Neural network feature maps with shared weights over 100 layers behaves similar to a phase space!
This story is from 2026-08-31. It is preserved in the archive; the latest stories are on the live feed.
I am currently researching by my own how neural networks work, in this part, I am researching how a shared-weight resiudal neural network's feature map behaves, curently, sharing the stage 3 blocks of ConvNext. It seems that it iteratively refines the feature map instead of computing different ones…
Read the full story at r/deeplearning ↗
Timeline · 1 report
- 2026-08-31 14:32 · r/deeplearning
Neural network feature maps with shared weights over 100 layers behaves similar to a phase space!
More stories
- Anthropic says Claude 'leads' 26 percent of its AI R&D work — Engadget
- Gemini Hacked Three Companies in First Known Breakout by Google’s AI — Wall Street Journal Technology
- Introducing Kimi K3 on Amazon Bedrock — AWS Machine Learning Blog
- Optimizing agent system prompts with Amazon Bedrock AgentCore — AWS Machine Learning Blog
- Introducing Amazon SageMaker HyperPod Inference Gateway — AWS Machine Learning Blog
- Introducing Astra for Law — OpenAI News
- Alibaba ships Qwen3.8-Omni-Flash to watch, listen and call tools — r/LocalLLM
- OpenAI reveals cases of ‘concerning’ AI behaviour as it announces new disclosure system — The Guardian AI
Get the daily brief of stories like this at 6:30 every morning →