Join us at our #ICLR2024 workshop: "Bridging the Gap Between Practice and Theory in Deep Learning"! Workshop website: sites.google.com/view/bgpt-iclr…

RNNs are popular but some limitations are known — RNNs cannot solve some algorithmic problems that Transformers can. Our new paper "RNNs Are NOT Transformers (Yet)" explores the representation gap between constant-memory RNNs and Transformers and potential ways to bridge the gap.

To reduce human bias in model architecture, we propose a simple, yet effective LLM-like visual framework, called GiT, applicable for various vision tasks (e.g., VL tasks and segmentation) only with a vanilla ViT. :) Code: github.com/Haiyang-W/GiT arxiv.org/abs/2403.09394

Great work!

#ICLR2024 Just arrived in Vienna! Don't miss our oral presentation tomorrow afternoon in room Halle A3, focusing on 𝗚𝗡𝗡𝘀 and their 𝗲𝘅𝗽𝗿𝗲𝘀𝘀𝗶𝘃𝗲 𝗽𝗼𝘄𝗲𝗿! Also, swing by our poster session (Poster272, Halle B). See you there! 🌟