Placeholder: A glitchy video message on the iconic multiple screens of Minority Report, inspired by "The Electronic Revolution", (William S. Burroughs), with fragmented words and disjointed images. The protagonists, watching the screens, maybe be aliens or phantasmagoric creatures from dreams A glitchy video message on the iconic multiple screens of Minority Report, inspired by "The Electronic Revolution", (William S. Burroughs), with fragmented words and disjointed images. The protagonists, watching the screens, maybe be aliens or phantasmagoric creatures from dreams

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@generalpha

Prompt

A glitchy video message on the iconic multiple screens of Minority Report, inspired by "The Electronic Revolution", (William S. Burroughs), with fragmented words and disjointed images. The protagonists, watching the screens, maybe be aliens or phantasmagoric creatures from dreams

distorted image, malformed body, malformed fingers

23 days ago

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Model

SSD-1B

Guidance Scale

7

Dimensions

1248 × 832

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A glitchy video message on the iconic multiple screens of Minority Report, inspired by "The Electronic Revolution", (William S. Burroughs), with fragmented words and disjointed images. The protagonists, watching the screens, maybe be aliens or phantasmagoric creatures from dreams
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A glitchy video poem on the iconic multiple smartphones of Minority Report, inspired by "The Electronic Revolution", (William S. Burroughs), with fragmented words and disjointed images.
[mahematics] In the context of universal approximation, two approaches can achieve similar results but with different parameter requirements. The overall system comprises data, architecture, and a loss function, interconnected by a learning procedure. Responsibilities within the system include acknowledging noisy or biased data, addressing the need for a large number of parameters in the architecture, and overcoming the principal-agent problem in the choice of the loss function.
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Local and global approaches in mathematics and machine learning are both universal approximators, but they differ in the number of parameters required to represent a given function accurately. The entire system, including data, architecture, and loss function, must be considered, as they are interconnected. Data can be noisy or biased, architecture may demand excessive parameters, and the chosen loss function may not align with the desired goal. To address these challenges, practitioners should
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