Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy

• URL: http://arxiv.org/abs/2402.00071v2
• Date: Fri, 12 Apr 2024 09:28:47 GMT
• Title: Unraveling the Impact of Initial Choices and In-Loop Interventions on Learning Dynamics in Autonomous Scanning Probe Microscopy
• Authors: Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Sergei V. Kalinin,
• Abstract summary: The current focus in Autonomous Experimentation (AE) is on developing robust to conduct the AE effectively. This paper presents an analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Learning (DKL) We illustrate the impact of the'seed effect' and in-loop seed interventions on the effectiveness of DKL in predicting material properties.
• Score: 0.8070353314073375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The current focus in Autonomous Experimentation (AE) is on developing robust workflows to conduct the AE effectively. This entails the need for well-defined approaches to guide the AE process, including strategies for hyperparameter tuning and high-level human interventions within the workflow loop. This paper presents a comprehensive analysis of the influence of initial experimental conditions and in-loop interventions on the learning dynamics of Deep Kernel Learning (DKL) within the realm of AE in Scanning Probe Microscopy. We explore the concept of 'seed effect', where the initial experiment setup has a substantial impact on the subsequent learning trajectory. Additionally, we introduce an approach of the seed point interventions in AE allowing the operator to influence the exploration process. Using a dataset from Piezoresponse Force Microscopy (PFM) on PbTiO3 thin films, we illustrate the impact of the 'seed effect' and in-loop seed interventions on the effectiveness of DKL in predicting material properties. The study highlights the importance of initial choices and adaptive interventions in optimizing learning rates and enhancing the efficiency of automated material characterization. This work offers valuable insights into designing more robust and effective AE workflows in microscopy with potential applications across various characterization techniques. The analysis code that supports the funding is publicly available at https://github.com/Slautin/2024_Seed_effect_DKL_BO.

Related papers

• Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian Processes [70.66864668709677]
  We consider the problem of active learning for global sensitivity analysis of expensive black-box functions. Since function evaluations are expensive, we use active learning to prioritize experimental resources where they yield the most value. We propose novel active learning acquisition functions that directly target key quantities of derivative-based global sensitivity measures.
  arXiv  Detail & Related papers  (2024-07-13T01:41:12Z)
• ACE : Off-Policy Actor-Critic with Causality-Aware Entropy Regularization [52.5587113539404]
  We introduce a causality-aware entropy term that effectively identifies and prioritizes actions with high potential impacts for efficient exploration. Our proposed algorithm, ACE: Off-policy Actor-critic with Causality-aware Entropy regularization, demonstrates a substantial performance advantage across 29 diverse continuous control tasks.
  arXiv  Detail & Related papers  (2024-02-22T13:22:06Z)
• Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments [5.488412825534217]
  We introduce an active learning approach: Active Learning in Causal Inference with Interference (ACI) ACI uses Gaussian process to flexibly model the direct and spillover treatment effects as a function of a continuous measure of neighbors' treatment assignment. We demonstrate its feasibility in achieving accurate effects estimations with reduced data requirements.
  arXiv  Detail & Related papers  (2024-02-20T04:13:59Z)
• Unraveling Key Factors of Knowledge Distillation [19.29311840930773]
  This study investigates how student model capacity, data complexity, and decoding strategies collectively influence distillation effectiveness. We empirically validate hypotheses related to the impact of these factors on knowledge distillation. Our research not only elucidates the significant influence of model capacity, data complexity, and decoding strategies on distillation effectiveness but also introduces a novel, optimized distillation approach.
  arXiv  Detail & Related papers  (2023-12-14T01:16:19Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• A dynamic Bayesian optimized active recommender system for curiosity-driven Human-in-the-loop automated experiments [8.780395483188242]
  We present the development of a new type of human in the loop experimental workflow, via a Bayesian optimized active recommender system (BOARS) This work shows the utility of human-augmented machine learning approaches for curiosity-driven exploration of systems across experimental domains.
  arXiv  Detail & Related papers  (2023-04-05T14:54:34Z)
• Active learning in open experimental environments: selecting the right information channel(s) based on predictability in deep kernel learning [0.0]
  Key tasks in experimental studies is establishing which of these channels is predictive of the behaviors of interest. Here we explore the problem of discovery of the optimal predictive channel for structure-property relationships in microscopy. This approach can be directly applicable to similar active learning tasks in automated synthesis and the discovery of quantitative structure-activity relations in molecular systems.
  arXiv  Detail & Related papers  (2022-03-18T22:36:40Z)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• Influence Functions in Deep Learning Are Fragile [52.31375893260445]
  influence functions approximate the effect of samples in test-time predictions. influence estimates are fairly accurate for shallow networks. Hessian regularization is important to get highquality influence estimates.
  arXiv  Detail & Related papers  (2020-06-25T18:25:59Z)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.