公开(公告)号：US20230259813A1

公开(公告)日：2023-08-17

申请号：US17674808

申请日：2022-02-17

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Yuan-Chi Chang ,  Venkata Nagaraju Pavuluri ,  Dharmashankar Subramanian ,  Timothy Rea Dinger

IPC: G06N20/00 ,  G06N3/08 ,  G06N3/04

CPC classification number: G06N20/00 ,  G06N3/08 ,  G06N3/0454

Abstract: A method of automatically tuning hyperparameters includes receiving a hyperparameter tuning strategy. Upon determining that one or more computing resources exceed their corresponding predetermined quota, the hyperparameter tuning strategy is rejected. Upon determining that the one or more computing resources do not exceed their corresponding predetermined quota, a machine learning model training is run with a hyperparameter point. Upon determining that one or more predetermined computing resource usage limits are exceeded for the hyperparameter point, the running of the machine learning model training is terminated for the hyperparameter point and the process returns to running the machine learning model training with a new hyperparameter point. Upon determining that training the machine learning model is complete, training results are collected and computing resource utilization metrics are determined. A correlation of the hyperparameters to the computing resource utilization is determined from the completed training of the machine learning model.

公开(公告)号：US20250005442A1

公开(公告)日：2025-01-02

申请号：US18344836

申请日：2023-06-29

Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION

Inventor： Venkata Nagaraju Pavuluri ,  Timothy Rea Dinger ,  Yuan-Chi Chang ,  Dharmashankar Subramanian

IPC: G06N20/00

Abstract: A product and methodology is contemplated for monitoring a multivariate process. The product has a computer readable storage medium with program instructions embodied therewith. The program instructions are executable by a computer processor to cause the device to: segment data obtained from the multivariate process into a time series of snapshot intervals, each snapshot interval further segmented into a predetermined plurality of zone intervals; compute a contrastive metric from the segmented data for each variable during each zone interval; compare the computed contrastive metrics to one or more predetermined threshold values to define representationally relevant zone intervals for each variable; apply representation learning to derive zone-based feature vectors for each variable during the corresponding relevant zone intervals; and concatenate the zone-based feature vectors into a representation vector for the multivariate process during the time series of snapshots.

公开(公告)号：US20220245440A1

公开(公告)日：2022-08-04

申请号：US17162626

申请日：2021-01-29

Applicant: International Business Machines Corporation

Inventor： Dharmashankar Subramanian ,  Venkata Nagaraju Pavuluri ,  Yuan-Chi Chang ,  Long Vu ,  Timothy Rea Dinger

IPC: G06N3/08 ,  G06K9/62 ,  G06F9/38

Abstract: A method of using a computing device to train a neural network to recognize features in variate time series data that includes receiving, by a computing device, variate time series data. The computing device further receives results associated with the variate time series data. The computing device determines an anchor of the variate time series data. The computing device additionally determines one or more portions of the variate time series data which lead to a positive result. The computing device further determines one or more portions of the variate time series data which lead to a negative result. The computing device trains a neural network to interpret results of future variate time series data based upon the anchor, the one or more portions of the variate time series data which lead to the positive result, and the one or more portions of the variate time series data which lead to the negative result.

公开(公告)号：US20220245409A1

公开(公告)日：2022-08-04

申请号：US17162649

申请日：2021-01-29

Applicant: International Business Machines Corporation

Inventor： Venkata Nagaraju Pavuluri ,  Dharmashankar Subramanian ,  Yuan-Chi Chang ,  Long Vu ,  Timothy Rea Dinger

IPC: G06K9/62 ,  G06N20/00 ,  G06F17/18 ,  G06F7/24

Abstract: A method of using a computing device to determine a window size in variate time series data that includes receiving, by a computing device, variate time series data associated with a machine learning model. The computing device sets a moving window size and a standard deviation for the variate time series data. The computing device further calculates a moving window average for the variate time series data. The computing device additionally calculates a standard deviation across all variate time series data. The computing device sorts the standard deviations calculated in descending order. The computing device further iterates indices for the standard deviations until the indices have been visited by at least one anchor. The computing device iteratively expands each anchor to cover neighbors' anchors which have been visited by previous anchors. The computing device determines a window size based upon the expanded anchors.

公开(公告)号：US11263172B1

公开(公告)日：2022-03-01

申请号：US17141116

申请日：2021-01-04

Applicant: International Business Machines Corporation

Inventor： Yuan-Chi Chang ,  Venkata Nagaraju Pavuluri ,  Dharmashankar Subramanian ,  Long Vu ,  Debarun Bhattacharjya ,  Timothy Rea Dinger

IPC: G06F16/11

Abstract: A method, computer program product, and/or computer system improves a future efficiency of a specific system. One or more processors receive multiple historical data snapshots that describe past operational states of a specific system. The processor(s) identify a time series pattern for the time series of data in the multiple historical snapshots and calculate their variability. The processor(s) then determine that the variability in a first sub-set of the time series pattern is larger than a predefined value, and determine that future values of the first set of the time series pattern are a set of non-forecastable future values. The processor(s) also determine that the variability in a second sub-set of the time series pattern for the data is smaller than the predefined value, and utilizes this second sub-set to modify the specific system at a current time.