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  2. Text-to-image model - Wikipedia

    en.wikipedia.org/wiki/Text-to-image_model

    A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Text-to-image models began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks.

  3. Flux (text-to-image model) - Wikipedia

    en.wikipedia.org/wiki/Flux_(text-to-image_model)

    Flux (also known as FLUX.1) is a text-to-image model developed by Black Forest Labs, based in Freiburg im Breisgau, Germany. Black Forest Labs were founded by former employees of Stability AI. As with other text-to-image models, Flux generates images from natural language descriptions, called prompts.

  4. Ideogram (text-to-image model) - Wikipedia

    en.wikipedia.org/wiki/Ideogram_(text-to-image_model)

    Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.

  5. Stable Diffusion - Wikipedia

    en.wikipedia.org/wiki/Stable_Diffusion

    Stable Diffusion is a deep learning, text-to-image model released in 2022 based on diffusion techniques. The generative artificial intelligence technology is the premier product of Stability AI and is considered to be a part of the ongoing artificial intelligence boom.

  6. T5 (language model) - Wikipedia

    en.wikipedia.org/wiki/T5_(language_model)

    T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [ 1 ] [ 2 ] Like the original Transformer model, [ 3 ] T5 models are encoder-decoder Transformers , where the encoder processes the input text, and the decoder generates the output text.

  7. Neuro-symbolic AI - Wikipedia

    en.wikipedia.org/wiki/Neuro-symbolic_AI

    Approaches for integration are diverse. [11] Henry Kautz's taxonomy of neuro-symbolic architectures [12] follows, along with some examples: . Symbolic Neural symbolic is the current approach of many neural models in natural language processing, where words or subword tokens are the ultimate input and output of large language models.

  8. Lymphocyte - Wikipedia

    en.wikipedia.org/wiki/Lymphocyte

    A lymphocyte is a type of white blood cell (leukocyte) in the immune system of most vertebrates. [1] Lymphocytes include T cells (for cell-mediated and cytotoxic adaptive immunity), B cells (for humoral, antibody-driven adaptive immunity), [2] [3] and innate lymphoid cells (ILCs; "innate T cell-like" cells involved in mucosal immunity and homeostasis), of which natural killer cells are an ...

  9. Follicular lymphoma - Wikipedia

    en.wikipedia.org/wiki/Follicular_lymphoma

    In situ follicular lymphoma is an accumulation of monoclonal B cells (i.e. cells descendent from a single ancestral cell) in the germinal centers of lymphoid tissue. These cells commonly bear a pathological genomic abnormality, i.e. a translocation between position 32 on the long (i.e. "q") arm of chromosome 14 and position 21 on chromosome 18's q arm.