Model predicts chemical reactions from patent data.
problem Predicting chemical reactions from patent data.
method Neural sequence-to-sequence model trained on 50,000 reactions.
result Model performs comparably with expert systems and overcomes their limitations.
New model predicts chemical reactions without human input.
problem Automating retrosynthetic planning in chemistry.
method Combined Molecular Transformer and hyper-graph exploration.
result Improved accuracy in predicting reactants, reagents, solvents, and catalysts.
Deep reinforcement learning optimizes retrosynthetic planning for chemical synthesis.
problem Optimizing chemical synthesis plans from molecular targets to simpler starting materials.
method Deep reinforcement learning to estimate synthesis costs and values of molecules.
result Trained neural networks outperform heuristic approaches in synthesizing unfamiliar molecules.
Retro* uses neural networks to efficiently find high-quality synthetic routes in organic chemistry.
problem Finding efficient synthetic routes in organic chemistry is challenging due to the vast search space.
method Retro* is a neural-based A*-like algorithm that learns a neural search bias to guide efficient best-first search.
result Retro* outperforms existing methods in both success rate and solution quality while being more efficient.
Model predicts diverse chemical reactions for target compounds.
problem Making generalizable and diverse retrosynthetic reaction predictions.
method Transformer architecture with novel pre-training methods and a latent variable model.
result Improves performance on USPTO-50k dataset, generating more diverse predictions.
Bayesian algorithm discovers synthetic routes from target molecules.
problem Identifying synthetic routes from desired products.
method Bayesian inference and combinatorial optimization.
result Algorithm rediscovered 80.3% and 50.0% of known synthetic routes.
Efficient private matrix analysis algorithms for recent variants.
problem Private analysis of recent matrix updates.
method Identifying sufficient conditions on positive semidefinite matrices.
result First efficient differentially private algorithms for various matrix analysis tasks.
Prototypal analysis improves archetypal analysis by penalizing distant prototypes, making it more robust and interpretable.
problem Sensitivity to outliers and non-locality in archetypal analysis limit its applicability as a learning tool.
method Prototypal analysis finds prototypes through convex combination of data points, penalizing distant prototypes.
result Prototypal analysis is more robust and interpretable than archetypal analysis.
Lecture notes on analysis tools for X-ray tomography.
problem Understanding X-ray tomography using mathematical analysis.
method Overview of analysis tools and ideas, minimal assumptions.
result Broad overview of analysis tools for X-ray tomography.
The paper explores machine learning in mobile big data analysis.
problem Challenges in mobile big data analysis.
method Discussion and review of existing methods.
result Identification of main challenges and future directions.
This paper introduces compositional data analysis for financial ratios, improving industry-level analysis.
problem Statistical issues with standard financial ratios at industry level.
method Compositional data analysis techniques for financial ratios.
result Improved analysis of financial ratios using compositional data methods.
In this dissertation, the main goal is visualisation of financial time series. We expect that visualisation of financial time series will be a useful auxiliary for technical analysis. Firstly, we review the technical analysis methods and test our trading rules, which are built by the essential concepts of technical ana…
Paper combines geometry and time-series analysis for spatiotemporal data.
problem Multivariate time-series data from multiple sensors.
method Combines manifold learning, Riemannian geometry, and spectral analysis.
result Proposes Riemannian multi-resolution analysis (RMRA) for dynamic mode extraction.
In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed. Based on this analysis the concept of a modularity component is defined, and its properties are developed. It is shown that modularity component analysis …
Paper uses dynamic analysis to detect malware with PHMMs.
problem Malware detection using static and dynamic analysis techniques.
method Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs) trained on API call sequences.
result PHMMs outperform HMMs in malware detection.
This paper simplifies complex geometry for shape analysis.
problem Understanding interactions between differential geometry and functional analysis.
method Provides an overview of infinite-dimensional Riemannian manifolds and metrics.
result Roadmap for beginners in computational anatomy and shape analysis.
Interactive DR framework for comparing datasets.
problem Limited flexibility in existing DR methods for comparative analysis.
method Unified linear comparative analysis (ULCA) with interactive optimization and visualization.
result ULCA and optimization algorithm improve comparative analysis efficiency and flexibility.
This paper reviews R packages for automating data analysis tasks.
problem Time-consuming Exploratory Data Analysis in large, noisy data sets.
method Systematic review of 12 R packages for autoEDA.
result Identifies automated tasks and areas for future development.
Improves transparency of deep neural networks through feature and consistency analysis.
problem Black-box nature of deep learning inference limits transparency for safety-critical systems.
method Structural and linguistic feature analysis, consistency analysis.
result 75% of human workers found input data and results consistent, 70% found inference and results consistent.
Proposes a method to optimize class mean preservation in kernel-based feature spaces.
problem Optimizing the selection of kernel subspace for better performance.
method Component analysis method for kernel-based dimensionality reduction that optimally preserves class mean distances.
result Discriminant analysis version of the proposed method provides insights into feature space properties.
Proposes a multivariate regression model for better analysis of multiple datasets.
problem Insufficient performance of single-dataset analysis in integrative studies.
method Sparse estimation for variable and group selection, alternating direction method of multipliers algorithm.
result Demonstrated improved performance through simulations and real data analysis.
Combines topological and geometric approaches to data analysis.
problem Understanding when and how geometric objects intersect.
method Connects topological and geometric concepts of curvature.
result Reconceptualizes curvature and links it to hyperconvexity.
New method uses topological data analysis to study stock market crashes.
problem Characterizing and predicting stock market crashes.
method Topological data analysis, persistence landscape, dynamic time series analysis.
result Demonstrates effectiveness of new method for Flash Crash characterization and prediction.
PARSEC compresses text for sentiment analysis with minimal loss in accuracy.
problem Compressing text data for sentiment analysis without losing accuracy.
method Uses Parts-of-Speech tags to compress text intelligently.
result Accurate compression is possible with minimal loss in sentiment classification accuracy.
A primer on navigating causal analysis problems.
problem Estimating causal effects from observational data.
method Four schools of thought for causal analysis.
result Conceptual map for causal analysis.
Novel sensitivity analysis for neural networks improves model interpretability.
problem Improving interpretability of complex probabilistic models.
method Bayesian neural networks with latent variables and sensitivity analysis.
result Increases interpretability of black-box probabilistic models.
Analyzes stock trends and e-commerce user behavior using Twitter data.
problem Understanding the relationship between stock prices, stock news, and e-commerce user behavior.
method Cross-domain analysis using Hadoop, Hive, and Tableau on three datasets.
result Identified correlations between stock sentiment, stock trends, and e-commerce user behavior.
Genetic programming optimizes Gaussian kernels for better sentiment analysis.
problem Improving accuracy of sentiment analysis in text.
method Genetic Programming applied to evolve more effective Gaussian kernels.
result The evolved kernels outperform traditional Gaussian Processes in sentiment analysis.
This work improves group data analysis using modified tensor decompositions.
problem Improving group data analysis models for better signal modeling.
method Introduces a new generalization of block tensor decomposition for group data analysis.
result Demonstrates improved performance in multilabel classification and clustering tasks.
Study analyzes Disney stock market performance using machine learning.
problem Forecasting stock market performance of Disney.
method Exploratory data analysis, feature engineering, model selection (linear regression).
result Linear regression model performed best.
This study analyzes data science vocabulary changes over 13 years.
problem Understanding evolution of data science terms over time.
method Exploratory Data Analysis, Latent Semantic Analysis, Latent Dirichlet Analysis, N-grams Analysis.
result Identified new vocabulary and its incorporation into scientific literature.
Analyzes changes in cryptocurrency market structure.
problem Understanding shifts in cryptocurrency market dynamics.
method Structural change analysis techniques.
result Identifies key structural changes in the market.
FinSphere improves stock analysis quality with AI and expert-curated data.
problem Lack of objective evaluation metrics and depth in stock analysis by FinLLMs.
method Developed AnalyScore, curated Stocksis dataset, and FinSphere AI agent.
result FinSphere outperforms general and domain-specific LLMs in generating high-quality stock analysis reports.
Random forest identifies key features for diagnosing machine faults.
problem Identifying specific factors causing machine faults.
method Gaussian mixture model clustering, spectrum analysis, random forest classification.
result Identified significant features for different machine states.
This study examines whether PCA can effectively identify nitrogen pollution sources in rivers.
problem Identifying pollution sources in rivers for effective environmental management.
method Principal Component Analysis and its modifications, along with Independent Component Analysis and Factor Analysis, are applied to nitrogen pollution source identification.
result PCA and related techniques can be powerful tools for uncovering nitrogen pollution sources in rivers.
Solves optimal stopping problem for financial technical analysis.
problem Optimal stopping problem for technical analysis models.
method Wide-class dynamics modeling support/resistance lines.
result Solution to optimal stopping problem for technical analysis.
We present a unifying framework which reduces the construction of probabilistic component analysis techniques to a mere selection of the latent neighbourhood, thus providing an elegant and principled framework for creating novel component analysis models as well as constructing probabilistic equivalents of deterministi…
Paper introduces probabilistic methods to approximate archetypal analysis, reducing complexity.
problem Inherent computational complexity of archetypal analysis limits its practical applicability.
method Two preprocessing techniques: dimensionality reduction and representation cardinality reduction, using probabilistic geometry.
result The method effectively reduces scaling and provides near-optimal solutions for prediction errors.
DeepCAM learns convolutional dictionaries for image processing.
problem Processing high-dimensional signals like images efficiently.
method Introduces a Deep Convolutional Analysis Dictionary Model (DeepCAM) using convolutional dictionaries.
result DeepCAM achieves performance comparable to other methods on single image super-resolution.
Improved LDA method for better classification and dimensionality reduction.
problem Improving linear discriminant analysis for better classification performance.
method Integrates spectrally-corrected covariance matrix and regularized discriminant analysis.
result SRLDA has a linear classification global optimal solution under spiked model assumption.
CDVI improves variational inference for survival analysis by considering censoring mechanisms.
problem Challenges in applying variational methods to survival data, especially the dependence on censoring.
method Censor-dependent variational inference (CDVI) tailored for latent variable models in survival analysis.
result Significant improvements in estimating individual survival distributions.
Survey of network analysis limits and optimal methods.
problem Graphon estimation, community detection, and hypothesis testing.
method Review of minimax optimal rates and procedures.
result Optimal algorithms for network analysis.
Survey on spectral embeddings for data analysis.
problem None explicitly stated in the abstract.
method Presentation of spectral embeddings from Riemannian geometry to data analysis.
result Survey of spectral embeddings and their applications.
Improved analysis of UCBVI algorithm with better empirical performance.
problem Improving the UCBVI algorithm's performance and understanding its bounds.
method Refined analysis of UCBVI algorithm with improved bonus terms and regret analysis.
result Improving multiplicative constants in UCBVI bounds enhances empirical performance.
The study applies Dimensional Analysis to the neoclassical economic growth model.
problem Inconsistency in the neoclassical economic growth model.
method Dimensional Analysis was used to evaluate and adjust the model.
result An adjustment to the neoclassical economic growth model is required to satisfy the principle of dimensional homogeneity.
Reduces survival analysis to common regression tasks.
problem Applying standard machine learning tools to survival analysis.
method Various reduction techniques to simplify survival analysis.
result Benchmark analysis shows improved predictive performance.
A new robust scaling approach improves downstream metabolomics analysis.
problem Challenges in choosing scaling techniques for metabolomics data.
method Introduces a weighted scaling approach robust to outliers.
result The proposed method outperforms traditional scaling techniques in both outlier-free and outlier-present datasets.
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
problem Inaccurate financial analysis by LLMs leading to investment and regulatory issues.
method Proposes FinAR-Bench, a benchmark dataset with three steps: key info extraction, financial indicator calculation, and logical reasoning.
result LLMs perform better in key info extraction and indicator calculation but struggle with logical reasoning.