Patents by Inventor Sakyasingha Dasgupta
Sakyasingha Dasgupta has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Patent number: 11080586Abstract: 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.Type: GrantFiled: November 6, 2017Date of Patent: August 3, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Sakyasingha Dasgupta, Takayuki Osogami
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Patent number: 10902311Abstract: Regularization of neural networks. Neural networks can be regularized by obtaining an original neural network having a plurality of first-in-first-out (FIFO) queues, each FIFO queue located between a pair of nodes among a plurality of nodes of the original neural network, generating at least one modified neural network, the modified neural network being equivalent to the original neural network with a modified length of at least one FIFO queue, evaluating each neural network among the original neural network and the at least one modified neural network, and determining which neural network among the original neural network and the at least one modified neural network is most accurate, based on the evaluation.Type: GrantFiled: December 28, 2016Date of Patent: January 26, 2021Assignee: International Business Machines CorporationInventors: Sakyasingha Dasgupta, Takayuki Osogami
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Patent number: 10891534Abstract: 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.Type: GrantFiled: January 11, 2017Date of Patent: January 12, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Sakyasingha Dasgupta, Takayuki Osogami
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Patent number: 10885111Abstract: A computer-implemented method, computer program product, and system are provided for learning mapping information between different modalities of data. The method includes mapping, by a processor, high-dimensional modalities of data into a low-dimensional manifold to obtain therefor respective low-dimensional embeddings through at least a part of a first network. The method further includes projecting, by the processor, each of the respective low-dimensional embeddings to a common latent space to obtain therefor a respective one of separate latent space distributions in the common latent space through at least a part of a second network. The method also includes optimizing, by the processor, parameters of each of the networks by minimizing a distance between the separate latent space distributions in the common latent space using a variational lower bound. The method additionally includes outputting, by the processor, the parameters as the mapping information.Type: GrantFiled: April 16, 2018Date of Patent: January 5, 2021Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar, Ryuki Tachibana
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Patent number: 10783660Abstract: Methods and a system are provided for detecting object pose. A method includes training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images. The method further includes training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images. The method also includes training, by the processor, a neural network (NN) to detect the object pose using the synthetic output images generated by the first and second AEs. The method additionally includes detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object.Type: GrantFiled: February 21, 2018Date of Patent: September 22, 2020Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATIONInventors: Tadanobu Inoue, Sakyasingha Dasgupta, Subhajit Chaudhury
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Publication number: 20200242449Abstract: A computer-implemented method is provided for autonomously making continuous trading decisions for assets using a first eligibility trace enabled Neural Network (NN). The method includes pretraining the first eligibility trace enabled NN, using asset price time series data, to generation predictions of future asset price time series data. The method further includes initializing a second eligibility trace enabled NN for reinforcement learning using learned parameters of the first eligibility trace enabled NN. The method also includes augmenting state information of the second eligibility trace enabled NN for reinforcement learning using an output from the first eligibility trace enabled NN. The method additionally includes performing continuous actions for trading assets at each of multiple time points.Type: ApplicationFiled: January 28, 2019Publication date: July 30, 2020Inventors: Sakyasingha Dasgupta, Rudy R. Harry Putra
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Publication number: 20200134498Abstract: 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.Type: ApplicationFiled: October 31, 2018Publication date: April 30, 2020Inventors: Rudy Raymond Harry Putra, Takayuki Osogami, Sakyasingha Dasgupta
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Publication number: 20200134464Abstract: 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.Type: ApplicationFiled: October 31, 2018Publication date: April 30, 2020Inventors: Rudy Raymond Harry Putra, Takayuki Osogami, Sakyasingha Dasgupta
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Publication number: 20190318040Abstract: A computer-implemented method, computer program product, and system are provided for learning mapping information between different modalities of data. The method includes mapping, by a processor, high-dimensional modalities of data into a low-dimensional manifold to obtain therefor respective low-dimensional embeddings through at least a part of a first network. The method further includes projecting, by the processor, each of the respective low-dimensional embeddings to a common latent space to obtain therefor a respective one of separate latent space distributions in the common latent space through at least a part of a second network. The method also includes optimizing, by the processor, parameters of each of the networks by minimizing a distance between the separate latent space distributions in the common latent space using a variational lower bound. The method additionally includes outputting, by the processor, the parameters as the mapping information.Type: ApplicationFiled: April 16, 2018Publication date: October 17, 2019Inventors: Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar, Ryuki Tachibana
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Publication number: 20190272465Abstract: A computer-implemented method, computer program product, and system are provided for estimating a reward in reinforcement learning. The method includes preparing a state prediction model trained to predict a state for an input using visited states in expert demonstrations performed by an expert. The method further includes inputting an actual state observed by an agent in reinforcement learning into the state prediction model to calculate a predicted state. The method also includes estimating a reward in the reinforcement learning based, at least in part, on similarity between the predicted state and an actual state observed by the agent.Type: ApplicationFiled: March 1, 2018Publication date: September 5, 2019Inventors: Daiki Kimura, Sakyasingha Dasgupta, Subhajit Chaudhury, Ryuki Tachibana
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Publication number: 20190259175Abstract: Methods and a system are provided for detecting object pose. A method includes training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images. The method further includes training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images. The method also includes training, by the processor, a neural network (NN) to detect the object pose using the synthetic output images generated by the first and second AEs. The method additionally includes detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object.Type: ApplicationFiled: February 21, 2018Publication date: August 22, 2019Inventors: Tadanobu Inoue, Sakyasingha Dasgupta, Subhajit Chaudhury
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Publication number: 20190156197Abstract: Adaptive exploration in deep reinforcement learning may be performed by inputting a current time frame of an action and observation sequence sequentially into a function approximator, such as a deep neural network, including a plurality of parameters, the action and observation sequence including a plurality of time frames, each time frame including action values and observation values, approximating a value function using the function approximator based on the current time frame to acquire a current value, updating an action selection policy through exploration based on an ?-greedy strategy using the current value, and updating the plurality of parameters.Type: ApplicationFiled: November 22, 2017Publication date: May 23, 2019Inventor: Sakyasingha Dasgupta
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Publication number: 20190042943Abstract: 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.Type: ApplicationFiled: August 4, 2017Publication date: February 7, 2019Inventors: Sakyasingha Dasgupta, TAKAYUKI OSOGAMI
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Publication number: 20190019082Abstract: Cooperative neural networks reinforcement learning may be performed by obtaining an action and observation sequence, inputting each time frame of the action and observation sequence sequentially into a first neural network including a plurality of first parameters and a second neural network including a plurality of second parameters, approximating an action-value function using the first neural network, and updating the plurality of second parameters to approximate a policy of actions by using updated first parameters.Type: ApplicationFiled: July 12, 2017Publication date: January 17, 2019Inventors: Sakyasingha Dasgupta, Tetsuro Morimura, Takayuki Osogami
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Publication number: 20180268286Abstract: Cooperative neural networks may be implemented by providing an input to a first neural network including a plurality of first parameters, and updating at least one first parameter based on an output from a recurrent neural network provided with the input, the recurrent neural network including a plurality of second parameters.Type: ApplicationFiled: November 6, 2017Publication date: September 20, 2018Inventor: Sakyasingha Dasgupta
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Publication number: 20180268285Abstract: Cooperative neural networks may be implemented by providing an input to a first neural network including a plurality of first parameters, and updating at least one first parameter based on an output from a recurrent neural network provided with the input, the recurrent neural network including a plurality of second parameters.Type: ApplicationFiled: March 20, 2017Publication date: September 20, 2018Inventor: Sakyasingha Dasgupta
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Publication number: 20180197079Abstract: A computer-implement method and an apparatus are provided for neural network reinforcement learning. The method includes inputting, by a processor, and 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 act on and observation sequence.Type: ApplicationFiled: January 11, 2017Publication date: July 12, 2018Inventors: Sakyasingha Dasgupta, Takayuki Osogami
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Publication number: 20180197083Abstract: 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.Type: ApplicationFiled: November 6, 2017Publication date: July 12, 2018Inventors: Sakyasingha Dasgupta, Takayuki Osogami
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Publication number: 20180075341Abstract: Regularization of neural networks. Neural networks can be regularized by obtaining an original neural network having a plurality of first-in-first-out (FIFO) queues, each FIFO queue located between a pair of nodes among a plurality of nodes of the original neural network, generating at least one modified neural network, the modified neural network being equivalent to the original neural network with a modified length of at least one FIFO queue, evaluating each neural network among the original neural network and the at least one modified neural network, and determining which neural network among the original neural network and the at least one modified neural network is most accurate, based on the evaluation.Type: ApplicationFiled: December 28, 2016Publication date: March 15, 2018Applicant: International Business Machines CorporationInventors: Sakyasingha Dasgupta, Takayuki Osogami