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Originally developed using Google's own Imagen text-to-image model, DreamBooth implementations can be applied to other text-to-image models, where it can allow the model to generate more fine-tuned and personalized outputs after training on three to five images of a subject. [1] [2] [3]
Widevine is a proprietary digital rights management (DRM) system that is included in most major web browsers and in the operating systems Android and iOS.It is used by streaming services such as Netflix, Amazon Prime, Hulu etc., to allow authorized users to view media while preventing them from creating unauthorized copies.
The ideas of teaching machines and programmed learning provided the basis for later ideas such as open learning and computer-assisted instruction. Illustrations of early teaching machines can be found in the 1960 sourcebook, Teaching Machines and Programmed Learning. [12] An "Autotutor" was demonstrated at the 1964 World's Fair. [13]
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.
The models and the code were released under Apache 2.0 license on GitHub. [4] An individual Inception module. On the left is a standard module, and on the right is a dimension-reduced module. A single Inception dimension-reduced module. The Inception v1 architecture is a deep CNN composed of 22 layers. Most of these layers were "Inception modules".
Neoverse V1 (code named Zeus [3]) is derived from the Cortex-X1 [4] and implements the ARMv8.4-A instruction set and some part of ARMv8.6-A. [5] It was officially announced by Arm on September 22, 2020. [6] It is said to be initially realized with a 7 nm process from TSMC. One of the changes from the X1 is that it supports SVE 2x256-bit.
The CM-1 and CM-2 design teams were led by Tamiko Thiel. [10] The physical form of the CM-1, CM-2, and CM-200 chassis was a cube-of-cubes, referencing the machine's internal 12-dimensional hypercube network, with the red light-emitting diodes (LEDs), by default indicating the processor status, visible through the doors of each cube.
JAX is a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).