Abstract: A schedule creation method is a method for creating a time schedule by executing a learning step multiple times. The learning step includes sequentially placing patterns each indicating a procedure in a processing sequence in a timetable for defining a time schedule for respective elements of a substrate processing apparatus. The sequentially placing patterns in a timetable includes: acquiring one or more placeable patterns that are allowed to be placed in the timetable from among the patterns based on a prescribed constraint condition; predicting and selecting through machine learning a pattern that makes an evaluation value maximum from among the one or more placeable patterns; and updating the timetable by placing the selected pattern in the timetable.

Abstract: A learning model optimization device includes a binarization matrix setting unit configured to set a binarization matrix m in which each element is a numerical value of “0” or “1” and a transformed matrix setting unit configured to set a transformed matrix M having, as an element, a product of each element of the parameter matrix and each element of the binarization matrix in the same row and the same column. The learning model optimization device further includes a learning unit configured to perform machine learning using the transformed matrix M and change a numerical value of each element of the binarization matrix m such that a result of the machine learning approaches teacher data, thereby optimizing the binarization matrix m, and a re-randomization processing unit configured to change again a parameter of the parameter matrix w.

Abstract: In a method of creating a learning model using a controller configured to perform pruning on a neural network, the pruning includes a first pruning process in which a pruning process is performed in units of channels of convolutional layers and a second pruning process in which a pruning process is performed in units of weight parameters.

Abstract: Apparatuses, systems, and techniques to determine whether to remove one or more neural network layers. In at least one embodiment, one or more neural network layers are determined to be removed based on, for example, a neural architecture search (NAS).

Abstract: A method of slicing a deep learning model for a heterogeneous embedded system includes collecting, by a model slicing apparatus, an execution time and power consumption when each layer corresponding to one layer of a deep learning model including a plurality of layers is executed in each computing device of the heterogeneous embedded system, predicting, by the model slicing apparatus, a performance cost and a power cost when each of the layers is executed in each of the computing devices using the execution time and the power consumption, predicting, by the model slicing apparatus, a communication cost when transmitting information from each of the layers to a next layer in each of the computing devices, and slicing, by the model slicing apparatus, the plurality of layers so that different sliced layers are allocated to each of the computing devices based on the performance cost, the power cost, and the communication cost in a given execution time limit condition using a reinforcement learning model.

Abstract: Systems, methods, and computer-readable medium are provided for healthcare analysis. Data corresponding to a plurality of patients is received. The data is parsed to generate normalized data for a plurality of variables, with normalized data generated for more than one variable for each patient. A causal relationship network model is generated relating the plurality of variables based on the generated normalized data using a Bayesian network algorithm. The causal relationship network model includes variables related to a plurality of medical conditions or medical drugs. In another aspect, a selection of a medical condition or drug is received. A sub-network is determined from a causal relationship network model. The sub-network includes one or more variables associated with the selected medical condition or drug. One or more predictors for the selected medical condition or drug are identified.

Abstract: A method performed by a local client computing device is provided. The method includes training a local model using data from the local client computing device, resulting in a local model update; sending the local model update to a central server computing device; receiving from the central server computing device a first updated global model; determining that the first updated global model does not meet a local criteria, wherein determining that the first updated global model does not meet a local criteria comprises computing a score based on the first updated global model, wherein the score exceeds a threshold; in response to determining that the first updated global model does not meet a local criteria, sending to the central server computing device context information; and receiving from the central server computing device a second updated global model.

Abstract: A learning apparatus according to the present application includes: a dividing unit that divides predetermined learning data features of which are to be learned by a model by training, into a plurality of sets in chronological order; and a training unit that trains the model to learn the features of the learning data included in the set obtained by the division by the dividing unit, for each of the divided sets, in a predetermined order.

Abstract: A method includes training a first control model by utilizing a first set of input data as first input, resulting in a trained first control model; copying the trained first control model to a second control model, wherein, after copying, the second input layer and the plurality of second hidden layers is identical to the plurality of first hidden layers, and the first output layer is replaced by the second output layer; freezing the plurality of second hidden layers; training the second control model by utilizing the first set of input data as second input, resulting in a trained second control model; and running the trained second control model by utilizing a second set of input data as second input, wherein the second output outputs the quality measure of the first control model.

Abstract: A parameter optimization device 800 optimizes input CNN structure information and outputs optimized CNN structure information, and includes stride and dilation use layer detection means 811 for extracting stride and dilation parameter information for each convolution layer from the input CNN structure information, and stride and dilation use position modification means 812 for changing the stride and dilation parameter information of the convolution layer.

Abstract: A multi-task deep learning network and a generation method thereof are provided. The generation method of a multi-task deep learning network includes: building at least one shared layer, where the shared layer is configured to receive a plurality of pieces of input information and generate a plurality of pieces of processed feature information; building a plurality of groups of task-specific layers, wherein the groups of task-specific layers compute and generate a plurality of groups of output information corresponding to a plurality of different tasks according to the pieces of processed feature information; and activating at least one of the groups of task-specific layers in stages according to a power supply state of an electronic apparatus.

Abstract: Techniques are provided for evaluation of machine learning models using agreement scores. One method comprises obtaining two or more of: (i) a first set of quantitative features characterizing model parameters of a machine learning model; (ii) a second set of quantitative features characterizing a training process used to train the machine learning model; and (iii) a third set of quantitative features characterizing a training dataset used to train the machine learning model; generating a score based on an aggregation of at least portions of the two or more of the first set, the second set and the third set, wherein the score is based on an agreement of the machine learning with designated characteristics; and initiating an automated action based on the score. The automated action may comprise updating the machine learning model; generating a notification in connection with an audit; and/or selecting a machine learning model for deployment.

Abstract: A method and system for odor visual expression based on electronic nose technology, and a storage medium are disclosed. The method includes: acquiring category information of an odor to be identified based on the electronic nose technology; determining demand information of the odor to be identified according to the category information; collecting corresponding relevant data according to the demand information so as to construct a database; constructing a knowledge map centered on odor identification according to the database; and converting the structured knowledge map into a visual node-link graph. The method and system for odor visual expression based on electronic nose technology and the storage medium according to this disclosure present related information of the identified odor to users in a form of visual content, and the visual content can facilitate the users to have more intuitive understanding of odors.

Abstract: A computing system includes a machine learning algorithm executing a machine learning model to predict a probability of a fracture driven interaction associated with a hydrocarbon well. The machine learning algorithm trains the machine learning model using well treatment pumping data, offset well production data, and well stage data. Feature extraction is performed on the pumping data, production data, and well stage data to produce a machine learning model that is used to predict the probability of a fracture driven interaction. The resulting machine learning model can be deployed for use in ongoing hydraulic fracturing operations to predict and reduce real-time fracture driven interactions.

Abstract: At least one processor can receive at least one preliminary response generated by a machine learning (ML) model having a predetermined level of randomness. The at least one processor can determine at least one transformation applying a new level of randomness, different from the predetermined level of randomness, to the at least one preliminary response. The at least one processor can generate at least one modified preliminary response, the generating comprising applying the at least one transformation to the at least one preliminary response. The at least one processor can replace the at least one preliminary response with the at least one modified preliminary response within the ML model, wherein the ML model generates a final response using the at least one modified preliminary response.

Abstract: Systems and methods for generating a knowledge base for facilitating interactions between users and an automated assistant. An example method is performed by one or more processors of a computing system. The example method may include receiving a transmission over a communications network from a computing device, the transmission including a plurality of transcripts of user interactions with the automated assistant or agents associated with the computing system, transforming ones of the transcripts into one or more question-and-answer (Q & A) pairs associated with a subject of the corresponding user interaction, and embedding ones of the Q & A pairs as vectors in a vector space for retrieval by the automated assistant during subsequent user interactions.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned identification of radio frequency (RF) signals.

Abstract: An iterative attention-based neural network training and processing method and system iteratively applies a focus of attention of a trained neural network on syntactical elements and generates probabilities associated with representations of the syntactical elements, which in turn inform a subsequent focus of attention of the neural network, resulting in updated probabilities. The updated probabilities are then applied to generate syntactical elements for delivery to a user. The user may respond to the delivered syntactical elements, providing additional training information to the trained neural network.

Abstract: An iterative attention-based neural network training and processing method and system iteratively applies a focus of attention of a trained neural network on syntactical elements and generates probabilities associated with representations of the syntactical elements, which in turn inform a subsequent focus of attention of the neural network, resulting in updated probabilities. The updated probabilities are then applied to generate syntactical elements for delivery to a user. The user may respond to the delivered syntactical elements, providing additional training information to the trained neural network.

Abstract: In accordance with some embodiments, systems, methods, and media for generating and using neural networks having improved efficiency for analyzing video are provided. In some embodiments, the method comprises: providing image data to a trained neural network; receiving, at a neuron, a delta-based input ?in from a previous layer; generating an output g(?in) of a linear transform g; generating an updated state variable a based on g(?in) and a current a; generating an output ƒ(a) of an activation function ƒ based on updated a; generating an updated state variable d based on a current d, a state variable b, and ƒ(a); generating an updated b based on output ƒ(a); transmitting d to a next layer based on a transmission policy and subtracting the value from d; and receiving an output from the trained neural network that represents a prediction based on the image data.