International Business Machines Corporation

Mattia Rigotti

Cicero Nogueira Dos Santos

Inkit Padhi

Youssef Mroueh

Tom Sercu

Wilfried Haensch

Tayfun Gokmen

Supriyo Chakraborty

Malte Johannes Rasch

A machine learning system that implements Sobolev Independence Criterion (SIC) for feature selection is provided. The system receives a dataset including pairings of stimuli and responses. Each stimulus includes multiple features. The system generates a correctly paired sample of stimuli and responses from the dataset by pairing stimuli and responses according to the pairings of stimuli and responses in the dataset. The system generates an alternatively paired sample of stimuli and responses from the dataset by pairing stimuli and responses differently than the pairings of stimuli and responses in the dataset. The system determines a witness function and a feature importance distribution across the features that optimizes a cost function that is evaluated based on the correctly paired and alternatively paired samples of the dataset. The system selects one or more features based on the computed feature importance distribution.

Feature selection using Sobolev Independence Criterion

Neural network protection mechanisms are provided. The neural network protection engine receives a pre-trained neural network computer model and forward propagates a dataset through layers of the pre-trained neural network computer model to compute, for each layer of the pre-trained neural network computer model, inputs and outputs of the layer. For at least one layer of the pre-trained neural network computer model, a differentially private distillation operation is performed on the inputs and outputs of the at least one layer to generate modified operational parameters of the at least one layer. The modified operational parameters of the at least one layer obfuscate aspects of an original training dataset used to train the pre-trained neural network computer model, present in original operational parameters of the at least one layer. The neural network protection engine generates a privatized trained neural network model based on the modified operational parameters.

Layer-wise distillation for protecting pre-trained neural network models

Mechanisms are provided for acceleration of convolutional neural networks on analog arrays. Input ports receive image signals from frames in an input image. Input memory arrays store the image signals received from the input ports into a respective input memory location to create a plurality of image sub-regions in input memory arrays. A distributor associated each of a set of analog array tiles in an analog array to a part of image sub-regions of the input memory arrays, so that one or more of a set of analog memory components is associated with the image signals in a distribution order to create a respective output signal. An assembler stores each of the respective output signals into one of a set of memory outputs in an output order that is determined by the distribution order.

Acceleration of convolutional neural networks on analog arrays

A machine learning system receives a witness function that is determined based on an initial sample of a dataset comprising multiple pairs of stimuli and responses. Each stimulus includes multiple features. The system receives a holdout sample of the dataset comprising one or more pairs of stimuli and responses that are not used to determine the witness function. The system generates a simulated sample based on the holdout sample. Values of a particular feature of the stimuli of the simulated sample are predicted based on values of features other than the particular feature of the stimuli of the simulated sample. The system applies the holdout sample to the witness function to obtain a first result. The system applies the simulated sample to the witness function to obtain a second result. The system determines whether to select the particular feature based on a comparison between the first result and the second result.

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New York, NY, United States of America

Mattia Rigotti