Abstract: A training method is provided. The training method includes clustering, by a processor, a plurality of items that each have an item attribute value, according to the item attribute value. The training method further includes generating, by the processor, for each item, a cluster attribute value corresponding to a cluster associated with the item. The training method also includes training, by the processor, an estimation model for estimating selection behavior of a target with respect to a choice set including two or more items, based on the cluster attribute value associated with each item included in the choice set, by using training data that includes a group of a choice set of items presented to the target and an item selected by the target from among the choice set.

Abstract: A computer-implemented method includes employing a dynamic Boltzmann machine (DyBM) to predict a higher-order moment of time-series datasets. The method further includes acquiring the time-series datasets transmitted from a source node to a destination node of a neural network including a plurality of nodes, learning, by the processor, a time-series generative model based on the DyBM with eligibility traces, and obtaining, by the processor, parameters of a generalized auto-regressive heteroscedasticity (GARCH) model to predict a time-varying second-order moment of the times-series datasets.

Abstract: A method, a computer program product, and a system of adversarial semi-supervised one-shot training using a data stream. The method includes receiving a data stream based on an observation, wherein the data stream includes unlabeled data and labeled data. The method also includes training a prediction model with the labeled data using stochastic gradient descent based on a classification loss and an adversarial term and training a representation model with the labeled data and the unlabeled data based on a reconstruction loss and the adversarial term. The adversarial term is a cross-entropy between the middle layer output data from the models. The classification loss is a cross-entropy between the labeled data and an output from the prediction model. The method further includes updating a discriminator with middle layer output data from the prediction model and the representation model and based on a discrimination loss, and discarding the data stream.

Abstract: A method is presented for predicting values of multiple input items. The method includes allowing a user to select a first set of variables and input first values therein and predicting second values for a second set of variables, the second values predicted in real-time as the first values are being inputted by the user. A tree-based prediction model is used to predict the second values. The tree-based prediction model is a regression tree or a decision tree.

Abstract: A computer-implemented method is provided for evaluating a next action of a target object in an environment. The method includes simulating, by a hardware processor for each of possible actions of the target object in the environment, a next state occurring thereafter to obtain a plurality of simulated next states, based on a pessimistic scenario which is randomly generated by sampling a plurality of costs from a distribution of cost. The distribution of cost is an area where the target object is likely to visit in a near future. The method further includes identifying, by the hardware processor, a safety area for the target object in each of the plurality of simulated next states. The method also includes evaluating, by the hardware processor, each of the possible actions of the target object, based on the safety area.

Abstract: A method is presented for predicting values of multiple input items. The method includes allowing a user to select a first set of variables and input first values therein and predicting second values for a second set of variables, the second values predicted in real-time as the first values are being inputted by the user. A tree-based prediction model is used to predict the second values. The tree-based prediction model is a regression tree or a decision tree.

Abstract: Fluid motion is simulated by performing a first fluid simulation without reflecting all of a plurality of forces acting on a fluid, to obtain a first velocity of the fluid at a current time step; estimating a velocity residue at the current time step by inputting a calculated velocity from the previous time step into a regression model. The regression model is trained to relate velocity obtained by performing a second fluid simulation reflecting the plurality of forces acting on the fluid to a difference between the velocity obtained by performing the first fluid simulation and the velocity obtained by performing the second fluid simulation; and calculating a velocity of the fluid at the current time step by adding the first velocity at the current time step and the velocity residue at the current time step.

Abstract: A processing apparatus is disclosed for representing cognitively biased selection behavior of a consumer as a learnable model with high prediction accuracy taking into account even feature values of a product and the consumer. The processing apparatus generates a selection model obtained by modeling selection behavior of a selection entity that selects at least one choice out of presented input choices. The processing apparatus includes an acquiring unit to acquire training data including a plurality of input feature vectors that indicate features of a plurality of the choices presented to the selection entity and an output feature vector that indicates a feature of an output choice. The processing apparatus further includes an input combining unit to combine the plurality of input vectors to generate an input combined vector, and a learning processing unit to learn a selection model on the basis of the input combined vector and the output vector.

Abstract: A computer-implemented method includes employing a dynamic Boltzmann machine (DyBM) to solve a maximum likelihood of generalized normal distribution (GND) of time-series datasets. The method further includes acquiring the time-series datasets transmitted from a source node to a destination node of a neural network including a plurality of nodes, learning, by the processor, a time-series generative model based on the GND with eligibility traces, and, performing, by the processor, online updating of internal parameters of the GND based on a gradient update to predict updated times-series datasets generated from non-Gaussian distributions.

Abstract: In an approach, a processor obtains a target base strategy for selecting actions of a target agent. A processor obtains an adversarial base strategy for selecting adversarial actions of an adversarial agent. A processor calculates, for each candidate action among a plurality of candidate actions of the target agent, a risk measure of the candidate action based on the adversarial base strategy and a payoff to the target agent in a case where the target agent takes the candidate action and the adversarial agent takes an adversarial action based on the adversarial base strategy. A processor generates a target strategy by adjusting the target base strategy based on the risk measure for each candidate action.

Abstract: A computer-implemented method for reducing computation cost associated with a machine learning task performed by a computer system by implementing continuous control of attention for a deep learning network includes initializing a control-value function, an observation-value function and a sequence of states associated with a current episode. If a current epoch associated with the current episode is odd, an observation-action is selected, the observation-action is executed to observe a partial image, and the observation-value function is updated based on the partial image and the control-value function. If the current epoch is even, a control-action is selected, the control-action is executed to obtain a reward corresponding to the control-action, and the control-value function is updated based on the reward and the observation-value function.

Abstract: Deep reinforcement learning of cooperative neural networks can be performed by obtaining an action and observation sequence including a plurality of time frames, each time frame including action values and observation values. At least some of the observation values of each time frame of the action and observation sequence can be input sequentially into a first neural network including a plurality of first parameters. The action values of each time frame of the action and observation sequence and output values from the first neural network corresponding to the at least some of the observation values of each time frame of the action and observation sequence can be input sequentially into a second neural network including a plurality of second parameters. An action-value function can be approximated using the second neural network, and the plurality of first parameters of the first neural network can be updated using backpropagation.

Abstract: Determinantal Point Process-based predictions are provided by training an asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by calculating an inverse matrix of a sum of the asymmetric kernel and a first identity matrix, the calculating using an inverse of a sum of the first identity matrix and a symmetric positive semidefinite matrix, a concatenated matrix made from a first matrix and a second matrix and a second identity matrix, the asymmetric kernel including the symmetric positive semidefinite matrix and a skewed-symmetric matrix, the skewed-symmetric matrix being calculated from the first matrix and the second matrix, to produce a prediction model, and outputting the asymmetric kernel as at least a part of the prediction model to make a prediction.

Abstract: A computer-implemented method is provided for evaluating a next action of a target object in an environment. The method includes simulating, by a hardware processor for each of possible actions of the target object in the environment, a next state occurring thereafter to obtain a plurality of simulated next states, based on a pessimistic scenario which is randomly generated by sampling a plurality of costs from a distribution of cost. The distribution of cost is an area where the target object is likely to visit in a near future. The method further includes identifying, by the hardware processor, a safety area for the target object in each of the plurality of simulated next states. The method also includes evaluating, by the hardware processor, each of the possible actions of the target object, based on the safety area.

Abstract: A method is provided for evaluating a next action of a target object in an environment. The method includes simulating, by a processor device for each of possible actions of the target object in the environment, a next state occurring thereafter to obtain a plurality of simulated next states, based on a pessimistic scenario in which all possible unfavorable actions of other objects occur in the next state in simulation. At least two of the possible unfavorable actions in the next state are unable to simultaneously occur in reality. The method further includes identifying, by the processor device, a safety area for the target object in each of the plurality of simulated next states. The method also includes evaluating, by the processor device, each of the possible actions of the target object, based on the safety area for the target object in each of the plurality of simulated next states.

Abstract: An apparatus, a computer readable medium, and a learning method for learning a model corresponding to time-series input data, including acquiring the time-series input data, which is a time series of input data including a plurality of input values, propagating, to a plurality of nodes in a model, each of a plurality of propagation values obtained by weighting each input value at a plurality of time points before one time point according to passage of time points, in association with the plurality of input values at the one time point, calculating a node value of a first node among the plurality of nodes by using each propagated value propagated to the first node, and updating a weight parameter used to calculate each propagation value propagated to the first node, by using a corresponding input value and a calculated error of the node value at the one time point.

Abstract: Rating models may be generated by obtaining a plurality of consumption values, obtaining a plurality of rating values, training a model that estimates consumption values and rating values by utilizing a plurality of consumer attributes for each consumer, a plurality of item attributes for each item, and a plurality of weights for each attribute of each combination of a consumer and an item. Each estimated consumption value is a function of the plurality of weights for each attribute of each combination of each consumer and each item that corresponds with the estimated consumption value, and each estimated rating value is a function of the plurality of consumer attributes of a consumer that corresponds with the estimated rating value, the plurality of item attributes of an item that corresponds with the estimated rating value, and the plurality of weights that corresponds with the estimated rating value.

Abstract: A method for a determinantal Point Process-based prediction includes obtaining, using a hardware processor, a training data set stored on one or more computer readable storage mediums operably coupled to the hardware processor, training an asymmetric kernel of a Determinantal Point Process (DPP) from a training data set by calculating an inverse matrix of a sum of the asymmetric kernel and an identity matrix in a recursive manner to reduce time and computational resources utilized, and determining a prediction model by training the asymmetric kernel as at least part of a prediction model to make a prediction.

Abstract: A computer-implement method and an apparatus are provided for neural network reinforcement learning. The method includes obtaining, by a processor, an action and observation sequence. The method further includes inputting, by the processor, each of a plurality of time frames of the action and observation sequence sequentially into a plurality of input nodes of a neural network. The method also includes updating, by the processor, a plurality of parameters of the neural network by using the neural network to approximate an action-value function of the action and observation sequence.

Abstract: A neuromorphic electric system includes a network of plural neuron circuits connected in series and in parallel to form plural layers. Each of the plural neuron circuits includes: a soma circuit that stores a charge supplied thereto and outputs a spike signal; and plural synapse circuits that supply a charge to the soma circuit according to a spike signal fed to the synapse circuits, a number of the plural synapse circuits being one more than a number of plural neuron circuits in a prior layer outputting the spike signal to the synapse circuits. One of the plural synapse circuits supplies a charge to the soma circuit in response to receiving a series of pulse signals, and the others of the plural synapse circuits supply a charge to the soma circuit in response to receiving a spike signal from corresponding neuron circuits in the prior layer.