Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text summarization neural network.

Abstract: Methods, systems and computer program products are provided for creating and retrieving an immutable digital testimony involving activating a process on a device with a unique identifier and authenticating the creator of an immutable digital testimony using a unique user ID registered with a testimony network. The device activates associated cameras to generate a media stream. Frames are hashed using a selected scheme to create stream hashes, forming a live stream hash, and capturing metadata to be recorded on a blockchain. The stream media and hashing scheme are transmitted to a cloud server for storage, with cloud or distributed file system (DFS) addresses retrieved to determine where the media is saved. The live stream media is encrypted and sent to the DFS, encrypted media addresses are received, and the stream media is saved locally on the device.

Abstract: A rent-to-own (RTO) transaction system and method includes a customer computer and retail computer. A product inventory is displayed on the customer computer, and a product selection is made by a customer via the customer computer. Identifying information is received from the customer, and based thereon, the identity of the customer is verified. An RTO agreement based on the product selection and the identifying information is developed and displayed on the customer computer. An execution of the RTO agreement is made by the customer via the customer computer and received by the retail computer.

Abstract: A method of determining a raw score of a pathology slide from a tissue sample includes receiving, by a regression system, a plurality of first slide features corresponding to the pathology slide, calculating, by the regression system, one or more second slide features corresponding to the pathology slide based on the plurality of first slide features, and determining, by the regression system, the raw score based on one or more features of an accumulated feature set including the plurality of first slide features and the one or more second slide features.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Embodiments disclosed herein generally relate to representative datasets for biomedical machine learning models. Particularly, aspects of the present disclosure are directed to identifying a representative distribution of characteristics for a disease, generating a dataset comprising a set of biomedical images, wherein the dataset has a distribution of the characteristics that corresponds to the representative distribution of the characteristics for the disease, processing the dataset using a trained machine learning model, and outputting a result of the processing, wherein the result corresponds to a prediction that a biomedical image of the dataset includes a depiction of a set of tumor cells or other structural and/or functional biological entities associated with the disease, the biomedical image is associated with a diagnosis of the disease, the biomedical image is associated with a classification of the disease, and/or the biomedical image is associated with a prognosis for the disease.

Abstract: The present disclosure relates to techniques for efficient development of initial models and efficient model update and/or adaptation to a different image domain using an adaptive learning framework. For efficient development of initial models, a two-step development strategy may be performed as follows: Phase 1: Model preconditioning, where an artificial intelligence system leverages existing annotated datasets and improves learning skills through training of these datasets; and Phase 2: Target-model training, where an artificial intelligence system utilizes the learning skills learned from Phase 1 to extend itself to a different image domain (target domain) with less number of annotations required in the target domain than conventional learning methods.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text summarization neural network.

Abstract: Methods, systems and computer program products are provided for creating and retrieving an immutable digital testimony involving activating a process on a device with a unique identifier and authenticating the creator of an immutable digital testimony using a unique user ID registered with a testimony network. The device activates associated cameras to generate a media stream. Frames are hashed using a selected scheme to create stream hashes, forming a live stream hash, and capturing metadata to be recorded on a blockchain. The stream media and hashing scheme are transmitted to a cloud server for storage, with cloud or distributed file system (DFS) addresses retrieved to determine where the media is saved. The live stream media is encrypted and sent to the DFS, encrypted media addresses are received, and the stream media is saved locally on the device.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text summarization neural network.

Abstract: Techniques for image segmentation of a digital pathology image may include accessing an input image that depicts a section of a tissue; and generating a segmentation image by processing the input image using a generator network, the generator network having been trained using a data set that includes a plurality of pairs of images. The segmentation image indicates, for each of a plurality of artifact regions of the input image, a boundary of the artifact region. At least one of the plurality of artifact regions depicts an anomaly that is not a structure of the tissue. Each pair of images of the plurality of pairs includes a first image of a section of a tissue, the first image including at least one artifact region, and a second image that indicates, for each of the at least one artifact region of the first image, a boundary of the artifact region.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating an output sequence from an input sequence. One of the methods includes, at each of a plurality of generation time steps: generating a combined sequence for the generation time step that includes the input sequence followed by the output tokens that have already been generated as of the generation time step; processing the combined sequence using a self-attention decoder neural network to generate a time step output that defines a score distribution over a set of possible output tokens; and selecting, using the time step output, an output token from the set of possible output tokens as the next output token in the output sequence.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a text summarization neural network.