Abstract: Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate usage dependent code embeddings. An example apparatus includes parsing circuitry to select a usage context of a code snippet including at least one line of code (LOC) before the code snippet or an LOC at which the code snippet is called, the code snippet, and at least one LOC after the code snippet or the LOC. The example apparatus additionally includes embedding circuitry to generate a first list of token embedding vectors for first tokens of a second list of tokens for the code snippet and a third list of token embedding vectors for second tokens of a fourth list of tokens for the usage context. The example apparatus also includes concatenation circuitry to concatenate a transformed token embedding vector of a close token and a fifth list of transformed token embedding vectors for the first list.

Abstract: The apparatus of an edge computing node, a system, a method and a machine-readable medium. The apparatus includes a processor to cause an initial set of weights for a global machine learning (ML) model to be transmitted a set of client compute nodes of the edge computing network; process Hessians computed by each of the client compute nodes based on a dataset stored on the client compute node; evaluate a gradient expression for the ML model based on a second dataset and an updated set of weights received from the client compute nodes; and generate a meta-updated set of weights for the global model based on the initial set of weights, the Hessians received, and the evaluated gradient expression.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to train a machine learning model. An example apparatus to generate adaptive hyper-parameters includes a model aggregator to, in response to obtaining at least one model trained using a first set of hyper-parameters of a probability distribution, generate a loss reduction, a hyper-parameter generator to, when the loss reduction satisfies a loss threshold, update the probability distribution and generate a second set of hyper-parameters using the updated probability distribution, and an interface to transmit the second set of hyper-parameters to a client.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to generate usage dependent code embeddings. An example apparatus includes parsing circuitry to select a usage context of a code snippet including at least one line of code (LOC) before the code snippet or an LOC at which the code snippet is called, the code snippet, and at least one LOC after the code snippet or the LOC. The example apparatus additionally includes embedding circuitry to generate a first list of token embedding vectors for first tokens of a second list of tokens for the code snippet and a third list of token embedding vectors for second tokens of a fourth list of tokens for the usage context. The example apparatus also includes concatenation circuitry to concatenate a transformed token embedding vector of a close token and a fifth list of transformed token embedding vectors for the first list.

Abstract: Various embodiments provide apparatuses, systems, and methods related to a first worker of a distributed neural network (NN), The first worker may execute a forward training pass of a first node of a distributed NN, wherein execution of the forward training pass includes generation of a first computational graph (CG) that is based on inputs related to a second node that is processed by a second worker of the distributed NN. The first worker may also delete, subsequent to the forward training pass of the first node, the CG. The first worker may also execute, a backward pass of the first node, wherein execution of the backward pass includes re-generation of at least a portion of the first CG. Other embodiments may be described and claimed.

Abstract: A compute-in-memory neural network architecture combines neural circuits implemented in CMOS technology and synaptic conductance crossbar arrays. The crossbar memory structures store the weight parameters of the neural network in the conductances of the synapse elements, which define interconnects between lines of neurons of consecutive layers in the network at the crossbar intersection points.

Abstract: Methods, systems, and computer programs are presented for implementing Personalized Mobility as a Service (PMaaS) to improve transportation services delivery. One storage medium includes instructions for detecting, by a mobility as a service (MaaS) system, a request for a trip from a user device of a user. The storage medium further includes instructions for mapping, using a model executing on the machine, the user to a persona from a plurality of persona models. Each persona model has one or more characteristics associated with users of the MaaS system. Further yet, the storage medium includes instructions for determining trip parameters for the trip based on the persona mapped to the user, the trip parameters defining one or more trip segments for the trip, and instructions for providing trip parameters to the user device.

Abstract: A convolution processing apparatus and method are disclosed. The convolution processing apparatus may include a controller configured to load a pixel of an input image and skip a process associated with the pixel in response to a value of the loaded pixel being 0, a filter bank including at least one filter and configured to extract at least one kernel element corresponding to the pixel from the at least one filter based on an index of the pixel and an input channel of the pixel, and a multiplier-accumulator (MAC) configured to perform a convolution operation based on the value of the pixel and a value of the at least one kernel element and accumulatively store an operation result of the convolution operation, the operation result corresponding to an output image.

Abstract: A convolution processing apparatus and method are disclosed. The convolution processing apparatus may include a controller configured to load a pixel of an input image and skip a process associated with the pixel in response to a value of the loaded pixel being 0, a filter bank including at least one filter and configured to extract at least one kernel element corresponding to the pixel from the at least one filter based on an index of the pixel and an input channel of the pixel, and a multiplier-accumulator (MAC) configured to perform a convolution operation based on the value of the pixel and a value of the at least one kernel element and accumulatively store an operation result of the convolution operation, the operation result corresponding to an output image.