Excited to share our NeurIPS 2024 Oral, Convolutional Differentiable Logic Gate Networks, leading to a range of inference efficiency records, including inference in only 4 nanoseconds 🏎️. We reduce model sizes by factors of 29x-61x over the SOTA.
Paper: https://t.co/c4xFC0SAid
I'm excited to share that our work on convolutional differentiable logic gate networks was covered by MIT Technology Review. 🎉
https://t.co/Vm6Fdjkwxm
@StefanoErmon@HildeKuehne
Excited to share our NeurIPS 2024 Oral, Convolutional Differentiable Logic Gate Networks, leading to a range of inference efficiency records, including inference in only 4 nanoseconds 🏎️. We reduce model sizes by factors of 29x-61x over the SOTA.
Paper: https://t.co/c4xFC0SAid
Ever wondered how training dynamics differ between LLMs 🖋️ and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable".
Paper📜 https://t.co/6yIeDfTBq8
Video🎥 https://t.co/CyVVHQTWnp
I'm excited to share our NeurIPS 2024 paper "Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms" 🤖.
Paper link 📜: https://t.co/rNAF9HQUST
Video link 🎥: https://t.co/fzcWAAYgn4
@StanfordAILab
1/6 🧵
Learn more in our paper 📑 (https://t.co/6yIeDfTBq8) and check out our paper video directly here on 𝕏!
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Thanks to my co-authors Christian Borgelt and @StefanoErmon.
@StanfordAILab
Ever wondered how training dynamics differ between LLMs 🖋️ and Vision 👁️ models? We explore this and close the gap between VMs and LLMs in our #NeurIPS2024 paper "TrAct: Making First-layer Pre-Activations Trainable".
Paper📜 https://t.co/6yIeDfTBq8
Video🎥 https://t.co/CyVVHQTWnp
...and it speeds up overall training by factors ranging from 1.25x (for large ViT pre-training) to 4x (for ConvNets).
We benchmark TrAct on a suite of 50 experimental settings.
I'm excited to share our NeurIPS 2024 paper "Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms" 🤖.
Paper link 📜: https://t.co/rNAF9HQUST
Video link 🎥: https://t.co/fzcWAAYgn4
@StanfordAILab
1/6 🧵