Abstract: The present disclosure relates to a method and apparatus for sparsification training of a neural network model, a board card, and a readable storage medium. The data processing apparatus of the present disclosure is implemented as a computing apparatus and included in a combined processing apparatus. The combined processing apparatus further includes an interface apparatus and other processing apparatus. The computing apparatus interacts with other processing apparatus to jointly complete a computing operation specified by a user. The combined processing apparatus further includes a storage apparatus. The storage apparatus is connected to the computing apparatus and other processing apparatus, respectively. The storage apparatus is used to store data of the computing apparatus and other processing apparatus.

Abstract: Methods, apparatus, and computer readable storage medium for navigating multiple objects from initial positions towards target positions are described in the present disclosure.

Abstract: A system and method for determining device attributes based on protocol string conventions. A method includes applying at least one machine learning model to an application data set extracted based on at least one first pair of strings, each first pair of strings including a protocol string and a key string indicated in respective fields of communications session data corresponding to a device, wherein each machine learning model is trained based on a training data set including second pairs of strings device attribute labels, wherein each device attribute label corresponds to one of the second pairs of strings, wherein each of the at least one machine learning model is configured to output a predicted device attribute for the device based on the first pair of strings; and determining, based on the output of the at least one machine learning model, at least one device attribute of the device.

Abstract: A system, method, and computer-readable medium for detecting a CRISPR-edited genome are disclosed. Certain embodiments of the system may include one or more processors configured to receive sequence data of a genome; generate an image representation of the sequenced data, the image being a plot of methylation variations as a function of methylation locations in the genome; apply the generated image representation to a trained convoluted neural network (CNN); generate, using the CNN, a score indicative of a probability that the genome was CRISPR-edited; and determine, based on the score, whether the genome contains a CRISPR-edited methylation region. A corresponding method and computer-readable medium are also provided.

Abstract: A data management system including a processor connected to a storage device that stores data usable for learning and inference in a machine learning model, the data management system including a compressor/decompressor for human that compresses and decompresses data to be usable for verification performed by human, in which the processor specifies data that is no longer used for learning and inference in the machine learning model among the data stored in the storage device, and compresses the specified data using the compressor/decompressor for human.

Abstract: Systems, methods, apparatuses, and computer program products for neural networks. In accordance with some example embodiments, an operational neuron model may comprise an artificial neuron comprising a composite nodal operator, a pool-operator, and an activation function operator. The nodal operator may comprise a linear function or non-linear function. In accordance with certain example embodiments, a generative neuron model may include a composite nodal-operator generated during the training using Taylor polynomial approximation without restrictions. In accordance with various example embodiments, a self-organized operational neural network (Self-ONN) may include one or more layers of generative neurons.

Abstract: Methods and devices of a function approximation unit configured to approximate a nonlinear function within a neural processing unit are described. According to one embodiment, the method includes storing an input value through an input register of the function approximation unit, transmitting the input value to a selected one of a plurality of preprocessing circuits of the unit according to a control signal, generating a preprocessing result corresponding to the input value by the selected one of the preprocessing circuits, transmitting the preprocessing result to a programmable function approximation circuit and a selected one of a plurality of post-processing circuits of the unit, generating an approximated function output based on the preprocessing result in the programmable function approximation circuit, and generating a final output value by post-processing the preprocessing result or the approximated function output in the selected one of the post-processing circuits.

Abstract: Analog memory-based activation function for an artificial neural network can be provided. An apparatus can include at least two non-volatile memory devices connected in parallel such that the current can flow through one of the two non-volatile memory devices depending on the voltage level driving the current. To control which branch an input current flows through, each of the two non-volatile memory devices can be connected to a circuit element that can function as a switch, for example, a diode such as a semiconductor diode, a transistor, or another circuit element. Such apparatus can implement an analog memory-based activation function, for example, for an analog memory-based artificial neural network.

Abstract: A prediction model (1) includes a first module (M1) that calculates, for each of a plurality of objects (xi) in a dataset (x), an index value (vi) corresponding to a combination of the object (xi) and attribute information (a) using a neural network, and a second module (M2) that calculates a prediction result (y) of an operation to be performed by a user by performing a predetermined process on a plurality of index values (v1, . . . , vN) obtained from the first module (M1) and corresponding to the respective plurality of objects (x1, . . . , xN).

Abstract: A temporal-aware or permutation-dependent Graph Neural Network (GNN) is disclosed. The example GNN is implemented by combining temporal-awareness with multi-layer neighborhood aggregation to further provide the GNN with inductive capabilities with respect to generating embeddings of a dynamic graph, all without creating multiple time snapshots of the graph. By using a temporal-aware message pass scheme involving a temporal-aware and permutation-dependent GNN, a set of temporal-aware local neighborhood aggregator functions may be effective trained and used for generating embeddings for unknow nodes and for providing more accurate embeddings for subsequent prediction tasks.

Abstract: An agent training method includes: obtaining environment information of a first agent and environment information of a second agent; generating first information based on the environment information of the first agent and the environment information of the second agent; and training the first agent by using the first information, so that the first agent outputs individual cognition information and neighborhood cognition information. The neighborhood cognition information of the first agent is consistent with neighborhood cognition information of the second agent.

Abstract: A neural network operation apparatus may include a receiver configured to receive input data to perform the neural network operation and a quantized Look Up Table (LUT) corresponding to a non-linear function comprised in the neural network operation, and a processor configured to perform scale-up on the input data based on a scale factor, to extract a quantized LUT parameter from the quantized LUT based on scaled-up input data, and to generate an operation result by performing a neural network operation based on the quantized LUT parameter.

Abstract: A method for approximating an activation function, the method including: receiving an input value of the activation function; determining that the input value is within a range, the range includes a set of non-uniform intervals; determining a selected interval from among the set of non-uniform intervals including the input value; retrieving, by a hardware accelerator, from a look-up table (LUT) associated with a type of the activation function, values of one or more quadratic interpolation parameters associated with the selected interval; performing a quadratic interpolation on the input value to approximate the input value using the values of the one or more quadratic interpolation parameters; and determining a first approximated output of the activation function based on a result of the quadratic interpolation performed on the input value.

Abstract: Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.

Abstract: A reservoir includes a common input layer, first and second output layers that outputs a first and a second readout values based on an input, a first partial reservoir including the input layer and the first output layer, and a second partial reservoir having a size between the input layer and the second output layer larger than the size of the first partial reservoir, and the training processing including: first calculating a third output weight that reduces a difference between a first product sum value of a third readout value and a first output weight; and second calculating a fourth output weight that reduces a difference between a second product sum value of a fourth readout value and a second output weight and differential teaching data that is a difference between a third product sum value of the third readout value and the third output weight and the teaching data.

Abstract: Systems and methods for adaptive calibration of quantum computing systems are disclosed. A system can include one or more processors coupled to non-transitory memory and configured to obtain telemetry data from a quantum processor of a quantum computing system, generate a spatio-temporal graph data structure based on the telemetry data and operational parameters, and provide at least a portion of the graph as input to a graph neural network (GNN) to generate parameter ranges for operation. The system can select a first set of test parameters using a Bayesian optimization function, execute calibration experiments to generate calibration results, generate updated operational parameters based on the results, and control the quantum processor according to the updated parameters.

Abstract: Systems and methods herein describe an image compression system. The image compression system generates a first generative adversarial network (GAN), identifies a threshold, based on the threshold, generates a second GAN by pruning channels of the first GAN, trains the second GAN using similarity-based knowledge distillation from the first GAN, and stores the trained second GAN.

Abstract: The present disclosure relates to a method of inter-layer format conversion for a neural network, the neural network comprising at least two computation layers including a first layer to process first data in a first data format and a second layer to process second data in a second data format, the method comprising: extracting data statistics from data output by the first layer, said data statistics being representative of the data output by the first layer; determining one or more conversion parameters based on the extracted data statistics and the second data format; and generating the second data for the second layer by modifying said data output by the first layer using the one or more conversion parameters.

Abstract: A system for cognitive inferencing for large language models (LLMs), the system including: a integration layer communicatively connected to a static LLM, wherein the integration layer includes a processor configured to receive one or more user requests from a user, wherein the one or more user requests include a user interaction with the static LLM, generate one or more cognitive inference (CI) units from the one or more user requests, assign a confidence-falsifiability (CF) delta for each of the one or more CI units using a Socratic engine and append the one or more CI units and the CF delta to a cognitive inference (CI) log associated with the user, wherein the CI log serves as an inference engine for the static LLM, wherein the integration layer is configured to query the CI log upon a subsequent user interaction with the static LLM.

Abstract: An information management system is disclosed herein that can use artificial intelligence to identify situations in which a performance metric may not be satisfied. For example, a storage manager of the information management system can maintain data related to historical, current, and/or future execution of secondary copy operations by secondary storage computing device(s) in the information management system. Using some or all of this data, the storage manager can train an artificial intelligence model (e.g., a neural network) to classify whether a current or future secondary copy operation job is likely to succeed or fail. Similarly, the storage manager can use some or all of this data to train another artificial intelligence model (e.g., a machine learning model) to predict the length of time for a current or future secondary copy operation job to complete. The trained models can be used to predict whether a performance metric will be satisfied.