In some implementations, a device may train the model to generate embeddings for log files associated with an application, and to enable the model to generate embeddings for sensitive information included in a set of training log files. The device may receive a log file associated with the application. The device may generate a compressed log file including a set of embedding vectors associated with records included in the log file, where a record that includes sensitive information is associated with one or more embedding vectors for the sensitive information and one or more embedding vectors for other information included in the record. The device may store the compressed log file including the set of embedding vectors where a size of the compressed file is less than a size of the log file, and the embedding vectors obfuscate the records included in the log file.

Search elements are extracted from requirement definitions of a requirement management tool for managing a project. The search elements may be extracted using natural language processing. The search elements are used to identify source code from source code repositories. Machine learning correlates the requirement definitions to source code subject matter. The extracted source code is confirmed by a stakeholder of the requirement management tool.

In some implementations, a collaborative knowledge system may receive a first set and a second set of privatized embeddings. The first set of privatized embeddings may be generated by a local model based on a first set of private documents associated with a first knowledge domain. The second set of privatized embeddings may be generated by a local model based on a second set of private documents associated with a second, different knowledge domain. The collaborative knowledge system may train, based on the first and second sets of privatized embeddings, a centralized model. The collaborative knowledge system may receive a query associated with the first knowledge domain or the second knowledge domain. The collaborative knowledge system may generate a response to the query based on processing the query with the centralized model. The collaborative knowledge system may provide the response to the query to a user device.

A cloud service system manages a filter repository including filters for encoding and decoding media content (e.g. text, image, audio, video, etc.). The cloud service system may receive a request from a client device to provide a filter for installation on a node such as an endpoint device (e.g. pipeline node). The request includes information such as a type of bitstream to be processed by the requested filter. The request may further include other information such as hardware configuration and functionality attribute. The cloud service system may access the filter repository that stores the plurality of filters including encoder filters and decoder filters and may select a filter that is configured to process the type of bitstream identified in the request and provide the selected filter to the client device.

A method includes obtaining data representative of a state or condition of an evaluation target. The method also includes providing first input based on the data to a trained classifier to generate a first result. The method further includes providing second input based on the data to an adaptive neuro-fuzzy inference system to generate a second result. The method also includes assigning a classification to the state or condition of the evaluation target based on the first result and the second result.

Techniques are described for generating multimodal training data cohorts tailored to specific clinical machine learning (ML) model inferencing tasks. In an embodiment, a method comprises accessing, by a system comprising a processor, multimodal clinical data for a plurality of subjects included in one or more clinical data sources. The method further comprises selecting, by the system, datasets from the multimodal clinical data based on the datasets respectively comprising subsets of the multimodal clinical data that satisfy criteria determined to be relevant to a clinical processing task. The method further comprises generating, by the system, a training data cohort comprising the datasets for training a clinical inferencing model to perform the clinical processing task.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing an input audio waveform using a generator neural network to generate an output audio waveform. In one aspect, a method comprises: receiving an input audio waveform; processing the input audio waveform using an encoder neural network to generate a set of feature vectors representing the input audio waveform; and processing the set of feature vectors representing the input audio waveform using a decoder neural network to generate an output audio waveform that comprises a respective output audio sample for each of a plurality of output time steps.

Disclosed herein are a method and apparatus for encoding/decoding a deep learning network. According to an embodiment, the method for decoding a deep learning network may include decoding network header information regarding the deep learning network; decoding layer header information regarding a plurality of layers in the deep learning network; decoding layer data information regarding specific information of the plurality of layers; and obtaining the deep learning network and a plurality of layers in the deep learning network, and the layer header information includes layer distinction information associated with distinguishing the plurality of layers.

A translation device includes a storage unit configured to store a plurality of pieces of learning data, a normalized sentence learning unit configured to perform learning on the plurality of pieces of learning data by combining original text for learning and a corresponding normalized sentence for learning, a translated sentence learning unit configured to perform learning on the plurality of pieces of learning data by combining the original text for learning and a corresponding translated sentence for learning, and a model generation unit configured to generate one normalization/translation model on the basis of a result of learning by the normalized sentence learning unit and the translated sentence learning unit, in which, on at least a part of the learning data, the translated sentence learning unit performs learning after the normalized sentence learning unit performs learning.

A method for training a machine learning model includes: sending, by a first node, first indication information, where the first indication information is used by a second node for determining a first quantization strategy, and the first quantization strategy is used for determining a parameter and/or an output result of the machine learning model.