Abstract: A computer-implemented method is provided for determining an action with respect to a given portfolio of items for supply chain management. The method includes acquiring, by a hardware processor, a feature vector for supply chain delivery trends, the given portfolio, and a current investment amount. The method further includes determining, by the hardware processor, whether a current supply chain delivery situation is normal or abnormal based on the feature vector. The method also includes performing a risk-avoidance action to reduce the current investment amount and avoid potential supply chain delivery losses, responsive to a determination that the current supply chain delivery situation is abnormal. The method additionally includes performing a risk adaptive action to increase the current investment amount and incur potential supply chain delivery gains by using a distributional reinforcement learning process, responsive to a determination that the current supply chain delivery situation is normal.

Abstract: A computer-implemented method is provided for determining an action with respect to a given portfolio of items for supply chain management. The method includes acquiring, by a hardware processor, a feature vector for supply chain delivery trends, the given portfolio, and a current investment amount. The method further includes determining, by the hardware processor, whether a current supply chain delivery situation is normal or abnormal based on the feature vector. The method also includes performing a risk-avoidance action to reduce the current investment amount and avoid potential supply chain delivery losses, responsive to a determination that the current supply chain delivery situation is abnormal. The method additionally includes performing a risk adaptive action to increase the current investment amount and incur potential supply chain delivery gains by using a distributional reinforcement learning process, responsive to a determination that the current supply chain delivery situation is normal.

Abstract: A technique for training a model includes obtaining a training example for a model having model parameters stored on one or more computer readable storage mediums operably coupled to the hardware processor. The training example includes an outcome and features to explain the outcome. A gradient is calculated with respect to the model parameters of the model using the training example. Two estimates of a moment of the gradient with two different time constants are computed for the same type of the moment using the gradient. Using a hardware processor, the model parameters of the model are updated using the two estimates of the moment with the two different time constants to reduce errors while calculating the at least two estimates of the moment of the gradient.

Abstract: A computer-implemented method is provided for determining an action with respect to a given portfolio of items for supply chain management. The method includes acquiring, by a hardware processor, a feature vector for supply chain delivery trends, the given portfolio, and a current investment amount. The method further includes determining, by the hardware processor, whether a current supply chain delivery situation is normal or abnormal based on the feature vector. The method also includes performing a risk-avoidance action to reduce the current investment amount and avoid potential supply chain delivery losses, responsive to a determination that the current supply chain delivery situation is abnormal. The method additionally includes performing a risk adaptive action to increase the current investment amount and incur potential supply chain delivery gains by using a distributional reinforcement learning process, responsive to a determination that the current supply chain delivery situation is normal.

Abstract: Methods and systems are provided for obtaining cleaned sequences showing trajectories of movement of a center of gravity and for estimating a biometric information pattern or value of a target. One of the methods includes removing noises from initial sequences showing trajectories of movement of a center of gravity to obtain the cleaned sequences. Another one of the methods includes reading cleaned sequences of the target into a memory, extracting features from the cleaned sequences, and estimating a biometric information pattern or value of the target from the extracted features, using a classification or regression model of biometric information patterns or values. The biometric information pattern may be a pattern derived from respiratory or circulatory organs of a target.

Abstract: A technique for training a model includes obtaining a training example for a model having model parameters stored on one or more computer readable storage mediums operably coupled to the hardware processor. The training example includes an outcome and features to explain the outcome. A gradient is calculated with respect to the model parameters of the model using the training example. Two estimates of a moment of the gradient with two different time constants are computed for the same type of the moment using the gradient. Using a hardware processor, the model parameters of the model are updated using the two estimates of the moment with the two different time constants to reduce errors while calculating the at least two estimates of the moment of the gradient.

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: Methods and systems are provided for obtaining cleaned sequences showing trajectories of movement of a center of gravity and for estimating a biometric information pattern or value of a target. One of the methods includes removing noises from initial sequences showing trajectories of movement of a center of gravity to obtain the cleaned sequences. Another one of the methods includes reading cleaned sequences of the target into a memory, extracting features from the cleaned sequences, and estimating a biometric information pattern or value of the target from the extracted features, using a classification or regression model of biometric information patterns or values. The biometric information pattern may be a pattern derived from respiratory or circulatory organs of a target.

Abstract: Similarity of items can be estimated by using a method including generating a prediction model that predicts an indicator of a target based on one or more attributes for items, by estimating a weight set, among weight sets, for each of the items, and estimating a similarity among the items for the target based on the weight sets of the prediction model.

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 method for implementing artificial intelligence agents to perform machine learning tasks using predictive analytics to leverage ensemble policies for maximizing long-term returns includes obtaining a set of inputs including a set of ensemble policies and a meta-policy parameter, selecting an action for execution within the system environment using a meta-policy function determined based in part on the set of ensemble policies and the meta-policy function parameter, causing the artificial intelligence agent to execute the selected action within the system environment, and updating the meta-policy function parameter based on the execution of the selected action.

Abstract: A computer-implemented method for producing a formulation based on a prior distribution of a number of ingredients used in the formulation includes grouping a set of energy functions based on a number of ingredients used in a formulation, generating a probability distribution using the set of energy functions, obtaining at least one sample of the formulation by sampling from the probability distribution based on a previous sample, and triggering fabrication of the formulation in accordance with the at least one sample.

Abstract: A method for selecting an action, includes reading, into a memory, a Partially Observed Markov Decision Process (POMDP) model, the POMDP model having top-k action IDs for each belief state, the top-k action IDs maximizing expected long-term cumulative rewards in each time-step, and k being an integer of two or more, in the execution-time process of the POMDP model, detecting a situation where an action identified by the best action ID among the top-k action IDs for a current belief state is unable to be selected due to a constraint, and selecting and executing an action identified by the second best action ID among the top-k action IDs for the current belief state in response to a detection of the situation. The top-k action IDs may be top-k alpha vectors, each of the top-k alpha vectors having an associated action, or identifiers of top-k actions associated with alpha vectors.

Abstract: Probability density ratios may be estimated by a method including obtaining a first sample set including a plurality of first samples and a second sample set including a plurality of second samples, wherein each of the first samples and the second samples is represented as a vector including a plurality of parameters, constructing at least one decision tree estimating a ratio of probability density p(x)/q(x) based on the first sample set and the second sample set, wherein p(x) is a probability density of the first samples corresponding to an input vector x and q(x) is a probability density of the second samples corresponding to the input vector x.

Abstract: Probability density ratios may be estimated by a method including obtaining a first sample set including a plurality of first samples and a second sample set including a plurality of second samples, wherein each of the first samples and the second samples is represented as a vector including a plurality of parameters, constructing at least one decision tree estimating a ratio of probability density p(x)/q(x) based on the first sample set and the second sample set, wherein p(x) is a probability density of the first samples corresponding to an input vector x and q(x) is a probability density of the second samples corresponding to the input vector x.

Abstract: Optimized learning settings of neural networks are efficiently determined by an apparatus including a processor and one or more computer readable mediums collectively including instructions that, when executed by the processor, cause the processor to train a first neural network with a learning setting; extract tentative weight data from the first neural network with the learning setting; calculate an evaluation value of the first neural network with the learning setting; and generate a predictive model for predicting an evaluation value of a second neural network with a new setting based on tentative weight data of the second neural network by using a relationship between the tentative weight data of the first neural network and the evaluation value of the first neural network.

Abstract: A technique for training a model includes obtaining a training example for a model having model parameters stored on one or more computer readable storage mediums operably coupled to the hardware processor. The training example includes an outcome and features to explain the outcome. A gradient is calculated with respect to the model parameters of the model using the training example. Two estimates of a moment of the gradient with two different time constants are computed for the same type of the moment using the gradient. Using a hardware processor, the model parameters of the model are updated using the two estimates of the moment with the two different time constants to reduce errors while calculating the at least two estimates of the moment of the gradient.

Abstract: A computer implemented method of generating new chemical compounds is provided. The method includes preparing a data-driven substructure feature vector for each of a plurality of chemical compounds for which a chemical or physical property is known. The method further includes preparing a predefined component feature vector, creating a regression model to predict a target value for the chemical or physical property, and performing a search algorithm to identify substructure features that affect the target value for the chemical or physical property. The method further includes generating a candidate structure having the target value for the chemical or physical property, and synthesizing the candidate structure.

Abstract: It is preferable to predict an objective variable by optimally selecting or combining the output of a plurality of models. A computer-implemented method is provided that calculates, for each of a plurality of models, a relevance of an output of the model with respect to a value of an objective variable based on the value of the objective variable and the output of the model in the past. The method also calculates, for each of the plurality of models, similarities between a current timing and a plurality of past timings based on the output of the model at the current timing, the output of the model at the plurality of past timings, and the relevance. Additionally, the method predicts the value of the objective variable at a target timing based on the similarities.

Abstract: A method is provided for rule creation that includes receiving (i) a MDP model with a set of states, a set of actions, and a set of transition probabilities, (ii) a policy that corresponds to rules for a rule engine, and (iii) a set of candidate states that can be added to the set of states. The method includes transforming the MDP model to include a reward function using an inverse reinforcement learning process on the MDP model and on the policy. The method includes finding a state from the candidate states, and generating a refined MDP model with the reward function by updating the transition probabilities related to the state. The method includes obtaining an optimal policy for the refined MDP model with the reward function, based on the reward policy, the state, and the updated probabilities. The method includes updating the rule engine based on the optimal policy.