Mete Erdoğan

Excited to share that our paper “Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism”—with @CPehlevan and @Alper_T_E—was accepted as a #NeurIPS2025 Spotlight! 🎉 Paper:🔗 arxiv.org/pdf/2504.11558

1/ Wonderful student projects from CS329H (Fall ’25) ML from Human Preferences at Stanford University! 🚀 @sangttruong @andreas_h0wpt and I introduced students to preference learning + alignment, culminating in final projects. Out of ~50, here are 5 standouts 👇

For anyone interested in understanding orthogonalization/spectral based methods here’s the slides from our #neurips25 oral that I tried to make more broadly about the topic.

Check out @Meterdogan27 's poster at NeurIPS. He just started his PhD at Stanford with one oral, one spotlight, and one poster presentation. Go talk to him at #909 today!

We are presenting our work on backprop alternatives, "Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism," co-authored with @Meterdogan27 & @CPehlevan, today at #Neurips2025! 📍 Poster S. 1, Exhibit Hall C,D,E - #2006 (Wed, 11am–2pm PST)

@CPehlevan @Alper_T_E If you’ll be at @NeurIPSConf, come say hi! We’d love to dive into the details of the paper, answer questions, or chat about any related ideas. 📍 Poster Session 1 🗓️ Wed Dec 3, 11am–2pm PST 📌 Exhibit Hall C,D,E — #2006 See you in San Diego! 🌴✈️

Excited to share that our paper “Error Broadcast and Decorrelation as a Potential Artificial and Natural Learning Mechanism”—with @CPehlevan and @Alper_T_E—was accepted as a #NeurIPS2025 Spotlight! 🎉 Paper:🔗 arxiv.org/pdf/2504.11558

@CPehlevan @Alper_T_E We see this as a step toward unifying theoretically grounded objectives, biologically inspired credit assignment, and more flexible, hardware-efficient architectures, all without requiring symmetric backward weights.

@CPehlevan @Alper_T_E The resulting gradients naturally take a three-factor form: • pre-synaptic activity • post-synaptic nonlinearity • a broadcast error signal This structure mirrors the three-factor learning rules observed in biological neural circuits.

@CPehlevan @Alper_T_E We build a theoretical foundation for broadcast learning by leveraging a core MMSE principle: at optimum, the prediction error is orthogonal to any nonlinear function of the input. We enforce this via layerwise objectives that decorrelate the broadcast error from the activations.

@CPehlevan @Alper_T_E Backprop enables neural network training, but relies on a precise backward pathway that mirrors forward weights—a constraint that’s rigid for hardware and unlikely in biological systems. Error-broadcast methods bypass this by sending the output error directly to hidden layers.

@caglarml and I are excited to share our lecture slides for EE-628 Training Large Language Models course: epfl.ch/labs/lions/tea… If you have any feedback, please reach out to us. I am also at #ICLR25.

Introducing the first NeRF-based 3D style transfer method that generalizes across scenes and styles! Our approach eliminates the time-consuming optimization for each scene or style, making it more efficient than previous NeRF-based methods. Check it out! #GCPR2024

@mehmetdurmaz007 Kafes dövüşüne davet etseydin direk