International Business Machines Corporation

Steven Nicholas Eliuk

Xiaolong Wang

Qinghan Xue

Abhishek Kolagunda

Seetharami R Seelam

Yang Sun

Christian Pinto

Andrea Reale

Yiannis Gkoufas

Kostas Katrinis

Yu Tian

Xin Guo

A method, a computer system, and a computer program product for using distinct paths with cross connections for distinct tasks to prevent catastrophic forgetting in class-incremental scenarios. Embodiments of the present invention may include receiving one or more tasks sequentially. Embodiments of the present invention may include applying one or more shareable blocks to the one or more tasks. Embodiments of the present invention may include learning one or more distinct paths for the one or more tasks. Embodiments of the present invention may include adding one or more cross connections between the one or more tasks. Embodiments of the present invention may include adding an aggregation block to collect one or more outputs from the distinct paths of each of the one or more tasks. Embodiments of the present invention may include providing a prediction.

Continual learning using cross connections

A method, a computer program product, and a computer system fuse features for multi-modal classifications for a plurality of modality inputs. The method includes receiving a request indicative of the modality inputs to be selected. The method includes performing an embeddings level fusion operation to concatenate features from the modality inputs. The method includes performing a multi-modal discriminative feature level fusion operation that integrates feature representations learned by applying different network structures on the modality inputs. The method includes determining weights of the concatenated features and the feature representations based on a measure of the concatenated features and the feature representations indicative of affecting a final prediction performance. The method includes generating fused features for the modality inputs based on the concatenated features, the feature representations, and the weights. The method includes generating a response to the request based on the fused features. The method includes transmitting the response.

Weighted deep fusion architecture

Various embodiments are provided for providing machine learning with noisy label data in a computing environment using one or more processors in a computing system. A label corruption probability of noisy labels may be estimated for selected data from a dataset using temporal inconsistency in a machine model prediction during a training operation in a neural network.

Intelligent learning system with noisy label data

Embodiments for data caching and data-aware placement for machine learning by a processor. Data may be cached in a distributed data store to one or more local compute nodes of cluster of nodes with the cached data. A new job may be scheduled, according to cache and data locality awareness, on the one or more local compute nodes with the cached data needed for execution.

Data caching and data-aware placement to accelerate machine learning applications

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San Jose, CA, United States of America

Steven Nicholas Eliuk