Zobrazeno 1 - 10
of 52 879
pro vyhledávání: '"State-Space Models"'
The newly introduced Visual State Space Model (VMamba), which employs \textit{State Space Mechanisms} (SSM) to interpret images as sequences of patches, has shown exceptional performance compared to Vision Transformers (ViT) across various computer v
Externí odkaz:
http://arxiv.org/abs/2411.17283
State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear state-space models. We introduce a new method, StateMixNN, that uses a pai
Externí odkaz:
http://arxiv.org/abs/2411.15638
Autor:
Sushma, Neeraj Mohan, Tian, Yudou, Mestha, Harshvardhan, Colombo, Nicolo, Kappel, David, Subramoney, Anand
Deep state-space models (Deep SSMs) have shown capabilities for in-context learning on autoregressive tasks, similar to transformers. However, the architectural requirements and mechanisms enabling this in recurrent networks remain unclear. This stud
Externí odkaz:
http://arxiv.org/abs/2410.11687
Recent work has shown that state space models such as Mamba are significantly worse than Transformers on recall-based tasks due to the fact that their state size is constant with respect to their input sequence length. But in practice, state space mo
Externí odkaz:
http://arxiv.org/abs/2410.11135
Autor:
Rusch, T. Konstantin, Rus, Daniela
We propose Linear Oscillatory State-Space models (LinOSS) for efficiently learning on long sequences. Inspired by cortical dynamics of biological neural networks, we base our proposed LinOSS model on a system of forced harmonic oscillators. A stable
Externí odkaz:
http://arxiv.org/abs/2410.03943
Autor:
Bhat, Siddhanth
State space models (SSMs) have emerged as a powerful framework for modelling long-range dependencies in sequence data. Unlike traditional recurrent neural networks (RNNs) and convolutional neural networks (CNNs), SSMs offer a structured and stable ap
Externí odkaz:
http://arxiv.org/abs/2410.03158
Autor:
Kitagawa, Genshiro
This paper explores a Bayesian self-organization method for state-space models, enabling simultaneous state and parameter estimation without repeated likelihood calculations. While efficient for low-dimensional models, high-dimensional cases like sea
Externí odkaz:
http://arxiv.org/abs/2411.16056
State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of observations related to the state is available. The
Externí odkaz:
http://arxiv.org/abs/2411.15637
Autor:
Beylkin, Gregory
We present a fast, robust algorithm for applying a matrix transfer function of a linear time invariant system (LTI) in time domain. Computing $L$ states of a multiple-input multiple-output (MIMO) LTI appears to require $L$ matrix-vector multiplicatio
Externí odkaz:
http://arxiv.org/abs/2411.17729