iKF method uncovers complex variable interactions for scientific discovery.
problem Limited interpretability of existing models in decision-making applications.
method Iterative Kings' Forests (iKF) method to uncover multi-order interactions.
result iKF provides strong interpretive power for explainable modeling.
Interpretable ML helps discover insights from big data.
problem Validating data-driven discoveries from complex datasets.
method Statistical and machine learning techniques for interpretable models.
result Challenges in validating data-driven discoveries remain.
New method extracts biological concepts from cell microscopy images.
problem Extracting meaningful concepts from vision foundation models trained on cell microscopy images.
method Sparse dictionary learning (DL) combined with PCA whitening pre-processing.
result Successfully retrieved biologically meaningful concepts like cell types and genetic perturbations.
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Theory-guided data science (TGDS) is an emerging paradigm that aims to leverage the wealth of scientific knowledge for improving the effectiveness of da…
Enhances KANs for accuracy and interpretability with multi-exit architecture.
problem Unclear optimal depth for KANs and difficulty in optimization and interpretation.
method Introduces multi-exit KANs with each layer having its own prediction branch.
result Multi-exit KANs outperform single-exit versions on various datasets.
The increasing size and complexity of scientific data could dramatically enhance discovery and prediction for basic scientific applications. Realizing this potential, however, requires novel statistical analysis methods that are both interpretable and predictive. We introduce Union of Intersections (UoI), a flexible, m…
Survey of deep learning models for scientific discovery.
problem Identifying which scientific problems are most suitable for deep learning.
method Overview of deep learning models, tasks, training methods, and techniques.
result Helps accelerate deep learning use in scientific domains.
Machine learning algorithms such as linear regression, SVM and neural network have played an increasingly important role in the process of scientific discovery. However, none of them is both interpretable and accurate on nonlinear datasets. Here we present contextual regression, a method that joins these two desirable …
Machine learning methods have been remarkably successful for a wide range of application areas in the extraction of essential information from data. An exciting and relatively recent development is the uptake of machine learning in the natural sciences, where the major goal is to obtain novel scientific insights and di…
AI methods broaden signal discovery in scientific data.
problem Limited coverage of possible signals in model-dependent searches.
method Model-agnostic AI strategies for broad exploration.
result Enhanced discovery potential in experimental science.
SVGP KAN integrates sparse variational GP with KANs for scalable probabilistic inference.
problem Lack of probabilistic outputs in standard KANs and cubic scaling of Gaussian Process methods.
method Sparse Variational GP-KAN combines KAN topology with sparse variational inference and permutation-based importance analysis.
result Enables probabilistic KANs to handle larger datasets with linear computational complexity.
Automated detection of new, interesting, unusual, or anomalous images within large data sets has great value for applications from surveillance (e.g., airport security) to science (observations that don't fit a given theory can lead to new discoveries). Many image data analysis systems are turning to convolutional neur…
Diamond method controls FDR for trustworthy feature interaction discovery in ML models.
problem Limited interpretability of ML models due to black box nature.
method Diamond method integrates model-X knockoffs framework to control FDR for non-additive interactions.
result Diamond method ensures accurate discovery of feature interactions with FDR control.
iPrompt uses LLMs to generate natural-language explanations of data patterns.
problem Finding and explaining patterns in data using natural language.
method Interpretable autoprompting (iPrompt) that generates natural-language explanations based on LLMs.
result iPrompt can accurately find and explain data patterns, improving upon human-written prompts.
New insights into ML models' accuracy and generalization for scientific problems.
problem Quantifying accuracy and generalization of ML models in scientific applications.
method Rigorous numerical analysis and theoretical bounds for linear differential equations.
result Different ML models can have opposing generalization behaviors, contrary to intuition.
Proposes LLM-DCD for improved causal discovery from data.
problem Challenges in discovering causal relationships from observational data.
method Uses LLM to initialize DCD optimization, incorporating priors.
result Higher accuracy on benchmark datasets compared to state-of-the-art.
Paper presents a workflow for reliable unsupervised learning in science.
problem Lack of standardization in unsupervised learning workflows for reproducible scientific discoveries.
method Structured workflow including data preparation, modeling, validation, and communication.
result Illustrates the importance of validation in unsupervised learning.
SGNNs use simulations to train neural networks, improving scientific forecasting and interpretability.
problem Combining precise theory and machine learning for robust scientific modeling.
method Pretraining neural networks on diverse mechanistic simulations as training data.
result SGNNs outperform data-driven and physics-constrained models in forecasting and interpretability.
New AI approach improves quantum device calibration by leveraging prior scientific discoveries.
problem Lack of abundant data in scientific disciplines hinders model generalizability.
method Introduces a new machine learning approach that combines prior scientific knowledge with data.
result Accuracy in predicting quantum device energy spectrum surpasses current state-of-the-art by over 20%.
Despite its importance, choosing the structural form of the kernel in nonparametric regression remains a black art. We define a space of kernel structures which are built compositionally by adding and multiplying a small number of base kernels. We present a method for searching over this space of structures which mirro…
Develops a Bayesian framework for symbolic regression of scientific expressions.
problem Lack of principled uncertainty quantification and interpretability in existing symbolic regression methods.
method Hierarchical Bayesian framework with tree-structured symbolic expressions and Markov chain Monte Carlo inference.
result Robust performance on various datasets, including single-atom catalysis.
A new framework for private Bayesian tests maintains interpretability and computational efficiency.
problem Lack of interpretability and inability to quantify evidence in confidential data.
method Differentially private Bayesian tests based on test statistics.
result Established results on Bayes factor consistency under the proposed framework.
NDM incorporates geometric structure into neural networks for better optimization and interpretability.
problem Efficient and interpretable deep learning architectures.
method NDM is a neural network architecture that explicitly incorporates geometric structure into its design, using a Coordinate Layer, Geometric Layer, and Evolution Layer.
result NDM provides intrinsic regularization, enhancing generalization and robustness.
AutoSciDACT detects scientific anomalies in noisy data.
problem Detecting anomalies in large, noisy scientific datasets.
method Contrastive pre-training for low-dimensional data representations, two-sample test using NPLM.
result Strong sensitivity to small anomalies across various scientific domains.
The paper argues for prioritizing identifying structure over complex models for scientific discovery.
problem Underdetermination of mechanisms in high-dimensional data, leading to unreliable explanations.
method Proposes concrete standards for 'mechanistic ML' to avoid collapsing explanations.
result Large language models (LLMs) can collapse large equivalence classes of explanations, making it hard to distinguish between mechanisms.
Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired o…
DiffATD efficiently discovers targets in partially observable environments using diffusion dynamics.
problem Efficiently discovering targets in partially observable environments with limited sampling.
method DiffATD uses diffusion dynamics to maintain a belief distribution over unobserved states, balancing exploration and exploitation.
result DiffATD outperforms baselines and supervised methods in diverse domains.
TRIM improves interpretability of deep neural networks in cosmology.
problem Understanding which features a deep neural network uses in a transformed space.
method TRIM (Transformation IMportance) attributes importances to features in a transformed space.
result Combining TRIM with contextual decomposition helps identify physical features learned by DNNs.
Study explores DNNs' reliance on existing vs. new features in physiological signals.
problem Understanding how deep neural networks discover new features in physiological signals.
method Proposes a method to remove hand-engineered features and force DNNs to learn new representations.
result DNNs often rediscover known features, but can also learn new ones.
PIMA autoencoders discover shared features in multimodal scientific data.
problem Discovering shared information in high-throughput scientific datasets.
method Physics-informed multimodal autoencoders (PIMA) with Gaussian mixture prior and product of experts formulation.
result Accurate cross-modal inference between images and mechanical stress-strain response in lattice metamaterials.
New method uses DNN for genetic variant identification, controlling randomness and improving interpretability.
problem Challenges in interpreting deep neural networks for genetic variant identification.
method Interpretable neural network model with controlled variable selection using ensembling, knockoffs, and de-randomization.
result The proposed method leads to more discoveries compared to conventional methods.
Polynomial chaos surrogates quantify epistemic uncertainty in AI-driven scientific models.
problem Uncertainty in reward estimates hinders interpretability in sequential generative models.
method Fit polynomial chaos expansions to trained models to propagate epistemic uncertainty and quantify sensitivity.
result Interpretable decomposition of reward components driving generative decisions.
In many scientific tasks we are interested in discovering whether there exist any correlations in our data. This raises many questions, such as how to reliably and interpretably measure correlation between a multivariate set of attributes, how to do so without having to make assumptions on distribution of the data or t…
Machine learning improves planetary space physics by incorporating physical knowledge.
problem Improving performance and interpretability of machine learning models for planetary space physics.
method Building on a previous semi-supervised physics-based classification, the team used varying data and physical information to improve machine learning performance and interpretability.
result Incorporating physical knowledge improves machine learning performance and interpretability, essential for deriving scientific meaning.
Proposes DCNAR for dynamic causal inference from neural time series.
problem Uncertainty and evolution of causal structure in real-world domains.
method Two-stage neural causal modeling integrating discovery and inference.
result Dynamic causal inferences are more stable and meaningful than alternatives.
SigTime learns interpretable signatures from time series data.
problem Discovering meaningful patterns in time series data with high complexity and limited interpretability.
method Jointly trains two Transformer models using shapelet-based and feature engineering representations.
result Learned shapelets serve as interpretable signatures for time series classification.
Framework evaluates AI proposals for drug discovery, finds no LLM advantage.
problem No principled framework exists for evaluating AI-guided scientific selection under budget constraints.
method Formally verified metric (BSDS/DQS) penalizes false discoveries and excessive abstention.
result LLMs provide no marginal value over existing classifiers in drug discovery.
New method controls false edge detections in Gaussian graphical models.
problem High false edge detections in well-established estimators.
method Nodewise variable selection approach to control false discovery rate.
result Significant gain in performance compared to competing methods.
A new algorithm learns causal relationships from multimodal data.
problem Discovering causal relationships in exploratory settings without prior information.
method causalPIMA algorithm using multimodal data and physics constraints.
result Learned causal structure and key features in fully unsupervised settings.
Unified taxonomy for ML uncertainty in physics, validated.
problem Uncertainty quantification in machine learning for physics.
method Unified taxonomy, principled validation tools.
result Illustrated validation tools with examples.
CausalEvolve improves efficiency and discovery in open-ended scientific tasks.
problem Lack of targeted guidance and knowledge organization in evolve-based agents.
method Causal scratchpad that identifies and reasons about guiding factors for evolution.
result Effective improvement in evolutionary efficiency and better solutions.
Quantum model discovery uses DQCs to solve equations from data.
problem Discovering differential equations from data using quantum computing.
method Differentiable quantum circuits (DQCs) to solve parameterized equations, regression on data and equations.
result Successful parameter inference and equation discovery on various systems.
In this paper we analyze both the scientific activities of Cahit Arf, a Turkish mathematician, and the social context in which he worked. We also discuss his work and social environment leading to the discovery of Arf invariant, Arf rings, Arf closure and Hasse-Arf theorem.
Scientific discovery is limited by hypothesis redundancy, and hybrid methods can exploit non-local exploration.
problem Limitation of scientific discovery due to hypothesis redundancy.
method Hybrid discovery systems combining structured local search with LLM-generated non-local proposals.
result Hybrid methods can exploit non-local exploration when three geometric conditions co-occur.
Since time immemorial, people have been looking for ways to organize scientific knowledge into some systems to facilitate search and discovery of new ideas. The problem was partially solved in the pre-Internet era using library classifications, but nowadays it is nearly impossible to classify all scientific and popular…
New benchmarks show LLMs struggle with causal discovery.
problem Leveraging LLMs for causal discovery is unreliable due to dataset leakage.
method Developing science-grounded benchmarks and hybrid methods combining LLM predictions with statistical analysis.
result LLMs perform poorly on novel, real-world scientific studies compared to classical methods.
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved amazing levels of accuracy on image recognition and natural language processing ta…
Cluster LOCO: A model-agnostic feature importance score for interpreting cluster outputs
problem Interpreting and auditing cluster outputs
method Cluster LOCO (Leave-One-Covariate-Out)
result More reliably recovers informative features than existing methods