📌 Poster Session:
⏰ When: TODAY, Thu, Dec 12, 4:30 p.m. – 7:30 p.m. PST
📍 Where: East Exhibit Hall A-C, #3705
📄 What: Geometry of Naturalistic Object Representations in Recurrent Neural Network Models of Working Memory
Hope to see you there!
@bashivan.bsky.social @takuito.bsky.social
Excited to be at #NeurIPS2024 in #Vancouver! Our poster session is TODAY—if you're interested in naturalistic representations in dynamic working memory models, please drop by and let’s chat!
👉 Check it out: arxiv.org/abs/2411.02685
📅 We’ll be at NeurIPS! Join us for our poster presentation on Thu 12 Dec, 7:30 p.m. EST — 10:30 p.m. EST.
#AI #CognitiveScience #WorkingMemory #DeepLearning #RepresentationGeometry #MultiTask
Our findings bridge cognitive science & AI, revealing how high-dimensional object information is encoded, retained, and recalled in recurrent models of working memory.
🎯 With training, RNNs implemented chronological memory subspaces allowing them to track object information using rotational dynamics—supporting resource-based models of working memory.
📐 Surprisingly, object features are less orthogonalized in RNN representations compared to perceptual space.
🧠 We found that multi-task RNNs (unlike single-task ones) retain both task-relevant & irrelevant info but reusable representations only emerged in simple gateless architectures.