I'm excited to share that our work on Convolutional Differentiable Logic Gate Networks was covered by MIT Technology Review. 🎉 www.technologyreview.com/2024/12/20/1... @hildekuehne.bsky.social

The next generation of neural networks could live in hardware

Researchers have devised a way to make computer vision systems more efficient by building networks out of computer chips’ logic gates.

technologyreview.com

Convolutional Differentiable Logic Gate Networks @FHKPetersen

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms @FHKPetersen

Join us at our poster session today, 11am-2pm, at East Exhibit Hall A-C *#1502*.

Felix Petersen@petersen.ai · 2y ago

Have you 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 link 📜: arxiv.org/abs/2410.23970 Video link 🎥: youtu.be/ZjTAjjxbkRY 🧵

Most innovative paper at #NeurIPS imho. Can we create a network that becomes the physical chip instead of running on a chip? Inference speedups and energy preservation are through the roof ! Oral on Friday at 10am PT neurips.cc/virtual/2024...

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

Felix Petersen@petersen.ai · 2y ago

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

Have you 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 link 📜: arxiv.org/abs/2410.23970 Video link 🎥: youtu.be/ZjTAjjxbkRY 🧵

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

Newton Losses: Using Curvature Information for Learning with Differentiable Algorithms

When training neural networks with custom objectives, such as ranking losses and shortest-path losses, a common problem is that they are, per se, non-differentiable. A popular approach is to continuou...

arxiv.org

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