Abstract: The present disclosure relates to an apparatus and a method for computing a neural network, a board card, and a readable storage medium. The computing apparatus of the present disclosure is included in an integrated circuit apparatus. The integrated circuit apparatus includes a general interconnection interface and other processing apparatus. The computing apparatus interacts with other processing apparatus to jointly complete a computing operation specified by a user. The integrated circuit apparatus further includes a storage apparatus. The storage apparatus is connected to the computing apparatus and other processing apparatus, respectively. The storage apparatus is used for data storage of the computing apparatus and other processing apparatus.

Abstract: A method and apparatus for training an online prediction model are provided. The method may include: acquiring an offline sample feature and an online sample feature of a user, the offline sample feature including a user portrait feature; offline training to obtain an offline recommendation model, based on the offline sample feature and the online sample feature of the user; acquiring a latest online feature of the user, and online training to obtain an online learning model based on the latest online feature of the user, the online learning model being used to adapt the latest online feature for use as an online sample feature to be input into the trained offline recommendation model; and synchronizing the offline recommendation model to online, and inputting the latest online feature output by the online learning model into the offline recommendation model to generate an online prediction model.

Abstract: Systems, apparatuses, methods, and computer program products are disclosed for determining a value corresponding to a composite object. Paths are determined based on initial values of underlying items of the composite object and random numbers. A DNN is trained by determining a final value for each path based on a final set of items corresponding to the path at the final time, iterating the value backward in time using a non-linear generator function from the final value to an initial value, defining a set of initial values comprising the initial value determined for each path and determining a statistical measure based on the set of initial values, and modifying parameters of the DNN based on the statistical measure. Value information comprising the value for the composite object at one or more times is determined based on output of the DNN. The value information is provided such that a user computing device receives it.

Abstract: Systems and methods of optimizing runtime of a neural network (NN) by at least one processor may include: receiving a space of untrained NN architectures, capable of performing a predefined NN function on a given computing device, after being trained; receiving a reference accuracy metric value; evaluating a latency value for each NN architecture of the architecture space; dividing the architecture space to a plurality of groups based on said evaluated latency; performing a search among the plurality of groups, to determine a group G* that corresponds to a minimal evaluated latency, and yet comprises at least one NN architecture that maintains an accuracy metric value that is at least equal to the reference accuracy metric value; and training at least one NN architecture of the determined group G* to perform the NN function.

Abstract: A semiconductor process prediction method and a semiconductor process prediction apparatus considering overall features and local features are provided. The semiconductor manufacturing process prediction method includes the following steps. Several equipment sensing curves are obtained. The equipment sensing curves are filtered to reduce the co-linearity of the equipment sensing curves. A Dynamic Time Warping (DTW) procedure is performed to align the equipment sensing curves. The equipment sensing curves which are aligned are inputted into a Convolutional Neural Network (CNN) model to obtain a first prediction result considering the local features. A statistical analysis procedure is performed on the equipment sensing curves to obtain several statistical data. The statistical data are inputted into an Artificial Neural Network (ANN) model to obtain a second prediction result considering the overall features.

Abstract: A method, computer system, and a computer program product for published content protection is provided. The present invention may include receiving a content file from a content management system (CMS). The present invention may include extracting a feature from the received content file. The present invention may include transforming, using an adversarial generation algorithm, the received content file into an adversarial content file. The present invention may include returning the adversarial content file to the CMS. The returned adversarial content file may represent an equivalent of the received content file to a content consumer. The present invention may include preventing an application of the returned adversarial content file in at least one machine learning task based on the adversarial noise included in the returned adversarial content file.

Abstract: Various embodiments include methods and devices for identity graphing of network genomes. Embodiments may include processing a node data set having unstructured node identifiers (IDs), determining potential node IDs from the node data set, determining node counts based on the potential node IDs, and classifying probabilities of accuracy of the node counts via a node count machine learning classifier model. Embodiments may further include classifying probabilities of accuracy of node IDs via a node ID machine learning classifier model. Embodiments may further include generating a data structure associating at least a first node ID of the classified node IDs with a network equipment ID. Embodiments may further include associating a classified probability accuracy of the first node ID with the first node ID.

Abstract: A system and method for automatically generating deep neural network architectures for time series prediction. The system includes a processor for: receiving a prediction context associated with a current use case; based on the associated prediction context, selecting a prediction model network configured for a current use case time series prediction task; replicating the selected prediction model network to create a plurality of candidate prediction model networks; inputting a time series data to each of the plurality of the candidate prediction model network; train, in parallel, each respective candidate prediction model network of the plurality with the input time series data; modifying each of the plurality of the candidate prediction model network by applying a respective different set of one or more model parameters while being trained in parallel; and determine a fittest modified prediction model network for solving the current use case time series prediction task.

Abstract: A method of pruning a pre-trained model comprises the steps of (a) constructing a stochastic super net and (b) training the stochastic super net to determine a particular candidate block selection that provides an optimal level of sparsity for each of the layers based upon a cost function. The stochastic super net generally represents a layer-wise search space with a fixed macro-architecture. A number of layers of the macro-architecture and input/output dimensions of each of the layers of the macro-architecture are essentially the same as the pre-trained model. Each layer comprises a plurality of candidate blocks. A sparsity of each of the candidate blocks in a respective layer is different. A training dataset used to train the pre-trained model is used to train the stochastic super net.

Abstract: A computer implemented method includes distributing a plurality of prediction models, where each of a plurality of clients initially includes at least one associated prediction model from the plurality of prediction models, among all of the plurality of clients to provide each of the plurality of clients with each of the plurality of prediction models. The plurality of prediction models is evaluated on at least a portion of a local dataset resident on each of the plurality of clients to output a quantification indicating how each of the prediction models fit at least the portion of the local dataset of each of the plurality of clients. An ensemble model is generated by applying weights to each of the plurality of prediction models based on a value, a gradient, and a Hessian matrix of a user-defined objective.

Abstract: The present disclosure relates to an apparatus that includes a neuromorphic spike integrator apparatus for neural networks. The apparatus receives at least one input signal encoding information in arrival time of the input signal at the apparatus. The received signal is weighted with a weight value corresponding to the arrival time. The weighted received signal is integrated into a current value of a state of the apparatus and a signal is output based on the current value of the state.

Abstract: A machine learning/artificial intelligence (ML/AI) system includes one or more controllers storing one or more neural networks in memory. The one or more neural networks include a plurality of layers including an input layer, one or more hidden layers, and an output layer, and one or more nodes provided for each of the plurality of layers of the one or more neural networks. Each of the plurality of layers are connected to a subsequent layer of the one or more neural networks by a connection. The connection connects a first node of an earlier layer with a second node of a later layer. The one or more neural networks also include plurality of weights, where each weight is associated with a connection and only a portion of the plurality of weights of the one or more neural networks are encrypted or protected.

Abstract: Embodiments of the present disclosure provide a method for determining causality, an apparatus for determining causality, an electronic device and a storage medium, and relates to a field of knowledge graph technologies. The method includes: obtaining event words expressing individual events and related words adjacent to the event words in a target text; inputting the event words and the related words into a graph neural network; and determining whether there is a causal relationship between any two events through the graph neural network.

Abstract: Non-limiting examples of the present disclosure describe analysis of venue data and prediction of trendiness of venues based on analyzing the venue data. As an example, one or more new venues are determined. The one or more new venues are determined by identification of a venue that has venue data existing for a period of time less than or equal to a predetermined time threshold. The venue data associated with the one or more new venues is evaluated. A predicted popularity for the one or more new venues is generated based on evaluation of the venue data. The generated predicted popularity may be provided to a processing device. In some examples, a ranked list of the one or more new venues is generated. The ranked list may display the one or more venues in a ranked order according to the generated predicted popularity. Other examples are also described.

Abstract: An approach is provided in which a method, system, and program product display, on a user interface, at least one of a set of node split parameters in response to receiving a first user selection that selects a node in a decision tree. The selected node branches to a set of child nodes in the decision tree based on the set of node split parameters. The method, system, and program product adjust at least one of the set of node split parameters of the selected node in response to receiving a second user selection. The method, system, and program product modify the decision tree based on the adjusted set of node split parameters. The modified decision tree includes a modified set of child nodes that branch from the selected node based on the adjusted set of node split parameters.

Abstract: Provided are a method, an apparatus, and a computer program for predicting interaction between a compound and a protein. A method for predicting interaction between a compound and a protein according to some embodiments of the present disclosure may comprises the steps of: acquiring learning data composed of compound data for learning, protein data for learning, and interaction scores; constructing a deep-learning model by using the acquired learning data; and predicting interaction of a given compound and protein through the constructed deep-learning model. Through the learning of the deep-learning mode with the exclusion of amino acid sequences associated with protein domains having a negative influence on interactions from amino acid sequences of proteins for learning, the interaction between a given compound and protein in the in vivo environment can be accurately predicted.

Abstract: A programmable, non-linear (PNL) activation engine for a neural network is capable of receiving input data within a circuit. In response to receiving an instruction corresponding to the input data, the PNL activation engine is capable of selecting a first non-linear activation function from a plurality of non-linear activation functions by decoding the instruction. The PNL activation engine is capable of fetching a first set of coefficients corresponding to the first non-linear activation function from a memory. The PNL activation engine is capable of performing a polynomial approximation of the first non-linear activation function on the input data using the first set of coefficients. The PNL activation engine is capable of outputting a result from the polynomial approximation of the first non-linear activation function.

Abstract: Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing predictive data analysis operations configured to integrate distinct clustering schemes given temporal variations. For example, certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive data analysis operations by generating integrative predicted scores based at least in part on at least one of: within-cluster consistency scores determined for clusters determined using a first clustering scheme (e.g., a service clustering scheme), within-cluster consistency scores determined for clusters determined using a second clustering scheme (e.g., a recipient clustering scheme), cross-cluster consistency scores, and cross-temporal consistency scores.

Abstract: Systems and methods for prompt tuning can utilize previously-learned prompts for the initialization of tuning for prompts on different tasks that may differ from the task associated with the previously-learned prompt. The prompt being utilized for initialization can be a generic prompt and/or may be a prompt selected based on a determined similarity between two or more task embeddings.

Abstract: Embodiments of the present disclosure relate to a method, device, and computer-readable storage medium for data processing. A method for data processing comprises obtaining a set of observed samples related to multiple factors, an observed sample in the set of observed samples comprising respective observed values of multiple factors. The method further comprises determining a set of dependency relationships between the multiple factors based on the set of observed samples, a dependency relationship in the set of dependency relationships indicating an interrelated factor pair among the multiple factors. The method further comprises determining a causality sequence of the multiple factors based on the set of dependency relationships, the causality sequence indicating that one factor is a cause of the other factor in the interrelated factor pair. Embodiments of the present disclosure further provide a device and computer-readable storage medium capable of performing the foregoing method.