From the 1 of 7 linked papers with an AI index.
7 papers
Online Neural Space Time Memory for Dynamic Novel View Synthesis
Baback Elmieh, Lynn Tsai, Zeman Li +8
The paper introduces an online neural space‑time memory system that periodically updates a persistent memory while applying it per frame, enabling real‑time novel view synthesis fo…
Mem3R: Streaming 3D Reconstruction with Hybrid Memory via Test-Time Training
Changkun Liu, Jiezhi Yang, Zeman Li +3
Streaming 3D perception is well suited to robotics and augmented reality, where long visual streams must be processed efficiently and consistently. Recent recurrent models offer a…
Memory Caching: RNNs with Growing Memory
Ali Behrouz, Zeman Li, Yuan Deng +3
Transformers have been established as the de-facto backbones for most recent advances in sequence modeling, mainly due to their growing memory capacity that scales with the context…
TNT: Improving Chunkwise Training for Test-Time Memorization
Zeman Li, Ali Behrouz, Yuan Deng +5
Recurrent neural networks (RNNs) with deep test-time memorization modules, such as Titans and TTT, represent a promising, linearly-scaling paradigm distinct from Transformers. Whil…
PiKE: Adaptive Data Mixing for Large-Scale Multi-Task Learning Under Low Gradient Conflicts
Zeman Li, Yuan Deng, Peilin Zhong +2
Modern foundation models are trained on diverse datasets to enhance generalization across tasks and domains A central challenge in this process is determining how to effectively mi…
ATLAS: Learning to Optimally Memorize the Context at Test Time
Ali Behrouz, Zeman Li, Praneeth Kacham +5
Transformers have been established as the most popular backbones in sequence modeling, mainly due to their effectiveness in in-context retrieval tasks and the ability to learn at s…