Partitioned Neural Networks for Faster Hyperparameter Tuning
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Solution Overview
Problem
Conventional hyperparameter tuning methods for machine learning models are slow, computationally expensive, and often lead to suboptimal results due to reliance on large validation sets and iterative training processes.
Innovation Solution
Partition machine learning models into subnetworks and use differentiable techniques to approximate marginal likelihood, enabling efficient gradient-based hyperparameter optimization using the training data itself without a validation set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional iterative hyperparameter tuning is used, then model performance can be evaluated, but the process is slow and computationally expensive
Solution Approach 1:
The patent segments the neural network into multiple subnetworks, each trained on different partitions of the training data. This segmentation enables parallel training and efficient computation of marginal likelihood, resolving the contradiction by allowing performance evaluation without requiring multiple full-model training iterations.
Solution Approach 2:
The patent replaces the mechanical iterative training process with a mathematical approximation approach using marginal likelihood computation. Instead of iteratively training models to evaluate hyperparameters, the system uses differentiable techniques to compute gradients of the marginal likelihood, substituting the iterative mechanical process with a more efficient mathematical formulation.
2Measurement precision
If large validation sets are used for hyperparameter optimization, then hyperparameter tuning can be performed, but additional computational resources and data are required
Solution Approach 1:
The patent makes the training data serve multiple functions: it is used both for training the subnetworks and for evaluating hyperparameter performance through marginal likelihood computation. This eliminates the need for separate validation data, as the training data itself provides the basis for hyperparameter optimization, resolving the contradiction between optimization accuracy and data quantity requirements.
Solution Approach 2:
The system enables the training data to serve itself by using the same data that trains the subnetworks to also evaluate hyperparameter performance. The marginal likelihood computation leverages the training data partitions to provide feedback for hyperparameter optimization, allowing the data to perform dual functions without requiring additional validation sets.
3Reliability
If multiple full model trainings are performed for hyperparameter tuning, then hyperparameter optimization can be achieved, but substantial computational resources are demanded
Solution Approach 1:
By segmenting the full model into multiple subnetworks that can be trained in parallel on data partitions, the patent reduces the computational burden of each individual training run. The marginal likelihood computation then efficiently aggregates results from these subnetworks, achieving reliable hyperparameter optimization without requiring multiple full-model trainings.
Solution Approach 2:
The patent computes the marginal likelihood using only the training data partitions assigned to each subnetwork, rather than requiring full model training on all data. This partial action approach provides sufficient information for hyperparameter optimization while significantly reducing computational resource consumption compared to multiple complete training cycles.
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for improved machine learning. A plurality of subnetworks, of a neural network is determined. Training of a first subnetwork of the plurality of subnetworks is facilitated using a first set of training exemplars from a plurality of sets of training exemplars, and training of a second subnetwork of the plurality of subnetworks is facilitated using a second set of training exemplars from the plurality of sets of training exemplars. A first loss is generated by processing the second set of training exemplars using the first subnetwork. An approximated marginal likelihood for the neural network is generated based at least in part on the first loss, and one or more hyperparameters of the neural network are refined based on the approximated marginal likelihood.