Felix Petersen

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: arxiv.org/abs/2411.04732

Join us today for Ubiquitous Ultra-Efficient Mobile Video Super-resolution + Denoising with Logic Gates at #ICCV2025 booth 247. 🌺

I'm excited to share that our work on convolutional differentiable logic gate networks was covered by MIT Technology Review. 🎉 technologyreview.com/2024/12/20/110… @StefanoErmon @HildeKuehne

The next generation of neural networks could live in hardware trib.al/65TrkHz

Convolutional Differentiable Logic Gate Networks @FHKPetersen

Join us today for our Oral presentation at 10:20am in East Ballroom A, B and our poster at 11am-2pm in East Exhibit Hall A-C #2411.

Join us now at our poster #1502 in East Exhibit Hall A-C 1502.

Join us on Wednesday, 11am-2pm for our poster on Newton Losses in West Ballroom A-D #6207. neurips.cc/virtual/2024/p…

Learn more in our paper 📑 (arxiv.org/abs/2410.23970) and check out our paper video directly here on 𝕏! --- Thanks to my co-authors Christian Borgelt and @StefanoErmon. @StanfordAILab

...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.

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📜 arxiv.org/abs/2410.23970 Video🎥 youtu.be/ZjTAjjxbkRY

Check out our 5 minute paper video directly here on 𝕏! 6/6 🧵

Also a shout-out to the methods we build on: Perturbed Optimizers @qberthet @mblondel_ml @BachFrancis NeuralSort @adityagrover_ @ecjwg SoftSort @eisenjulian 5/6 🧵

I'm excited to share our NeurIPS 2024 paper "Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms" 🤖. Paper link 📜: arxiv.org/abs/2410.19055 Video link 🎥: youtube.com/watch?v=7aFPIM… @StanfordAILab 1/6 🧵