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162
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2.032
Unterkategorien
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Begriffe
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Self-Supervised Learning

Learning paradigm where the system generates its own labels from input data to create supervision signals without human intervention.

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Contrastive Pre-training

Learning method that maximizes similarity between representations of positive instances and minimizes that of negative instances in latent space.

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Masked Modeling

Technique where parts of the input data are masked and the model learns to reconstruct or predict these missing segments.

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Reconstruction Learning

Approach where the network learns representations by training to reconstruct the original input from a degraded or encoded version.

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Time Series

Ordered collection of data points indexed chronologically, exhibiting intrinsic temporal dependencies and correlations.

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Temporal Embedding

Dense vector representation that captures dynamic features and temporal relationships of sequential data.

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Long-Term Memory

Property of advanced architectures enabling the preservation and access to distant information in the temporal sequence.

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Future Frame Prediction

Task specific to video sequences where the model generates or anticipates subsequent frames based on previous frames.

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Spatio-Temporal Encoding

Process of transforming video data into vector representations simultaneously capturing spatial and temporal relationships.

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Unsupervised Learning

Paradigm where the model learns relevant representations without using manual labels provided by human experts.

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Pretext Task

Auxiliary learning objective designed to enable the acquisition of useful representations in the absence of direct supervision.

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Dynamic Representations

Learned features that evolve and adapt to capture changes and transitions in temporal data.

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Temporal Generative Model

Architecture capable of generating new realistic temporal sequences by learning the underlying data distribution.

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Temporal Normalization

Preprocessing technique that standardizes temporal features to stabilize training and improve convergence.

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Multi-Horizon Prediction

Model's ability to generate predictions for different future time intervals simultaneously from a single present state.

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