Machine learning and other gibberish
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
See also: https://sharing.leima.is
Archives: https://datumorphism.leima.is/amneumarkt/
Diffusion Models for Handwriting Generation
https://github.com/tcl9876/Diffusion-Handwriting-Generation
What is going on with trolli? Was ist passiert?
https://www.visualcapitalist.com/gen-z-favorite-brands-compared-with-older-generations/
Liu, Yong, Haixu Wu, Jianmin Wang, and Mingsheng Long. 2022. “Non-Stationary Transformers: Exploring the Stationarity in Time Series Forecasting.” ArXiv [Cs.LG], May. https://doi.org/10.48550/ARXIV.2205.14415.
https://github.com/thuml/Nonstationary_Transformers
#ml
https://arxiv.org/abs/2210.10101v1
Bernstein, Jeremy. 2022. “Optimisation & Generalisation in Networks of Neurons.” ArXiv [Cs.NE], October. https://doi.org/10.48550/ARXIV.2210.10101.
https://arxiv.org/abs/2210.10101v1
Bernstein, Jeremy. 2022. “Optimisation & Generalisation in Networks of Neurons.” ArXiv [Cs.NE], October. https://doi.org/10.48550/ARXIV.2210.10101.
#ml
😱😱😱 Fancy
Video: https://fb.watch/giO0tV4N4T
Press: https://research.facebook.com/publications/dressing-avatars-deep-photorealistic-appearance-for-physically-simulated-clothing/
Xiang, Donglai, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, et al. 2022. “Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing.” ArXiv [Cs.GR], June. https://arxiv.org/abs/2206.15470
😱😱😱 Fancy
Video: https://fb.watch/giO0tV4N4T
Press: https://research.facebook.com/publications/dressing-avatars-deep-photorealistic-appearance-for-physically-simulated-clothing/
Xiang, Donglai, Timur Bagautdinov, Tuur Stuyck, Fabian Prada, Javier Romero, Weipeng Xu, Shunsuke Saito, et al. 2022. “Dressing Avatars: Deep Photorealistic Appearance for Physically Simulated Clothing.” ArXiv [Cs.GR], June. https://arxiv.org/abs/2206.15470
#ml
https://developer.nvidia.com/blog/how-optimize-data-transfers-cuda-cc/
I find this post very useful. I have always wondered what happens after my dataloader prepared everything for the GPU. I didn’t know that CUDA has to copy the data again to create page-locked memory.
I used to set pin_memory=True in a PyTorch DataLoader and benchmark it. To be honest, I have only observed very small improvements in most of my experiments. So I stopped caring about pin_memory.
After some digging, I also realized that performance from setting pin_memory=True in DataLoader is ticky. If we don’t use multiprocessing nor reuse the page-locked memory, it is hard to expect any performance gain.
(some other notes: https://datumorphism.leima.is/cards/machine-learning/practice/cuda-memory/)
https://developer.nvidia.com/blog/how-optimize-data-transfers-cuda-cc/
I find this post very useful. I have always wondered what happens after my dataloader prepared everything for the GPU. I didn’t know that CUDA has to copy the data again to create page-locked memory.
I used to set pin_memory=True in a PyTorch DataLoader and benchmark it. To be honest, I have only observed very small improvements in most of my experiments. So I stopped caring about pin_memory.
After some digging, I also realized that performance from setting pin_memory=True in DataLoader is ticky. If we don’t use multiprocessing nor reuse the page-locked memory, it is hard to expect any performance gain.
(some other notes: https://datumorphism.leima.is/cards/machine-learning/practice/cuda-memory/)
NVIDIA Technical Blog
How to Optimize Data Transfers in CUDA C/C++
In the previous three posts of this CUDA C & C++ series we laid the groundwork for the major thrust of the series: how to optimize CUDA C/C++ code. In this and the following post we begin our…
#ml
Amazon has been updating their Machine Learning University website. It is getting more and more interesting.
They have added an article about linear regression recently. There is a section in this article about interpreting linear models and it is just fun.
https://mlu-explain.github.io/
( Time machine: https://t.me/amneumarkt/293 )
Amazon has been updating their Machine Learning University website. It is getting more and more interesting.
They have added an article about linear regression recently. There is a section in this article about interpreting linear models and it is just fun.
https://mlu-explain.github.io/
( Time machine: https://t.me/amneumarkt/293 )
#showerthoughts
I've never thought about dark mode in LaTeX. It sounds weird at first, but now thinking about this, it's actually a great style.
This is a dark style from Dracula.
https://draculatheme.com/latex
I've never thought about dark mode in LaTeX. It sounds weird at first, but now thinking about this, it's actually a great style.
This is a dark style from Dracula.
https://draculatheme.com/latex
Draculatheme
LaTeX
The most famous dark theme for LaTeX and an ever-growing selection of apps! 🦇
#ML
This is interesting.
Toy Models of Superposition. [cited 15 Sep 2022]. Available: https://transformer-circuits.pub/2022/toy_model/index.html#learning
This is interesting.
Toy Models of Superposition. [cited 15 Sep 2022]. Available: https://transformer-circuits.pub/2022/toy_model/index.html#learning
Germany is so small.
My GitHub profile ranks 102 in Germany by public contributions.
https://github.com/gayanvoice/top-github-users
> Tracking of an Eagle over a 20 year period.
Source:
https://twitter.com/Loca1ion/status/1566346534651924480?s=20&t=AKXn9U-L3fyhrJzeAXySlA
#fun
Some results from the stable difussion model. See comments for some examples.
https://huggingface.co/CompVis/stable-diffusion
Some results from the stable difussion model. See comments for some examples.
https://huggingface.co/CompVis/stable-diffusion
huggingface.co
CompVis/stable-diffusion · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Hmm not so many contributions from wild animals.
Source: https://www.weforum.org/agenda/2021/08/total-biomass-weight-species-earth
Data from this paper: https://www.pnas.org/doi/10.1073/pnas.1711842115#T1
#ml
https://ai.googleblog.com/2022/08/optformer-towards-universal.html?m=1
I find this work counter intuitive. They took some descriptions of the optimization in machine learning and trained a transformer to "guesstimate" the hyperparameters of a model.
I understand that human being has some "feeling" of the hyperparameters after working with the data and model for a while. But it is usually hard to extrapolate such knowledge when we have completely new data and models.
I guess our brain is doing some statistics based on our historical experiments. And we call this intuition. My "intuition" is that there is little generalizable knowledge in this problem. 🙈 It would have been so great if they investigated the saliency maps.
https://ai.googleblog.com/2022/08/optformer-towards-universal.html?m=1
I find this work counter intuitive. They took some descriptions of the optimization in machine learning and trained a transformer to "guesstimate" the hyperparameters of a model.
I understand that human being has some "feeling" of the hyperparameters after working with the data and model for a while. But it is usually hard to extrapolate such knowledge when we have completely new data and models.
I guess our brain is doing some statistics based on our historical experiments. And we call this intuition. My "intuition" is that there is little generalizable knowledge in this problem. 🙈 It would have been so great if they investigated the saliency maps.
#fun
I became a beta tester of DALLE. Played with it for a while and it is quite fun. See the comments for some examples.
Comment if you would like to test some prompts.
I became a beta tester of DALLE. Played with it for a while and it is quite fun. See the comments for some examples.
Comment if you would like to test some prompts.
#fun
> participants who spent more than six hours working on a tedious and mentally taxing assignment had higher levels of glutamate — an important signalling molecule in the brain. Too much glutamate can disrupt brain function, and a rest period could allow the brain to restore proper regulation of the molecule
https://www.nature.com/articles/d41586-022-02161-5
> participants who spent more than six hours working on a tedious and mentally taxing assignment had higher levels of glutamate — an important signalling molecule in the brain. Too much glutamate can disrupt brain function, and a rest period could allow the brain to restore proper regulation of the molecule
https://www.nature.com/articles/d41586-022-02161-5
Nature
Why thinking hard makes us feel tired
Nature - Difficult tasks can lead to build-up of a signalling molecule in the brain, triggering fatigue.
