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.

Abstract: Methods for automatic language detection for handwritten text are performed by systems and devices. Such automatic language detection is performed prior to sending representations of the handwritten text to a language recognition engine. Handwritten inputs including one or more writing strokes are received from an input interface, and are associated with coordinates of the inputs and times that the inputs are made. The handwritten inputs are grouped into words based on the coordinates and times. Writing strokes are normalized, and then the words are individually transformed to generate language vectors, such as through a recurrent neural network. The language vectors are used to determine language probabilities for the handwritten inputs. Based on the language probabilities, the handwritten inputs are provided to a specific language recognition engine to determine the language thereof prior to translation or transcription.

Abstract: Various embodiments described herein support or provide for content genome generation and management operations of a media asset, such as accessing content data of a media asset and a set of input cues associated with the media asset; identifying a set of elements based on the content data of the media asset and the set of input cues; assigning a set of weight values to the set of elements based on a priority rule; identifying one or more relevant elements from the set of elements based on a ranking of the set of weight values; analyzing the one or more relevant elements to generate a set of classified features based on a set of tags; and using a machine learning algorithm to generate a content genome of the media asset based on the set of classified features.

Abstract: Computer hardware and/or software that performs the following operations: (i) updating a machine learning model by synchronously applying, to the machine learning model, a first set of training results received from a set of trainers having respective training datasets; (ii) receiving, from one or more trainers of the set of trainers, a first set of metrics pertaining to at least some of the training results of the first set of training results; and (iii) based, at least in part, on the first set of metrics, determining to subsequently update the machine learning model via asynchronous application of subsequent training results received from respective trainers of the set of trainers.

Abstract: A method may include a processor training a machine learning model with a training data set, computing a data distribution of the training data set, processing a stream of new data to determine a likelihood of the new data from the data distribution that is computed, incrementing a counter when the likelihood of the new data is less than a first threshold, and retraining the machine learning model when the counter exceeds a second threshold.

Abstract: A method, a device, and a computer program for predicting the interaction between a compound and a protein are provided. A method for predicting the interaction between a compound and a protein, according to some embodiments of the present disclosure, may include: acquiring compound data for training, protein data for training, and training data including interaction scores; constructing a deep-learning model by using the acquired training data; and predicting the interaction between the given compound and protein by using the constructed deep-learning model. The interaction between the given compound and protein in an in vivo environment can be accurately predicted by training the deep-learning model, while excluding, from an amino acid sequence of the protein for training, amino acid sequences associated with a protein domain having a negative influence on the interaction.

Abstract: Techniques are disclosed relating to exhaustive learning techniques for machine learning algorithms. In various embodiments, the disclosed techniques include performing an iterative machine learning operation that includes training a first version of a machine learning model (e.g., a decision tree model) based on a current version of a training dataset, where the first version of the machine learning model includes a plurality of decision branches, identifying a first subset of data samples that satisfy evaluation criteria included in a first one of the plurality of decision branches, and removing this first subset of data samples to generate an updated version of the training dataset. In various embodiments, the disclosed techniques include repeating the iterative machine learning operation using the updated version of the training dataset to produce a final trained version of the machine learning model.

Abstract: A method includes building a mud-gas hydrocarbon oil fraction database comprising historical data, training a machine learning model using the historical data in the mud-gas hydrocarbon oil fraction database, drilling a new wellbore, processing drilling mud returns, from the new wellbore, through a gas sampler comprising a gas chromatograph and a gas mass spectrometer, retrieving real-time mud-gas data from the gas sampler, and generating a real-time hydrocarbon oil fraction log for the new wellbore by processing the real-time mud-gas data through the trained machine learning model and producing estimated hydrocarbon oil fraction data.

Abstract: Techniques are disclosed relating to feature selection based on feedback-assisted optimization models. In various embodiments, for example, the disclosed techniques include accessing a training dataset that includes a plurality of data samples that include data values for a plurality of features, and a set of labels corresponding to the plurality of data samples. In some embodiments, a computer system performs feature-selection operations to select, from the plurality of features, a subset of features to include in a reduced feature set. For example, in some embodiments the feature-selection operations include processing the training dataset based on an optimization model, where an objective function utilized in the optimization model utilizes performance feedback information corresponding to machine learning models that are trained based on candidate feature sets.

Abstract: The described technology is generally directed towards accelerating distributed principal components in the presence of noisy channels. A federated training based method is disclosed. The method can calculate a desired common subspace for edge devices under the coordination of a server. The server can be connected to the edge devices via noisy wireless channels. A broadband communication system can be used, wherein devices can transmit local gradients by linear analog modulation over sub-channels in communication rounds for over-the-air aggregation. Before each communication round, the server can detect information of a current region. Based on the region information, an online region-adaptive power control scheme can be applied to accelerate the process.

Abstract: Methods, apparatus, and processor-readable storage media for automatically detecting workload type-related information in storage systems using machine learning techniques are provided herein.

Abstract: Described are a method, system, and computer program product for generating robust graph neural networks using universal adversarial training. The method includes receiving a graph neural network (GNN) model and a bipartite graph including an adjacency matrix, initializing model parameters of the GNN model, initializing perturbation parameters, and sampling a subgraph of a complementary graph based on the bipartite graph. The method further includes repeating until convergence of the model parameters: drawing a random variable from a uniform distribution; generating a universal perturbation matrix based on the subgraph, the random variable, and the perturbation parameters; determining Bayesian Personalized Ranking (BPR) loss by inputting the bipartite graph and the universal perturbation matrix to the GNN model; updating the perturbation parameters based on stochastic gradient ascent; and updating the model parameters based on stochastic gradient descent.

Abstract: A device and method for machine learning using an artificial neural network. For a calculation hardware for the artificial neural network, a layer description is provided, which defines at least one part of a layer of the artificial neural network, the layer description defining a tensor for input values of at least one part of this layer, a tensor for weights of at least one part of this layer, and a tensor for output values of at least one part of this layer, in particular of its starting address. A message that includes a start address of the tensor for the input values, or of the tensor for the weighs, or of the tensor for the output values is sent by the calculation hardware for transfer of the input values, or the weights, or the output values, is sent by the calculation hardware.

Abstract: Training a machine learning model can include receiving time series data. A knowledge graph structure can be received including nodes and edges, the nodes representing entities associated with the time series data, the edges representing relationships between the nodes connected by the edges. A machine learning model can be structured to forecast a prediction using the time series data. The machine learning model can be structured to integrate the knowledge graph structure as an error term in the machine learning model. The machine learning model can be trained to forecast the prediction based on the time series data and the knowledge graph structure. The error term representing the knowledge graph structure can be regularized for sparsity during training.