Abstract: An apparatus of operating a computational network is configured to determine a low-rank approximation for one or more layers of the computational network based at least in part on a set of residual targets. A set of candidate rank vectors corresponding to the set of residual targets may be determined. Each of the candidate rank vectors may be evaluated using an objective function. A candidate rank vector may be selected and used to determine the low rank approximation. The computational network may be compressed based on the low-rank approximation. In turn the computational network may be operated using the one or more compressed layers.

Abstract: Methods, apparatus, systems, and articles of manufacture to iteratively search for an artificial intelligence-based architecture are disclosed. An example apparatus includes an interface to access a first subgroup of architecture configurations from a search space; instructions; and processor circuitry to execute the instructions to: train first predictors based on the first subgroup; generate a first plurality of candidate architecture configurations using the trained first predictors; and generate a second subgroup of architecture configurations by selecting a number of the plurality of candidate architecture configurations; train second predictors based on the first subgroup and the second subgroup; and generate a second plurality of candidate architecture configurations using the trained second predictors.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed for designing hardware.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve neural architecture searches. An example apparatus includes similarity verification circuitry to identify candidate networks based on a combination of a target platform type, a target workload type to be executed by the target platform type, and historical benchmark metrics corresponding to the candidate networks, the candidate networks associated with performance metrics. The example apparatus also includes likelihood verification circuitry to categorize (a) a first set of the candidate networks based on a first one of the performance metrics corresponding to first tier values, and (b) a second set of the candidate networks based on a second one of the performance metrics corresponding to second tier values, and extract first features corresponding to the first set of the candidate networks and extract second features corresponding to the second set of the candidate networks.

Abstract: The present disclosure is related to framework for automatically and efficiently finding machine learning (ML) architectures that are optimized to one or more specified performance metrics and/or hardware platforms. This framework provides ML architectures that are applicable to specified ML domains and are optimized for specified hardware platforms in significantly less time than could be done manually and in less time than existing ML model searching techniques. Furthermore, a user interface is provided that allows a user to search for different ML architectures based on modified search parameters, such as different hardware platform aspects and/or performance metrics. Other embodiments may be described and/or claimed.

Abstract: The present disclosure is related to framework for automatically and efficiently finding machine learning (ML) architectures that generalize well across multiple artificial intelligence (AI) and/or ML domains, AI/ML tasks, and datasets. The ML architecture search framework accepts a list of tasks and corresponding datasets as inputs, and may also include relevancy scores/weights for each item in the input. A combined performance metric is generated, where this combined performance metric quantifies the performance of the ML architecture across all the specified AI/ML domains, AI/ML tasks, and datasets. The system then performs a multi-objective ML architecture search with the combined performance metric, along with hardware-specific performance metrics as the objectives. Other embodiments may be described and/or claimed.

Abstract: An apparatus of operating a computational network is configured to determine a low-rank approximation for one or more layers of the computational network based at least in part on a set of residual targets. A set of candidate rank vectors corresponding to the set of residual targets may be determined. Each of the candidate rank vectors may be evaluated using an objective function. A candidate rank vector may be selected and used to determine the low rank approximation. The computational network may be compressed based on the low-rank approximation. In turn the computational network may be operated using the one or more compressed layers.

Abstract: According to one embodiment, a computer-implemented method for cleaning up a data set having a possible incorrect label includes: selecting a plurality of training documents; estimating a quality of an organization of a plurality of categories; and determining whether the quality of the organization is greater than a predetermined quality threshold. Corresponding system and computer program product embodiments are also presented. Other aspects and advantages of the present invention will become apparent from the following detailed description, which, when taken in conjunction with the drawings, illustrate by way of example the principles of the invention.

Abstract: A method of online training of a classifier includes determining a distance from one or more feature vectors of an object to a first predetermined decision boundary established during off-line training for the classifier. The method also includes updating a decision rule as a function of the distance. The method further includes classifying a future example based on the updated decision rule.

Abstract: This fertilizer is a mixture of organic matter from animal and plant sources, nematode controllers, carbohydrates, minerals, and mycorrhizal inoculum. It contains guano, kelp meal, neem cake, dry molasses, clay, magnesium sulfate and mycorrhizae. In one embodiment, the mixture is made of the following amounts by weight: 55% high-nitrogen bat guano; 12.5% high-phosphorous bat guano; 12.5% kelp meal; 10% neem cake; and 2.5% each of dry molasses, montmorillonite clay, magnesium sulfate, and mycorrhizae of the Glomus genus. Versions of the fertilizer directed to specific plant species may contain additional ingredients, such as indole-3-butyric acid.

Abstract: A method of generating executable code for a target platform in a neural network includes receiving a spiking neural network description. The method also includes receiving platform-specific instructions for one or more target platforms. Further, the method includes, generating executable code for the target platform(s) based on the platform-specific instructions and the network description.

Abstract: According to one embodiment, a computer-implemented method for cleaning up a data set having a possible incorrect label includes: selecting a plurality of training documents; estimating a quality of an organization of a plurality of categories; and determining whether the quality of the organization is greater than a predetermined quality threshold. Corresponding system and computer program product embodiments are also presented. Other aspects and advantages of the present invention will become apparent from the following detailed description, which, when taken in conjunction with the drawings, illustrate by way of example the principles of the invention.

Abstract: According to one embodiment, a computer-implemented method for confirming/rejecting a most relevant example includes: generating a binary decision model by training a binary classifier using a plurality of training documents; classifying one or more test documents into one of a plurality of categories using the binary decision model, wherein the one or more test documents lack a user-defined category label; selecting a most relevant example of the classified test documents from among the classified test documents; displaying, using a display of the computer, the most relevant example of the classified test documents to a user; receiving, via the computer and from the user, a confirmation or a negation of a classification label of the most relevant example of the classified test documents; and storing the confirmation or the negation of the classification label of the most relevant example of the classified test documents to a memory of the computer.

Abstract: Certain aspects of the present disclosure support efficient implementation of common neuron models. In an aspect, a first memory layout can be allocated for parameters and state variables of instances of a first neuron model, and a second memory layout different from the first memory layout can be allocated for parameters and state variables of instances of a second neuron model having a different complexity than the first neuron model.

Abstract: According to one embodiment, a computer-implemented method for confirming/rejecting a most relevant example includes: generating a binary decision model by training a binary classifier using a plurality of training documents; classifying one or more test documents into one of a plurality of categories using the binary decision model, wherein the one or more test documents lack a user-defined category label; selecting a most relevant example of the classified test documents from among the classified test documents; displaying, using a display of the computer, the most relevant example of the classified test documents to a user; receiving, via the computer and from the user, a confirmation or a negation of a classification label of the most relevant example of the classified test documents; and storing the confirmation or the negation of the classification label of the most relevant example of the classified test documents to a memory of the computer.

Abstract: A method for dynamically setting a neuron value processes a data structure including a set of parameters for a neuron model and determines a number of segments defined in the set of parameters. The method also includes determining a number of neuron types defined in the set of parameters and determining at least one boundary for a first segment.

Abstract: A method of detecting unknown classes is presented and includes generating a first classifier for multiple first classes. In one configuration, an output of the first classifier has a dimension of at least two. The method also includes designing a second classifier to receive the output of the first classifier to decide whether input data belongs to the multiple first classes or at least one second class.

Abstract: Methods and apparatus are provided for training a neural device having an artificial nervous system by modulating at least one training parameter during the training. One example method for training a neural device having an artificial nervous system generally includes observing the neural device in a training environment and modulating at least one training parameter based at least in part on the observing. For example, the training apparatus described herein may modify the neural device's internal learning mechanisms (e.g., spike rate, learning rate, neuromodulators, sensor sensitivity, etc.) and/or the training environment's stimuli (e.g., move a flame closer to the device, make the scene darker, etc.). In this manner, the speed with which the neural device is trained (i.e., the training rate) may be significantly increased compared to conventional neural device training systems.

Abstract: A method for dynamically modifying synaptic delays in a neural network includes initializing a delay parameter and operating the neural network. The method further includes dynamically updating the delay parameter based on a program which is based on a statement including the delay parameter.

Abstract: A method of adaptively selecting a configuration for a machine learning process includes determining current system resources and performance specifications of a current system. A new configuration for the machine learning process is determined based at least in part on the current system resources and the performance specifications. The method also includes dynamically selecting between a current configuration and the new configuration based at least in part on the current system resources and the performance specifications.