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Sebastian Risi

@risi1979 • 10,155 subscribers

Researching @ ITU Copenhagen, @SakanaAILabs, Co-founder @ https://t.co/EeVHNpCcDq. Collective Intelligence | Self-Organizing AI | Neuroevolution. ERC GROW-AI.

Shorts

Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:

Introducing Neural Developmental Programs (NDPs)🧬🧠Instead of neural networks with fixed architectures, we allow neural networks to grow through a dynamic self-organizing process, inspired by how biological nervous systems develop👇 PDF:

239,469 Aufrufe

RL is back! But is it always the best choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).

RL is back! But is it always the best choice? In a new paper, we investigate under what circumstances neuroevolution outperforms RL in transfer learning tasks. See more details in the thread below 🧵 While NE performs best in simpler domains, it will be interesting to see if the lessons learned here can also be applied to more complex systems/tasks (LLMs?).

11,130 Aufrufe

Videos

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Imagine you're building something with Lego and the bricks automatically recognize what structure is being build! Introducing Smart Cellular Bricks: Project page: PDF:

Imagine you're building something with Lego and the bricks automatically recognize what structure is being build! Introducing Smart Cellular Bricks: Project page: PDF:

11,637 Aufrufe • vor 8 Monaten

Excited to share our latest work: “Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots” 🪲🦎 We show that Hebbian learning outperforms LSTM-based adaptation for real-world transfer. It even works without domain randomization! It can handle: ✅ Uneven terrain ✅ Morphological damage ✅ Sim-to-real gaps

Excited to share our latest work: “Bio-Inspired Plastic Neural Networks for Zero-Shot Out-of-Distribution Generalization in Complex Animal-Inspired Robots” 🪲🦎 We show that Hebbian learning outperforms LSTM-based adaptation for real-world transfer. It even works without domain randomization! It can handle: ✅ Uneven terrain ✅ Morphological damage ✅ Sim-to-real gaps

14,387 Aufrufe • vor 1 Jahr

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