Framework for inverting complex software simulators using probabilistic programming.
problem Inverting complex software simulators is difficult and requires custom algorithms.
method Formulation of inversion as approximate inference in a simple probabilistic model; four inference strategies implemented.
result Framework implemented in under 20 lines of probabilistic code.
A machine learning environment for detecting autonomous vehicle corner cases.
problem Testing autonomous driving software in the real world is difficult.
method Connecting CARLA simulation software to TensorFlow and custom AI client software.
result The system can identify situations where AI software fails to understand the scenario.
Improving software quality through effective organizational learning.
problem Lack of reliable quantification methods for software evolution.
method Leveraging application lifecycle management data to identify and address managerial practices.
result Effective learning from past processes improves software quality indirectly.
SABCEMM simulates economic market models with millions of agents.
problem Efficiently simulating large-scale economic market models.
method Object-oriented C++ implementation, XML configuration, abstract model class.
result Different random number generators significantly impact ABCEM model run time.
EvaSylv software evaluates forest management with natural risk considerations.
problem Evaluating forest management under increased natural risk due to climate change.
method User-friendly software simulates forest management scenarios, integrating natural risk using a Poisson process and Faustmann approach.
result Software optimizes forest management criteria like Faustmann value and Averaged yield value.
Software aids in estimating speciation parameters using trait-dependent models.
problem Lack of robust software to estimate numerous speciation parameters.
method Approximate Bayesian Computation approach implemented in R package pcmabc.
result Software allows estimation of parameters of trait-dependent speciation processes.
FEDHC learns Bayesian networks efficiently for continuous data.
problem Efficiently learning Bayesian networks for continuous data.
method Forward Early Dropping Hill Climbing (FEDHC) algorithm.
result FEDHC is computationally efficient and produces accurate Bayesian networks.
A new Python-C++ framework for agent-based simulation.
problem Understanding market dynamics and effects of delays.
method User-friendly Python API with efficient C++ implementation, message-driven architecture.
result Investigated the role of order processing delay in financial markets.
VegasFlow accelerates complex simulations across various hardware platforms.
problem Complex calculations and simulations requiring high-dimensional integrals.
method Monte Carlo integration techniques using Vegas algorithm and TensorFlow.
result Significantly faster performance on various hardware platforms.
Virtual reality explores non-Euclidean Sol geometry.
problem Exploring non-Euclidean geometries in virtual reality.
method Developed a VR software for Sol geometry.
result Demonstrated the feasibility of non-Euclidean geometry in VR.
Wield simplifies reinforcement learning task design.
problem Lack of suitable task representations for reinforcement learning.
method Introduces Wield, a system with software primitives and staged randomization protocol.
result Facilitates practical reinforcement learning through decoupling system and task design.
Software helps finance students construct optimal portfolios using VBA.
problem Finding the best portfolio of assets considering risk and return.
method Two methods: Markowitz and El-Khatib-Hatemi-J, both optimizing risk-adjusted return.
result Software constructs all possible portfolios and helps investors choose the best one.
Quantum computing speeds up multi-period asset allocation.
problem High computational complexity in classic computing for multi-period asset allocation.
method Applied quantum computing to simulate multi-asset portfolio using historic data.
result Quantum computing offers significant advantages over classical computing in finance.
Hierarchical spiking networks resist physical distortions for neuromorphic computing.
problem Distortions in physical neuromorphic implementations of spiking networks.
method Used hierarchical leaky integrate-and-fire neurons to create robust spiking networks.
result Hierarchical spiking networks are robust to physical distortions.
Study stylized facts in ABCEM models using a new simulator.
problem Understanding stylized facts in economic market models.
method Used SABCEMM tool to simulate and recombine existing ABCEM models.
result Identified and recombined stylized facts in ABCEM models.
Efficiently implements large-scale CNNs using memristors.
problem Implementing high-dimensional convolution in deep neural networks efficiently.
method Developed a method to convert CNN kernels to memristor-based circuits, optimized for accuracy and minimized error.
result 8-bit ADC/DAC is necessary to preserve software level classification accuracy in memristor-based CNNs.
Deep learning improves gamma-ray energy estimation and event selection.
problem Improving gamma-ray event selection and energy estimation.
method Adapted convolutional neural networks (CNN) for gamma-ray astronomy.
result Significant improvement in gamma-ray energy estimation and event selection.
Paper proposes a new Markov model for efficient PLC system design.
problem Efficient estimation of Markov model parameters for bursty error channels.
method Introduced a Block Diagonal Markov model and a modified Baum-Welch algorithm.
result Efficient estimation of state transition matrix Λ for PLC system design. Optical ESNs enable flexible, efficient machine learning with reduced energy.
problem Implementing universal computational capabilities in machine learning.
method Optical implementation of ESNs leveraging stimulated Brillouin scattering.
result Efficient, scalable, and memory-capable optical reservoir computing.
Inconsistent constraints in GJR-GARCH models lead to uncoherent parameter estimates.
problem Inconsistency in applying Nelson-Cao constraints in GJR-GARCH models.
method Monte Carlo simulations to demonstrate parameter coherence issues.
result Parameter estimates violate Nelson-Cao constraints, leading to uncoherent results.
DRIFT uses RL to automate functional software testing efficiently.
problem Efficient and reliable automated software testing.
method DRIFT employs Q-learning with Graph Neural Networks on symbolic UI representations.
result DRIFT can robustly test software functionalities in a fully automated manner.
Adaptive RL optimizes testing resource allocation for dynamic software environments.
problem Optimizing resource allocation for evolving software testing environments.
method Integrates Q-learning with hybrid reward design for sequential decision-making.
result Consistently outperforms static and optimization-based baselines in simulation studies.
Multimodal deep learning improves flaw detection in software programs.
problem Current flaw detection relies on single software representations.
method Adapted multimodal deep learning models for flaw detection.
result Multimodal models outperform traditional deep learning models.
Bayesian approach improves car-following model calibration and validation.
problem Insufficient data and computational constraints limit accurate model calibration.
method Bayesian machine learning and probabilistic programming.
result Unique parameter sets estimated for each driver, outperforming standard approaches.
JAX MD enables differentiable physics simulations for molecular dynamics.
problem Performing efficient and differentiable physics simulations for molecular dynamics.
method Differentiable physics simulation environments, interaction potentials, neural networks, flexible primitives.
result Differentiable physics simulations can be used for meta-optimization and scaling to large particle systems.
Machine learning speeds up GPR simulations.
problem Computational demands of simulating practical GPR problems.
method Automatic ML-based forward solver framework using gprMax.
result Near-real-time GPR simulations achieved.
KnotPlot helps beginners and veterans use software for visualizing knots.
problem None explicitly stated in the abstract.
method Software introduction and background for users.
result Provides new and veteran users with KnotPlot functionality.
Deep model improves software code understanding.
problem Software code's long context challenges existing models.
method Inspired by human memory, uses LSTM for long-term dependencies.
result Demonstrated effectiveness on Java project corpus.
Firefly algorithm improves software effort estimation models.
problem Improving accuracy of software effort estimation models.
method Using Firefly Algorithm to optimize COCOMO-based models.
result High accuracy and significant error minimization of Firefly Algorithm.
This paper evaluates methods to categorize similar software packages in npm's network.
problem Finding similar packages in npm's large software dependency network.
method Using graph structure to categorize packages, focusing on npm's 216,911 packages.
result Developed methods for categorizing similar packages, useful for recommendation systems.
Simulation-based inference aids in predicting disease dynamics for health policy.
problem Predicting disease dynamics to inform public health interventions.
method Simulation-based inference using machine learning.
result Potential for efficient interpretable Bayesian inference in epidemiological models.
Research proposes an ensemble learning model for efficient software defect prediction.
problem Efficient and cost-effective software testing to minimize project resources.
method Machine learning analysis on different datasets using KNN, Decision Tree, SVM, and Naïve Bayes.
result Ensemble learning model outperforms other techniques in accuracy, precision, recall, and F1-score.
Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.
problem Urban traffic congestion in expanding cities.
method Behavioral system theory, data-driven control, DeePC algorithm.
result DeePC outperforms existing traffic control methods in travel time and CO2 emissions.
TinyCNN accelerates CNN models on embedded FPGA with 15x speedup.
problem Limited memory on embedded FPGAs restricts CNN performance.
method Software and hardware design tool for FPGA resource-aware CNN accelerator.
result 3% accuracy loss with 15.75x speedup on image classification.
Method controls extrapolation in prediction profiles for statistical and machine learning models.
problem Avoiding invalid predictions due to extrapolation in prediction profiles.
method Genetic algorithm optimization over constrained factor regions.
result Optimal factor settings without constraint are often invalid and extrapolated.
DeepSoft aims to model software development for risk prediction and intervention.
problem Manual feature engineering and traditional classification problems in software analytics.
method End-to-end deep learning framework using Long Short Term Memory architecture.
result DeepSoft predicts future risks and recommends interventions in software development.
Improved software flaw detection using NAS on multimodal DL models.
problem Software flaw detection in multimodal deep learning models.
method Adapted NAS framework for multimodal learning, combined with multimodal deep learning models.
result Improved performance on the Juliet Test Suite.
A new method of event reweighting using boosted decision trees is presented.
problem Quality control of reweighting step in machine learning models.
method Novel event reweighting method based on boosted decision trees.
result Improved quality control of reweighting step in machine learning models.
Generative Adversarial Network repairs software bugs without labeled data.
problem Automated repair of software vulnerabilities.
method Adversarial learning approach mapping between source and target domains.
result Effective at repairing software vulnerabilities, close to seq2seq approaches.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
problem Predicting time-varying hardware resource availability in control software.
method Path structured multimarginal Schrödinger bridge (MSBP) for learning stochastic resource usage.
result Guaranteed linear convergence to accurate prediction of hardware resource utilization.
This paper tackles co-design of neural hardware and software to improve efficiency.
problem Designing efficient deep learning systems that consider both hardware and software optimizations together.
method Developed a constrained Bayesian optimization framework to automatically identify profitable design points in the joint hardware/software design space.
result Improved energy-delay product by 18% (ResNet) and 40% (DQN) over hand-tuned systems.
Develops regression trees for estimating cumulative incidence curves in competing risks.
problem Estimating cumulative incidence functions in competing risks settings.
method Uses augmented estimators of the Brier score risk to build and prune regression trees.
result Demonstrates the utility of the proposed methods through simulation studies and real data.
Machine learning enhances fuzzing for better software testing.
problem Challenges in traditional fuzzing.
method Applications of machine learning to improve fuzzing.
result Machine learning tools have successfully addressed fuzzing bottlenecks.
Survey of software developers' experience with Github Copilot tool.
problem Investigate developers' acceptance of AI-generated code.
method Survey with 18 questions distributed to 42 programmers.
result Mixed developer opinions, mostly positive but reluctance to use.
Optuna introduces a new hyperparameter optimization framework.
problem Efficient hyperparameter optimization for machine learning models.
method Define-by-run API, efficient implementation, versatile architecture.
result Optuna is the first software meeting next-generation hyperparameter optimization criteria.
Real-time scene understanding solved using Approximate Bayesian Computation.
problem Predicting human actions, object poses, and pedestrian crossings from depth images.
method Bayesian error model, neural surrogates, and adaptive discretization.
result Real-time inference on real-world problems is feasible.
Adapts ISO 26262 for machine learning in automotive software.
problem Conflict between innovation and safety in automotive software development.
method Detailed assessment and adaptation of ISO 26262 for machine learning, analyzing and proposing new requirements.
result Adapted ISO 26262 for machine learning, addressing safety and innovation in automotive software.
RAMANMETRIX simplifies Raman spectroscopy data analysis.
problem Complexity of Raman spectroscopy data processing.
method User-friendly software with chemometric analysis workflow.
result Facilitates integration of Raman spectroscopy into clinical routines.