Abstract: Video processing such as encoding and/or decoding a picture can involve deriving an inter-prediction parameter based on first and second merge candidates used to generate a pairwise merge candidate, wherein the inter-prediction parameter comprises at least one of an index for weighted bi-prediction or an interpolation filter index; and encoding at least a portion of the picture information based on the inter-prediction parameter.

Abstract: A method for decoding. the method comprising: obtaining an ordered list of a plurality of positions in a spatial neighborhood of a current block in a picture; parsing the list in order until first motion information is available at one of the positions and second motion information is available at a position in a reference picture designated by the first motion information; using the second motion information to obtain at least one motion vector predictor candidate to be inserted in at least one list of motion vector predictor candidates used for predicting a motion vector used for the current block.

Abstract: In a video coding system, it is proposed to adapt video coding tools to the use of Reference Picture Re-scaling where a reference picture has a different size than the current picture to be coded or decoded. Different embodiments are proposed hereafter, introducing some tools modifications to increase coding efficiency and improve the codec consistency when RPR is enabled. A video encoding method, a decoding method, a video encoder and a video decoder are described.

Abstract: Techniques to predictively respond to user requests using natural language processing are described. In one embodiment, an apparatus may comprise a client communication component operative to receive a user service request from a user client; an interaction processing component operative to submit the user service request to a memory-based natural language processing component; generate a series of user interaction exchanges with the user client based on output from the memory-based natural language processing component, wherein the series of user interaction exchanges are represented in a memory component of the memory-based natural language processing component; and receive one or more operator instructions for the performance of the user service request from the memory-based natural language processing component; and a user interface component operative to display the one or more operator instructions in an operator console. Other embodiments are described and claimed.

Abstract: To generate an embedding model for entities in an online system, a first set of partitions is generated. Each partition of the first set of partitions includes a subset of entities of the online system. Each partition of at least a subset of partitions of the first set of partitions is assigned to embedding workers. Each of the embedding worker determines embedding vectors for each entity in the partition assigned to the embedding worker. A second set of partitions is generated. Each partition of at least a subset of partitions of the second set of partitions are assigned to embedding workers. Each embedding worker retrieves embedding vectors for the entities in the partition assigned to embedding worker, and determines updated embedding vectors for each of the entities based on the retrieved embedding vectors and information about interaction between the entities.

Abstract: Embodiments are disclosed for providing a machine-generated response (e.g., answer) to an input (e.g., question) based on long-term memory information. A method according to some embodiments include receiving an input; converting the input into an input feature vector in an internal feature representation space; updating a memory data structure by incorporating the input feature vector into the memory data structure; generating an output feature vector in the internal feature representation space, based on the updated memory data structure and the input feature vector; converting the output feature vector into an output object; and providing an output based on the output object as a response to the input.

Abstract: To generate an embedding model for entities in an online system, a first set of partitions is generated. Each partition of the first set of partitions includes a subset of entities of the online system. Each partition of at least a subset of partitions of the first set of partitions is assigned to embedding workers. Each of the embedding worker determines embedding vectors for each entity in the partition assigned to the embedding worker. A second set of partitions is generated. Each partition of at least a subset of partitions of the second set of partitions are assigned to embedding workers. Each embedding worker retrieves embedding vectors for the entities in the partition assigned to embedding worker, and determines updated embedding vectors for each of the entities based on the retrieved embedding vectors and information about interaction between the entities.

Abstract: Techniques to predictively respond to user requests using natural language processing are described. In one embodiment, an apparatus may comprise a client communication component operative to receive a user service request from a user client; an interaction processing component operative to submit the user service request to a memory-based natural language processing component; generate a series of user interaction exchanges with the user client based on output from the memory-based natural language processing component, wherein the series of user interaction exchanges are represented in a memory component of the memory-based natural language processing component; and receive one or more operator instructions for the performance of the user service request from the memory-based natural language processing component; and a user interface component operative to display the one or more operator instructions in an operator console. Other embodiments are described and claimed.

Abstract: In one embodiment, a computing system may generate a query vector representation of an input (e.g., a question). The system may generate relevance measures associated with a set of key-value memories based on comparisons between the query vector representation and key vector representations of the keys in the memories. The system may generate an aggregated result based on the relevance measures and value vector representations of the values in the memories. Through an iterative process that iteratively updates the query vector representation used in each iteration, the system may generate a final aggregated result using a final query vector representation. A combined feature representation may be generated based on the final aggregated result and the final query vector representation. The system may select an output (e.g., an answer to the question) in response to the input based on comparisons between the combined feature representation and a set of candidate outputs.

Abstract: Techniques to predictively respond to user requests using natural language processing are described. In one embodiment, an apparatus may comprise a client communication component operative to receive a user service request from a user client; an interaction processing component operative to submit the user service request to a memory-based natural language processing component; generate a series of user interaction exchanges with the user client based on output from the memory-based natural language processing component, wherein the series of user interaction exchanges are represented in a memory component of the memory-based natural language processing component; and receive one or more operator instructions for the performance of the user service request from the memory-based natural language processing component; and a user interface component operative to display the one or more operator instructions in an operator console. Other embodiments are described and claimed.

Abstract: Embodiments are disclosed for providing a machine-generated response (e.g., answer) to an input (e.g., question) based on long-term memory information. A method according to some embodiments include receiving an input; converting the input into an input feature vector in an internal feature representation space; updating a memory data structure by incorporating the input feature vector into the memory data structure; generating an output feature vector in the internal feature representation space, based on the updated memory data structure and the input feature vector; converting the output feature vector into an output object; and providing an output based on the output object as a response to the input.