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Ken OtwellI thought the claim WAS fraud by Twitter? Twitter fraudulently under-reporting the bot numbers.

Mike Lorreymisrepresenting real user numbers is, actually, fraud, so he gets out of the billion dollar fee.

Shubham Ghosh Roy shared a post.

Michael MacLauchlan shared a link to the group: Futuristic Cities.


Recent text-to-image generation methods provide a simple yet exciting conversion capability between text and image domains. While these methods have incrementally improved the generated image fidelity and text relevancy, several pivotal gaps remain unanswered, limiting applicability and quality. We propose a novel text-to-image method that addresses these gaps by (i) enabling a simple control mechanism complementary to text in the form of a scene, (ii) introducing elements t… See more.


Recent text-to-image generation methods provide a simple yet exciting.

Conversion capability between text and image domains. While these methods have.

Incrementally improved the generated image fidelity and text relevancy, several.

Existing pre-trained models are generally geared towards a particular class of problems. To date, there seems to be still no consensus on what the right architecture and pre-training setup should be. This paper presents a unified framework for pre-training models that are universally effective across datasets and setups. We begin by disentangling architectural archetypes with pre-training objectives — two concepts that are commonly conflated. Next, we present a generalized and unified perspective for self-supervision in NLP and show how different pre-training objectives can be cast as one another and how interpolating between different objectives can be effective. We then propose Mixture-of-Denoisers (MoD), a pre-training objective that combines diverse pre-training paradigms together.