Abstract: A method and apparatus for training an online prediction model are provided. The method may include: acquiring an offline sample feature and an online sample feature of a user, the offline sample feature including a user portrait feature; offline training to obtain an offline recommendation model, based on the offline sample feature and the online sample feature of the user; acquiring a latest online feature of the user, and online training to obtain an online learning model based on the latest online feature of the user, the online learning model being used to adapt the latest online feature for use as an online sample feature to be input into the trained offline recommendation model; and synchronizing the offline recommendation model to online, and inputting the latest online feature output by the online learning model into the offline recommendation model to generate an online prediction model.

Abstract: A neural network computation apparatus includes a first processing block including a plurality of processing units that each perform a matrix multiplication operation on input data and weights, and a second processing block including a plurality of element-wise operation processing groups. The element-wise operation processing group selectively perform a first neural network computation operation and a second neural network computation operation. The first neural network computation operation comprises the matrix multiplication operation on the input data and the weights and an activation operation on a result value of the matrix multiplication operation, and the second neural network computation operation comprises an activation operation on the result value of the matrix multiplication operation, which is transferred from the first processing block, and an element-wise operation.

Abstract: Embodiments of the present disclosure provide a method for determining causality, an apparatus for determining causality, an electronic device and a storage medium, and relates to a field of knowledge graph technologies. The method includes: obtaining event words expressing individual events and related words adjacent to the event words in a target text; inputting the event words and the related words into a graph neural network; and determining whether there is a causal relationship between any two events through the graph neural network.

Abstract: A programmable, non-linear (PNL) activation engine for a neural network is capable of receiving input data within a circuit. In response to receiving an instruction corresponding to the input data, the PNL activation engine is capable of selecting a first non-linear activation function from a plurality of non-linear activation functions by decoding the instruction. The PNL activation engine is capable of fetching a first set of coefficients corresponding to the first non-linear activation function from a memory. The PNL activation engine is capable of performing a polynomial approximation of the first non-linear activation function on the input data using the first set of coefficients. The PNL activation engine is capable of outputting a result from the polynomial approximation of the first non-linear activation function.