The present disclosure provides computing systems and techniques for validating a decision model against a canon of regulation. A server can deconstruct a decision model into a number of branching decisions and also generate a Markov chain comprising a number of sequences from a canon of regulation. The server can compare the branching decisions to the sequences and can validate the decision model with the canon of regulation based on the comparison.

A mechanism is described for facilitating slimming of neural networks in machine learning environments. A method of embodiments, as described herein, includes learning a first neural network associated with machine learning processes to be performed by a processor of a computing device, where learning includes analyzing a plurality of channels associated with one or more layers of the first neural network. The method may further include computing a plurality of scaling factors to be associated with the plurality of channels such that each channel is assigned a scaling factor, wherein each scaling factor to indicate relevance of a corresponding channel within the first neural network. The method may further include pruning the first neural network into a second neural network by removing one or more channels of the plurality of channels having low relevance as indicated by one or more scaling factors of the plurality of scaling factors assigned to the one or more channels.

Various embodiments include methods and devices for transforming a data block into weights for a neural network. Some embodiments may include training a first neural network of a cybernetic engram to reproduce the data block, and replacing the data block in memory with weights used by the first neural network to reproduce the data block.

A parallel recursive neural network, including: a memory configured to store data and processing instructions; and a parallel computer processor configured to: receive a set of input values; apply a recursive layer function individually on each of the set of input values in parallel to produce a set of hidden states; apply a reduction function on pairs of adjacent hidden states in the set of hidden states in parallel to produce a new set of hidden states; and repeat applying the reduction function of pairs of adjacent states in the new set of hidden states in parallel until a single output hidden state results.

Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.

A method for identifying chromosomal abnormalities in an embryo, is disclosed. Sample genomic sequence information obtained from an embryo is received, wherein the sample genomic sequence information is comprised of a plurality of genomic sequence reads. The sample genomic sequence information is aligned against a reference genome. The sample genomic sequence information is normalized against baseline genomic sequence information to correct the sample genomic sequence information for locus effects and generate a normalized sample genomic sequence information dataset. One or more correction factors derived from a regression analysis of error factors is applied to the normalized sample genomic sequence information dataset to correct for technical effects and generate de-noised sample genomic sequence information dataset. Copy number variations in the de-noised sample genomic sequence information dataset is identified when a frequency of genomic sequence reads aligned to a chromosomal position on the reference genome deviates from a frequency threshold.

A method of subgraph tile fusion in a convolutional neural network, including partitioning a network into at least one subgraph node, determining a layer order of at least one layer of the at least one subgraph node, determining a input layer of the at least one subgraph node, determining a weight layer of the at least one subgraph node, determining a output layer of the at least one subgraph node and fusing the at least one subgraph node, the input layer, the weight layer and the output layer in the layer order.

Methods and systems for generating recommendation data to address behaviors exhibited by an entity are described. A processor may construct a finite automaton based on entity data associated with the entity. Each state of the finite automaton may represent a sentiment, and the finite automaton may accept a language representing a set of behaviors. The processor may receive a request comprising an input behavior string. The processor may apply the input behavior string on the finite automata to determine an output string. The processor may identify at least one neural model mapped to the output string, where the identified neural model comprises logic that facilitates interpretation of a cause of the behaviors among the input behavior string. The processor may generate the recommendation data using the identified neural model, where the recommendation data comprises a recommendation to address the behaviors among the input behavior string.

Embodiments include methods performed by a processor of a vehicle for allocating processing resources to concurrently-executing neural networks. The methods may include determining a priority of each of a plurality of neural networks executing on a vehicle processing system based on a contribution of each neural network to overall vehicle safety performance, and allocating computing resources to the plurality of neural networks based on the determined priority of each neural network. In some embodiments, the methods may dynamically adjust hyperparameters of one or more neural networks.

Some embodiments provide a compiler for optimizing the implementation of a machine-trained network (e.g., a neural network) on an integrated circuit (IC). The compiler of some embodiments receives a specification of a machine-trained network including multiple layers of computation nodes and generates a graph representing options for implementing the machine-trained network in the IC. The compiler, as part of generating the graph, in some embodiments, determines whether any set of channels contains no non-zero values (i.e., contains only zero values). For sets of channels that include no non-zero values, some embodiments perform a zero channel removal operation to remove all-zero channels wherever possible. In some embodiments, zero channel removal operations include removing input channels, removing output channels, forward propagation, and backward propagation of channels and constants.