From the 1 of 16 linked papers with an AI index.
1 citations · 1 across the 6 of their papers we have counts for
14 papers · 1 filter
Language Models Need Sleep: Learning to Self-Modify and Consolidate Memories
Ali Behrouz, Farnoosh Hashemi, Adel Javanmard +1
The past few decades have witnessed significant advances in the design of machine learning algorithms, from early studies on task-specific shallow models to more general deep Large…
Tapered Language Models
Reza Bayat, Ali Behrouz, Aaron Courville
Modern language models, including transformer, recurrent, and memory-based variants, share a common chassis: a stack of identical layers in which parameters are allocated uniformly…
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…
Nested Learning: The Illusion of Deep Learning Architectures
Ali Behrouz, Meisam Razaviyayn, Peilin Zhong +1
Despite the recent progresses, particularly in developing Language Models, there are fundamental challenges and unanswered questions about how such models can continually learn/mem…
Trellis: Learning to Compress Key-Value Memory in Attention Models
Mahdi Karami, Ali Behrouz, Praneeth Kacham +1
Transformers, while powerful, suffer from quadratic computational complexity and the ever-growing Key-Value (KV) cache of the attention mechanism. This paper introduces Trellis, a…
MS-SSM: A Multi-Scale State Space Model for Efficient Sequence Modeling
Mahdi Karami, Ali Behrouz, Peilin Zhong +2
State-space models (SSMs) have recently attention as an efficient alternative to computationally expensive attention-based models for sequence modeling. They rely on linear recurre…