Omri Azencot A Koopman Approach To Understanding Sequence Neural Models

(PDF) A Koopman Approach To Understanding Sequence Neural Models

(PDF) A Koopman Approach To Understanding Sequence Neural Models We introduce a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method. motivated by the relation between time series models and self maps, we compute approximate koopman operators that encode well the latent dynamics. Key to our method is the koopman operator, which is a linear object that globally represents the dominant behavior of the network dynamics. the linearity of the koopman operator facilitates analysis via its eigenvectors and eigenvalues.

Figure 1 From A Koopman Approach To Understanding Sequence Neural Models | Semantic Scholar

Figure 1 From A Koopman Approach To Understanding Sequence Neural Models | Semantic Scholar Speaker: omri azencot title:: a koopman approach to understanding sequence neural models summary: deep learning models are often treated as 'black boxes'. existing app more. In this paper, we introduce a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method. at the core of our method lies the koopman operator, which is linear, yet it encodes the dominant features of the network latent dynamics. We introduce a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method. motivated by the relation between time series. Abstract we introduce a new approach to understanding trained sequence neural models: the koopman anal ysis of neural networks (kann) method. motivated by the relation between time series models and self maps, we compute approximate koopman operators which encode well the latent dynamics.

Figure 1 From A Koopman Approach To Understanding Sequence Neural Models | Semantic Scholar

Figure 1 From A Koopman Approach To Understanding Sequence Neural Models | Semantic Scholar We introduce a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method. motivated by the relation between time series. Abstract we introduce a new approach to understanding trained sequence neural models: the koopman anal ysis of neural networks (kann) method. motivated by the relation between time series models and self maps, we compute approximate koopman operators which encode well the latent dynamics. To evaluate our proposed consistent dynamic koopman ae, we perform a comprehensive study using various datasets and compare to state of the art koopman based approaches as well as other baseline sequential models. One paper accepted to aistats! one paper was accepted to aistats: "a multi task learning approach to linear multivariate forecasting." by…. This work introduces a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method, and shows that the operator eigendecomposition is instrumental in exploring the dominant features of the network. We introduce a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method. motivated by the relation between time series models and self maps, we compute approximate koopman operators that encode well the latent dynamics.

Figure 1 From A Koopman Approach To Understanding Sequence Neural Models | Semantic Scholar

Figure 1 From A Koopman Approach To Understanding Sequence Neural Models | Semantic Scholar To evaluate our proposed consistent dynamic koopman ae, we perform a comprehensive study using various datasets and compare to state of the art koopman based approaches as well as other baseline sequential models. One paper accepted to aistats! one paper was accepted to aistats: "a multi task learning approach to linear multivariate forecasting." by…. This work introduces a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method, and shows that the operator eigendecomposition is instrumental in exploring the dominant features of the network. We introduce a new approach to understanding trained sequence neural models: the koopman analysis of neural networks (kann) method. motivated by the relation between time series models and self maps, we compute approximate koopman operators that encode well the latent dynamics.

Omri Azencot: A Koopman Approach to Understanding Sequence Neural Models