Abstract: A method, system, and computer program product, including generating, using a linear probe, confidence scores through flattened intermediate representations and theoretically-justified weighting of samples during a training of the simple model using the confidence scores of the intermediate representations.

Abstract: An artificial intelligence (AI) job recommender system and methods implement neural network machine learning by generating and utilizing actual and synthetic training data to identify, learn, and apply latent job-to-job transition information and trends to improve job recommendations. The AI job recommender system and method represent technological advances that, for example, identify data representations, identify multiple instances of latent information in actual data, develop synthetic training data, create a directed graph from latent, directional information, embed the directed graph into a vector space, and apply machine learning algorithms to technologically advance and transform a machine into a specialized machine that learns and improves job recommendations across the vector space.

Abstract: A method, apparatus and computer program product for establishing a logical connection between an indirect utterance and a transaction is described. An indirect utterance is received from a user as an input to a conversational system. The indirect utterance is parsed to a first logical form. A first set of predicates and terms is mapped from the first logical form to a first subgraph in a knowledge graph. A second set of predicates and terms is mapped from a second logical form belonging to a transaction to a second subgraph of the knowledge graph. A best path in the knowledge graph between the first subgraph and the second subgraph is searched for while transforming the first logical form using the node and edge labels along the best path to generate an intermediate logical form. A system action is performed for a transaction if a graph structure of the intermediate logical form matches the graph structure of the logical form of the transaction above a threshold.

Abstract: A computer-implemented method and system having computer-executable instructions stored in a memory for processing user behavior features by neural networks to identify user segments. The method includes receiving user datasets from a database along with respective user identifiers, retention labels, static user features and interactive user features associated with an online product during a time period. A first neural network processes the interactive user features to generate a time distributed concatenation representation. A second neural network is configured to generate a vector by embedding the time distributed concatenation representation and the static user features through an embedding layer. The second neural network is configured to process the vector through a plurality of layers. A cluster model is used to determine user segments based on values extracted from nodes of a second to last layer of the second neural network.

Abstract: The disclosed technology relates identifying causes of an observed outcome. A system is configured to receive an indication of a user experience problem, wherein the user experience problem is associated with observed operations data including an observed outcome. The system generates, based on the observed operations data, a predicted outcome according to a model, determines that the observed outcome is within range of the predicted outcome, and identifies a set of candidate causes of the user experience problem when the observed outcome is within range of the predicted outcome.

Abstract: An exemplary embodiment provides an autoencoder which is explainable. An exemplary autoencoder may explain the degree to which each feature of the input attributed to the output of the system, which may be a compressed data representation. An exemplary embodiment may be used for classification, such as anomaly detection, as well as other scenarios where an autoencoder is input to another machine learning system or when an autoencoder is a component in an end-to-end deep learning architecture. An exemplary embodiment provides an explainable generative adversarial network that adds explainable generation, simulation and discrimination capabilities. The underlying architecture of an exemplary embodiment may be based on an explainable or interpretable neural network, allowing the underlying architecture to be a fully explainable white-box machine learning system.

Abstract: The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize recurrent neural networks to determine the existence of one or more open intents in a text input, and then extract the one or more open intents from the text input. In particular, in one or more embodiments, the disclosed systems utilize a trained intent existence neural network to determine the existence of an actionable intent within a text input. In response to verifying the existence of an actionable intent, the disclosed systems can apply a trained intent extraction neural network to extract the actionable intent from the text input. Furthermore, in one or more embodiments, the disclosed systems can generate a digital response based on the intent identified from the text input.

Abstract: A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to receive an indication to execute an artificial intelligence (AI) agent of a digital twin, the digital twin including the AI agent and a graph data structure, the graph data structure including nodes representing entities of a building and edges between the nodes representing relationships between the entities of the building. The instructions cause the one or more processors to execute the AI agent based on data of the building to generate an inference that is a prediction of a future data value of a data point of the building for a future time and store at least one of the inference, or a link to the inference, in the graph data structure.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for neural network compression. In one aspect, a method comprises receiving a neural network and identifying a particular set of multiple weights of the neural network. Multiple anchor points are determined based on current values of the particular set of weights of the neural network. The neural network is trained by, at each of multiple training iterations, performing operations comprising adjusting the values of the particular set of weights by backpropagating gradients of a loss function. The loss function comprises a first loss function term based on a prediction accuracy of the neural network and a second loss function term based on a similarity of the current values of the particular set of weights to the anchor points. After training, the values of the particular set of weights are quantized based on the anchor points.

Abstract: Methods, systems, and devices for distributed hyperparameter tuning and load balancing are described. A device (e.g., an application server) may generate a first set of combinations of hyperparameter values associated with training a mathematical model. The mathematical model may include a machine learning model, an optimization model, or any combination. The device may identify a subset of combinations from the first set of combinations that are associated with a computational runtime that exceeds a first threshold and may distribute the subset of combinations across a set of machines. The device may then test each of the first set of combinations in a parallel processing operation to generate a first set of validation error values and may test a second set of combinations of hyperparameter values using an objective function that is based on the first set of validation error values.

Abstract: Systems and methods for flexible parameter sharing for multi-task learning are provided. A training method can include obtaining a test input, selecting a particular task from one or more tasks, and training a multi-task machine-learned model for the particular task by performing a forward pass using the test input and one or more connection probability matrices to generate a sample distribution of test outputs, training the components of the machine-learned model based at least in part on the sample distribution, and performing a backwards pass to train a connection probability matrix of the multi-task machine-learned model using a straight-through Gumbel-softmax approximation.

Abstract: Systems, methods, and computer program products for estimating a Bayesian hierarchical regression model using parallelized and distributed Gibbs sampling are described. The techniques can be implemented to solve use cases where there is a response variable, e.g., number of store visits or web page visits, which is a variable of interest, and multiple explanatory variables, e.g., locations, temperatures, or prices, that may predict the response variable. The disclosed techniques build a model that explains and quantifies effects of the explanatory variables on the response variable on a distributed system. For instance, the disclosed techniques can build a model which has the capability to estimate that an X-degree increase in temperature at a certain time of year predicts a Y-percent increase in store visits. This estimation process is performed in parallel on multiple nodes of the distributed system.

Abstract: A computer-implemented method for automatically determining a neural network architecture represents a neural network architecture as a data structure defining a hierarchical set of directed acyclic graphs in multiple levels. Each graph has an input, an output, and a plurality of nodes between the input and the output. At each level, a corresponding set of the nodes are connected pairwise by directed edges which indicate operations performed on outputs of one node to generate an input to another node. Each level is associated with a corresponding set of operations. At a lowest level, the operations associated with each edge are selected from a set of primitive operations. The method includes repeatedly generating new sample neural network architectures, and evaluating their fitness. The modification is performed by selecting a level, selecting two nodes at that level, and modifying, removing or adding an edge between those nodes according to operations associated with lower levels of the hierarchy.

Abstract: A method includes receiving a set of training data and selecting a first machine learning platform based on a first optimization function that metrics past machine learning platforms used for training on the set of training data. The method also includes selecting a first algorithm supported by the first machine learning platform based on a second optimization function that metrics past algorithms used for training on the set of training data. Further, the method includes determining one or more hyperparameters supported by the first algorithm based on a third optimization function that metrics past combinations of hyperparameters from the set of hyperparameters used for training on the set of training data. The method also includes training a machine learning model on the set of training data using the first machine learning platform, the first algorithm, and the one or more hyperparameters.

Abstract: A system assigns experts of a mixture-of-experts artificial intelligence model to processing devices in an automated manner. The system includes an orchestrator component that maintains priority data that stores, for each of a set of experts, and for each of a set of execution parameters, ranking information that ranks different processing devices for the particular execution parameter. In one example, for the execution parameter of execution speed, and for a first expert, the priority data indicates that a central processing unit (“CPU”) executes the first expert faster than a graphics processing unit (“GPU”). In this example, for the execution parameter of power consumption, and for the first expert, the priority data indicates that a GPU uses less power than a CPU. The priority data stores such information for one or more processing devices, one or more experts, and one or more execution characteristics.

Abstract: In one implementation, a method is implemented by a neural network device and includes inputting a representation of topological structures in patterns of activity in a source neural network, wherein the activity is responsive to an input into the source neural network, processing the representation, and outputting a result of the processing of the representation. The processing is consistent with a training of the neural network to process different such representations of topological structures in patterns of activity in the source neural network.

Abstract: Automated development and training of deep forest models for analyzing data by growing a random forest of decision trees using data, determining Out-of-bag (OOB) predictions for the forest, appending the OOB predictions to the data set, and growing an additional forest using the data set including the appended OOB predictions, and combining the output of the additional forest, then utilizing the model to classify data outside the training data set.

Abstract: Provided are methods and system for obtaining, by a computer system, a machine learning/machine learning model; obtaining, by the computer system, a training data set; training, with the computer system, an obfuscation transform based on the machine learning/machine learning model and the training data set; and storing, with the computer system, the obfuscation transform in memory.

Abstract: A computing device selects new test configurations for testing software. (A) First test configurations are generated using a random seed value. (B) Software under test is executed with the first test configurations to generate a test result for each. (C) Second test configurations are generated from the first test configurations and the test results generated for each. (D) The software under test is executed with the second test configurations to generate the test result for each. (E) When a restart is triggered based on a distance metric value computed between the second test configurations, a next random seed value is selected as the random seed value and (A) through (E) are repeated. (F) When the restart is not triggered, (C) through (F) are repeated until a stop criterion is satisfied. (G) When the stop criterion is satisfied, the test result is output for each test configuration.

Abstract: Generate an automorphism of the problem graph, determine an embedding of the automorphism to the hardware graph and modify the embedding of the problem graph into the hardware graph to correspond to the embedding of the automorphism to the hardware graph. Determine an upper-bound on the required chain strength. Calibrate and record properties of the component of a quantum processor with a digital processor, query the digital processor for a range of properties. Generate a bit mask and change the sign of the bias of individual qubits according to the bit mask before submitting a problem to a quantum processor, apply the same bit mask to the bit result. Generate a second set of parameters of a quantum processor from a first set of parameters via a genetic algorithm.