Abstract: A method for obtaining a user portrait includes: obtaining a user feature vector of a target user and tag feature vectors of content tags of multimedia content in a target application, and determining an alternative tag of the target user according to similarities between the user feature vector and the tag feature vectors, to further determine a user portrait of the target user according to the alternative tag.

Abstract: A computer-implemented device and corresponding method are provided for processing a multi-agent system input to form an at least partially optimised output indicative of an action policy. The method comprises receiving the multi-agent system input, the multi-agent system input comprising a definition of a multi-agent system and defining behaviour patterns of a plurality of agents based on system states; receiving an indication of an input system state; performing an iterative machine learning process to estimate a single aggregate function representing the behaviour patterns of the plurality of agents over a set of system states; and iteratively processing the single aggregate function for the input system state to estimate an at least partially optimised set of actions for each of the plurality of agents in the input system state. This may allow policies corresponding to the Nash equilibrium to be learned.

Abstract: One example method includes registering, by a customer, with a service provider, receiving, by the customer from the service provider, a global machine learning model, running, by the customer, the global machine learning model as a local machine learning model, collecting, by the customer, unlabeled data generated by edge devices operating in a customer domain, checking, by the customer, to determine if the customer domain has changed, and when it is determined that the customer domain has changed, performing, by the customer, a model adaptation process on the local machine learning model, and transmitting to the service provider, by the customer, gradients that comprise customer implemented changes to the local machine learning model.

Abstract: A method is provided for determining a control sequence for performing a multiqudit algorithm on a quantum computer, the multi-qudit algorithm expressible as a series of one or more k-qudit interactions. The method comprises, for each of the k-qudit interactions, decomposing the k-qudit interaction into a sequence of single-qudit unitary rotations and/or two-qudit unitary rotations from the continuous family of controllable unitary rotations generated by underlying physical interactions in the hardware of the quantum computer subject to a specified minimum interaction time, said sequence being physically implementable on the quantum computer. The method further comprises combining the sequences to form a combined interaction sequence. The method further comprises determining, based on the combined interaction sequence, the control sequence for performing the multiqudit algorithm on the quantum computer.

Abstract: A method of training a machine learning system for an object recognition device. The method includes: providing sensing element data; and training a machine learning system, using the provided sensing element data; at least one object being recognized from the sensing element data; and signal intensities of the sensing element data being used together with a reflection and/or absorption factor associated with the object.

Abstract: A quantum computing system includes a classical computer coupled in combination with a quantum computer, wherein the quantum computing system is configurable to execute program instructions to process input data to generate corresponding output data. The program instructions include one or more arithmetic functions to be executed using the quantum computer. The quantum computing system is configured to apply a transformation to transform the one or more arithmetic functions into a series of Fourier components that are executable using the quantum computer by using one or more quantum circuits utilizing rotation gates acting on qubits representing the Fourier components, and to process outputs from the one or more quantum circuits to generate results of the one or more arithmetic functions, wherein the results are used to generate the corresponding output data.

Abstract: A computation apparatus that includes a spiking neuron model. A spiking neuron model varies an index value of a signal output based on an input condition of a signal during an input time interval and outputs, based on the index value, a signal during an output time interval that starts after the input time interval ends.

Abstract: Various embodiments are provided for providing enhanced data allocation for machine learning operations in a computing environment by one or more processors in a computing system. One or more data sampling strategies may be determined based on a dataset. One or more enhanced training data allocations may be suggested for machine learning operations in a cloud computing environment based on the one or more data sampling strategies.

Abstract: Techniques for auto composing using a transformer-based language model having a parameter sharing decoder pair (PSDP) that reduces a number of parameters of the model and maintains the capability of generating understandable and reasonable compositions. In one particular aspect, a method is provided that includes obtaining a full encoder sequence, and inputting the full encoder sequence into a transformer model having a PSDP. The PSDP includes: a first decoder having parameters that are shared across all N layers of the first decoder; and a second decoder having parameters that are shared across all N layers of the first decoder. The parameters of the first decoder are different from the parameters of the second decoder. The method further includes using the transformer model to predict sequence elements based on the full encoder sequence, generate an output sequence comprising the sequence elements, and output an output sequence different from the full encoder sequence.

Abstract: Distributed quantum file consolidation is disclosed. A controlling quantum computing system (QCS) determines to consolidate a quantum file that includes a plurality of qubits implemented on a plurality of quantum computing systems (QCSs) onto a target QCS, the plurality of qubits including at least a first qubit implemented on a first QCS of the plurality of QCSs. The controlling QCS causes a transfer of quantum information contained in each qubit of the plurality of qubits that is not currently implemented on the target QCS to a corresponding qubit on the target QCS. Quantum file update information that indicates the qubits that compose the quantum file are located on the target QCS is communicated to at least the first QCS.

Abstract: Techniques are described for neural networks based on Progressive Neural ODEs (PODEs). In an example, a method to progressively train a neural ordinary differential equation (NODE) model comprises processing, by a machine learning system executed by a computing system, first training data, the first training data having a first complexity, to perform training of a first layer for the NODE model; and after performing the first training, processing second training data, the second training data having a second complexity that is higher than the first complexity, to perform training of a second layer for the NODE model.

Abstract: Methods, systems, and computer program products perform classification based on an imbalanced dataset. In a method, machine learning models are generated based on positive samples included in an imbalanced dataset. An amount of the positive samples is less than an amount of negative samples that are included in the imbalanced dataset. Each sample in the positive and negative samples includes parameters. Multiple influential parameter groups are identified from the parameters for the positive samples, respectively. A final predictive model is determined based on the machine learning models and the multiple influential parameter groups. The final predictive model is used for classifying a sample as a positive type or a negative type.

Abstract: Generally, the present disclosure provides systems and methods for performing machine learning in hyperbolic space. Specifically, techniques are provided which enable the learning of a classifier (e.g., large-margin classifier) for data defined within a hyperbolic space (e.g., which may be particularly beneficial for data possessing a hierarchical structure).

Abstract: A quantum computing system that supports efficient multitasking receives messages from a classical computing system to a pool of qubits. Each received message is associated with a partition identifier. The system configures a first set of qubits in the pool of qubits to perform a first computing task based on received messages that are associated with a first partition identifier and a second set of qubits in the pool of qubits to perform a second computing task based on received messages that are associated with a second partition identifier. The system acquires a first set of measurements from the first set of qubits and a second set of measurements from the second set of qubits. The system relays the first and second sets of measurements to the classical computing system.

Abstract: A method for predicting a molecular structure includes: preparing a learning data set including first learning data including an eigenvector value and a quantum mechanics calculation value for a monoatomic and molecular structural model, a bulk structural model, a slab structural model, and a nanoparticle structural model of a material including a plurality of elements; learning an artificial neural network using the learning data set to obtain a potential value; and predicting a molecular structure of another material by using the potential value.

Abstract: A method, an apparatus, and a system for configuring a neural network across heterogeneous processors are provided. The method includes creating a unified neural network profile for the plurality of processors; receiving at least one request to perform at least one task using the neural network; determining a type of the requested at least one task as one of an asynchronous task and a synchronous task; and parallelizing processing of the neural network across the plurality of processors to perform the requested at least one task, based on the type of the requested at least one task and the created unified neural network profile.

Abstract: A grammar is used in a grammatical evolution of a set of parent neural network models to generate a set of child neural network models. A generation of neural network models is tested based on a set of test data, where the generation includes the set of child neural network models. Respective values for each one of a plurality of attributes are determined for each neural network in the generation, where one of the attributes includes a validation accuracy value determined from the test. Multi-objective optimization is performed based on the values of the plurality of attributes for the generation of neural networks and a subset of the generation of neural network models is selected based on the results of the multi-objective optimization.

Abstract: A method for the inference computation of a plurality of neural networks on a hardware platform. Each of the neural networks comprise a plurality of neurons, which respectively aggregate inputs into a network input using a transfer function characterized by weights and process this network input into an activation using an activation function. The method includes: identifying at least one unit, which comprises one or multiple transfer functions and/or complete neurons and exists in at least two of the networks in the same form or in a form that is similar according to a predefined criterion; performing a single inference computation for the unit on the hardware platform so that the unit provides a set of outputs; processing this set of outputs in the respective networks as an output of the unit. A method for the simultaneous execution of multiple applications is also provided.

Abstract: Methods and apparatuses for automatic speech recognition are provided. The method includes: generating a weight matrix for a layer of a plurality of layers in a neural network; dividing the weight matrix into a plurality of blocks, each block including a plurality of weights; selecting a set of blocks from the plurality of blocks for block-wise pruning by minimizing a cost function subject to a pre-determined block-wise constraint; and generating a block-wise pruned weight matrix by setting one or more weights in the set of blocks to zero. The weight matrix includes a set of weights associated with the layer, the plurality of layers includes a first layer receiving a first input associated with one or more audio feature sequences, and the plurality of layers are executed on one or more processors.

Abstract: Methods, apparatus, systems, and articles of manufacture for modifying a machine learning model are disclosed. An example apparatus includes a supervised branch inserter to insert a supervised branch into a machine learning model at an identified location, a first cluster generator to generate a first cluster of the inserted supervised branch using a first clustering technique, a second cluster generator to generate a second cluster of the inserted supervised branch using a second clustering technique, the second clustering technique different from the first clustering technique, a cluster joiner to join the first cluster and the second cluster to form a clustering block, the clustering block appended to an end of the supervised branch, and a propagation strategy executor to execute a propagation training strategy to modify a parameter of the machine learning model.