29 citations · 94 across the 25 of their papers we have counts for
7 papers · 1 filter
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…
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…
Hydra: Dual Exponentiated Memory for Multivariate Time Series Analysis
Asal Meskin, Alireza Mirrokni, Ali Najar +1
In recent years, effectively modeling multivariate time series has gained significant popularity, mainly due to its wide range of applications, ranging from healthcare to financial…
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…
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
Ali Behrouz, Meisam Razaviyayn, Peilin Zhong +1
Designing efficient and effective architectural backbones has been in the core of research efforts to enhance the capability of foundation models. Inspired by the human cognitive p…