1073
Hot data science related posts every hour. Chat: https://telegram.me/r_channels
D Why do current LLMs work well in discrete space but not in continuous space?
One interesting observation is that LMs are trained to predict tokens over a categorical distribution and then a sampling algorithm is used to discretize the distribution to produce an output. If we try this in a continuous domain, e.g., predict pixels directly with L2 loss, it doesn't work, the output gets very blurry. It seems that the descretization via sampling is crucial to make things work during inference. Recent papers like GIVT can model the output as a gaussian mixture instead of a categorical distribution, but sampling is still necessary to make it work.
I'm sure this isn't some new observation, are there any resources out there that can help explain why this is the case?
/r/MachineLearning
https://redd.it/18vfj7k
D Do we really know how token probability leads to reasoning? For example, when we give GPT4 a riddle and it selves it using non-intuitive logic, how is that happening?
GPT4 can solve the below very basic riddle/question with ease.
Example riddle: You have a cup and a ball. You place the ball on the table and place the cup over the ball. You then place the cup on the kitchen counter. Where is the ball?
Answer: It's still on the original table of course.
How does a probability engine know that reasoning?
/r/MachineLearning
https://redd.it/18q9ucf
D What are 2023's top innovations in ML/AI outside of LLM stuff?
What really caught your eye so far this year? Both high profile applications but also research innovations which may shape the field for decades to come.
/r/MachineLearning
https://redd.it/18hnh8p
PubMedBERT Embeddings - Semantic search and retrieval augmented generation for medical literature
https://huggingface.co/NeuML/pubmedbert-base-embeddings
/r/LanguageTechnology
https://redd.it/17y4a5s
How do you keep up with the advancements with/in LLMs?
I would like to know how everyone can stay (almost) up-to-date with the advancements in LLMs? Do you follow survey papers/any blogs/newsletters/channels etc?
/r/LanguageTechnology
https://redd.it/18akb71
Converting multiview images to 3d model
Hi, I'm looking for a solution to convert multi-view images that look like this:
https://preview.redd.it/wbquazk95i4c1.png?width=1762&format=png&auto=webp&s=5a9ded99bc0eb8891e73f7519317a7aeae572044
into 3D models (obj, glb, etc.)
I'm pretty sure I can do this with NeRF but I'm fairly new to the field (coming from a classic software engineer background) so I'm looking for suggestions and some advice on this topic.
I've also been suggested to use COLMAP for this purpose but from what I understand COLMAP seems to be a different kind of tool that is meant to reconstruct 3d from multiple poses / camera pictures.
I'd also welcome any existing projects that could help me save some time doing so. Thanks!
/r/computervision
https://redd.it/18bftod
We've programmed our DIY smartwatch to take the wheel and steer the Space Rover around 🚀🌌
/r/computervision
https://redd.it/186q043
[R]eading List for Andrej Karpathy’s “Busy person’s intro to Large Language Models” Video
I loved Andrej’s talk about in his “Busy person’s intro to Large Language Models” video, so I decided to create a reading list to dive in deeper to a lot of the topics. I feel like he did a great job of describing the state of the art for anyone from an ML Researcher to any engineer who is interested in learning more.
The full talk can be found here:
https://youtu.be/zjkBMFhNj_g?si=fPvPyOVmV-FCTFEx
Here’s the reading list:
https://blog.oxen.ai/reading-list-for-andrej-karpathys-intro-to-large-language-models-video/
Let me know if you have any other papers you would add!
/r/MachineLearning
https://redd.it/184qeuo
Who wants to take on Duolingo?
Hello /r/LanguageTechnology \-
I've recently built my prototype for an NLP based language learning application which was received very positively. I've decided to go full time on it and make it a reality, and am looking for another technical co-founder to join me on this journey as CTO.
I'm a technical founder, but as I'm currently doing all the development, design, admin, sales, support, funding, etc, I'm looking for someone to just focus on the tech. The tech includes:
\- python backend
\- clojurescript frontend
\- NLP (currently via stanza, although partial support for spacy as well)
\- lots of LLM integrations
\- lots of TTS integrations
\- lots of vector embeddings
​
If any polyglots out there are passionate about NLP/LLMs/AI, genuinely believe they can change the world, dream in docker files and model weights, and just want to work on really cool tech, please send me a DM 🙏 let's change the world together.
Site can be found at https://phrasing.app. The app is currently in private beta, if you're interested in the application but not the job, fill out the beta application and send me a DM and I'll prioritize your access 😁
PS: I messaged mods first for permission, but heard no response. I hope this post is well received.
​
/r/LanguageTechnology
https://redd.it/180h4s9
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Have a live conversation about a basketball game with GPT4V, Whisper, TTS
/r/computervision
https://redd.it/17ywiwp
Open Source Multicamera Calibration in a GUI: pyxy3d
/r/computervision
https://redd.it/18rcwy4
D Deep dive into the MMLU ("Are you smarter than an LLM?")
After all the hubbub around the MMLU (for example my article) I thought I would make an interface for seeing how humans do versus even middle of the pack LLM. It's called Are You Smarter Than An LLM?
It presents you random questions from the MMLU and compares your answers to the LLM. Click the "what is this" button at the bottom for more details on how it works.
Feedback appreciated!
/r/MachineLearning
https://redd.it/18ntia7
KL divergence
Why do we use KL divergence in VAEs, it seems to me that we could just minimize the cross entropy between our models distribution and the Gaussian (or whatever distribution we choose for latent space). Is it simply because KL divergence has a minimum of 0 and we already know the entropy of the Gaussian? I guess more what I am getting at is how do we choose between kl divergence and cross entropy as a loss function if minimizing one always minimizes the other?
/r/deeplearning
https://redd.it/18k82sv
3D Pose Estimation of Two Interacting Hands from a Monocular Event Camera
/r/computervision
https://redd.it/18fvkv5
D Thoughts on Mamba?
I ran the NanoGPT of Karparthy replacing Self-Attention with Mamba on his TinyShakespeare Dataset and within 5 minutes it started spitting out the following:
​
https://preview.redd.it/4r96tp6lxx4c1.png?width=836&format=png&auto=webp&s=10f2f61cd4cea96f4f903cb2070835fc5d1df951
​
https://preview.redd.it/32ler5vnxx4c1.png?width=622&format=png&auto=webp&s=dd00e53f43dd0afa058758a987901ee6789d2258
​
https://preview.redd.it/sc96i4xoxx4c1.png?width=678&format=png&auto=webp&s=94d2ed279054363d3ed2b6beed65be89468582b0
So much faster than self-attention, and so much smoother, running at 6 epochs per second. I'm honestly gobsmacked.
https://colab.research.google.com/drive/1g9qpeVcFa0ca0cnhmqusO4RZtQdh9umY?usp=sharing
​
Some loss graphs:
Multihead attention without truncation\(x is iterations in 10s, and y is loss\)
Multihead attention with truncation\(x is iterations in 10s, and y is loss\)
Mamba loss graph\(x is iterations in 10s, and y is loss\)
​
​
/r/MachineLearning
https://redd.it/18d65bz
After two years of self-study, my first independent paper: Cross-Axis Transformer with 2D Rotary Embeddings
https://arxiv.org/pdf/2311.07184v1.pdf
/r/computervision
https://redd.it/189bo8i
A good modern textbook to get me up to speed on NLP in Python?
Hey everyone,
I have an MS in Statistics, but the focus was not on NLP - more classical models with a little machine learning. I'm not sure what's hip in the NLP circles, but I don't want to go down a bunch of rabbit holes trying to find out. Any suggestions?
/r/LanguageTechnology
https://redd.it/17xa49p
I built an open source motion capture system that costs $20 and runs at 150fps! Details in comments
/r/computervision
https://redd.it/17xi20j
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553. @blue_archive_reddit
554. @r_disneyvacation
555. @r_marvelstudios
556. @r_heraldry
557. @r_progmetal
558. @ya_metro
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561. @r_kgbtr
562. @r_bitcoin
563. @r_mechanicalkeyboards
564. @r_osugame
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567. @r_LiverpoolFC
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578. @reddit_cartoons
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580. @r_zig
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585. @r_edgerunners
586. @r_homeassistant
587. @reddit_fashion
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589. @r_publicfreakout
590. @r_tamamo
591. @DetroitBecomeHumanbackup
592. @r_porn
593. @r_channels_tifu
594. @r_antimeme
595. @trueoffmychest
596. @r_foxes
597. @r_PlipPlip
598. @r_grandorder
599. @r_punee
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306. @r_vexillologycirclejerk
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194. @r_hololive
195. @brasildob
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197. @rJackSucksAtLife
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🎂🎂🎂🎂🎂🎂🎂
🎁 Today @datascientology is 7 years old.
🎉 Congratulations! 🎈
Serverless development experience for embedded computer vision
I recently published the version 1.0 of the Pipeless framework. It provides the development experience of serverless web frameworks to create computer vision applications that run directly on devices.
What that means is you provide some Python functions and those are executed when there is a new video frame. The framework manages everything for you including parallelization, streams management, executing your functions when they have to be executed, etc. You can run it in your devices and provide input streams via a CLI or REST API. It supports multi-stream processing, dynamic stream configuration, ships some inference runtimes so you just need to provide a model, and a bunch of other cool features.
It is working at a very decent performance. I have reached real-time 15 FPS in a CPU of 4 cores with a YOLO model. When using GPU it more than doubles that.
If someone is interested I really appreciate feedback!
You can find the repo here: https://github.com/pipeless-ai/pipeless
/r/computervision
https://redd.it/17vemga
