-
公开(公告)号：US20190213482A1
公开(公告)日：2019-07-11
申请号：US16355290
申请日：2019-03-15
Applicant: salesforce.com, inc.
Inventor： Richard SOCHER , Caiming XIONG , Kai Sheng TAI
Abstract: A method of classifying three-dimensional (3D) data includes receiving three-dimensional (3D) data and processing the 3D data using a neural network that includes a plurality of subnetworks arranged in a sequence and the data is processed through each of the subnetworks. Each of the subnetworks is configured to receive an output generated by a preceding subnetwork in the sequence, process the output through a plurality of parallel 3D convolution layer paths of varying convolution volume, process the output through a parallel pooling path, and concatenate output of the 3D convolution layer paths and the pooling path to generate an output representation from each of the subnetworks. Following processing the data through the subnetworks, the method includes processing the output of a last one of the subnetworks in the sequence through a vertical pooling layer to generate an output and classifying the received 3D data based upon the generated output.
-
22.发明申请公开(公告)号：US20190130312A1
公开(公告)日：2019-05-02
申请号：US15885727
申请日：2018-01-31
Applicant: salesforce.com, inc.
Inventor： Caiming XIONG , Tianmin SHU , Richard SOCHER
Abstract: The disclosed technology reveals a hierarchical policy network, for use by a software agent, to accomplish an objective that requires execution of multiple tasks. A terminal policy learned by training the agent on a terminal task set, serves as a base task set of the intermediate task set. An intermediate policy learned by training the agent on an intermediate task set serves as a base policy of the top policy. A top policy learned by training the agent on a top task set serves as a base task set of the top task set. The agent is configurable to accomplish the objective by traversal of the hierarchical policy network. A current task in a current task set is executed by executing a previously-learned task selected from a corresponding base task set governed by a corresponding base policy, or performing a primitive action selected from a library of primitive actions.
-
公开(公告)号：US20180082171A1
公开(公告)日：2018-03-22
申请号：US15421016
申请日：2017-01-31
Applicant: salesforce.com, inc.
Inventor： Stephen Joseph MERITY , Caiming XIONG , James BRADBURY , Richard SOCHER
CPC classification number: G06N3/0445 , G06F17/277 , G06N3/0454 , G06N3/0472 , G06N3/08 , G06N3/084 , G06N7/005
Abstract: The technology disclosed provides a so-called “pointer sentinel mixture architecture” for neural network sequence models that has the ability to either reproduce a token from a recent context or produce a token from a predefined vocabulary. In one implementation, a pointer sentinel-LSTM architecture achieves state of the art language modeling performance of 70.9 perplexity on the Penn Treebank dataset, while using far fewer parameters than a standard softmax LSTM.
-
公开(公告)号：US20210141781A1
公开(公告)日：2021-05-13
申请号：US16680302
申请日：2019-11-11
Applicant: salesforce.com, inc.
Inventor： Ankit CHADHA , Zeyuan CHEN , Caiming XIONG , Ran XU , Richard SOCHER
Abstract: Embodiments described herein provide unsupervised density-based clustering to infer table structure from document. Specifically, a number of words are identified from a block of text in an noneditable document, and the spatial coordinates of each word relative to the rectangular region are identified. Based on the word density of the rectangular region, the words are grouped into clusters using a heuristic radius search method. Words that are grouped into the same cluster are determined to be the element that belong to the same cell. In this way, the cells of the table structure can be identified. Once the cells are identified based on the word density of the block of text, the identified cells can be expanded horizontally or grouped vertically to identify rows or columns of the table structure.
-
公开(公告)号：US20210103816A1
公开(公告)日：2021-04-08
申请号：US17122894
申请日：2020-12-15
Applicant: salesforce.com, inc.
Inventor： James BRADBURY , Stephen Joseph MERITY , Caiming XIONG , Richard SOCHER
Abstract: The technology disclosed provides a quasi-recurrent neural network (QRNN) encoder-decoder model that alternates convolutional layers, which apply in parallel across timesteps, and minimalist recurrent pooling layers that apply in parallel across feature dimensions.
-
Patent Agency Ranking
公开(公告)号：US20200372116A1
公开(公告)日：2020-11-26
申请号：US16531343
申请日：2019-08-05
Applicant: salesforce.com, inc.
Inventor： Mingfei GAO , Richard SOCHER , Caiming Xiong
Abstract: Systems and methods are provided for weakly supervised natural language localization (WSNLL), for example, as implemented in a neural network or model. The WSNLL network is trained with long, untrimmed videos, i.e., videos that have not been temporally segmented or annotated. The WSNLL network or model defines or generates a video-sentence pair, which corresponds to a pairing of an untrimmed video with an input text sentence. According to some embodiments, the WSNLL network or model is implemented with a two-branch architecture, where one branch performs segment sentence alignment and the other one conducts segment selection.
-
公开(公告)号：US20200272940A1
公开(公告)日：2020-08-27
申请号：US16398757
申请日：2019-04-30
Applicant: salesforce.com, inc.
Inventor： Lichao Sun , Caiming XIONG , Jia LI , Richard SOCHER
Abstract: Approaches for private and interpretable machine learning systems include a system for processing a query. The system includes one or more teacher modules for receiving a query and generating a respective output, one or more privacy sanitization modules for privacy sanitizing the respective output of each of the one or more teacher modules, and a student module for receiving a query and the privacy sanitized respective output of each of the one or more teacher modules and generating a result. Each of the one or more teacher modules is trained using a respective private data set. The student module is trained using a public data set. In some embodiments, human understandable interpretations of an output from the student module is provided to a model user.
-
公开(公告)号：US20200084465A1
公开(公告)日：2020-03-12
申请号：US16687405
申请日：2019-11-18
Applicant: Salesforce.com, inc.
Inventor： Yingbo ZHOU , Luowei ZHOU , Caiming XIONG , Richard SOCHER
IPC: H04N19/46 , H04N19/132 , H04N19/126 , H04N19/33 , H04N21/81 , H04N19/187 , H04N19/60 , H04N19/44
Abstract: Systems and methods for dense captioning of a video include a multi-layer encoder stack configured to receive information extracted from a plurality of video frames, a proposal decoder coupled to the encoder stack and configured to receive one or more outputs from the encoder stack, a masking unit configured to mask the one or more outputs from the encoder stack according to one or more outputs from the proposal decoder, and a decoder stack coupled to the masking unit and configured to receive the masked one or more outputs from the encoder stack. Generating the dense captioning based on one or more outputs of the decoder stack. In some embodiments, the one or more outputs from the proposal decoder include a differentiable mask. In some embodiments, during training, error in the dense captioning is back propagated to the decoder stack, the encoder stack, and the proposal decoder.
-
公开(公告)号：US20190188568A1
公开(公告)日：2019-06-20
申请号：US15926768
申请日：2018-03-20
Applicant: salesforce.com, inc.
Inventor： Nitish Shirish KESKAR , Richard SOCHER
CPC classification number: G06N3/082 , G06N3/0427
Abstract: Hybrid training of deep networks includes a multi-layer neural network. The training includes setting a current learning algorithm for the multi-layer neural network to a first learning algorithm. The training further includes iteratively applying training data to the neural network, determining a gradient for parameters of the neural network based on the applying of the training data, updating the parameters based on the current learning algorithm, and determining whether the current learning algorithm should be switched to a second learning algorithm based on the updating. The training further includes, in response to the determining that the current learning algorithm should be switched to a second learning algorithm, changing the current learning algorithm to the second learning algorithm and initializing a learning rate of the second learning algorithm based on the gradient and a step used by the first learning algorithm to update the parameters of the neural network.
-
公开(公告)号：US20180144248A1
公开(公告)日：2018-05-24
申请号：US15817165
申请日：2017-11-18
Applicant: salesforce.com, inc.
Inventor： Jiasen LU , Caiming XIONG , Richard SOCHER
Abstract: The technology disclosed presents a novel spatial attention model that uses current hidden state information of a decoder long short-term memory (LSTM) to guide attention and to extract spatial image features for use in image captioning. The technology disclosed also presents a novel adaptive attention model for image captioning that mixes visual information from a convolutional neural network (CNN) and linguistic information from an LSTM. At each timestep, the adaptive attention model automatically decides how heavily to rely on the image, as opposed to the linguistic model, to emit the next caption word. The technology disclosed further adds a new auxiliary sentinel gate to an LSTM architecture and produces a sentinel LSTM (Sn-LSTM). The sentinel gate produces a visual sentinel at each timestep, which is an additional representation, derived from the LSTM's memory, of long and short term visual and linguistic information.
-
-
-
-
-
-
-
-
-
Search Results
33
records
(took 0.018 seconds)
