Abstract: Described herein are systems and methods for state change implementation. In some embodiments, an apparatus may obtain system data and classify the system data to descriptors. In some embodiments, an apparatus may determine descriptor ratios as a function of the elements of system data classified to descriptors, weightings associated with the elements of system data, or both. In some embodiments, an apparatus may determine a growth model as a function of a plurality of descriptor ratios.

Abstract: An apparatus for enhancing longevity, wherein the apparatus includes at least a processor and a memory communicatively connected to the processor, the memory containing instructions configuring the at least a processor to receive a longevity measurement related to a user and calculate a longevity parameter as a function of the longevity measurement. The memory containing instructions further configuring the processor to assign the user a longevity level, including training a longevity classifier using a longevity training data containing a plurality of data entries correlating examples of longevity parameters to examples of longevity levels, classifying the longevity parameter to the longevity level using the longevity classifier, and assigning the user the longevity level as a function of the classification. The memory containing instructions further configuring the processor to generate a longevity plan as a function of the longevity parameter and longevity level.

Abstract: Systems and methods ingest extensive data regarding parties and contextual data to determine correlation between a variety of data types, parameters related thereto, and member service representative (MSR) contact events and corresponding staffing levels. The appropriate level of staffing, based on real-time and historical context to meet demand for calls, emails, messages, and other contact or servicing can be calculated without overstaffing.

Abstract: An object is to make it possible to accurately detect abnormality of event data. A training unit (105) trains a parameter of a model based on a plurality of event series that are event data in a time series and labels that indicate abnormality or normality with respect to event data of each of the plurality of event series, the model outputting a degree of abnormality of a target event series when the target event series is input, the target event series being an event series of which the degree of abnormality is to be predicted, the parameter being trained to optimize an objective function that represents a relationship between a probability of occurrence of an event at each time point in the time series and a degree of abnormality of each of the plurality of event series.

Abstract: Ideographic contrastive autoencoder for large language model fine-tuning is disclosed, including: obtaining a set of user activities according to a specified task; obtaining respective sets of input features from the set of user activities; using an encoder network of an autoencoder to encode the respective sets of input features into a set of words; prompting a machine learning model to perform the specified task using the set of words, wherein the machine learning model has been fine-tuned using a custom lexicographical vocabulary associated with the autoencoder; and presenting, at a user interface, a message determined based at least in part on an output result from the machine learning model.

Abstract: One embodiment provides a method, including: providing, from the central server to a machine-learning model, a training set of samples having known values for at least one target protected attribute, wherein the training set includes a first set of samples having a first value for the at least one target protected attribute and a second set of samples having a second value for the at least one target protected attribute; receiving, at the central server from the machine-learning model, an output classification for each of the samples within the training set of samples; and generating, at the central server using the output classification, a set of rules delineating a region within the machine-learning model as discriminatory, wherein the region includes a classification region where the machine-learning model classifies received samples differently based upon a value of the at least one protected attribute.

Abstract: A system and related methods for generating class-specific data are disclosed. The data belong to an input space with an unknown initial probability distribution and a known classification scheme. From a relatively small, unbalanced dataset of samples with respect to the classification scheme in the input space, the system is programmed to learn a series of invertible transformations from the input space to a target space, a target probability distribution for the samples in the target space, and a trainable parameter probability distribution for each parameter of the target probability distribution to represent uncertainty information related to the target probability distribution. The system is programmed to further identify how to sample from each parameter probability distribution, which determine how to sample from the target probability distribution, to generate samples in the input space that are more likely to belong to specific classes.

Abstract: The invention relates to a computer-implemented method (100) for configuring a neural network model, wherein the method comprises the following steps: providing (102) a neural network model; splitting (104) the neural network model into a first portion and a second portion, the second portion comprising a first head for classifying a first type of classification data and a second head for classifying the second type of classification data; pre-processing (106), in a training phase, the second type of classification data in the first portion, processing (108) the pre-processed second type of classification data in the first and second heads and determining a first result of the processing of first type of classification data in the first head and a second result of the processing of first type of classification data in the second head; calculating (110) the consistency between the first result and the second result; and configuring (112) the neural network model by updating a value of at least one parameter of

Abstract: Features are used to train one or more ML models in a modelling layer. In a feature selection layer, each generated ML model is analyzed to determine, for each input feature, a degree of importance of the feature on the results generated by the ML model. Features with low importance are identified and the information is propagated backward to the data source and feature engineering layers. In response, the data source and feature engineering layers refrain from gathering or generating the unimportant features. Based on a confidence measure of the determination that each feature is important or unimportant, a number of periods between reevaluation of the feature importance is determined. After the number of periods has elapsed, a removed feature is restored to the pipeline.

Abstract: Managing and applying human resources data comprising aggregating employee transaction data for an organization. A number of human resources-related attributes are evaluated across heterogeneous transaction data. The employee transaction data is classified via statistical machine learning into a number of normalized codes according to the human resources-related attributes, a user interface is presented to adjust a number of organizational operating procedures according to the normalized codes.

Abstract: A neural network processing method, comprising the following steps: obtaining a model dataset and model structure parameters of an original network (S100); obtaining an operational attribute of each compute node in the original network; operating the original network according to the model dataset and the model structure parameters of the original network and the operational attribute of each compute node, to obtain an instruction corresponding to each compute node in the original network (S200); and if the operational attribute of the current compute node is a first operational attribute, storing a network weight and the instruction corresponding to the current compute node into a first non-volatile memory, so as to obtain a first offline model corresponding to the original network (S300). Further provided are a computer system and a storage medium.

Abstract: A computerized method includes receiving a dialog session. The dialog session comprises a set of new inbound messages. The method feeds the dialog session into tokenizer. The method, with the tokenizer, generates a set of tokens by breaking the new inbound messages into a sequence of tokens. The method provides the tokens to a DAG frame labeler cascade. With the DAG frame labeler cascade, the method uses a sequence of tokens to generate a set of token labels. The method passes the token labels and tokens to an entity interpreter. With the entity interpreter, the method generates a DAG frame. With the DAG frame, the method outputs a structured information from a multiturn dialogue.

Abstract: An embodiment includes identifying an initial plurality of sets of hyperparameter values at which to evaluate an objective function that relates hyperparameter values to performance values of a neural network. The embodiment also executes training processes on the neural network with the hyperparameters set to the each of the initial sets of hyperparameter values such that the training process provides an initial set of the performance values for the objective function. The embodiment also generates an approximation of the objective function using splines at selected performance values. The embodiment approximates a point at which the approximation of the objective function reaches a maximum value, then determines an updated set of hyperparameter values associated with the maximum value. The embodiment then executes a runtime process using the neural network with the hyperparameters set to the updated set of hyperparameter values.

Abstract: Systems and methods are provided for improving autotuning procedures using stateless processing with a remote key-value store. For example, the system can implement a task launcher, a scheduler, and an agent to launch, schedule, and execute decomposed autotuning stages, respectively. The scheduling policy implemented by the scheduler may perform operations beyond a simple scheduling policy (e.g., a FIFO-based scheduling policy), which produces a high queuing delay. Compared to the traditional systems, by leveraging autotuning specific domain knowledge, queueing delay is reduced and resource utilization is improved.

Abstract: Reinforcement learning methods can use actor-critic networks where (1) additional laboratory-only state information is used to train a policy that much act without this additional laboratory-only information in a production setting; and (2) complex resource-demanding policies are distilled into a less-demanding policy that can be more easily run at production with limited computational resources. The production actor network can be optimized using a frozen version of a large critic network, previously trained with a large actor network. Aspects of these methods can leverage actor-critic methods in which the critic network models the action value function, as opposed to the state value function.

Abstract: A method, apparatus and system for object detection in sensor data having at least two modalities using a common embedding space includes creating first modality vector representations of features of sensor data having a first modality and second modality vector representations of features of sensor data having a second modality, projecting the first and second modality vector representations into the common embedding space such that related embedded modality vectors are closer together in the common embedding space than unrelated modality vectors, combining the projected first and second modality vector representations, and determining a similarity between the combined modality vector representations and respective embedded vector representations of features of objects in the common embedding space to identify at least one object depicted by the captured sensor data. In some instances, data manipulation of the method, apparatus and system can be guided by physics properties of a sensor and/or sensor data.

Abstract: Methods, apparatus, and processor-readable storage media for predicting component lifespan information by processing user install base data and environment-related data using machine learning techniques are provided herein. An example computer-implemented method includes obtaining install base data associated with at least one system component and environment-related data associated with usage of the at least one system component; performing feature analysis on at least a portion of the obtained data using a first set of machine learning techniques; clustering, based on the feature analysis, at least a portion of the install base data and at least a portion of the environment-related data into one or more groups using a second set of machine learning techniques; generating at least one lifespan information prediction attributed to the at least one system component based on the clustering; and performing at least one automated action based on the at least one lifespan information prediction.

Abstract: The present disclosure relates to a learning task compiling method of artificial intelligence processors and related products. The learning task compiling method of artificial intelligence processors includes fusing a redundant neural network layer to a convolution layer, optimizing a structure of a convolution neural network, and compiling a learning task of an artificial intelligence processor based on the optimized convolution neural network. The method may achieve high efficiency for learning task compiling of artificial intelligence processors, and may reduce data exchange during processing when being executed on a device.

Abstract: The disclosed embodiments are related to storing critical data in a memory device such as Flash or DRAM memory device. In one embodiment, a device comprising a plurality of parallel processors is disclosed, the plurality of parallel processors configured to: perform a search and match operation, the search and match operation loading a plurality of synaptic identifier bit strings and a plurality of spike identifier bit strings, the search and match operation further generating a plurality of bitmasks; perform a synaptic integration phase, the synaptic integration phase generating a plurality of synaptic current vectors based on the plurality of bitmasks, the synaptic current vectors associated with respective synthetic neurons; solve a neural membrane equation for each of the synthetic neurons; and update membrane potentials associated with the synthetic neurons, the membrane potentials stored in a memory device.

Abstract: Methods, apparatus, and processor-readable storage media for detecting container incidents using machine learning techniques are provided herein.