yusuke1997/dblp-id-title huggingface dataset:
Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework
Anomaly detection in aeronautics data with quantum-compatible discrete deep generative model
Inception neural network for complete intersection Calabi-Yau 3-folds
Are queries and keys always relevant? A case study on transformer wave functions
Physics informed token transformer for solving partial differential equations
Physics-informed Bayesian optimization of expensive-to-evaluate black-box functions
regAL: Python package for active learning of regression problems
Automating the discovery of partial differential equations in dynamical systems
Time-warping invariant quantum recurrent neural networks via quantum-classical adaptive gating
Toward a theory of machine learning
Propagation of priors for more accurate and efficient spectroscopic functional fits and their application to ferroelectric hysteresis
Probability flow solution of the Fokker-Planck equation
Neural scaling laws from large- N field theory: solvable model beyond the ridgeless limit
Determination of droplet size from wide-angle light scattering image data using convolutional neural networks
Robust errant beam prognostics with conditional modeling for particle accelerators
Enhancing anomaly detection with topology-aware autoencoders
Guided quantum compression for high dimensional data classification
An atomic cluster expansion potential for twisted multilayer graphene
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces
A novel multi-layer modular approach for real-time fuzzy-identification of gravitational-wave signals
Self-adaptive physics-informed quantum machine learning for solving differential equations
Machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids
Machine learning for neutron scattering at ORNL
Applications of physics informed neural operators
Multi-perspective feedback-attention coupling model for continuous-time dynamic graphs
Optimizing data acquisition: a Bayesian approach for efficient machine learning model training
Benchmarking of quantum fidelity kernels for Gaussian process regression
FDDM: unsupervised medical image translation with a frequency-decoupled diffusion model
Incorporating background knowledge in symbolic regression using a computer algebra system
Stochastic black-box optimization using multi-fidelity score function estimator
Deterministic versus stochastic dynamical classifiers: opposing random adversarial attacks with noise
NbBench: benchmarking language models for comprehensive nanobody tasks
Metric learning for kernel ridge regression: assessment of molecular similarity
Spectral density classification for environment spectroscopy
Optimised feature subset selection via simulated annealing
Characterizing out-of-distribution generalization of neural networks: application to the disordered Su-Schrieffer-Heeger model
Redefining the North Atlantic Oscillation index generation using autoencoder neural network
A path towards quantum advantage in training deep generative models with quantum annealers
Autoencoder-assisted study of directed percolation with spatial long-range interactions
Comment on 'Physics-based representations for machine learning properties of chemical reactions'
Anti-correlated noise in epoch-based stochastic gradient descent: implications for weight variances in flat directions
Planning with tensor networks based on active inference
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Improving materials property predictions for graph neural networks with minimal feature engineering *
Pile-up mitigation using attention
Towards reflectivity profile inversion through artificial neural networks
Interpretable classification of Levantine ceramic thin sections via neural networks
Reinforcement learning pulses for transmon qubit entangling gates
Exploiting data diversity in multi-domain federated learning
Learning the dynamics of a one-dimensional plasma model with graph neural networks
Building robust machine learning models for small chemical science data: the case of shear viscosity of fluids
Scientific AI in materials science: a path to a sustainable and scalable paradigm
3D positioning and autofocus of the particle field based on the depth-from-defocus method and the deep networks
A finite element-convolutional neural network model (FE-CNN) for stress field analysis around arbitrary inclusions
Analysis of machine learning prediction reliability based on sampling distance evaluation with feature decorrelation
On the role of gradients for machine learning of molecular energies and forces
Medical image segmentation assisted with clinical inputs via language encoder in a deep learning framework
Saddle hierarchy in dense associative memory
Multi-type point cloud autoencoder: a complete equivariant embedding for molecule conformation and pose
Unsupervised learning of quantum many-body scars using intrinsic dimension
Adaptive active Brownian particles searching for targets of unknown positions
Machine and deep learning performance in out-of-distribution regressions
IFE.PTML: advancing neurotherapeutic nanoparticle release-system design for enhanced brain delivery
High-precision prediction of non-resonant high-order harmonics energetic particle modes via stacking ensemble strategies
Assessing the quality of random number generators through neural networks
Geometric GNNs for charged particle tracking at GlueX
CResU-Net: a method for landslide mapping using deep learning
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes
CSS-Net: a collaborative framework for synthesis and segmentation of missing contrast-enhanced image with error-prediction consistency
The information of attribute uncertainties: what convolutional neural networks can learn about errors in input data
An efficient Wasserstein-distance approach for reconstructing jump-diffusion processes using parameterized neural networks
Outlook for artificial intelligence and machine learning at the NSLS-II
Calculated solvation and ionization energies for thousands of organic molecules relevant to battery design
Global system errors to simultaneously improve the identification of subsystems with mixed data Gaussian process regression
Randomized radial basis function neural network for solving multiscale elliptic equations
Range-aware graph positional encoding via high-order pretraining: theory and practice
Fast Bayesian inference for neutrino non-standard interactions at dark matter direct detection experiments
Improving resilience of sensors in planetary exploration using data-driven models
Accuracy vs memory advantage in the quantum simulation of stochastic processes
Atomic permutationally invariant polynomials for fitting molecular force fields
Analysis and Benchmarking of feature reduction for classification under computational constraints
Reliable and efficient inverse analysis using physics-informed neural networks with normalized distance functions and adaptive weight tuning
Convergence analysis of deep ReLU networks for solving integral equations
Proton dose deposition matrix prediction using multi-source feature driven deep learning approach
The probabilistic tensor decomposition toolbox
A bin and hash method for analyzing reference data and descriptors in machine learning potentials
Deep-learning-based quantum vortex detection in atomic Bose-Einstein condensates
Reinforcement learning enhanced quantum-inspired algorithm for combinatorial optimization
Qudit machine learning
Inferring interpretable models of fragmentation functions using symbolic regression
Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction
A deep learning functional estimator of optimal dynamics for sampling large deviations
Domain-specific large language model for predicting band gap and formation energy of III-VIIIB and III-IVA nitrides based on fine-tuned GPT-3.5-turbo
LatentDE: latent-based directed evolution for protein sequence design
Predicting the thermal Sunyaev-Zel'dovich field using modular and equivariant set-based neural networks
Quantum machine learning for image classification
Semi-supervised segmentation of abdominal organs and liver tumor: uncertainty rectified curriculum labeling meets X-fuse
A machine learning based Bayesian optimization solution to non-linear responses in dusty plasmas
Quaternion-based machine learning on topological quantum systems
A method for finding the background potential of quantum devices from scanning gate microscopy data using machine learning
An attention-based spatio-temporal neural operator for evolving physics
Chemical transformer compression for accelerating both training and inference of molecular modeling
SBDFNet: a deep learning-based multiclass segmentation method via boundary distance fields
Gradient-based grand canonical optimization enabled by graph neural networks with fractional atomic existence
Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning
SICGNN: structurally informed convolutional graph neural networks for protein classification
Datacube segmentation via deep spectral clustering
Transferability of atom-based neural networks
Feature learning and generalization in deep networks with orthogonal weights
Improving SOH estimation for lithium-ion batteries using TimeGAN
Noise-aware physics-informed machine learning for robust PDE discovery
Machine learning-guided construction of an analytic kinetic energy functional for orbital free density functional theory
Quantum resources of quantum and classical variational methods
Machine learning the computational cost of quantum chemistry
Latent space dynamics learning for stiff collisional-radiative models
Machine learning-based prediction of microparticle dynamics in externally driven strongly-coupled dusty plasmas
Review and experimental benchmarking of machine learning algorithms for efficient optimization of cold atom experiments
Machine learning modeling of materials with a group-subgroup structure
Region versus query based instance segmentation models: application to the estimation of aggregated TiO 2 particles size distribution measured by SEM
Multi-fidelity Gaussian process surrogate modeling for regression problems in physics
Generating stable molecules using imitation and reinforcement learning
Hyperparameter optimisation in deep learning from ensemble methods: applications to proton structure
Graph convolutional networks applied to unstructured flow field data
A new formulation of gradient boosting
Laryngeal cancer diagnosis based on improved YOLOv8 algorithm
Laziness, barren plateau, and noises in machine learning
Multi-fidelity learning for atomistic models via trainable data embeddings
A recipe for cracking the quantum scaling limit with machine learned electron densities
Exploring the nexus of many-body theories through neural network techniques: the tangent model
Physics-enhanced neural networks for equation-of-state calculations
Density estimation via binless multidimensional integration
New gravitational wave discoveries enabled by machine learning
Using deep learning to enhance event geometry reconstruction for the telescope array surface detector
Deep learning model with L1 penalty for predicting breast cancer metastasis using gene expression data
Incompleteness of graph neural networks for points clouds in three dimensions
MeGen - generation of gallium metal clusters using reinforcement learning
Architecture-aware minimization (A 2 M): how to find flat minima in neural architecture search
Machine-learning-based diabetes classification method using blood flow oscillations and Pearson correlation analysis of feature importance
Robust and provably monotonic networks
Phases of learning dynamics in artificial neural networks in the absence or presence of mislabeled data
Learning tree structures from leaves for particle decay reconstruction
DeepRICH: learning deeply Cherenkov detectors
Geometric neural operators (gnps) for data-driven deep learning in non-euclidean settings
Robust anomaly detection for particle physics using multi-background representation learning
Interpolation of environmental data using deep learning and model inference
GNADET: A geospatial neural advection-diffusion equation framework with graph transformer for surface temperature forecasting
Benchmarking machine learning interatomic potentials via phonon anharmonicity
Categorical representation learning and RG flow operators for algorithmic classifiers
Improving detection of Parkinson's disease with acoustic feature optimization using particle swarm optimization and machine learning
Unsupervised classification of single-molecule data with autoencoders and transfer learning
Multi-target property prediction and optimization using latent spaces of generative model
Graph2Mat: universal graph to matrix conversion for electron density prediction
Towards automated analysis for neutron reflectivity
Towards a robust and reliable deep learning approach for detection of compact binary mergers in gravitational wave data
PASSer: prediction of allosteric sites server
Closed-loop Koopman operator approximation
Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *
Modular post-hoc enhancements for zero-shot histopathology classification using vision-language models
SAGIPS: a physics-inspired scalable asynchronous generative inverse-problem solver
Actively learning costly reward functions for reinforcement learning
Ultra-low latency recurrent neural network inference on FPGAs for physics applications with hls4ml
Hybrid data-driven and physics-informed regularized learning of cyclic plasticity with neural networks
Multiresolution equivariant graph variational autoencoder
Numerical metrics for complete intersection and Kreuzer-Skarke Calabi-Yau manifolds
Efficient hyperparameter tuning for kernel ridge regression with Bayesian optimization
Emergence of chemotactic strategies with multi-agent reinforcement learning
MLGN: multi-scale local-global feature learning network for long-term series forecasting
Optimising entanglement distribution policies under classical communication constraints assisted by reinforcement learning
Towards a machine-learned Poisson solver for low-temperature plasma simulations in complex geometries
Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo
Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data
Tackling multimodal device distributions in inverse photonic design using invertible neural networks
On the benefit of attention in inverse design of thin films filters
Quantum autoencoders with enhanced data encoding
A detailed study of interpretability of deep neural network based top taggers
Quantum support vector data description for anomaly detection
A machine-learning approach to setting optimal thresholds and its application in rolling bearing fault diagnosis
Olympus: a benchmarking framework for noisy optimization and experiment planning
Gaussian-process-regression-based method for the localization of exceptional points in complex resonance spectra
Dark solitons in Bose-Einstein condensates: a dataset for many-body physics research
Residual attention U-Net for predicting fluid dynamics and permeability in two-dimensional digital porous rocks
Sparse autoregressive neural networks for classical spin systems
Unifying O(3) equivariant neural networks design with tensor-network formalism
Physics-informed neural networks for solving forward and inverse Vlasov-Poisson equation via fully kinetic simulation
Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks
Decision tree insights analytics (DTIA) tool: an analytic framework to identify insights from large data records across fields of science
Predicting drug properties with parameter-free machine learning: pareto-optimal embedded modeling (POEM)
BCTVNet: a 3D Hybrid segmentation neural network for clinical target volume delineation of cervical cancer brachytherapy
Improving sample and feature selection with principal covariates regression
Guided graph compression for quantum graph neural networks
Prediction of flow stress of Ta-W alloys using machine learning
Towards virtual painting recolouring using vision transformer on x-ray fluorescence datacubes
Quantum extreme learning of molecular potential energy surfaces and force fields
Coherent optical communications enhanced by machine intelligence
Generative diffusion model surrogates for mechanistic agent-based biological models
Scalable neural quantum states architecture for quantum chemistry
Convolutional neural network based non-iterative reconstruction for accelerating neutron tomography
On the impact of selected modern deep-learning techniques to the performance and celerity of classification models in an experimental high-energy physics use case
Neural networks and quantum field theory
Non-perturbative renormalization for the neural network-QFT correspondence
Physics-inspired machine learning detects 'unknown unknowns' in networks: discovering network boundaries from observable dynamics
Chaotic attractor reconstruction using small reservoirs - the influence of topology
Multi-modal cascade feature transfer for polymer property prediction
Machine learning spatio-temporal epidemiological model to evaluate Germany-county-level COVID-19 risk
Physics-informed neural networks for an optimal counterdiabatic quantum computation
Federated data processing and learning for collaboration in the physical sciences
Transferable foundation models for geometric tasks on point cloud representations: geometric neural operators
Reinforcement learning for semi-autonomous approximate quantum eigensolver
Learning on the correctness class for domain inverse problems of gravimetry
The OpenLAM Challenges: LAM Crystal Philately competition
The role of feature space in atomistic learning
In operando active learning of interatomic interaction during large-scale simulations
Solving Newton's equations of motion with large timesteps using recurrent neural networks based operators
Density matrix emulation of quantum recurrent neural networks for multivariate time series prediction
Establishing an evaluation metric to quantify climate change image realism
Neural force functional for non-equilibrium many-body colloidal systems
Bridging text and crystal structures: literature-driven contrastive learning for materials science
Sparse optical flow outliers elimination method based on Borda stochastic neighborhood graph
Interpretable surrogate models to approximate the predictions of convolutional neural networks in glaucoma diagnosis
Modeling sequences with quantum states: a look under the hood
Using positional tracking to improve abdominal ultrasound machine learning classification
CrysMTM: a multiphase, temperature-resolved, multimodal dataset for crystalline materials
Diversity-based two-phase pruning strategy for maximizing image segmentation generalization with applications in transmission electron microscopy
Probing transfer learning with a model of synthetic correlated datasets
A graph neural network simulation of dispersed systems
Reliability of deep learning models for scanning electron microscopy analysis
Decoupled coordinates for machine learning-based molecular fragment linking
Prediction of molecular field points using SE(3)-transformer model
LoGAN: local generative adversarial network for novel structure prediction
An informativeness score for optimal mixed datasets using Gaussian process regression
Uncertainty estimation of machine learning spatial precipitation predictions from satellite data
High-resolution imaging in acoustic microscopy using deep learning
Quantum neural networks force fields generation
Noise classification in three-level quantum networks by Machine Learning
Autonomous victim detection system based on deep learning and multispectral imagery
A method for quantifying the generalization capabilities of generative models for solving Ising models
TomOpt: differential optimisation for task- and constraint-aware design of particle detectors in the context of muon tomography
Designing the next generation of polymers with machine learning and physics-based models
Fast 3D particle reconstruction using a convolutional neural network: application to dusty plasmas
Data-driven Lie point symmetry detection for continuous dynamical systems
Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits
How deep convolutional neural networks lose spatial information with training
ELUQuant: event-level uncertainty quantification in deep inelastic scattering
Suppressing simulation bias in multi-modal data using transfer learning
Efficient solving of Schrödinger equation using deep convolutional neural network model with an attention mechanism and transfer learning
Formation energy prediction of neutral single-atom impurities in 2D materials using tree-based machine learning
A duality connecting neural network and cosmological dynamics
Machine learning for reparameterization of multi-scale closures
Automated detection of potential artifacts in machine learning based bio-image segmentation
Machine-learning Kohn-Sham potential from dynamics in time-dependent Kohn-Sham systems
Graph neural network-based resource allocation strategies for multi-object spectroscopy
Quantum mechanics and neural networks
Diverse explanations from data-driven and domain-driven perspectives in the physical sciences
First-passage approach to optimizing perturbations for improved training of machine learning models
Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experiments
Pixel-wise classification in graphene-detection with tree-based machine learning algorithms
Configurable calorimeter simulation for AI applications
Differentiable simulation of a liquid argon time projection chamber
Explainable physics-based constraints on reinforcement learning for accelerator optimization
The influence of sorbitol doping on aggregation and electronic properties of PEDOT: PSS: a theoretical study
Deep learning methods for Hamiltonian parameter estimation and magnetic domain image generation in twisted van der Waals magnets
DiffLense: a conditional diffusion model for super-resolution of gravitational lensing data
Multimodal multi-output ordinal regression for discovering gravitationally-lensed transients
Explainability for deep learning in mammography image quality assessment
Reinforcement learning decoders for fault-tolerant quantum computation
FINETUNA: fine-tuning accelerated molecular simulations
Unification of symmetries inside neural networks: transformer, feedforward and neural ODE
Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers
Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models
Quantum state reconstruction via disentanglement with sequential optimization algorithm
The reusability prior: comparing deep learning models without training
Generating artificial displacement data of cracked specimen using physics-guided adversarial networks
Multivariate prediction intervals for bagged models
Bridging the synthesizability gap in perovskites by combining computations, literature data, and PU learning
Coot optimization based Enhanced Global Pyramid Network for 3D hand pose estimation
Explainable deep learning for the analysis of MHD spectrograms in nuclear fusion
A neural network approach for line detection in complex atomic emission spectra measured by high-resolution Fourier transform spectroscopy
Learning to unknot
The impact of memory on learning sequence-to-sequence tasks
Coincident learning for unsupervised anomaly detection of scientific instruments