Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases

Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca Bencomo, Jake Snell, Thomas L. Griffiths·May 29, 2024

Summary

The paper presents a Bayesian approach to incorporate human inductive biases into machine learning models using contrastive learning with generative similarity. This method addresses the challenge of scaling up training with limited psychological data by defining a similarity measure based on the likelihood of data points coming from the same distribution. The authors apply this to tasks like geometric shapes, abstract drawing styles, and probabilistic programs, showing improved performance and alignment with human cognition. The study demonstrates the effectiveness of generative similarity in capturing human-like regularities, outperforming supervised and finetuned models in tasks like shape recognition and abstract drawing classification. The work aims to bridge the gap between human and machine intelligence by leveraging Bayesian models in a scalable contrastive learning framework, with potential applications in various domains. Future research will focus on extending the approach to multi-layer hierarchical models and other modalities.

Key findings

8

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of imbuing machine models with human inductive biases by using contrastive learning with generative similarity . This problem is not entirely new, as the paper builds upon existing research on incorporating human similarity judgments and Bayesian models of reasoning into machine learning models . The paper emphasizes the importance of instilling strong inductive biases in machine models to enhance their intelligence and alignment with human cognition .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to instilling human inductive biases into machine learning models by introducing a Bayesian notion of generative similarity to define a contrastive learning objective . The goal is to learn spatial embeddings that express specific inductive biases, focusing on capturing human inductive biases for geometric shapes and distinguishing different abstract drawing styles parameterized by probabilistic programs . The study addresses the challenge of finding effective training procedures to imbue neural networks with these inductive biases, leveraging the concept of generative similarity to improve model performance on various benchmarks such as few-shot learning, robustness, and alignment .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases" introduces a novel approach based on contrastive learning to imbue neural networks with human inductive biases . This approach leverages a Bayesian notion of generative similarity to define a contrastive learning objective, where data points are designated as "same" or "different" to learn representations that encourage similar data points to be closer together and different data points to be further apart . By using generative similarity based on Bayesian inference, the paper demonstrates how this approach can be applied to complex generative processes, including probabilistic programs, to learn spatial embeddings that express specific inductive biases .

Furthermore, the paper addresses the challenge of incorporating human judgments in model objectives, which can be costly and challenging to scale for modern machine learning datasets . The proposed approach provides a scalable solution by utilizing contrastive learning, which is a widely used training procedure in machine learning, to learn representations that capture human inductive biases . This method enables the learning of spatial embeddings that reflect the generative similarity between data points, even in cases where the exact form of similarity is intractable .

Overall, the paper introduces a novel framework that combines contrastive learning with generative similarity to capture human inductive biases in neural networks, providing a scalable and effective method for improving model performance on various benchmarks, including few-shot learning, robustness, and alignment with human representations . The paper "Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases" introduces a novel approach that offers several key characteristics and advantages compared to previous methods .

  1. Bayesian Notion of Generative Similarity: The paper introduces a Bayesian notion of generative similarity, where two data points are considered similar if they are likely to have been sampled from the same distribution . This measure enables the definition of a contrastive learning objective, even in cases where the exact form of similarity is intractable, facilitating the learning of spatial embeddings that express specific inductive biases .

  2. Improved Representations: The approach demonstrates improved representations of probabilistic programs synthesizing abstract drawings compared to standard contrastive paradigms like SimCLR . By leveraging generative similarity based on Bayesian inference, the method enhances the quality of representations of complex generative processes, showcasing its effectiveness in capturing human inductive biases .

  3. Flexibility and Scalability: The framework is designed to be flexible and scalable, allowing for the application of generative similarity and contrastive learning to various domains beyond vision, such as language models for logical and causal reasoning . This versatility enables the method to capture human inductive biases across different modalities and applications .

  4. Addressing Complex Domains: The paper acknowledges the complexity of generative processes and the need to explore richer hierarchies in Bayesian models, highlighting the potential for future work to extend the framework to models with multiple layers of hierarchy . This forward-looking approach aims to enhance the understanding and application of inductive biases in machine learning models across diverse domains .

In summary, the paper's approach stands out for its innovative use of generative similarity, scalability, flexibility across domains, and potential for addressing complex generative processes, offering a promising avenue for imbuing neural networks with human inductive biases .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of capturing human inductive biases using contrastive learning with generative similarity. Noteworthy researchers in this field include Mohammad Norouzi, Geoffrey Hinton, Charles Kemp, Joshua B Tenenbaum, and Thomas L. Griffiths . These researchers have contributed to studies on contrastive learning of visual representations, generative theory of similarity, and incorporating human judgments in model objectives.

The key to the solution mentioned in the paper involves defining a principled notion of similarity based on Bayesian inference and implementing it in a contrastive learning framework, even when the exact form is intractable. This approach uses a hierarchical generative model of the data to define generative similarity between pairs of samples, encouraging the representation of "same" datapoints to be closer together and "different" datapoints to be further apart . By leveraging this framework, the study aims to capture human inductive biases for geometric shapes and distinguish different abstract drawing styles parameterized by probabilistic programs.


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on contrastive learning with generative similarity to capture human inductive biases. The experiments involved testing an array of domains that varied in complexity to showcase the flexibility of the approach . The domains examined included Gaussian mixtures, geometric shapes, and probabilistic programs, each serving as motivating examples to illustrate the contrastive training procedure with generative similarity . The experiments aimed to improve neural network representations by incorporating human similarity judgments and instilling the right inductive biases in machine models . The study utilized different training objectives, such as a standard contrastive learning objective from SimCLR and one based on a Monte-Carlo estimate of generative similarity, to enhance the representations of the programs compared to standard contrastive paradigms . The experiments also involved finetuning models like CorNet to induce human geometric regularity biases and test the model's performance on tasks like the Oddball task .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study discusses training logistic regression models and ridge regression models for classification and prediction tasks . The code used in the study is not explicitly stated to be open source in the provided context. Further information regarding the dataset and the open-source status of the code may require additional details or a direct reference to the dataset or code repository.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study explores the domain of probabilistic programs synthesizing abstract drawings and demonstrates improved representations compared to standard contrastive paradigms like SimCLR . The research highlights the importance of characterizing generative models underlying human judgments to instill the right inductive biases in machine models . Additionally, the study acknowledges the need for testing richer generative processes beyond the examined domains to enhance the flexibility of the approach .

Moreover, the paper discusses the potential application of the framework in various domains beyond vision, indicating its generalizability to other modalities or applications . The incorporation of generative similarity in contrastive loss functions is shown to be effective in optimizing embedding parameters to minimize differences between generative similarity and embedding similarity . This approach enables the learning of spatial embeddings that express specific inductive biases, as demonstrated in capturing human inductive biases for geometric shapes and abstract drawing styles .

Overall, the experiments and results in the paper provide a strong foundation for verifying scientific hypotheses related to instilling human inductive biases in machine learning models through generative similarity and contrastive learning approaches .


What are the contributions of this paper?

The paper "Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases" makes several key contributions:

  • Introducing a Bayesian notion of generative similarity to define a contrastive learning objective, enabling the learning of spatial embeddings that express specific inductive biases .
  • Demonstrating how generative similarity can be utilized to capture human inductive biases for geometric shapes and distinguish different abstract drawing styles parameterized by probabilistic programs .
  • Highlighting the importance of finding effective training procedures to instill neural networks with inductive biases, leveraging the literature on modeling human inductive biases with Bayesian models, and distilling these biases into machine learning models .
  • Showing that the network trained using the proposed approach excels at the Oddball task at a superhuman level due to the overlap between the training paradigm and the task, although it lacks the regularity effect observed in human-like models .

What work can be continued in depth?

Further research in this area can delve deeper into multiple aspects:

  • Exploring the generative models underlying human judgments in various domains to instill the right inductive biases in machine models .
  • Testing richer generative processes beyond the domains examined in the current work to enhance the flexibility of the approach .
  • Applying the framework to domains beyond vision, as it is general enough to be applicable to other modalities or applications, such as language models for logical and causal reasoning .
  • Investigating the alignment of human representations to support robust few-shot learning and improve neural network representations using human similarity judgments .
  • Considering the implications of instilling human inductive biases into machine models, ensuring that undesirable biases are not inadvertently incorporated .

Tables

1

Introduction
Background
Human inductive biases in learning
Challenges with limited psychological data
Objective
To develop a scalable method using Bayesian and contrastive learning
Improve model performance and alignment with human cognition
Method
Data Collection
Psychological data acquisition
Selection of tasks: geometric shapes, abstract drawing styles, probabilistic programs
Data Preprocessing
Defining generative similarity measure
Likelihood-based similarity for data distribution
Generative Similarity
Bayesian framework integration
Contrastive learning implementation
Model Architecture
Bayesian contrastive learning model
Hierarchical structure for scalability
Performance Evaluation
Shape recognition and abstract drawing classification tasks
Comparison with supervised and finetuned models
Results
Improved performance in tasks reflecting human-like regularities
Alignment with human cognition demonstrated
Applications
Bridging the gap between human and machine intelligence
Potential use in various domains
Future Research Directions
Extension to multi-layer hierarchical models
Exploration of other modalities
Conclusion
Summary of key findings and contributions
Implications for the field of machine learning and human-centered AI.
Basic info
papers
neurons and cognition
machine learning
artificial intelligence
Advanced features
Insights
What type of learning approach is used in the paper to integrate human inductive biases into machine learning models?
What is the primary goal of the study, and what are the potential applications of this work?
In what tasks does the authors demonstrate the effectiveness of their method, and how does it perform compared to supervised and finetuned models?
How does the method address the challenge of limited psychological data during training?

Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases

Raja Marjieh, Sreejan Kumar, Declan Campbell, Liyi Zhang, Gianluca Bencomo, Jake Snell, Thomas L. Griffiths·May 29, 2024

Summary

The paper presents a Bayesian approach to incorporate human inductive biases into machine learning models using contrastive learning with generative similarity. This method addresses the challenge of scaling up training with limited psychological data by defining a similarity measure based on the likelihood of data points coming from the same distribution. The authors apply this to tasks like geometric shapes, abstract drawing styles, and probabilistic programs, showing improved performance and alignment with human cognition. The study demonstrates the effectiveness of generative similarity in capturing human-like regularities, outperforming supervised and finetuned models in tasks like shape recognition and abstract drawing classification. The work aims to bridge the gap between human and machine intelligence by leveraging Bayesian models in a scalable contrastive learning framework, with potential applications in various domains. Future research will focus on extending the approach to multi-layer hierarchical models and other modalities.
Mind map
Comparison with supervised and finetuned models
Shape recognition and abstract drawing classification tasks
Contrastive learning implementation
Bayesian framework integration
Exploration of other modalities
Extension to multi-layer hierarchical models
Performance Evaluation
Generative Similarity
Selection of tasks: geometric shapes, abstract drawing styles, probabilistic programs
Psychological data acquisition
Improve model performance and alignment with human cognition
To develop a scalable method using Bayesian and contrastive learning
Challenges with limited psychological data
Human inductive biases in learning
Implications for the field of machine learning and human-centered AI.
Summary of key findings and contributions
Future Research Directions
Alignment with human cognition demonstrated
Improved performance in tasks reflecting human-like regularities
Model Architecture
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Applications
Results
Method
Introduction
Outline
Introduction
Background
Human inductive biases in learning
Challenges with limited psychological data
Objective
To develop a scalable method using Bayesian and contrastive learning
Improve model performance and alignment with human cognition
Method
Data Collection
Psychological data acquisition
Selection of tasks: geometric shapes, abstract drawing styles, probabilistic programs
Data Preprocessing
Defining generative similarity measure
Likelihood-based similarity for data distribution
Generative Similarity
Bayesian framework integration
Contrastive learning implementation
Model Architecture
Bayesian contrastive learning model
Hierarchical structure for scalability
Performance Evaluation
Shape recognition and abstract drawing classification tasks
Comparison with supervised and finetuned models
Results
Improved performance in tasks reflecting human-like regularities
Alignment with human cognition demonstrated
Applications
Bridging the gap between human and machine intelligence
Potential use in various domains
Future Research Directions
Extension to multi-layer hierarchical models
Exploration of other modalities
Conclusion
Summary of key findings and contributions
Implications for the field of machine learning and human-centered AI.
Key findings
8

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the challenge of imbuing machine models with human inductive biases by using contrastive learning with generative similarity . This problem is not entirely new, as the paper builds upon existing research on incorporating human similarity judgments and Bayesian models of reasoning into machine learning models . The paper emphasizes the importance of instilling strong inductive biases in machine models to enhance their intelligence and alignment with human cognition .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to instilling human inductive biases into machine learning models by introducing a Bayesian notion of generative similarity to define a contrastive learning objective . The goal is to learn spatial embeddings that express specific inductive biases, focusing on capturing human inductive biases for geometric shapes and distinguishing different abstract drawing styles parameterized by probabilistic programs . The study addresses the challenge of finding effective training procedures to imbue neural networks with these inductive biases, leveraging the concept of generative similarity to improve model performance on various benchmarks such as few-shot learning, robustness, and alignment .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases" introduces a novel approach based on contrastive learning to imbue neural networks with human inductive biases . This approach leverages a Bayesian notion of generative similarity to define a contrastive learning objective, where data points are designated as "same" or "different" to learn representations that encourage similar data points to be closer together and different data points to be further apart . By using generative similarity based on Bayesian inference, the paper demonstrates how this approach can be applied to complex generative processes, including probabilistic programs, to learn spatial embeddings that express specific inductive biases .

Furthermore, the paper addresses the challenge of incorporating human judgments in model objectives, which can be costly and challenging to scale for modern machine learning datasets . The proposed approach provides a scalable solution by utilizing contrastive learning, which is a widely used training procedure in machine learning, to learn representations that capture human inductive biases . This method enables the learning of spatial embeddings that reflect the generative similarity between data points, even in cases where the exact form of similarity is intractable .

Overall, the paper introduces a novel framework that combines contrastive learning with generative similarity to capture human inductive biases in neural networks, providing a scalable and effective method for improving model performance on various benchmarks, including few-shot learning, robustness, and alignment with human representations . The paper "Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases" introduces a novel approach that offers several key characteristics and advantages compared to previous methods .

  1. Bayesian Notion of Generative Similarity: The paper introduces a Bayesian notion of generative similarity, where two data points are considered similar if they are likely to have been sampled from the same distribution . This measure enables the definition of a contrastive learning objective, even in cases where the exact form of similarity is intractable, facilitating the learning of spatial embeddings that express specific inductive biases .

  2. Improved Representations: The approach demonstrates improved representations of probabilistic programs synthesizing abstract drawings compared to standard contrastive paradigms like SimCLR . By leveraging generative similarity based on Bayesian inference, the method enhances the quality of representations of complex generative processes, showcasing its effectiveness in capturing human inductive biases .

  3. Flexibility and Scalability: The framework is designed to be flexible and scalable, allowing for the application of generative similarity and contrastive learning to various domains beyond vision, such as language models for logical and causal reasoning . This versatility enables the method to capture human inductive biases across different modalities and applications .

  4. Addressing Complex Domains: The paper acknowledges the complexity of generative processes and the need to explore richer hierarchies in Bayesian models, highlighting the potential for future work to extend the framework to models with multiple layers of hierarchy . This forward-looking approach aims to enhance the understanding and application of inductive biases in machine learning models across diverse domains .

In summary, the paper's approach stands out for its innovative use of generative similarity, scalability, flexibility across domains, and potential for addressing complex generative processes, offering a promising avenue for imbuing neural networks with human inductive biases .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of capturing human inductive biases using contrastive learning with generative similarity. Noteworthy researchers in this field include Mohammad Norouzi, Geoffrey Hinton, Charles Kemp, Joshua B Tenenbaum, and Thomas L. Griffiths . These researchers have contributed to studies on contrastive learning of visual representations, generative theory of similarity, and incorporating human judgments in model objectives.

The key to the solution mentioned in the paper involves defining a principled notion of similarity based on Bayesian inference and implementing it in a contrastive learning framework, even when the exact form is intractable. This approach uses a hierarchical generative model of the data to define generative similarity between pairs of samples, encouraging the representation of "same" datapoints to be closer together and "different" datapoints to be further apart . By leveraging this framework, the study aims to capture human inductive biases for geometric shapes and distinguish different abstract drawing styles parameterized by probabilistic programs.


How were the experiments in the paper designed?

The experiments in the paper were designed with a focus on contrastive learning with generative similarity to capture human inductive biases. The experiments involved testing an array of domains that varied in complexity to showcase the flexibility of the approach . The domains examined included Gaussian mixtures, geometric shapes, and probabilistic programs, each serving as motivating examples to illustrate the contrastive training procedure with generative similarity . The experiments aimed to improve neural network representations by incorporating human similarity judgments and instilling the right inductive biases in machine models . The study utilized different training objectives, such as a standard contrastive learning objective from SimCLR and one based on a Monte-Carlo estimate of generative similarity, to enhance the representations of the programs compared to standard contrastive paradigms . The experiments also involved finetuning models like CorNet to induce human geometric regularity biases and test the model's performance on tasks like the Oddball task .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is not explicitly mentioned in the provided context. However, the study discusses training logistic regression models and ridge regression models for classification and prediction tasks . The code used in the study is not explicitly stated to be open source in the provided context. Further information regarding the dataset and the open-source status of the code may require additional details or a direct reference to the dataset or code repository.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that need to be verified. The study explores the domain of probabilistic programs synthesizing abstract drawings and demonstrates improved representations compared to standard contrastive paradigms like SimCLR . The research highlights the importance of characterizing generative models underlying human judgments to instill the right inductive biases in machine models . Additionally, the study acknowledges the need for testing richer generative processes beyond the examined domains to enhance the flexibility of the approach .

Moreover, the paper discusses the potential application of the framework in various domains beyond vision, indicating its generalizability to other modalities or applications . The incorporation of generative similarity in contrastive loss functions is shown to be effective in optimizing embedding parameters to minimize differences between generative similarity and embedding similarity . This approach enables the learning of spatial embeddings that express specific inductive biases, as demonstrated in capturing human inductive biases for geometric shapes and abstract drawing styles .

Overall, the experiments and results in the paper provide a strong foundation for verifying scientific hypotheses related to instilling human inductive biases in machine learning models through generative similarity and contrastive learning approaches .


What are the contributions of this paper?

The paper "Using Contrastive Learning with Generative Similarity to Learn Spaces that Capture Human Inductive Biases" makes several key contributions:

  • Introducing a Bayesian notion of generative similarity to define a contrastive learning objective, enabling the learning of spatial embeddings that express specific inductive biases .
  • Demonstrating how generative similarity can be utilized to capture human inductive biases for geometric shapes and distinguish different abstract drawing styles parameterized by probabilistic programs .
  • Highlighting the importance of finding effective training procedures to instill neural networks with inductive biases, leveraging the literature on modeling human inductive biases with Bayesian models, and distilling these biases into machine learning models .
  • Showing that the network trained using the proposed approach excels at the Oddball task at a superhuman level due to the overlap between the training paradigm and the task, although it lacks the regularity effect observed in human-like models .

What work can be continued in depth?

Further research in this area can delve deeper into multiple aspects:

  • Exploring the generative models underlying human judgments in various domains to instill the right inductive biases in machine models .
  • Testing richer generative processes beyond the domains examined in the current work to enhance the flexibility of the approach .
  • Applying the framework to domains beyond vision, as it is general enough to be applicable to other modalities or applications, such as language models for logical and causal reasoning .
  • Investigating the alignment of human representations to support robust few-shot learning and improve neural network representations using human similarity judgments .
  • Considering the implications of instilling human inductive biases into machine models, ensuring that undesirable biases are not inadvertently incorporated .
Tables
1
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