Abstract: One example method includes receiving, at a decision tree trained with a group of training observations, a group of new observations, traversing the decision tree with the new observations, calculating, for one or more nodes of the decision tree, a respective local diversity score, and aggregating the local diversity scores to create an aggregate diversity score, and the aggregate diversity score indicates an extent to which one or more of the new observations are similar, in one or more respects, to the group of training observations.

Abstract: An outlier detection mechanism is disclosed that improves transparency and explainability in machine learning models. The outlier detection mechanism can quantify, at prediction time, how a new observation differs from training observations. The outlier detection mechanism can also provide a way to aggregate outputs from decision trees by weighting the outputs of the decision trees based on their explainability.

Abstract: A logistics system for generating hazard alerts. Nodes in an environment generate position data. Local alerts are generated based on the node's position data and recent position data concerning other nodes. The position data is sent to a central node, which receives position data from the other nodes. The central node can generate a second level alert and send the alert to at least the affected nodes in the environment.

Abstract: Classifying unknown samples for scalable automatic labeling are disclosed. Unknown samples are soft labeled at edge nodes. When a node cannot soft label a sample, a candidate node is selected. The candidate node is selected based on why the sample cannot be labelled. The sample is communicated to the candidate node for labeling. If the candidate node is unsuccessful, a different candidate node may be identified to process and label the sample.

Abstract: Testing very large machine models is disclosed. A framework is provided that allows changes to very large machine learning models to be evaluated using compressed machine learning models and automatic or semi-automatic unit testing.

Abstract: Techniques described herein relate to a method for managing data nodes of data node clusters. The method includes obtaining, by a data node manager, a request to deploy a model to a data node; in response to obtaining the model deployment request: identifying, by the data node manager, a data node cluster associated with the data node; making a first determination, by the data node manager, that the data node cluster is associated with an available distilled dataset; and in response to the first determination: generating, by the data node manager, a model using the available distilled dataset; and deploying, by the data node manager, the model to the data node.

Abstract: An information handling system for managing detection of objects includes a storage and a processor. The storage is for storing an encoder; a critical class classifier; a general classifier; and a decoder. The processor obtains data that may include one or more of the objects; encodes the data using the encoder to obtain encoded data; obtains a critical class classification for the encoded data using the critical class classifier; obtains a general classification for the encoded data using the general classifier; conditions the encoded data to obtain conditioned encoded data; decodes the conditioned encoded data using the decoder to obtain reconstructed data; makes a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification; classifies the data as being an unknown classification based on the determination; and performs an action set based on the unknown classification of the data.

Abstract: One example method includes performing, at a central node operable to communicate with edge nodes of an edge computing environment, operations that include signaling the edge nodes to share their respective data distributions to the central node, collecting the data distributions, performing a Bayesian clustering operation with respect to the edge nodes to define clusters that group some of the edge nodes, and one of the edge nodes in each cluster is a representative edge node of that cluster, and sampling data from the representative edge nodes.

Abstract: One example method includes mapping of a set of environment constraints to various elements of a dataset distillation process, and then performing the dataset distillation process based upon the mapping, and the dataset distillation process is performed in a distributed manner by a group of edge nodes and a central node with which the edge nodes communicate. The environment constraints may include computing resource availability, and privacy constraints concerning the data of the dataset.

Abstract: One example method includes identifying active device data streams, based on the active device data streams identified, selecting applicable ML models, obtaining a respective classification inference from each of the ML models, wherein each of the classification inferences applies to a respective active device data stream, identifying a current agreement between the respective classification inferences generated by the models, comparing the classification inferences associated with the current agreement with historical classification inferences associated with historical agreements, and based on the comparing, determining whether or not one or more of the data streams comprises a piece of data of an unknown class.

Abstract: One example method includes, for each document in a group of annotated documents, extracting a set of words from the annotated document, and each of the words is positioned in a respective field of the annotated document. The method further includes using an aggregation function to determine, for one of the fields, a similarity of each one of the annotated documents to all of the other annotated documents, creating a document layout graph with nodes that each correspond to a respective annotated document, and each node is connected to all other nodes for which a similarity threshold for the one field has been met, and running an algorithm on the document layout graph to identify a clique of the annotated documents, and each annotated document in the clique has a similar layout to respective layouts of the other annotated documents in the clique.

Abstract: A global prediction manager for generating predictions using data from data zones includes storage for storing a model repository comprising a global model set and a prediction manager. The prediction manager obtains a local model set from a data zone of the data zones indicating that the global model set is unacceptable; makes a determination that the local model set is acceptable; in response to the determination: distributes the local model set to at least one second data zone of the data zones; obtains compressed telemetry data, that was compressed using the local model set, from the data zone and the at least one second data zone; and generates a global prediction regarding a future operating condition of the data zones using: the compressed local telemetry data and the local model set.

Abstract: One example method includes, at a node, installing a default parametrization configuration that facilitates performance of a domain task, obtaining, by the node, a distilled dataset, and obtaining the distilled dataset is either: obtaining the distilled dataset from another node; or leveraging a synthetic state assembled in the node to select the distilled dataset from another node based on state similarity of the node to the another node. The example method further includes training a model at the node, and the training is performed using the distilled dataset, and the trained model is operable to leverage information received by the node to propose changes to the parametrization configuration so as to optimize execution of a task by the node.

Abstract: Techniques described herein relate to a method for managing data nodes of data node clusters. The method includes obtaining, by a data node manager, a request to deploy a model to a data node; in response to obtaining the model deployment request: identifying, by the data node manager, a data node cluster associated with the data node; making a first determination, by the data node manager, that the data node cluster is associated with an available distilled dataset; and in response to the first determination: generating, by the data node manager, a model using the available distilled dataset; and deploying, by the data node manager, the model to the data node.

Abstract: Techniques described herein relate to a method for managing data nodes. The method includes identifying, by a data node manager associated with a plurality of data nodes, a model deployment event; in response to identifying the model deployment event: generating, by the data node manager, a new model using model generation information from a subset of the plurality of data nodes specified by a data node registry; deploying, by the data node manager, the new model to the plurality of data nodes; and updating the data node registry based on the new model to obtain an updated data node registry.

Abstract: Techniques described herein relate to a method for managing data of data nodes. The method includes obtaining, by a data node manager, a soft labeling request; in response to obtaining the soft labeling request: sending, by the data node manager, requests for processed data to data nodes associated with the data node manager; obtaining, by the data node manager, processed data from the data nodes; merging, by the data node manager, the processed data to obtain processed data; performing, by the data node manager, clustering on the processed data to obtain soft label metadata; associating, by the data node manager, the soft label metadata with live data associated with the data nodes; and performing, by the data node manager, labeling actions using the live data and the soft label metadata.

Abstract: Techniques described herein relate to a method for predicting field values of documents. The method may include identifying a field prediction model generation request; obtaining, training documents from a document manager; selecting a first training document; making a first determination that the first training document is a text-based document; performing text-based data extraction to identify first words and first boxes included in the first training document; identifying first keywords and first candidate words included in the first training document based on the first words and the first boxes; and generating a first annotated training document using the first keywords and the first candidate words, wherein the first annotated training document comprises color-based representation masks for the first keywords, the first candidate words, and first general words included in the first training document.

Abstract: Techniques described herein relate to a method for model updating in a federated learning environment. The method may include distributing, by a model coordinator, a current model to a plurality of client nodes; receiving, by the model coordinator and in response to distributing the current model, a set of gradient K-quant vectors, wherein each gradient K-quant vector of the first set of gradient K-quant vectors is received from one client node of the plurality of client nodes. The gradient K-quant vectors may be compressed representations of gradient vectors. The compression may be performed by determining a bin index value corresponding to the gradient vector values, based on a K value and range received from the model coordinator. The model coordinator may use the gradient K-quant vectors to generate an updated model, and send the updated model to the client nodes for use in the next training cycle.

Abstract: Techniques described herein relate a method for explainability for Random Forest (RF) classifiers. The method may include generating a plurality of class labels for a target variable; training a RF classifier using the plurality of class labels and a historical dataset to obtain a trained RF classifier; building a transaction database using the trained RF classifier; identifying a plurality of class association rules using the transaction database; identifying a portion of the plurality of class association rules that have minimum confidence values greater than a minimum confidence value threshold; and presenting the portion of the plurality of class association rules to an interested entity as explainability results.

Abstract: An autoregressor that compresses input data for a specific purpose. Input data is compressed using a compression/decompression framework and by accounting for a purpose of a prediction model. The compression aspect of the framework is distributed and the decompression aspect of the framework may be centralized. The compression/decompression framework and a machine learning prediction model can be centrally trained. The compressor is distributed to nodes such that the input data can be compressed and transmitted to a central node. The model and the compression/decompression framework are continually trained on new data. This allows for lossy compression and higher compression rates while maintaining low prediction error rates.