Abstract: A task-based learning using task-directed prediction network can be provided. Training data can be received. Contextual information associated with a task-based criterion can be received. A machine learning model can be trained using the training data. A loss function computed during training of the machine learning model integrates the task-based criterion, and minimizing the loss function during training iterations includes minimizing the task-based criterion.

Abstract: Provided in the present disclosure are a message management method and terminal, the method comprising: receiving a first input of an operating body for a target message on a group communication interface; in response to the first input, displaying a message management widget, the message management widget comprising processing progress information of the target message; when a processing feedback message of at least one message receiving subject for the target message is received, updating display content of the message management widget.

Abstract: Techniques for image processing and transformation are provided. A plurality of images and a plurality of maps are received, and a system of neural networks is trained based on the plurality of images and the plurality of maps. A first image is received, and a first map is generated by processing the first image using the system of neural networks.

Abstract: Disclosed in this application are a method for embedding a watermark in video data and apparatus, a method for extracting a watermark in video data and apparatus, a device, and a storage medium. The method for embedding the watermark includes: acquiring a target image frame in video data; performing time-frequency transformation on the target image frame to obtain target frequency domain data, the target frequency domain data comprising a matrix formed by frequency domain coefficients; changing the frequency domain coefficients in the target frequency domain data according to watermark data to obtain watermarked frequency domain data; performing inverse time-frequency transformation on the watermarked frequency domain data to obtain a watermarked image frame; and synthesizing watermarked video data according to the watermarked image frame.

Abstract: Hessian matrix-free sample-based techniques for model explanations that are faithful to the model are provided. In one aspect, a method for explaining a machine learning model {circumflex over (?)} (e.g., for natural language processing) is provided. The method includes: training the machine learning model {circumflex over (?)} with training data D; obtaining a decision of the machine learning model {circumflex over (?)}; and explaining the decision of the machine learning model {circumflex over (?)} using training examples from the training data D.

Abstract: Sample-based model explanation techniques are provided using arbitrary spans of training data at any granularity as an explanation with increased interpretability. In one aspect, a method for explaining a machine learning model {circumflex over (?)} includes: training the machine learning model {circumflex over (?)} with training data D; obtaining a decision of the machine learning model {circumflex over (?)}; masking one or more datapoints in the training data D; determining whether a new decision of the machine learning model {circumflex over (?)} obtained after the masking is same as the decision of the machine learning model {circumflex over (?)} obtained prior to the masking; and using the masking to explain which of the one or more datapoints in the training data D are significant. Namely, the one or more datapoints in the training data D that, when masked, change the decision of the machine learning model {circumflex over (?)} are significant.

Abstract: Systems, computer-implemented methods, and/or computer program products to facilitate updating, such as averaging and/or training, of one or more statistical sets are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a computing component that updates a first statistical set with an additional statistical set from an additional system. The additional statistical set can have been generated from a parent statistical set that is based on underlying data. To update the first statistical set, the additional statistical set can be obtained by the system without obtaining the parent statistical set and without obtaining the underlying data. According to an embodiment, the first statistical set can be a model parameter set generated from a first parent statistical set that is an analytical model.

Abstract: A machine learning model can be trained to predict one or more financial indicators using earnings call transcripts augmented with counterfactual information. Using faithful gradient-based method, prediction results with respect to a particular counterfactual information can be explained. Based on the explanation, the counterfactual information determined to have most impact on prediction results can be selected for updating the machine learning model.

Abstract: From metadata of a corpus of natural language text documents, a relativity matrix is constructed, a row-column intersection in the relativity matrix corresponding to a relationship between two instances of a type of metadata. An encoder model is trained, generating a trained encoder model, to compute an embedding corresponding to a token of a natural language text document within the corpus and the relativity matrix, the encoder model comprising a first encoder layer, the first encoder layer comprising a token embedding portion, a relativity embedding portion, a token self-attention portion, a metadata self-attention portion, and a fusion portion, the training comprising adjusting a set of parameters of the encoder model.

Abstract: Computer hardware and/or software that performs the following operations: (i) updating a machine learning model by synchronously applying, to the machine learning model, a first set of training results received from a set of trainers having respective training datasets; (ii) receiving, from one or more trainers of the set of trainers, a first set of metrics pertaining to at least some of the training results of the first set of training results; and (iii) based, at least in part, on the first set of metrics, determining to subsequently update the machine learning model via asynchronous application of subsequent training results received from respective trainers of the set of trainers.

Abstract: An electronic device and a focusing method are provided. The electronic device includes at least two cameras, and each of the at least two cameras is provided with a phase difference (PD) point pair set having one or more PD point pairs. The one or more PD point pairs in the PD point pair set of one camera are located in different areas of the one camera, and areas of another camera corresponding to the different areas where the one or more PD point pairs of the one camera are located do not have PD point pairs located therein, and the another camera is a camera other than the one camera in the at least two cameras.

Abstract: Time series data can be received. A machine learning model can be trained using the time series data. A contaminating process can be estimated based on the time series data, the contaminating process including outliers associated with the time series data. A parameter associated with the contaminating process can be determined. Based on the trained machine learning model and the parameter associated with the contaminating process, a single-valued metric can be determined, which represents an impact of the contaminating process on the machine learning model's future prediction. A plurality of different outlier detecting machine learning models can be used to estimate the contaminating process and the single-valued metric can be determined for each of the plurality of different outlier detecting machine learning models. The plurality of different outlier detecting machine learning models can be ranked according to the associated single-valued metric.

Abstract: Systems, computer-implemented methods, and computer program products to facilitate updating, such as averaging and/or training, of one or more statistical sets are provided. According to an embodiment, a system can comprise a memory that stores computer executable components and a processor that executes the computer executable components stored in the memory. The computer executable components can include a computing component that averages a statistical set, provided by the system, with an additional statistical set, that is compatible with the statistical set, to compute an averaged statistical set, where the additional statistical set is obtained from a selected additional system of a plurality of additional systems. The computer executable components also can include a selecting component that selects the selected additional system according to a randomization pattern.

Abstract: Embodiments for implementing a softmax function in an analog circuit. The analog circuit may comprise a plurality of input nodes to accept voltage inputs; a plurality of diodes connected to each of the plurality of input nodes to perform a current adding function; a log amplifier coupled to the plurality of diodes; a plurality of analog adders coupled to the voltage inputs and an output of the log amplifier; and a plurality of exponential amplifiers, each of the plurality of exponential amplifiers coupled to one of the plurality of analog adders.

Abstract: A method for editing text and a mobile terminal are provided. The method includes: receiving a first input of a user on target text; displaying a preset text deletion control on a first preset side of the target text in response to the first input; receiving a second input of the user on the text deletion control; and deleting, in response to the second input, target sub-text selected by the second input, where the target text includes the target sub-text.

Abstract: A method for power saving and encryption during analysis of media captured by an information capture device using a partitioned neural network includes replicating, by an information capture device, an artificial neural network (ANN) from a computer server to the information capture device. The ANN on the computer server and a replicated ANN, both, include M layers. The method further includes, in response to captured data being input to be processed, partially processing, by the information capture device, the captured data by executing a first k layers using the replicated ANN, wherein only the k layers are selected to execute on the information capture device. The method further includes transmitting, by the information capture device, an output of the k-th layer to the computer server, which partially processes the captured data by executing the remainder of the M layers using the ANN and the output of the k-th layer.

Abstract: Techniques are provided for decentralized parallel min/max optimizations. In one embodiment, the techniques involve generating gradients based on a first set of weights associated with a first node of a neural network, exchanging the first set of weights with a second set of weights associated with a second node, generating an average weight based on the first set of weights and the second set of weights, and updating the first set of weights and the second set of weights via a decentralized parallel optimistic stochastic gradient (DPOSG) algorithm based on the gradients and the average weight.

Abstract: The invention relates to energy-resolved X-ray imaging apparatus and method. The present disclosure provides an apparatus for electromagnetic irradiation imaging. The apparatus includes one or more pixels, each pixel including a plurality of detector cells arranged in a row extending in a row direction. The row is configured to receive photons at an incident surface at one end of the row, and the received photons penetrate the plurality of detector cells in the row direction.

Abstract: Embodiments of a method are disclosed. The method includes performing a batch of decentralized deep learning training for a machine learning model in coordination with multiple local homogenous learners on a deep learning training compute node, and in coordination with multiple super learners on corresponding deep learning training compute nodes. The method also includes exchanging communications with the super learners in accordance with an asynchronous decentralized parallel stochastic gradient descent (ADPSGD) protocol. The communications are associated with the batch of deep learning training.

Abstract: Embodiments of a method are disclosed. The method includes performing distributed deep learning training on a batch of training data. The method also includes determining training times representing an amount of time between a beginning batch time and an end batch time. Further, the method includes modifying a communication aspect of the communication straggler to reduce a future network communication time for the communication straggler to send a future result of the distributed deep learning training on a new batch of training data in response to the centralized parameter server determining that the learner is the communication straggler.