Hyperparameter Optimization via Distance-Based Candidate Selection
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Solution Overview
Problem
Conventional methods for optimizing hyperparameters of computational models are inefficient, particularly due to the manual nature of hyperparameter definition and the infeasibility of identifying optimal values for large numbers of hyperparameters within feasible time and computational constraints.
Innovation Solution
The approach involves sampling hyperparameter values, determining candidate values based on distance thresholds in the data space, and using parallel processing to train and validate computational models, thereby improving the identification of optimal hyperparameter combinations and reducing computational resources required.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If brute force search algorithms are used to identify hyperparameter values, then the computational model may achieve better performance, but the time and computation required become infeasible
Solution Approach 1:
The patent applies preliminary action by performing an initial sampling phase before the main optimization process. A subset of hyperparameter values is sampled and evaluated in advance to build a preliminary performance model, which then guides subsequent candidate value selection, avoiding exhaustive search from scratch
Solution Approach 2:
The patent changes parameters by transitioning from evaluating all possible hyperparameter values to evaluating only sampled values and their distant candidates. The distance threshold parameter controls the exploration-exploitation balance, allowing the system to adapt the search strategy based on performance metrics rather than exhaustively searching the entire parameter space
2Adaptability or versatility
If the number of hyperparameters is large, then the computational model may have greater flexibility and performance potential, but conventional approaches to identify optimal values become infeasible
Solution Approach 1:
The patent segments the hyperparameter optimization process into distinct phases: initial sampling, candidate generation based on distance thresholds, and iterative evaluation. This segmentation breaks down the complex task of optimizing many hyperparameters into manageable stages, reducing overall complexity
Solution Approach 2:
The patent introduces an intermediary mechanism - the distance-based candidate selection system - that mediates between the large space of possible hyperparameter values and the limited computational resources available. This intermediary filters and prioritizes candidate values, making the optimization of numerous hyperparameters feasible
3Ease of manufacture
If manual processes are used to definehyperparameter values, then the process may be simple to implement, but it is inefficient and time-consuming
Solution Approach 1:
The patent implements self-service by creating an automated system that selects and evaluates hyperparameter candidates based on distance thresholds and performance metrics. The system serves itself by using its own output (performance data from sampled values) to guide its next actions (candidate selection), eliminating the need for manual intervention while maintaining implementation simplicity
Data Source
AI summary
Systems, methods, apparatuses, and computer-readable media for computational model optimization. A plurality of sampled values for a hyperparameter of a computational model may be received, the plurality of sampled values comprising a subset of a plurality of possible values for the hyperparameter, each sampled value associated with a performance metric for the computational model with the sampled value assigned to the hyperparameter. A first candidate value from the plurality of possible values may be determined, the first candidate value having a distance to a first sampled value of the plurality of sampled values that exceeds a threshold distance, wherein the distance is in a space comprising the plurality of possible values. The first candidate value may be assigned to the hyperparameter of the computational model. A first performance metric may be determined for the computational model with the first candidate value assigned to the hyperparameter.