Patents by Inventor Yanqi ZHOU
Yanqi ZHOU 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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Publication number: 20240112027Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.Type: ApplicationFiled: September 28, 2023Publication date: April 4, 2024Inventors: Yanqi Zhou, Yanping Huang, Yifeng Lu, Andrew M. Dai, Siamak Shakeri, Zhifeng Chen, James Laudon, Quoc V. Le, Da Huang, Nan Du, David Richard So, Daiyi Peng, Yingwei Cui, Jeffrey Adgate Dean, Chang Lan
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Publication number: 20240005129Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for jointly determining neural network architectures and hardware accelerator architectures.Type: ApplicationFiled: October 1, 2021Publication date: January 4, 2024Inventors: Yanqi Zhou, Amir Yazdanbakhsh, Berkin Akin, Daiyi Peng, Yuxiong Zhu, Mingxing Tan, Xuanyi Dong
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Publication number: 20230409867Abstract: Implementations are described herein for performing joint optimization of multi-task learning of dense predictions (MT-DP) and hardware-aware neural architecture search (NAS). In various implementations, a set of tasks to be performed using a resource-constrained edge computing system may be determined. Based on a base multi-task dense-prediction (MT-DP) architecture template, the set of tasks, and a plurality of hardware-based constraints of a target edge computing system, a network architecture search (NAS) may be used to sample candidate MT-DP architecture(s) from a search space of neural network architecture components. Each sampled candidate MT-DP architecture may include a distinct assembly of sampled neural network architecture components applied to the base MT-DP architecture template. Image data may be processed using the candidate MT-DP architecture(s) to determine performance metrics. These performance metrics may be used to jointly train the MT-DP architecture(s) and/or the NAS.Type: ApplicationFiled: June 15, 2022Publication date: December 21, 2023Inventors: Chunfeng Wen, Yueqi Li, Zhiqiang Yuan, Minh Thanh Vu, Yanqi Zhou
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Publication number: 20230376664Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining architectures of hardware accelerators.Type: ApplicationFiled: October 11, 2021Publication date: November 23, 2023Inventors: Amir YAZDANBAKHSH, Christof ANGERMUELLER, Berkin AKIN, Yanqi ZHOU, James LAUDON, Ravi NARAYANASWAMI
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Publication number: 20230306266Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.Type: ApplicationFiled: May 22, 2023Publication date: September 28, 2023Inventors: Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Lin-Kit Wong, Chao Ma, Qiumin Xu, Azalia Mirhoseini
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Patent number: 11741342Abstract: Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy but lacked consideration of computational resource use. Presented herein are embodiments of a Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA embodiments use a policy network to process the network embeddings to generate new configurations. Example demonstrates of RENA embodiments on image recognition and keyword spotting (KWS) problems are also presented herein. RENA embodiments can find novel architectures that achieve high performance even with tight resource constraints. For the CIFAR10 dataset, the tested embodiment achieved 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size was less than 3M parameters.Type: GrantFiled: March 8, 2019Date of Patent: August 29, 2023Assignee: Baidu USA LLCInventors: Yanqi Zhou, Siavash Ebrahimi, Sercan Arik, Haonan Yu, Hairong Liu, Gregory Diamos
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Publication number: 20230176840Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compiler optimizations using a compiler optimization network. One of the methods includes receiving an input program, wherein the input program defines a graph of operation modules, wherein each node in the graph is a respective operation module, and each edge between nodes in the graph represents one operation module receiving the output generated by another operation module. The input program is processed by a compiler optimization network comprising a graph-embedding network that is configured to encode operation features and operation dependencies of the operation modules of the input program into a graph embedding representation and a policy network that is configured to generate an optimization action for each of one or more nodes encoded in the graph embedding representation.Type: ApplicationFiled: June 7, 2021Publication date: June 8, 2023Inventors: Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Lin-Kit Wong, Chao Ma, Qiumin Xu, Hanxiao Liu, Phitchaya Mangpo Phothilimthana, Shen Wang, Anna Darling Goldie, Azalia Mirhoseini, James Laudon
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Patent number: 11657289Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.Type: GrantFiled: April 3, 2020Date of Patent: May 23, 2023Assignee: Google LLCInventors: Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Lin-Kit Wong, Chao Ma, Qiumin Xu, Azalia Mirhoseini
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Patent number: 11651763Abstract: Described herein are systems and methods for augmenting neural speech synthesis networks with low-dimensional trainable speaker embeddings in order to generate speech from different voices from a single model. As a starting point for multi-speaker experiments, improved single-speaker model embodiments, which may be referred to generally as Deep Voice 2 embodiments, were developed, as well as a post-processing neural vocoder for Tacotron (a neural character-to-spectrogram model). New techniques for multi-speaker speech synthesis were performed for both Deep Voice 2 and Tacotron embodiments on two multi-speaker TTS datasets—showing that neural text-to-speech systems can learn hundreds of unique voices from twenty-five minutes of audio per speaker.Type: GrantFiled: November 2, 2020Date of Patent: May 16, 2023Assignee: Baidu USA LLCInventors: Sercan O. Arik, Gregory Diamos, Andrew Gibiansky, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou
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Patent number: 11593655Abstract: As deep learning application domains grow, a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements is extremely beneficial. Presented herein are large-scale empirical study of error and model size growth as training sets grow. Embodiments of a methodology for this measurement are introduced herein as well as embodiments for predicting other metrics, such as compute-related metrics. It is shown herein that power-law may be used to represent deep model relationships, such as error and training data size. It is also shown that model size scales sublinearly with data size. These scaling relationships have significant implications on deep learning research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and underscore the importance of continued computational scaling.Type: GrantFiled: November 30, 2018Date of Patent: February 28, 2023Assignee: Baidu USA LLCInventors: Joel Hestness, Gregory Diamos, Hee Woo Jun, Sharan Narang, Newsha Ardalani, Md Mostofa Ali Patwary, Yanqi Zhou
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Patent number: 11238843Abstract: Voice cloning is a highly desired capability for personalized speech interfaces. Neural network-based speech synthesis has been shown to generate high quality speech for a large number of speakers. Neural voice cloning systems that take a few audio samples as input are presented herein. Two approaches, speaker adaptation and speaker encoding, are disclosed. Speaker adaptation embodiments are based on fine-tuning a multi-speaker generative model with a few cloning samples. Speaker encoding embodiments are based on training a separate model to directly infer a new speaker embedding from cloning audios, which is used in or with a multi-speaker generative model. Both approaches achieve good performance in terms of naturalness of the speech and its similarity to original speaker—even with very few cloning audios.Type: GrantFiled: September 26, 2018Date of Patent: February 1, 2022Assignee: Baidu USA LLCInventors: Sercan O. Arik, Jitong Chen, Kainan Peng, Wei Ping, Yanqi Zhou
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Publication number: 20210248445Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.Type: ApplicationFiled: April 3, 2020Publication date: August 12, 2021Inventors: Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Lin-Kit Wong, Chao Ma, Qiumin Xu, Azalia Mirhoseini
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Publication number: 20210049999Abstract: Described herein are systems and methods for augmenting neural speech synthesis networks with low-dimensional trainable speaker embeddings in order to generate speech from different voices from a single model. As a starting point for multi-speaker experiments, improved single-speaker model embodiments, which may be referred to generally as Deep Voice 2 embodiments, were developed, as well as a post-processing neural vocoder for Tacotron (a neural character-to-spectrogram model). New techniques for multi-speaker speech synthesis were performed for both Deep Voice 2 and Tacotron embodiments on two multi-speaker TTS datasets—showing that neural text-to-speech systems can learn hundreds of unique voices from twenty-five minutes of audio per speaker.Type: ApplicationFiled: November 2, 2020Publication date: February 18, 2021Applicant: Baidu USA LLCInventors: Sercan O. ARIK, Gregory DIAMOS, Andrew GIBIANSKY, John MILLER, Kainan PENG, Wei PING, Jonathan RAIMAN, Yanqi ZHOU
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Patent number: 10896669Abstract: Described herein are systems and methods for augmenting neural speech synthesis networks with low-dimensional trainable speaker embeddings in order to generate speech from different voices from a single model. As a starting point for multi-speaker experiments, improved single-speaker model embodiments, which may be referred to generally as Deep Voice 2 embodiments, were developed, as well as a post-processing neural vocoder for Tacotron (a neural character-to-spectrogram model). New techniques for multi-speaker speech synthesis were performed for both Deep Voice 2 and Tacotron embodiments on two multi-speaker TTS datasets—showing that neural text-to-speech systems can learn hundreds of unique voices from twenty-five minutes of audio per speaker.Type: GrantFiled: May 8, 2018Date of Patent: January 19, 2021Assignee: Baidu USA LLCInventors: Sercan O. Arik, Gregory Diamos, Andrew Gibiansky, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou
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Publication number: 20200175374Abstract: As deep learning application domains grow, a deeper understanding of the relationships between training set size, computational scale, and model accuracy improvements is extremely beneficial. Presented herein are large-scale empirical study of error and model size growth as training sets grow. Embodiments of a methodology for this measurement are introduced herein as well as embodiments for predicting other metrics, such as compute-related metrics. It is shown herein that power-law may be used to represent deep model relationships, such as error and training data size. It is also shown that model size scales sublinearly with data size. These scaling relationships have significant implications on deep learning research, practice, and systems. They can assist model debugging, setting accuracy targets, and decisions about data set growth. They can also guide computing system design and underscore the importance of continued computational scaling.Type: ApplicationFiled: November 30, 2018Publication date: June 4, 2020Applicant: Baidu USA LLCInventors: Joel HESTNESS, Gregory DIAMOS, Hee Woo JUN, Sharan NARANG, Newsha ARDALANI, Md Mostofa Ali PATWARY, Yanqi ZHOU
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Publication number: 20190354837Abstract: Neural Architecture Search (NAS) is a laborious process. Prior work on automated NAS targets mainly on improving accuracy but lacked consideration of computational resource use. Presented herein are embodiments of a Resource-Efficient Neural Architect (RENA), an efficient resource-constrained NAS using reinforcement learning with network embedding. RENA embodiments use a policy network to process the network embeddings to generate new configurations. Example demonstrates of RENA embodiments on image recognition and keyword spotting (KWS) problems are also presented herein. RENA embodiments can find novel architectures that achieve high performance even with tight resource constraints. For the CIFAR10 dataset, the tested embodiment achieved 2.95% test error when compute intensity is greater than 100 FLOPs/byte, and 3.87% test error when model size was less than 3M parameters.Type: ApplicationFiled: March 8, 2019Publication date: November 21, 2019Applicant: Baidu USA LLCInventors: Yanqi ZHOU, Siavash EBRAHIMI, Sercan ARIK, Haonan YU, Hairong LIU, Gregory DIAMOS
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Publication number: 20190251952Abstract: Voice cloning is a highly desired capability for personalized speech interfaces. Neural network-based speech synthesis has been shown to generate high quality speech for a large number of speakers. Neural voice cloning systems that take a few audio samples as input are presented herein. Two approaches, speaker adaptation and speaker encoding, are disclosed. Speaker adaptation embodiments are based on fine-tuning a multi-speaker generative model with a few cloning samples. Speaker encoding embodiments are based on training a separate model to directly infer a new speaker embedding from cloning audios, which is used in or with a multi-speaker generative model. Both approaches achieve good performance in terms of naturalness of the speech and its similarity to original speaker—even with very few cloning audios.Type: ApplicationFiled: September 26, 2018Publication date: August 15, 2019Applicant: Baidu USA LLCInventors: Sercan O. ARIK, Jitong CHEN, Kainan PENG, Wei PING, Yanqi ZHOU
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Publication number: 20180336880Abstract: Described herein are systems and methods for augmenting neural speech synthesis networks with low-dimensional trainable speaker embeddings in order to generate speech from different voices from a single model. As a starting point for multi-speaker experiments, improved single-speaker model embodiments, which may be referred to generally as Deep Voice 2 embodiments, were developed, as well as a post-processing neural vocoder for Tacotron (a neural character-to-spectrogram model). New techniques for multi-speaker speech synthesis were performed for both Deep Voice 2 and Tacotron embodiments on two multi-speaker TTS datasets—showing that neural text-to-speech systems can learn hundreds of unique voices from twenty-five minutes of audio per speaker.Type: ApplicationFiled: May 8, 2018Publication date: November 22, 2018Applicant: Baidu USA LLCInventors: Sercan O. ARIK, Gregory DIAMOS, Andrew GIBIANSKY, John MILLER, Kainan PENG, Wei PING, Jonathan RAIMAN, Yanqi ZHOU