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Found 11 papers, 5 papers with code
It's All Connected: A Journey Through Test-Time Memorization, Attentional Bias, Retention, and Online Optimization
Going beyond these objectives, we present a set of alternative attentional bias configurations along with their effective approximations to stabilize their training procedure.
Titans: Learning to Memorize at Test Time
Over more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention.
Best of Both Worlds: Advantages of Hybrid Graph Sequence Models
Our theoretical evaluations of the representation power of Transformers and modern recurrent models through the lens of global and local graph tasks show that there are both negative and positive sides for both types of models.
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models
Despite recent attempts to improve the expressive power of SSMs by using deep structured SSMs, the existing methods are either limited to univariate time series, fail to model complex patterns (e. g., seasonal patterns), fail to dynamically model the dependencies of variate and time dimensions, and/or are input-independent.
MambaMixer: Efficient Selective State Space Models with Dual Token and Channel Selection
Motivated by the success of SSMs, we present MambaMixer, a new architecture with data-dependent weights that uses a dual selection mechanism across tokens and channels, called Selective Token and Channel Mixer.
Graph Mamba: Towards Learning on Graphs with State Space Models
Motivated by the recent success of State Space Models (SSMs), such as Mamba, we present Graph Mamba Networks (GMNs), a general framework for a new class of GNNs based on selective SSMs.
CAT-Walk: Inductive Hypergraph Learning via Set Walks
Our evaluation on 10 hypergraph benchmark datasets shows that CAT-Walk attains outstanding performance on temporal hyperedge prediction benchmarks in both inductive and transductive settings.
CS-TGN: Community Search via Temporal Graph Neural Networks
The evolution of these networks over time has motivated several recent studies to identify local communities in temporal networks.
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease Prediction
The problem of identifying anomalies in dynamic networks is a fundamental task with a wide range of applications.
CS-MLGCN : Multiplex Graph Convolutional Networks for Community Search in Multiplex Networks
Existing CS approaches in multiplex networks adopt pre-defined subgraph patterns to model the communities, which cannot find communities that do not have such pre-defined patterns in real-world networks.
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design
Specifically, they rely on the discreteness of the loss function, which means that real-valued weights cannot be directly used.
