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Every machine learning algorithm with any ability to generalize beyond the training data that it sees has some type of inductive bias, which are.
Table of contents
- Explicit Inductive Bias for Transfer Learning with Convolutional Networks
- Your Answer
- Inductive bias
- On Inductive Biases in Deep Reinforcement Learning | OpenReview
- Inductive Inference
This approach allowed them to also introduce the very first large-scale dataset for training algorithms on human decision-making tasks. After this step, the neural network will already be nearly as predictive as the behavioral model, and is now in a place to make the most of further learning from real examples of human behavior.
Using 'cognitive model priors' the researchers attained state-of-the-art results on two existing benchmark datasets. These findings suggest that it is indeed possible for ML models to make accurate decision-making predictions, even if available datasets are small.
Explicit Inductive Bias for Transfer Learning with Convolutional Networks
In their case, this was achieved by pre-training models on artificial data derived from cognitive models. We hope that this will encourage greater collaboration between the machine learning and behavioral science communities by providing a way to evaluate a wider class of models of human decision making.
- Inductive Bias | SpringerLink;
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In their study, Bourgin, Peterson and their colleagues have made significant advancements in the study of ML tools for capturing human behavior, with their approach achieving unprecedented performance on two restricted datasets of human decisions. They also presented a new dataset that contains , human judgments across 13, decision problems, which could be used by other research groups to train their own ML models.
From a practical standpoint, their work could save researchers the significant amount of time that is typically spent on collecting data for ML human prediction models. Please sign in to add a comment. Registration is free, and takes less than a minute. Read more. Your feedback will go directly to Tech Xplore editors. Thank you for taking your time to send in your valued opinion to Science X editors. You can be assured our editors closely monitor every feedback sent and will take appropriate actions. Your opinions are important to us. We do not guarantee individual replies due to extremely high volume of correspondence.
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On Inductive Biases in Deep Reinforcement Learning | OpenReview
They then trained a neural network to predict this synthetic behavior, effectively transferring the behavioral model into the network. Once the network finished learning the synthetic data they fine tuned it on real human data, allowing it to further build upon the cognitive model and achieve better predictions.
Credit: Bourgin et al. Explore further.
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More information: David D. Bourgin et al. Cognitive model priors for predicting human decisions. This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.
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Ewald, V. In the deep learning framework, many natural tasks such as object, image, and speech recognition that were impossible to be performed by classical ML algorithms in the previous decades can now be be done by typical home personal computer. However, in many other domains such as aircraft visual inspection, such a large dataset is not easily available and this clearly restricts deep learning to perform well to recognize material damage in aircraft structures.
As many computer science researchers believe, we also think that in order to achieve a performance similar to human-level intelligence, AI could and should not start from scratch. Introducing an inductive bias into deep learning might be one solution to achieve that humanlevel intelligence.