29 citations · 94 across the 25 of their papers we have counts for
6 papers · 1 filter
Titans: Learning to Memorize at Test Time
Ali Behrouz, Peilin Zhong, Vahab Mirrokni
Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data in…
Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
Ali Behrouz, Ali Parviz, Mahdi Karami +3
Modern sequence models (e.g., Transformers, linear RNNs, etc.) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational po…
A Unified Core Structure in Multiplex Networks: From Finding the Densest Subgraph to Modeling User Engagement
Farnoosh Hashemi, Ali Behrouz
In many complex systems, the interactions between objects span multiple aspects. Multiplex networks are accurate paradigms to model such systems, where each edge is associated with…
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models
Ali Behrouz, Michele Santacatterina, Ramin Zabih
Modeling multivariate time series is a well-established problem with a wide range of applications from healthcare to financial markets. Traditional State Space Models (SSMs) are cl…
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Ali Behrouz, Michele Santacatterina, Ramin Zabih
Recent advances in deep learning have mainly relied on Transformers due to their data dependency and ability to learn at scale. The attention module in these architectures, however…
Graph Mamba: Towards Learning on Graphs with State Space Models
Ali Behrouz, Farnoosh Hashemi
Graph Neural Networks (GNNs) have shown promising potential in graph representation learning. The majority of GNNs define a local message-passing mechanism, propagating information…