New method uses machine learning to predict CO2 reduction catalysts without expensive ab initio calculations.
problem Predicting catalytic activity for CO2 reduction reactions using computationally expensive ab initio methods.
method Combining muffin-tin orbital theory descriptors with machine learning (ANN and KRR) for large-scale screening.
result Predicted CO adsorption energy with 0.05 eV mean absolute deviation, significantly improved over previous methods.
Catalyst accelerates convex optimization methods with practical guidelines.
problem Improving convergence of gradient-based optimization methods.
method Catalyst scheme based on inexact accelerated proximal point algorithm.
result Faster convergence rates for various convex optimization methods.
Catalyst.RL accelerates RL research with efficient training.
problem Efficient reinforcement learning training in complex environments.
method Open-source PyTorch framework with distributed training and RL algorithms.
result Catalyst.RL achieved 2nd place in a computationally expensive RL challenge.
Accelerates stochastic optimization for convex and strongly convex problems.
problem Improving convergence rates in noisy stochastic optimization.
method Extends Catalyst approach to stochastic settings, handles inexact proximal operators.
result Achieves optimal worst-case complexity for noise-dominated regions.
catalyst.RL simplifies RL research, enabling reproducibility and efficiency.
problem Difficulty in reproducing and comparing RL algorithms.
method Open-source framework with distributed training, flexible configurations, and efficient RL algorithms.
result Demonstrated effectiveness on AI for Prosthetics Challenge, achieving 3rd place.
New method uses machine learning to analyze catalyst reactions.
problem Understanding complex reaction mechanisms in catalytic materials.
method Combining transient kinetics and machine learning.
result Correct estimates of micro-kinetic coefficients and mechanism.
Large GNNs trained with Graph Parallelism improve atomic simulation accuracy.
problem Training memory-intensive GNNs for high-order atomic interactions.
method Graph Parallelism to distribute graphs across GPUs.
result Graph-parallelized models achieve state-of-the-art results on catalyst datasets.
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.
Unified framework for variance reduction to solve monotone operator problems.
problem Large-scale monotone inclusion problems with finite sum structure.
method Developed a general framework for variance-reduced forward-backward splitting algorithms.
result Linear convergence rate under mild assumptions, with Catalyst acceleration and asynchronous implementation.
Gryffin optimizes categorical variables in materials design, leveraging expert knowledge.
problem Optimizing categorical variables in complex design choices like molecule selection.
method Bayesian optimization with smooth approximations to categorical distributions, incorporating expert knowledge.
result Gryffin accelerates discovery of promising molecules and materials, highlighting relevant correlations.
New algorithm speeds up recovery of low-rank matrices.
problem Nonconvex low-rank matrix recovery problems.
method Stochastic variance-reduced gradient descent with semi-stochastic gradient.
result Linear convergence rate to unknown low-rank matrix.
A new method for distributed optimization reduces communication rounds without minibatches.
problem Efficient training in distributed machine learning with different data distributions.
method A primal-dual method (GA-MSGD) applied to the Lagrangian of distributed optimization.
result Achieves linear convergence in communication rounds for strongly convex objectives.
Deep filtering improves robustness of models from noisy, sparse data.
problem Challenges in understanding microscopic interactions from noisy, sparse, and biased data.
method Statistical approach based on deep filtering of nonlinear feature networks.
result Physicochemical models are more robust, transparent, and generalize better.
AutoML struggles with climate change data, but offers potential improvements.
problem Improving machine learning for climate change applications.
method Benchmarked AutoML libraries on climate modeling, wind power, and catalyst discovery.
result Current AutoML techniques fail to surpass human-designed models in climate change applications.
New scheme uses gradient-based methods for non-convex optimization.
problem Non-convex optimization problems in machine learning and signal processing.
method Generic scheme for gradient-based algorithms on weakly convex objectives.
result Guaranteed to produce a stationary point with near-optimal convergence rate.
The paper studies geometric properties of Φ(3)-harmonic maps and proves Liouville type results.
problem Exploring geometric properties of Φ(3)-harmonic maps. method Unified geometric analytic methods, first and second variation formulas, stress-energy tensor, conservation law, monotonicity formula, asymptotic assumption, extrinsic average variational method.
result Proves Liouville type results for Φ(3)-harmonic maps. Company mergers and acquisitions are often perceived to act as catalysts for corporate growth in free markets systems: it is conventional wisdom that those activities lead to better and more efficient markets. However, the broad adoption of this perception into corporate strategy is prone to result in a less diverse an…
Precision farming uses data analysis to optimize crop management.
problem Meeting increased food demand with sustainable practices.
method Statistical tools and machine learning for pattern recognition and prediction.
result Optimized farming processes in real-time reduce resource use.
New algorithm speeds up solving saddle-point problems with large condition numbers.
problem Solving saddle-point problems with large condition numbers.
method Proposes a stochastic proximal point algorithm that accelerates variance reduction methods.
result Reduces logarithmic term of condition number for iteration complexity.
Study shows activist board representation improves Japanese companies' performance.
problem Lack of innovation and improvement in Japanese companies.
method Examined two Japanese companies with activist board representation, analyzing performance metrics.
result Companies with activist board representation experienced significant improvements in stock returns and operational metrics.
Norm-range partition improves MIPS search efficiency by reducing query complexity.
problem Efficiently searching for maximum inner product in large datasets.
method Norm-range partition technique that divides datasets into sub-datasets with similar norms and builds independent hash indexes.
result Significantly reduces the number of probed buckets for LSH-based MIPS algorithms.
Study detects endogenous bubbles in meme stocks using CI.
problem Detecting endogenous bubbles in meme stocks.
method Used Log-Periodic Power Law (LPPL) Confidence Indicator (CI).
result CI detected numerous bubbles in meme stocks but struggled with predicting exogenous rallies.
We study optimal buying and selling strategies in target zone models. In these models the price is modeled by a diffusion process which is reflected at one or more barriers. Such models arise for example when a currency exchange rate is kept above a certain threshold due to central bank intervention. We consider the op…
Deep RL finds efficient pathways for sugar to chemicals.
problem Finding efficient pathways from sugar to value-added chemicals.
method Markov decision process with deep reinforcement learning.
result Promising preliminary results in efficient biomass conversion.
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.
SAPPHIRE tackles ill-conditioned rERM problems with faster convergence.
problem Ill-conditioned objectives and non-smooth regularizers in rERM.
method Sketch-based preconditioning and scaled proximal mapping.
result Achieves condition-number-free linear convergence.
Study examines how war impacts financial entropy.
problem Analyzing sudden events' impact on financial markets.
method Investigates entropy changes in Polish stock market data.
result Entropy analysis reveals market changes due to extreme events.
Two new algorithms improve federated optimization under second-order similarity.
problem Federated learning under communication constraints and second-order similarity.
method SVRP and Catalyzed SVRP algorithms combining proximal point evaluations, client sampling, and variance reduction.
result Achieves superior performance and uniformly improves upon existing algorithms for federated optimization under second-order similarity and strong convexity.
Laplace approximation improves deep learning efficiency without sacrificing performance.
problem Bayesian deep learning's practical implementation and efficiency.
method Review and implementation of Laplace approximation (LA) in PyTorch.
result Laplace approximation is competitive with popular alternatives in performance but significantly more efficient.
Paper proposes ExsdHawkes to model LOBs, capturing volatility dynamics.
problem Modeling volatility signature plots in LOBs with high-frequency trading dynamics.
method Extended State-Dependent Hawkes Process (ExsdHawkes) with relaxed constraints.
result ExsdHawkes uniquely reproduces volatility signature plots, identifying MLOs as catalysts.
Advances deep learning for financial model calibration.
problem Calibrating rough stochastic volatility models.
method Two-step approach: deep learning for pricing map, traditional methods for model parameters.
result Demonstrates a new neural network-based calibration method for rough volatility models.
Deep learning detects face swapping with high accuracy and uncertainty.
problem Photo-realistic face swapping without consent.
method Deep transfer learning for face swapping detection, human subject rankings for comparison.
result True positive rates >96% with minimal false alarms, uncertainty provided for each prediction.
Soft-constrained PINN solves ODEs with minimal data, improving efficiency and robustness.
problem Sparse and noisy data in experiments and simulations.
method Soft-constrained Physics-informed Neural Network (PINN) with minimal labeled data.
result Soft-constrained PINN reduces need for labeled data and achieves strong generalization.
A framework for faster, better infographic design by non-experts and experts alike.
problem Designing infographics is time-consuming and tedious for non-experts and even professionals.
method Semi-automated infographic framework for structured and flow-based designs, including automatic design ranking and customization options.
result Designers from all expertise levels can generate generic infographic designs faster than existing methods while maintaining quality.
A new RL method helps designers solve complex tasks.
problem Design process gap between problem and solution.
method Deep Reinforcement Learning (RL) for task-oriented design.
result Method achieves satisfactory design even with multiple goals.
The paper proposes a method to reliably select design algorithms for machine learning-guided design tasks.
problem Choosing the right design algorithm for machine learning-guided design tasks.
method Combining designs' predicted property values with held-out labeled data to reliably forecast characteristics of the label distributions produced by different design algorithms.
result The method is guaranteed to return design algorithms that yield successful label distributions.
Unified approach to experimental design using interlacing polynomials.
problem Experimental design problems, especially D/A/E-design and E-design.
method Unified deterministic approach using interlacing polynomials.
result Improved approximation guarantees for various experimental design objectives.
Design-by-Morphing creates radical airfoil designs without geometric constraints.
problem Design constraints limit airfoil design novelty and small changes.
method Design-by-Morphing (DbM) creates a search space without geometric constraints.
result DbM generates radical airfoils with remarkable lift-over-drag ratio and stall angle tolerance.
MO-PaDGAN generates diverse, high-performance designs with multiple metrics.
problem Challenges in generating diverse, high-performance designs with multiple metrics.
method MO-PaDGAN uses a new Determinantal Point Processes based loss function for probabilistic modeling of diversity and performances.
result MO-PaDGAN expands the design space towards high-performance regions and generates new designs with high diversity and performances.
MCD automates counterfactual design searches for multi-modal tasks.
problem Designing for multi-objective goals and complex constraints.
method Model-agnostic counterfactual search method for multi-modal design modifications.
result MCD streamlines and automates counterfactual search, recommending effective design modifications.
PaDGAN generates diverse, high-quality designs with improved performance.
problem Lack of diversity and performance improvement in generated designs.
method Integrates Determinantal Point Processes for diversity and quality, using GAN framework.
result PaDGAN generates higher quality designs with better diversity and without mode collapse.
New approach designs optimal structures using GANs and CNNs.
problem Topology design optimization requires many iterations and is impractical for real-world applications.
method Integrates Generative Adversarial Networks (GANs) and convolutional neural networks for topology design.
result Optimal structures generated effectively and rapidly.
ANN with GA optimizes flexible disc design for lower mass and stress.
problem Design flexible disc elements for lower mass and stress without compromising torque transmission and misalignment.
method Artificial Neural Network (ANN) coupled with Genetic Algorithm (GA) for design exploration.
result Optimized designs meet specified criteria with minimum mass and stress.
DeepCloud uses machine learning to generate design alternatives without explicit designer input.
problem Designing with explicit specifications limits innovation potential.
method Developed a data-driven generative system combining autoencoder for point clouds and web-based interface.
result DeepCloud learns design alternatives from existing solutions without designer input.
New method for mixed-variable GSA improves material design efficiency.
problem Designing materials with both quantitative and qualitative variables.
method Integrates LVGP with Sobol' analysis for mixed-variable GSA.
result Accelerates exploration of novel MOF candidates in combinatorial design spaces.
Deep learning models enhance engineering design automation.
problem Design optimization and customization across various industries.
method Leveraging deep neural networks, GANs, VAEs, and DRL for design synthesis.
result Recent advances in DGMs show promise in structural optimization, materials design, and shape synthesis.
Design automation optimizes deep learning models for various hardware.
problem Designing efficient deep learning models requires balancing algorithm and hardware.
method Proposes design automation techniques for specialized neural networks, including auto pruning and quantization.
result Learning-based automation achieves superior performance and efficiency compared to human design.
SentRNA improves RNA design by integrating human strategies.
problem Designing sequences for large or complex RNA targets.
method SentRNA uses a neural network trained on human-designed RNA sequences.
result SentRNA solves complex targets previously unsolvable by machines.