Paper: https://arxiv.org/abs/2403.16933 Abstract: Effective learning in neuronal networks requires the adaptation of individual synapses given their relative contribution to solving a task. However, physical neuronal systems -- whether biological or artificial -- are constrained by spatio-temporal locality. How such networks can perform efficient credit...

I'm a 20-year-old Brazilian student who has always admired the field of machine learning engineering. I'm more interested in the field of computer vision, but the more I research, the more lost I become with so much content that comes my way. So I decided to go to dear reddit and ask you... can you give me tips, roadmaps, or anything that will help...

https://youtu.be/D_jt-xO_RmI Extremely good lecture, highest signal to noise of historical architecture advances of DL. submitted by /u/lkhphuc [link] [comments]

Recently I've heard many people saying reinforcement learning itself hasn't shown any improvement in many years (maybe alphago was the last big thing). Whereas other field of AI has seen many SOTA architectures like 'Transformers' for Sequence based tasks and 'ResNet', 'Diffusers' & 'VAE' like architectures for Computer vision tasks. Thought I...

TorchFix is a Python code static analysis tool - a linter with autofix capabilities - for users of PyTorch. It can be used to find and fix issues like usage of deprecated PyTorch functions and non-public symbols, and to adopt PyTorch best practices in general: https://github.com/pytorch-labs/torchfix submitted by /u/kit1980 [link] [comments]

Hey everyone! It looks like in a few years, the basic large language models (LLMs) we use will get commoditised, and it won't really matter which one you pick. The next big thing could be LLMs that use Retrieval-Augmented Generation (RAG), which means they need a ton of data to work well. Given that Google has access to loads of data through its search...