Meet the new ALVI Interface: a breakthrough in intuitive prosthetic control.

This technology offers individuals with hand differences a new movement experience:
✨ Wrist rotation.
🖐 Finger movement.
🕹 Interaction with objects in VR.

Discover how we're turning futuristic dreams into today's reality. Be among the first to step into this new era of possibilities.

Recent demo:
https://youtu.be/Dx_6Id2clZ0?si=jF9pX3u7tSiKobM5

Brain-To-Text Competition 2024

This is the most fascinating BCI competition yet, organized by Stanford. Everyone has one month to develop the world's best brain-to-speech decoder!

Task: Predict attempted speech from brain activity.

Deadline: June 2, 2024

Dataset: They've recorded 12,100 sentences from a patient who can no longer speak intelligibly due to amyotrophic lateral sclerosis (ALS).

For each sentence, we provide the transcript of what the participant was attempting to say, along with the corresponding time series of neural spiking activity recorded from 256 microelectrodes in speech-related areas of cortex.

Just letting you know we're jumping into this challenge!
Together with @Altime, @kovalev_alvi and the team of ALVI Labs, we're going to create something interesting.

Like this post if you want to follow our updates❤️

Muscle-To-Motion Competition 2024

I’m happy to tell you about new competition!

This is the BCI competition, organized by ALVI Labs and BCI Initiative. You've got 21 days to cook up the world's best prosthetic control system.

Task: Predict finger movements from the muscle activation(EMG).

Deadline: August 5, 2024

Dataset: We've recorded 10+ hours of data from both healthy and amputee subjects.

We asked people to make different finger movements (simple moves, gestures, static moves). We recorded muscle activity using Myo Thalmic Armband ( 8 electrodes, 200Hz). Plus, we captured hand positions with 20 DoF at 25 Hz.

This isn't just classification - it's a full regression challenge. We'll test your model on a real amputee. Your code could directly impact someone's life.

Time to create some next-level shit and maybe, just maybe, revolutionize prosthetics. Let’s fun.❤️

kaggle: https://www.kaggle.com/c/bci-initiative-alvi-hci-challenge/overview

Github with code: https://github.com/BCI-I/BCI_ALVI_challenge

Сделай сам

Мы подготовили подборку полезных каналов на YouTube, где есть реализации архитектур с нуля, и, конечно, поделились ссылками на их примеры. Она поможет вам:
- детальнее погрузиться в тему архитектуры трансформеров для разных задач, именно этой архитектуре посвящена большая часть видео в нашей подборке
- попробовать применить трансформеры на практике для различных задач
- самостоятельно создать нужную для вас архитектуру с нуля с помощью представленных туториалов

Читайте новый пост по ссылке: https://deepschool-pro.notion.site/b1f197579cf648baa81c34a6005ad379?pvs=4

enhancing intuition in diffusion

Tutorial on Diffusion Models for Imaging and Vision
what: good and comprehensive tutorial about modern diffusion models.

Diffusion is spectral autoregression
what: blogpost with python notebook.

Diffusion Models are Evolutionary Algorithms
what: another view on diffusion
- the denoising process in diffusion models can be seen as a combination of selection and mutation.

if you know good blogs please write in comments

#knowledge

[2/4]
tasty transformer papers - october 2024

Differential Transformer
what: small modification for self attention mechanism.
- focuses on the most important information, ignoring unnecessary details.
- it does this by subtracting one attention map from another to remove "noise."
link: https://arxiv.org/abs/2410.05258

Pixtral-12B
what: good multimodal model with simple arch.
- Vision Encoder with ROPE-2D: Handles any image resolution/aspect ratio natively.
- Break Tokens: Separates image rows for flexible aspect ratios.
- Sequence Packing: Batch-processes images with block-diagonal masks, no info “leaks.”
link: https://arxiv.org/abs/2410.07073

Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
what: maskGIT with continual tokens.
- get vae with quantized loss but do not use quantization in decoder ( stable diffusion)
- propose BERT-like model to generate in random-order.
- ablation shows that bert-like better than gpt-like for images(tbh small improvements)
link: https://arxiv.org/abs/2410.13863

UniMTS: Unified Pre-training for Motion Time Series
what: one model to handle different device positions, orientations, and activity types.
- use graph convolution encoder to work with all devices
- contrastive learning with text from LLMs to “get” motion context.
- rotation-invariance: doesn’t care about device angle.
link: https://arxiv.org/abs/2410.19818

my thoughts

I'm really impressed with the Differential Transformer metrics. They made such a simple and clear modification. Basically, they let the neural network find not only the most similar tokens but also the irrelevant ones. Then they subtract one from the other to get exactly what's needed.

This approach could really boost brain signal processing. After all, brain activity contains lots of unnecessary information, and filtering it out would be super helpful. So it looks promising.

Mistral has really nailed how to build and explain models. Clear, brief, super understandable. They removed everything unnecessary, kept just what's needed, and got better results. The simpler, the better!

Конференция local:llama!

Oдни из лучших докладов из того что я видел за последнее время, слушать стоит почти всё, но особое внимание я бы уделил: quantizing your gguf,
history and advances of quantization in llama.cpp

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