxiv.org/abs/2001.09977 Blog: https://ai.googleblog.com/2020/01/towards-conversational-agent-that-can.html …pic.twitter.com/5SOBa58qx3
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Mikhail Grankin proslijedio/la je Tweet
New blog post: Contrastive Self-Supervised Learning. Contrastive methods learn representations by encoding what makes two things similar or different. I find them very promising and go over some recent works such as DIM, CPC, AMDIM, CMC, MoCo etc.https://ankeshanand.com/blog/2020/01/26/contrative-self-supervised-learning.html …
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Mikhail Grankin proslijedio/la je Tweet
Amazing work. PS: we're all so screwed.https://twitter.com/quocleix/status/1222268281932828672 …
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Mikhail Grankin proslijedio/la je Tweet
@GoogleAI has officially released the Dataset discovery engine that helps Data scientists find useful datasets in a matter of a few clicks. If you have not tried it yet, check it out https://datasetsearch.research.google.com/ Fuel your#MachineLearning models with a massive amount of data nowHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
Mikhail Grankin proslijedio/la je Tweet
Check out Meena, a new state-of-the-art open-domain conversational agent, released along with a new evaluation metric, the Sensibleness and Specificity Average, which captures basic, but important attributes for normal conversation. Learn more below!https://goo.gle/36zB8Wj
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