Abstract: Displaying visual indications of one or more files that are associated with a file being viewed via an augmented reality headset may be facilitated. In some embodiments, an image of a file being viewed by a user may be received. The system may determine whether a file is associated with a first error indicating an inconsistency between the file and one or more other files related to the file. Based on determining that the file is associated with the first error, the system may retrieve, based on one or more file identifiers corresponding to the one or more other files, one or more other files associated with the inconsistency. The system may then generate for display (i) a visual indicator indicating the first error and (ii) one or more visual indications of the one or more other files associated with the inconsistency.

Abstract: Some embodiments provide a method that receives a specification of a neural network for execution by an integrated circuit. The integrated circuit includes a neural network inference circuit for executing the neural network to generate an output based on an input, an input processing circuit for providing the input to the neural network inference circuit, a microprocessor circuit for controlling the neural network inference circuit and the input processing circuit, and a unified memory accessible by the microprocessor circuit, the neural network inference circuit, and the input processing circuit. The method determines usage of the unified memory by the neural network inference circuit while executing the neural network. Based on the determined usage by the neural network inference circuit, the method allocates portions of the unified memory to the microprocessor circuit and input processing circuit.

Abstract: A network management system includes a non-transitory computer readable medium configured to store instructions thereon. The network management system further includes a processor connected to the non-transitory computer readable medium. The processor is configured to execute the instructions for receiving first log data from a first component in a network, wherein the first log data includes first log information; and parsing the first log data using a trained neural network to define parsed log data, wherein parsing the first log data includes organizing the first log information into a predefined sequence of information, and the parsed log data includes at least a signal source and a signal message. The processor is configured to execute the instructions for generating a unified model language (UML) diagram based on the parsed log data; and determining whether an error is present in the first component based on the UML diagram.

Abstract: Multiple artificial neural networks can be compiled as a single workload. A respective throughput for each of the artificial neural networks can be changed at runtime. The multiple artificial neural networks can be partially compiled individually and then later compiled just-in-time according to changing throughput demands for the artificial neural networks. The multiple artificial neural networks can be deployed on a deep learning accelerator hardware device.

Abstract: A data analysis apparatus using a first neural network comprises: a setting unit configured to receive output data from the first input layer, set a weight of each layer in the first intermediate layer based on the output data and a second learning parameter, and output said weight to the first output layer; a weight processing unit included in the first output layer, the weight processing unit being configured to weight each output data with the weight of each layer of the first intermediate layer that was set by the setting unit; and a calculation unit included in the first output layer, the calculation unit being configured to calculate prediction data based on each output data that was weighted by the weight processing unit and a third learning parameter.

Abstract: Embodiments described herein provide systems and methods for learning representation from unlabeled videos. Specifically, a method may comprise generating a set of strongly-augmented samples and a set of weakly-augmented samples from the unlabeled video samples; generating a set of predictive logits by inputting the set of strongly-augmented samples into a student model and a first teacher model; generating a set of artificial labels by inputting the set of weakly-augmented samples to a second teacher model that operates in parallel to the first teacher model, wherein the second teacher model shares one or more model parameters with the first teacher model; computing a loss objective based on the set of predictive logits and the set of artificial labels; updating student model parameters based on the loss objective via backpropagation; and updating the shared parameters for the first teacher model and the second teacher model based on the updated student model parameters.

Abstract: Systems and methods for image captioning are described. One or more aspects of the systems and methods include generating a training caption for a training image using an image captioning network; encoding the training caption using a multi-modal encoder to obtain an encoded training caption; encoding the training image using the multi-modal encoder to obtain an encoded training image; computing a reward function based on the encoded training caption and the encoded training image; and updating parameters of the image captioning network based on the reward function.

Abstract: A response style component removal device capable of removing a response style that does not depend on content of a questionnaire is provided. The response style component removal device generates a probability of rating values for question items from raters who have rated questionnaires. Specifically, learning data for training the device include rating values for a plurality of question items from a plurality of raters who have rated a plurality of types of questionnaires. The device is configured to learn a rater parameter ?k that indicates a tendency of each rater, an item parameter ?k that indicates a tendency of each question item, and a response style parameter ? indicates a tendency of a response style, the rater parameter ?k, the item parameter ?k. The device is further configured to remove the response style parameter ? to generate a probability distribution of the rating value.

Abstract: A method for processing a video includes receiving a video as an input at a first layer of an artificial neural network (ANN). A first frame of the video is processed to generate a first label. Thereafter, the artificial neural network is updated based on the first label. The updating is performed while concurrently processing a second frame of the video. In doing so, the temporal inconsistency between labels is reduced.

Abstract: Embodiments of the disclosure provide a robot control method, apparatus and device, a computer storage medium and a computer program product and relate to the technical field of artificial intelligence. The method includes: acquiring environment interaction data and an actual target value, indicating a target that is actually reached by executing an action corresponding to action data in the environment interaction data; determining a return value after executing the action according to state data, action data and the actual target value at the first time of two adjacent times; updating a return value in the environment interaction data by using the return value after executing the action; training an agent corresponding to a robot control network by using the updated environment interaction data, and controlling the action of a target robot by using the trained agent.

Abstract: An online concierge system trains a user interaction model to predict a probability of a user performing an interaction after one or more content items are displayed to the user. This provides a measure of an effect of displaying content items to the user on the user performing one or more interactions. The user interaction model is trained from displaying content items to certain users of the online concierge system and withholding display of the content items to other users of the online concierge system. To train the user interaction model, the user interaction model is applied to labeled examples identifying a user and value based on interactions the user performed after one or more content items were displayed to the user and interactions the user performed when one or more content items were not used.

Abstract: The performance of a neural network (NN) and/or deep neural network (DNN) can limited by the number of operations being performed as well as management of data among the various memory components of the NN/DNN. A sparsity-inducing regularization optimization process is performed on a machine learning model to generate a compressed machine learning model. A machine learning model is trained using a first set of training data. A sparsity-inducing regularization optimization process is executed on the machine learning model. Based on the sparsity-inducing regularization optimization process, a compressed machine learning model is received. The compressed machine learning model is executed to generate one or more outputs.

Abstract: A semiconductor device includes a first transistor including a first channel layer of a first conductivity type, a second transistor provided in parallel with the first transistor and including a second channel layer of a second conductivity type, and a third transistor stacked on the first and second transistors. The third transistor may include a gate insulating film including a ferroelectric material. The third transistor may include third channel layer and a gate electrode that are spaced apart from each other in a thickness direction with the gate insulating film therebetween.

Abstract: A computing system is disclosed that includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the processor to perform several acts. The acts include receiving, by a generative model, input set forth by a user of a client computing device that is in network communication with the computing system. The acts also include generating, by the generative model, a query based upon the input set forth by the user; providing the query to a search engine. The acts further include receiving, by the generative model and from the search engine, content identified by the search engine based upon the query. The acts additionally include generating, by the generative model, an output based upon a prompt, where the prompt includes the content identified by the search engine based upon the query. The acts also include transmitting the output to the client computing device for presentment to the user.

Abstract: A hybrid personal watercraft combines features of pontoon boats and deck boats, in a cost-effective and versatile package. The watercraft includes port and starboard sponsons which combine a pair of outboard flotation cavities. A space below the deck and above the hull bottom creates at least one, and potentially up to three additional flotation cavities, which may also be used as storage areas accessible by an access door in the bow of the watercraft and/or a set of hatches in the deck. The watercraft may be efficiently produced assembled from polymer materials, such as thermoplastic polyolefin (TPO).

Abstract: Systems, devices, and methods related to a deep learning accelerator and memory are described. An integrated circuit may be configured with: a central processing unit, a deep learning accelerator configured to execute instructions with matrix operands; random access memory configured to store first instructions of an artificial neural network executable by the deep learning accelerator and second instructions of an application executable by the central processing unit; one or more connections among the random access memory, the deep learning accelerator and the central processing unit; and an input/output interface to an external peripheral bus. While the deep learning accelerator is executing the first instructions to convert sensor data according to the artificial neural network to inference results, the central processing unit may execute the application that uses inference results from the artificial neural network.

Abstract: An electronic device according to an example embodiment includes a processor, and a memory operatively connected to the processor and including instructions executable by the processor, wherein when the instructions are executed, the processor is configured to collect an EEG signal measuring brain activity and an fNIRS signal measuring the brain activity, and output a result of determining a type of the brain activity from a trained neural network model using the EEG signal and the fNIRS signal, and the neural network model may be trained to, extract an EEG feature from the EEG signal, extract an fNIRS feature from the fNIRS signal, extract a fusion feature based on the EEG signal and the fNIRS signal, and output the result of determining the type of the brain activity based on the EEG feature and the fusion feature.

Abstract: A computing device includes one or more processors, a first random access memory (RAM) comprising magnetic random access memory (MRAM), a second random access memory of a type distinct from MRAM, and a non-transitory computer-readable storage medium storing instructions for execution by the one or more processors. The computing device receives first data on which to train an artificial neural network (ANN) and trains the ANN by, using the first RAM comprising the MRAM, performing a first set of training iterations to train the ANN using the first data, and, after performing the first set of training iterations, using the second RAM of the type distinct from MRAM, performing a second set of training iterations to train the ANN using the first data. The computing device stores values for the trained ANN. The trained ANN is configured to classify second data based on the stored values.

Abstract: Embodiments for automatically converting printed documents into electronic format using artificial intelligence techniques disclosed herein include: (i) receiving a plurality of images of documents; (ii) for each received image, using an image classification algorithm to classify the image as one of (a) an image of a first type of document, or (b) an image of a second type of document; (iii) for each image classified as an image of the first type of document, using an object localization algorithm to identity an area of interest in the image; (iv) for an identified area of interest, using an optical character recognition algorithm to extract text from the identified area of interest; and (v) populating a record associated with the document with the extracted text.

Abstract: A model adapted to a predetermined system is adapted to another system with an environment or an agent similar to that of the predetermined system. Specifically, a first model adapted to a first system that is operated based on a first condition including a specific environment and a specific agent is corrected using a correction model to generate a second model. The second model is adapted to a second system that is operated based on a second condition, where the second condition is partially different from the first condition.

Abstract: The disclosed computer-implemented method for identifying and remediating security threats against graph neural network models may include (i) analyzing an input format for model data utilized by a graph neural network (GNN) model on a target computing system, (ii) generating probing data corresponding to the input format, (iii) querying the GNN model utilizing the probing data, (iv) building, based on a query response output of the GNN model utilizing the probing data, one or more shadow GNN models, (v) verifying a performance metric of the shadow GNN models against a target performance metric associated with the GNN model, and (vi) performing a security action that protects against a potential security threat against the GNN model when the performance metric of the shadow GNN models is similar to target performance metric associated with the GNN model. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: Hyperparameters for tuning a machine learning system may be optimized using Bayesian optimization with constraints. The hyperparameter optimization may be performed for a received training set and received constraints. Respective probabilistic models for the machine learning system and constraint functions may be initialized, then hyperparameter optimization may include iteratively identifying respective values for hyperparameters using analysis of the respective models performed using an acquisition function implementing entropy search on the respective models, training the machine learning system using the identified values to determine measures of accuracy and constraint metrics, and updating the respective models using the determined measures.

Abstract: A machine learning model engine executes a machine learning model that has been trained with training data and processes scoring data to generate predictions. A machine learning model analyzer is configured to evaluate the machine learning model. The machine learning model analyzer determines a plurality of drift metrics for the plurality of input variables to compare the distribution of the training data to the distribution of the scoring data. Each of the plurality of drift metrics is associated with one of the plurality of input variables. The machine learning model analyzer also determines an overall drift metric for the combination of the input variables. The plurality of input variables are weighted in the overall drift metric in accordance with the plurality of feature importances. The machine learning model analyzer generates an alert based on the overall distribution of the training data relative to the overall distribution of the scoring data.

Abstract: Volumetric quantification can be performed for various parameters of an object represented in volumetric data. Multiple views of the object can be generated, and those views provided to a set of neural networks that can generate inferences in parallel. The inferences from the different networks can be used to generate pseudo-labels for the data, for comparison purposes, which enables a co-training loss to be determined for the unlabeled data. The co-training loss can then be used to update the relevant network parameters for the overall data analysis network. If supervised data is also available then the network parameters can further be updated using the supervised loss.

Abstract: A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

Abstract: Hardware implementations of DNNs and related methods with a variable output data format. Specifically, in the hardware implementations and methods described herein the hardware implementation is configured to perform one or more hardware passes to implement a DNN wherein during each hardware pass the hardware implementation receives input data for a particular layer, processes that input data in accordance with the particular layer (and optionally one or more subsequent layers), and outputs the processed data in a desired format based on the layer, or layers, that are processed in the particular hardware pass. In particular, when a hardware implementation receives input data to be processed, the hardware implementation also receives information indicating the desired format for the output data of the hardware pass and the hardware implementation is configured to, prior to outputting the processed data convert the output data to the desired format.

Abstract: A method includes obtaining a speech segment. The method also includes generating, using at least one processing device of an electronic device, context-independent features and context-dependent features of the speech segment. The method further includes decoding, using the at least one processing device of the electronic device, a first viseme based on the context-independent features. The method also includes decoding, using the at least one processing device of the electronic device, a second viseme based on the context-dependent features and the first viseme. In addition, the method includes generating, using the at least one processing device of the electronic device, an output viseme based on the first and second visemes, where the output viseme is associated with a visual animation of the speech segment.

Abstract: Provided is a method and system for weight memory mapping for a streaming operation of giant generative artificial intelligence hardware. A weight memory mapping system may include a weight memory configured to store a weight matrix for a pretrained artificial intelligence model; an input register configured to store a plurality of input data; a first hardware operator configured to process a matrix multiplication operation between the plurality of input data and the weight matrix and to compute a lane-level final sum during the progress of the matrix multiplication operation by reusing a partial sum of the matrix multiplication operation; and a second hardware operator configured to preprocess a next matrix multiplication operation during the progress of the matrix multiplication operation using the final sum.

Abstract: The present disclosure provides a training and application method of a multi-layer neural network model, apparatus and a storage medium. In a forward propagation of the multi-layer neural network model, the number of input feature maps is expanded and a data computation is performed by using the expanded input feature maps.

Abstract: Mechanisms are provided for performing machine learning training of a computer model. A perturbation generator generates a modified training data comprising perturbations injected into original training data, where the perturbations cause a data corruption of the original training data. The modified training data is input into a prediction network of the computer model and processing the modified training data through the prediction network to generate a prediction output. Machine learning training is executed of the prediction network based on the prediction output and the original training data to generate a trained prediction network of a trained computer model. The trained computer model is deployed to an artificial intelligence computing system for performance of an inference operation.

Abstract: A system and method are disclosed for providing a private multi-modal artificial intelligence platform. The method includes splitting a neural network into a first client-side network, a second client-side network and a server-side network and sending the first client-side network to a first client. The first client-side network processes first data from the first client, the first data having a first type. The method includes sending the second client-side network to a second client. The second client-side network processes second data from the second client, the second data having a second type. The first type and the second type have a common association. Forward and back propagation occurs between the client side networks and disparate data types on the different client side networks and the server-side network to train the neural network.

Abstract: The present disclosure relates to systems and methods for providing vector-wise sparsity in neural networks. In some embodiments, an exemplary method for providing vector-wise sparsity in a neural network, comprises: dividing a matrix associated with the neural network into a plurality of vectors; selecting a first subset of non-zero elements from the plurality of vectors to form a pruned matrix; and outputting the pruned matrix for executing the neural network using the pruned matrix.

Abstract: Systems and methods of data processing are provided. The method comprises receiving an input data to be processed by a series of operations, identifying a first operation from the series of operations, selecting at least one second operation from the series of operations to be grouped with the first operation based at least in part on an amount of an input data and an output data of the grouped operations and the capacity of the memory unit, and processing a portion of the input data of the grouped operations. An efficiency of the series of data operations can be improved by ensuring the input data and output data of any data operations are both stored in the memory unit.

Abstract: A method for compressing a deep neural network includes determining a pruning ratio for a channel and a mixed-precision quantization bit-width based on an operational budget of a device implementing the deep neural network. The method further includes quantizing a weight parameter of the deep neural network and/or an activation parameter of the deep neural network based on the quantization bit-width. The method also includes pruning the channel of the deep neural network based on the pruning ratio.

Abstract: A method of construction of a feedforward neural network includes a step of initialization of a neural network according to an initial topology, and at least one topological optimization phase, of which each phase includes: an additive phase including a modification of the network topology by adding at least one node and/or a connection link between the input of a node of a layer and the output of a node of any one of the preceding layers, and/or a subtractive phase including a modification of the network topology by removing at least one node and/or a connection link between two layers. Each topology modification includes the selection of a topology modification among several candidate modifications, based on an estimation of the variation in the network error between the previous topology and each topology modified according to a candidate modification.

Abstract: The present disclosure relates to a training method for a parameter configuration model, a parameter configuration method, and a parameter configuration device.

Abstract: Disclosed is a disentangled personalized federated learning method via consensus representation extraction and diversity propagation provided by embodiments of the present application. The method includes: receiving, by a current node, local consensus representation extraction models and unique representation extraction models corresponding to other nodes, respectively; extracting, by the current node, the representations of the data of the current node by using the unique representation extraction models of other nodes respectively, and calculating first mutual information between different sets of representation distributions, determining similarity of the data distributions between the nodes based on the size of the first mutual information, and determining aggregation weights corresponding to the other nodes based on the first mutual information; the current node obtains the global consensus representation aggregation model corresponding to the current node.

Abstract: Provided are an apparatus and method of compressing an artificial neural network. According to the method and the apparatus, an optimal compression rate and an optimal operation accuracy are determined by compressing an artificial neural network, determining a task accuracy of a compressed artificial neural network, and automatically calculating a compression rate and a compression ratio based on the determined task accuracy. The method includes obtaining an initial value of a task accuracy for a task processed by the artificial neural network, compressing the artificial neural network by adjusting weights of connections among layers of the artificial neural network included in information regarding the connections, determining a compression rate for the compressed artificial neural network based on the initial value of the task accuracy and a task accuracy of the compressed artificial neural network, and re-compressing the compressed artificial neural network according to the compression rate.

Abstract: A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive sets of inputs individually responsive to a respective subset of queries. The hardware processor can generate a predictive model using the set of input data. The hardware processor can calculate predictive outcomes individually associated with a respective user by applying the predictive model to each respective set of inputs of the sets of inputs. The hardware processor can generate a list ranked according to the predictive outcomes for the particular asset.

Abstract: A computer-implemented method for enforcing an idempotent-constrained characteristic during training of a neural network may be provided. The method comprises training of a neural network by minimizing a loss function, wherein the loss function comprises an additional term imposing an idempotence-based regularization to the neural network during the training.

Abstract: An object of the present invention is to provide a learning apparatus and a learning method capable of appropriately learning pieces of data that belong to the same category and are acquired under different conditions. In a learning apparatus according a first aspect of the present invention, first data and second data are respectively input to a first input layer and a second input layer that are independent of each other, and feature quantities are calculated. Thus, the feature quantity calculation in one of the first and second input layers is not affected by the feature quantity calculation in the other input layer. In addition to feature extraction performed in the input layers, each of a first intermediate feature quantity calculation process and a second intermediate feature quantity calculation process is performed at least once in an intermediate layer that is shared by the first and second input layers.

Abstract: A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive a set of inputs individually responsive to a respective subset of a plurality of queries for a particular user. The hardware processor can generate a predictive model based on the set of input data. The hardware processor can calculate a predictive outcome for the particular user by applying the predictive model to the set of inputs. The hardware processor can identify a target score impacting the predictive outcome for the particular user. The hardware processor can assign a training program to the particular user corresponding to the target score.

Abstract: Methods and apparatus for performing matrix transforms within a memory fabric. Various embodiments of the present disclosure are directed to converting a memory array into a matrix fabric for matrix transformations and performing matrix operations therein. Exemplary embodiments described herein perform matrix transformations within a memory device that includes a matrix fabric and matrix multiplication unit (MMU). In one exemplary embodiment, the matrix fabric uses a “crossbar” construction of resistive elements. Each resistive element stores a level of impedance that represents the corresponding matrix coefficient value. The crossbar connectivity can be driven with an electrical signal representing the input vector as an analog voltage. The resulting signals can be converted from analog voltages to a digital values by an MMU to yield a vector-matrix product. In some cases, the MMU may additionally perform various other logical operations within the digital domain.

Abstract: In an approach to improve the generation of a virtual object in a three-dimensional virtual environment, embodiments of the present invention identify a virtual object to be generated in a three-dimensional virtual environment based on a natural language utterance. Additionally, embodiments generate the virtual object based on a CLIP-guided Generative Latent Space (CLIP-GLS) analysis, and monitor usage of the generated virtual object in the three-dimensional virtual space. Moreover, embodiments infer human perception data from the monitoring, and generate a utility score for the virtual object based on the human perception data.

Abstract: A method of using a computing device to compare performance of multiple algorithms. The method includes receiving, by a computing device, multiple algorithms to assess. The computing device further receives a total amount of resources to allocate to the multiple algorithms. The computing device additionally assigns a fair share of the total amount of resources to each of the multiple algorithms. The computing device still further executes each of the multiple algorithms using the assigned fair share of the total amount of resources. The computing device additionally compares the performance of each of the multiple based on at least one of multiple hardware relative utility metrics describing a hardware relative utility of any given resource allocation for each of the multiple algorithms.

Abstract: Disclosed is a method of determining a characteristic of interest relating to a structure on a substrate formed by a lithographic process, the method comprising: obtaining an input image of the structure; and using a trained neural network to determine the characteristic of interest from said input image. Also disclosed is a reticle comprising a target forming feature comprising more than two sub-features each having different sensitivities to a characteristic of interest when imaged onto a substrate to form a corresponding target structure on said substrate. Related methods and apparatuses are also described.

Abstract: Modifying digital content based on predicted future user behavior is provided. Trends in propagation values corresponding to a layer of nodes in an artificial neural network are identified based on measuring the propagation values at each run of the artificial neural network. The trends in the propagation values are forecasted to generate predicted propagation values at a specified future point in time. The predicted propagation values are applied to the layer of nodes in the artificial neural network. Predicted website analytics values corresponding to a set of website variables of interest for the specified future point in time are generated based on running the artificial neural network with the predicted propagation values. A website corresponding to the set of website variables of interest is modified based on the predicted website analytics values corresponding to the set of website variables of interest for the specified future point in time.

Abstract: A unified model for a neural network can be used to predict a particular value, such as a customer value. In various instances, customer value may have particular sub-components. Taking advantage of this fact, a specific learning architecture can be used to predict not just customer value (e.g. a final objective) but also the sub-components of customer value. This allows improved accuracy and reduced error in various embodiments.

Abstract: An interface circuit includes an integrator circuit and a buffer circuit. The integrator circuit is configured to be electrically coupled to a column of memory cells, receive a signal corresponding to a sum of currents flowing through the memory cells of the column, and integrate the signal over time to generate an intermediate voltage. The buffer circuit is electrically coupled to an output of the integrator circuit to receive the intermediate voltage, and is configured to be electrically coupled to a row of further memory cells, generate an analog voltage corresponding to the intermediate voltage, and output the analog voltage to the further memory cells of the row.

Abstract: Automatic multi-objective hardware optimization for processing a deep learning network is disclosed. An example of a storage medium includes instructions for obtaining client preferences for a plurality of performance indicators for processing of a deep learning workload; generating a workload representation for the deep learning workload; providing the workload representation to machine learning processing to generate a workload executable, the workload executable including hardware mapping based on the client preferences; and applying the workload executable in processing of the deep learning workload.