Abstract: A method is described for using artificial intelligence (AI) components in association with first a transceiver node in a wireless network, where the first node is configured to send data over a wireless channel and to initiate a training procedure for an artificial intelligent component in a second node. The first node, having an encoder, transmits, to a decoder in the second node, a plurality of ordered training pairs, the transmission in response to a detection of a trigger condition based on a reconstruction loss value determined by the first node. The first node receives, from the second node, partially processed training information corresponding to the transmitted training pairs. The first node updates learnable parameters of the encoder based on the received partially processed training information to reduce the reconstruction loss value.

Abstract: The disclosure relates to a method comprising quantizing parameters of an input tensor, said quantizing using a codebook whose size is obtained according to a distortion value determined between the at least one tensor and a quantized version of said at least one tensor. The disclosure also relates to a method for quantizing parameters of the input tensor using a pdf-based initialization bounded according to at least one first pdf factor, said first pdf factor being selected among several candidate bounding pdf factors according to resulting entropy. The disclosure also relates to corresponding signal; bitstream, storage media and encoder and/or decoder devices.

Abstract: The disclosure relates to a method comprising, responsive to a determination that at least one first tensor of at least one layer of at least one Deep Neural Network is decomposed into a second tensor and a third tensor whose parameters are encoded in a bitstream, decoding from the bitstream a size of at least one of the second tensor and the third tensor, and decoding the at least one of the second tensor and the third tensor from the bitstream based on the decoded size. Corresponding apparatus, encoding method, signal; bitstream, storage media and encoder and/or decoder devices are also provided.

Abstract: The disclosure relates to a method for compression including codebook-based quantization of a data set and corresponding decompression method, signal; bitstream, and encoder and/or decoder device.

Abstract: The disclosure relates, according to a first aspect, to a method for compressing data including encoding at least one information representative of a use, during the compression, of a compressed sparse format. The disclosure relates, according to a second aspect, to a method for decompressing input data comprising obtaining information representative of zero or non-zero values in at least a part of the input data, and using only the non-zero values of the zero or non-zero values for a further processing of the part of the input data, based on the representative information. Corresponding devices, system, non-transitory program product, computer storage medium and signal are also disclosed.

Abstract: The present disclosure relates to a method including reshaping a first tensor of weights, by using one or more second tensor having a lower dimension than the first tensor dimension and encoding the second tensor in a signal The present disclosure relates to a method including obtaining a first tensor of weights by reshaping one or more second tensor hav ing a lower dimension than the first tensor dimension, the one or more second tensor being decoded from a signal. The present disclosure further relates to the corresponding dev ices, signal, and computer readable storage media.

Abstract: Different implementations are described, particularly implementations for video encoding and decoding based on linear weighted intra prediction, also called matrix based intra prediction, are presented. Accordingly, for a block being encoded or decoded in linear weighted intra prediction, obtaining intra predicted samples from at least two matrix-vector products between at least two selected weight matrices of reduced size and a set of neighboring reference samples. Advantageously, such arrangement allows to reduce the amount of memory for storing data and to reduce the complexity of the intra prediction samples computation.

Abstract: A method and apparatus for performing deep neural network compression of convolutional and fully connected layers using a linear approximation of their outputs with information, such as in matrices representing weights, biases and non-linearities, to iteratively compress a pre-trained deep neural network by low displacement rank based approximation of the network layer weight matrices. Extension of the technique enables consecutive layers to be compressed jointly, allowing compression and speeding inference by reducing the number of channels/hidden neurons in the network.

Abstract: A method and apparatus for conveying information in a bitstream for deep neural network compression, such as in matrices representing weights, biases and non-linearities, to iteratively compress a pre-trained deep neural network by low displacement rank based approximation of the network layer weight matrices. The low displacement rank approximation allows for replacement of an original layer weight matrices of the pre-trained deep neural network as the sum of small number of structured matrices, allowing compression and low inference complexity. A decoder stage parses a bitstream for inference.

Abstract: A method and an apparatus for performing deep neural network compression use an approximation training set along with information, such as in matrices representing weights, biases and non-linearities, to iteratively compress a pre-trained deep neural network by low displacement rank based approximation of the network layer weight matrices. The low displacement rank approximation allows for replacement of an original layer weight matrices of the pre-trained deep neural network as the sum of a small number of structured matrices, allowing compression and low inference complexity.

Abstract: The present disclosure provides a method (1100) and apparatus (702) for inhibiting the interruption of the content being consumed by a user that is provided by an entertainment system. The method (1100) and apparatus (702) of the present disclosure may detect an interruption event and determine, based on the current state of a user, whether the interruption event matches the current state of the user. If the method (1100) and apparatus (702) of the present disclosure determine that the interruption event does not match the current state of the user, the method (1100) and apparatus (702) of the present disclosure may inhibit the interruption of the content being consumed by the user.

Abstract: A method for correlating Galvanic Skin Response (GSR) signals from multiple users watching the same content to identify scenes of interest in such content includes filtering GSR signals from each user by subtracting consecutive GSR signal samples from each other. The user reaction portion and baseline portion of the GSR signals for the users are collectively optimized to recover non-zero user responses for the users. The locations in the content having the non-zero responses for the users are then identified as the scenes of interest in such content for the users.

Abstract: The present disclosure provides a method (1100) and apparatus (702) for inhibiting the interruption of the content being consumed by a user that is provided by an entertainment system. The method (1100) and apparatus (702) of the present disclosure may detect an interruption event and determine, based on the current state of a user, whether the interruption event matches the current state of the user. If the method (1100) and apparatus (702) of the present disclosure determine that the interruption event does not match the current state of the user, the method (1100) and apparatus (702) of the present disclosure may inhibit the interruption of the content being consumed by the user.

Abstract: The present disclosure provides a method (500) and apparatus (1000, 250) for using the biometric responses of a first group of users watching and/or listening to digital multimedia content to generate a summary of the digital multimedia content by randomly selecting segments from the digital multimedia content as a function of segment scores determined from the biometric responses. In another method (600) and apparatus (1000, 250) of the present disclosure, the biometric responses of a second group of users being shown the generated summary of the digital multimedia content can be used to extrapolate the biometric responses of the second group of users to the entire digital multimedia content. Furthermore, yet another method (650) and apparatus (1000, 250) of the present disclosure can use the biometric responses of at least one user in the second group of users to generate recommendations.

Abstract: The present disclosure provides a method (500) and apparatus (1000, 250) for using the biometric responses of a first group of users watching and/or listening to digital multimedia content to generate a summary of the digital multimedia content by randomly selecting segments from the digital multimedia content as a function of segment scores determined from the biometric responses. In another method (600) and apparatus (1000, 250) of the present disclosure, the biometric responses of a second group of users being shown the generated summary of the digital multimedia content can be used to extrapolate the biometric responses of the second group of users to the entire digital multimedia content. Furthermore, yet another method (650) and apparatus (1000, 250) of the present disclosure can use the biometric responses of at least one user in the second group of users to generate recommendations.

Abstract: Interactions with a multimedia content provider are tracked and communication is made with a physical activity tracking device to obtain movement information. The interactions and physical activity level provide insight as to a length of time a user has been continuously watching multimedia content. When a level of inactivity exceeds a threshold, access to multimedia content is adjusted, for example, by limiting access to the multimedia content and/or displaying a notification to the user regarding their level of inactivity.

Abstract: The present principles generally relate to detection and analysis of sound events in a user's environment to automate changes to a multi-media player's state or action. The multimedia player characterizes ambient sound that it receives. The state or the action of the multimedia player is adaptively initiated or changed according to the characterization of the ambient sound and the location of the player, thus allowing adaptive adjustment of the sound of the audio/video content.

Abstract: A method for recommending content from among a collection of available content includes determining for each of available content at least one of a first, second and third content ratings related to (1) content characteristics that interfere with sleep patterns; (2) excitement level of the content; and (3) dynamic range of content audio, respectively. Thereafter, the content having a content rating that least interfere with sleep patterns is established. The available content having the content rating that least interferes with sleep patterns is recommended.