Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search

• URL: http://arxiv.org/abs/2103.13939v1
• Date: Thu, 25 Mar 2021 16:00:23 GMT
• Title: Recovering Quantitative Models of Human Information Processing with Differentiable Architecture Search
• Authors: Sebastian Musslick
• Abstract summary: We introduce an open-source pipeline for the automated construction of quantitative models. We find that these methods are capable of recovering basic quantitative motifs from models of psychophysics, learning and decision making.
• Score: 0.3384279376065155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of behavioral phenomena into mechanistic models of cognitive function is a fundamental staple of cognitive science. Yet, researchers are beginning to accumulate increasing amounts of data without having the temporal or monetary resources to integrate these data into scientific theories. We seek to overcome these limitations by incorporating existing machine learning techniques into an open-source pipeline for the automated construction of quantitative models. This pipeline leverages the use of neural architecture search to automate the discovery of interpretable model architectures, and automatic differentiation to automate the fitting of model parameters to data. We evaluate the utility of these methods based on their ability to recover quantitative models of human information processing from synthetic data. We find that these methods are capable of recovering basic quantitative motifs from models of psychophysics, learning and decision making. We also highlight weaknesses of this framework, and discuss future directions for their mitigation.

Related papers

• AI-Aided Kalman Filters [65.35350122917914]
  The Kalman filter (KF) and its variants are among the most celebrated algorithms in signal processing. Recent developments illustrate the possibility of fusing deep neural networks (DNNs) with classic Kalman-type filtering. This article provides a tutorial-style overview of design approaches for incorporating AI in aiding KF-type algorithms.
  arXiv  Detail & Related papers  (2024-10-16T06:47:53Z)
• Axiomatic Causal Interventions for Reverse Engineering Relevance Computation in Neural Retrieval Models [20.29451537633895]
  We propose the use of causal interventions to reverse engineer neural rankers. We demonstrate how mechanistic interpretability methods can be used to isolate components satisfying term-frequency axioms.
  arXiv  Detail & Related papers  (2024-05-03T22:30:15Z)
• Model Compression Techniques in Biometrics Applications: A Survey [5.452293986561535]
  Deep learning algorithms have extensively empowered humanity's task automatization capacity. The huge improvement in the performance of these models is highly correlated with their increasing level of complexity. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation.
  arXiv  Detail & Related papers  (2024-01-18T17:06:21Z)
• Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus [55.2480439325792]
  We propose a framework that combines Symbolic Regression (SR) and Discrete Exterior Calculus (DEC) for the automated discovery of physical models. DEC provides building blocks for the discrete analogue of field theories, which are beyond the state-of-the-art applications of SR to physical problems. We prove the effectiveness of our methodology by re-discovering three models of Continuum Physics from synthetic experimental data.
  arXiv  Detail & Related papers  (2023-10-10T13:23:05Z)
• AI-Assisted Discovery of Quantitative and Formal Models in Social Science [6.39651637213537]
  We show that our system can be used to discover interpretable models from real-world data in economics and sociology. We propose that this AI-assisted framework can bridge parametric and non-parametric models commonly employed in social science research.
  arXiv  Detail & Related papers  (2022-10-02T16:25:47Z)
• Advancing Reacting Flow Simulations with Data-Driven Models [50.9598607067535]
  Key to effective use of machine learning tools in multi-physics problems is to couple them to physical and computer models. The present chapter reviews some of the open opportunities for the application of data-driven reduced-order modeling of combustion systems.
  arXiv  Detail & Related papers  (2022-09-05T16:48:34Z)
• EINNs: Epidemiologically-Informed Neural Networks [75.34199997857341]
  We introduce a new class of physics-informed neural networks-EINN-crafted for epidemic forecasting. We investigate how to leverage both the theoretical flexibility provided by mechanistic models as well as the data-driven expressability afforded by AI models.
  arXiv  Detail & Related papers  (2022-02-21T18:59:03Z)
• Supply of engineering techniques and software design patterns in psychoanalysis and psychometrics sciences [0.0]
  The purpose of this study is to introduce software technologies and models and artificial intelligence algorithms to improve the weaknesses of CBT (Cognitive Behavior Therapy) method in psychotherapy. The presentation method for this purpose is the implementation of psychometric experiments in which the hidden human variables are inferred from the answers of tests.
  arXiv  Detail & Related papers  (2021-08-16T08:36:37Z)
• Using Data Assimilation to Train a Hybrid Forecast System that Combines Machine-Learning and Knowledge-Based Components [52.77024349608834]
  We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data is noisy partial measurements. We show that by using partial measurements of the state of the dynamical system, we can train a machine learning model to improve predictions made by an imperfect knowledge-based model.
  arXiv  Detail & Related papers  (2021-02-15T19:56:48Z)
• Model-Based Deep Learning [155.063817656602]
  Signal processing, communications, and control have traditionally relied on classical statistical modeling techniques. Deep neural networks (DNNs) use generic architectures which learn to operate from data, and demonstrate excellent performance. We are interested in hybrid techniques that combine principled mathematical models with data-driven systems to benefit from the advantages of both approaches.
  arXiv  Detail & Related papers  (2020-12-15T16:29:49Z)
• Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective [5.597839822252915]
  We study supervised regression models that combine rigid-body mechanics with data-driven modelling techniques. We provide a unified view on the combination of data-driven regression models, such as neural networks and Gaussian processes, with analytical model priors.
  arXiv  Detail & Related papers  (2020-12-11T11:26:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.