yusuke1997/dblp-id-title huggingface dataset:
Strategies for the construction of machine-learning potentials for accurate and efficient atomic-scale simulations
High-quality tomographic image reconstruction integrating neural networks and mathematical optimization
Adaptive sampling for accelerating neutron diffraction-based strain mapping *
Deep learning in electron microscopy
Explainable representation learning of small quantum states
Core-Collapse supernova gravitational-wave search and deep learning classification
Atom cloud detection and segmentation using a deep neural network
Atomic orbits in molecules and materials for improving machine learning force fields
Deep learning-based spatiotemporal multi-event reconstruction for delay line detectors
Deep Bayesian experimental design for quantum many-body systems
Estimating the probability of coincidental similarity between atomic displacement parameters with machine learning
Optimizing training trajectories in variational autoencoders via latent Bayesian optimization approach *
Physics-guided dual implicit neural representations for source separation
SYMBA: symbolic computation of squared amplitudes in high energy physics with machine learning
Multi-fidelity transfer learning for quantum chemical data using a robust density functional tight binding baseline
STG-MTL: scalable task grouping for multi-task learning using data maps
Importance nested sampling with normalising flows
Unpaired image translation to mitigate domain shift in liquid argon time projection chamber detector responses
Multidimensional analysis of excitonic spectra of monolayers of tungsten disulphide: toward computer-aided identification of structural and environmental perturbations of 2D materials
Deep learning for structure-preserving universal stable Koopman-inspired embeddings for nonlinear canonical Hamiltonian dynamics
Randomized tensor network reservoir computing: validity and learnability phase transitions
Prediction of 4D stress field evolution around additive manufacturing-induced porosity through progressive deep-learning frameworks
Enhancing friction stir-based techniques with machine learning: a comprehensive review
Spatially-resolved fluorescence (SRF) and deep-learning algorithms for the measurement of agents' concentrations
Towards automating structural discovery in scanning transmission electron microscopy *
Mammo-MX: an x-ray mammography dataset for computer-aided diagnosis of breast cancer
Application of kernel principal component analysis for optical vector atomic magnetometry
Interpretation of chemical reaction yields with graph neural additive network
Equivariant tensor network potentials
Fast convolutional neural networks on FPGAs with hls4ml
A filter-based feature selection approach in multilabel classification
Rapid likelihood free inference of compact binary coalescences using accelerated hardware
Physical data embedding for memory efficient AI
Scalable multi-task learning for particle collision event reconstruction with heterogeneous graph neural networks
Nearest-neighbors neural network architecture for efficient sampling of statistical physics models
Transfer learning for multi-material classification of transition metal dichalcogenides with atomic force microscopy
Solving deep-learning density functional theory via variational autoencoders
In situ compression artifact removal in scientific data using deep transfer learning and experience replay
RG-Flow: a hierarchical and explainable flow model based on renormalization group and sparse prior
Decoding the shift-invariant data: applications for band-excitation scanning probe microscopy *
On application of deep learning to simplified quantum-classical dynamics in electronically excited states
Unified lookup tables: training foundation models on encoded data
Corrigendum: Impact of non-normal error distributions on the benchmarking and ranking of quantum machine learning models (2020 Mach. Learn.: Sci. Technol. 1 035011)
Molecular machine learning with conformer ensembles
Normalizing flows for atomic solids
Quantum generative adversarial networks with dual generators
Scalable quantum measurement error mitigation via conditional independence and transfer learning
Generative adversarial networks for data-scarce radiative heat transfer applications
Collective variables of neural networks: empirical time evolution and scaling laws
Classifying rock types by geostatistics and random forests in tandem
Deep learning-based detection and identification of brain tumor biomarkers in quantitative MR-images
Background suppression for volcano muography with machine learning
Framelet pooling aided deep learning network: the method to process high dimensional medical data
Inverting the Kohn-Sham equations with physics-informed machine learning
Rediscovering orbital mechanics with machine learning
Simulation-based inference on virtual brain models of disorders
Classifying global state preparation via deep reinforcement learning
rule4ml: an open-source tool for resource utilization and latency estimation for ML models on FPGA
Accelerating scientific discovery with generative knowledge extraction, graph-based representation, and multimodal intelligent graph reasoning
Transfer learning application of self-supervised learning in ARPES
Exploring machine learning to hardware implementations for large data rate x-ray instrumentation
Machine learning for the solution of the Schrödinger equation
DeepAir: deep learning and satellite imagery to estimate high-resolution PM 2.5 at scale
Efficient ensemble uncertainty estimation in Gaussian processes regression
Generation model meets swin transformer for unsupervised low-dose CT reconstruction
Physics-inspired self-learning framework for unsupervised depth estimation on non-Lambertian surfaces
Machine learning and excited-state molecular dynamics
Logic replicant: a new machine learning algorithm for multiclass classification in small datasets
Active particles using reinforcement learning to navigate in complex motility landscapes
A comprehensive machine learning-based investigation for the index-value prediction of 2G HTS coated conductor tapes
Interpreting variational quantum models with active paths in parameterized quantum circuits
Metric flows with neural networks
Learning latent functions for causal discovery
Neural quasiprobabilistic likelihood ratio estimation with negatively weighted data
Restricted Boltzmann machine representation for the groundstate and excited states of Kitaev Honeycomb model
Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys
AMCG: a graph dual atomic-molecular conditional molecular generator
Deep learning of chaos classification
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
Generating quantum reservoir state representations with random matrices
Multi-fidelity learning for interatomic potentials: low-level forces and high-level energies are all you need *
Observing Schrödinger's cat with artificial intelligence: emergent classicality from information bottleneck
Applying machine learning optimization methods to the production of a quantum gas
Watch and learn - a generalized approach for transferrable learning in deep neural networks via physical principles
LHC hadronic jet generation using convolutional variational autoencoders with normalizing flows
Algorithms for tensor network contraction ordering
Methods for comparing uncertainty quantifications for material property predictions
Autoencoder-enhanced joint dimensionality reduction for constrained Bayesian optimisation
Federated learning with tensor networks: a quantum AI framework for healthcare
Learning a general model of single phase flow in complex 3D porous media
QR-DeepONet: resolve abnormal convergence issue in deep operator network
Differentiable physics-enabled closure modeling for Burgers' turbulence
Deep artificial neural network-powered phase field model for predicting damage characteristic in brittle composite under varying configurations
Stereological 3D modeling of nano-scale catalyst particles using TEM projections
About the generalizability of deep learning based image quality assessment in mammography
Wilsonian renormalization of neural network Gaussian processes *
Quadratic hyper-surface kernel-free large margin distribution machine-based regression and its least-square form
An autoencoder for compressing angle-resolved photoemission spectroscopy data
Implicit quantile networks for emulation in jet physics
Molecular quantum chemical data sets and databases for machine learning potentials
Generative super-resolution AI accelerates nanoscale analysis of cells
BenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
Training multi-layer binary neural networks with random local binary error signals
Training-free hyperparameter optimization of neural networks for electronic structures in matter
ICEGAN: inverse covariance estimating generative adversarial network
Unified representation of molecules and crystals for machine learning
Optimal equivariant architectures from the symmetries of matrix-element likelihoods
Spherical convolutions on molecular graphs for protein model quality assessment
Data augmentation with Mobius transformations
Deep learning enhanced noise spectroscopy of a spin qubit environment
Multimodal neural network-based predictive modeling of nanoparticle properties from pure compounds
A novel dynamic machine learning-based explainable fusion monitoring: application to industrial and chemical processes
No-reference perceptual CT image quality assessment based on a self-supervised learning framework
Predicting thermoelectric transport properties from composition with attention-based deep learning
Automation of quantum dot measurement analysis via explainable machine learning
Time-resolved classification of plasma crystals in a direct current discharge using an advanced graph neural network
Inverse model based error detection in beamline optics
CycleGAN-driven transfer learning for electronics response emulation in high-purity germanium detectors
Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems
Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml
Random feedback alignment algorithms to train neural networks: why do they align?
Fractional deep neural network via constrained optimization
Resolving turbulent magnetohydrodynamics: a hybrid operator-diffusion framework
A generative adversarial network to speed up optical Monte Carlo simulations
Artificial intelligent identification of apatite fission tracks based on machine learning
Deep energy-pressure regression for a thermodynamically consistent EOS model
Neural ordinary differential equation and holographic quantum chromodynamics
Standardizing chemical compounds with language models
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems *
Generation of conformational ensembles of small molecules via surrogate model-assisted molecular dynamics
Learning from survey propagation: a neural network for MAX-E-3-SAT
Simulation-based inference with approximately correct parameters via maximum entropy
Exploring spatial reasoning performances of CNN on linear layout dataset
Hessian-based toolbox for reliable and interpretable machine learning in physics
Laplace HypoPINN: physics-informed neural network for hypocenter localization and its predictive uncertainty
Machine learning phases in swarming systems
Vortex detection in atomic Bose-Einstein condensates using neural networks trained on synthetic images
Dynamic deep learning based super-resolution for the shallow water equations
Reflection equivariant quantum neural networks for enhanced image classification
Gaming the beamlines - employing reinforcement learning to maximize scientific outcomes at large-scale user facilities
Prediction of mild cognitive impairment using EEG signal and BiLSTM network
Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling
Automated gadget discovery in the quantum domain
Multi-scale tensor network architecture for machine learning
A deep neural network to search for new long-lived particles decaying to jets
Towards XAI agnostic explainability to assess differential diagnosis for Meningitis diseases
The R-mAtrIx Net
Shift-curvature, SGD, and generalization
Polynomial differentiation decreases the training time complexity of physics-informed neural networks and strengthens their approximation power
Ordered embeddings and intrinsic dimensionalities with information-ordered bottlenecks
Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models
Automated design of digital filters using convolutional neural networks for extracting ringdown gravitational waves
Large deviations in the perceptron model and consequences for active learning
Deep learning symmetries and their Lie groups, algebras, and subalgebras from first principles
Harnessing the power of gradient-based simulations for multi-objective optimization in particle accelerators
Noise2Filter: fast, self-supervised learning and real-time reconstruction for 3D computed tomography
Baryon density extraction and isotropy analysis of cosmic microwave background using deep learning
Deep learning for enhanced free-space optical communications
FFTL-Net: a network for the classification of skin diseases based on feature fusion and transfer learning
Distributed deep reinforcement learning for simulation control
Predicting nonequilibrium Green's function dynamics and photoemission spectra via nonlinear integral operator learning
Theoretical characterization of uncertainty in high-dimensional linear classification
Improving the generative performance of chemical autoencoders through transfer learning
scGMM-VGAE: a Gaussian mixture model-based variational graph autoencoder algorithm for clustering single-cell RNA-seq data
Decoding characteristics of key physical properties in silver nanoparticles by attaining centroids for cytotoxicity prediction through data cleansing
Robust prediction of chaotic systems with random errors using dynamical system deep learning
Comparison of neural network architectures for feature extraction from binary black hole merger waveforms
A free-energy principle for representation learning
MPGVAE: improved generation of small organic molecules using message passing neural nets
A genetic algorithm to generate maximally orthogonal frames in complex space
Simulation driven adaptive sampling for neutron-diffraction based strain mapping of additively manufactured parts *
WEISS: Wasserstein efficient sampling strategy for LLMs in drug design
Uncertainty quantification by direct propagation of shallow ensembles
Machine learning-based multi-classification of space signals from the solar orbiter mission
Neural architecture codesign for fast physics applications
Uncovering obscured phonon dynamics from powder inelastic neutron scattering using machine learning
Ten years of generative adversarial nets (GANs): a survey of the state-of-the-art
An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients
Many-body expansion based machine learning models for octahedral transition metal complexes
Optimal spectroscopic measurement design: Bayesian framework for rational data acquisition
Machine learning at the (sub)atomic scale: next generation scanning probe microscopy
A machine learning workflow for molecular analysis: application to melting points
A smart alarm for particle accelerator beamline operations
VISION: a modular AI assistant for natural human-instrument interaction at scientific user facilities
Efficient determination of the Hamiltonian and electronic properties using graph neural network with complete local coordinates
Validating large-scale quantum machine learning: efficient simulation of quantum support vector machines using tensor networks
Exploring explainable AI: category theory insights into machine learning algorithms
Towards recognizing the light facet of the Higgs boson
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments
Quantum Kerr learning
Universal adversarial perturbations for multiple classification tasks with quantum classifiers
A data-lean machine learning approach for damage extent estimation and classification in composite structures under multiple failure modes
Masked particle modeling on sets: towards self-supervised high energy physics foundation models
Neural-prior stochastic block model
Data-driven dynamics reconstruction using RBF network *
Symmetric and antisymmetric kernels for machine learning problems in quantum physics and chemistry
Improving parametric neural networks for high-energy physics (and beyond)
Predicting the von Neumann entanglement entropy using a graph neural network
Large language models for causal hypothesis generation in science
Neural network enhanced measurement efficiency for molecular groundstates
A frequentist simulation-based inference treatment of sterile neutrino global fits
Rescuing off-equilibrium simulation data through dynamic experimental data with dynAMMo
Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem
Deep learning neural network for approaching Schrödinger problems with arbitrary two-dimensional confinement
A reinforcement learning approach to the design of quantum chains for optimal energy and state transfer
Machine learning approach to reconstruct density matrices from quantum marginals
A generative modeling approach to reconstructing 21 cm tomographic data
FAIR AI models in high energy physics
Artificial neural networks exploiting point cloud data for fragmented solid objects classification
Theoretical physics benchmark (TPBench) - a dataset and study of AI reasoning capabilities in theoretical physics
Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model
Improving the prediction of spatio-temporal chaos by combining parallel reservoir computing with dimensionality reduction
CYJAX: A package for Calabi-Yau metrics with JAX
Vision-language model approach for few-shot learning of attention deficit hyperactivity disorder using EEG connectivity-based featured images
An adaptive particle swarm optimization with information interaction mechanism
Twin neural network regression is a semi-supervised regression algorithm
Towards a phenomenological understanding of neural networks: data
GPU coprocessors as a service for deep learning inference in high energy physics
Sequential learning on a tensor network Born machine with trainable token embedding
Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap
Simulation-based inference with deep ensembles: evaluating calibration uncertainty and detecting model misspecification
Feature extraction and classification of digital rock images via pre-trained convolutional neural network and unsupervised machine learning
Investigating the ability of PINNs to solve Burgers' PDE near finite-time blowup
Neural network enhanced cross entropy benchmark for monitored circuits
Quantum architecture search for optimizing quantum generators in quantum GAN
Discovering interpretable physical models using symbolic regression and discrete exterior calculus
Detecting underdetermination in parameterized quantum circuits
Many-body dynamics with explicitly time-dependent neural quantum states
Automatic differentiation to simultaneously identify nonlinear dynamics and extract noise probability distributions from data
Quantum computation with machine-learning-controlled quantum stuff
CNN-Based vortex detection in atomic 2D Bose gases in the presence of a phononic background
Dynamics of supercooled liquids from static averaged quantities using machine learning
Machine learning for neutron reflectometry data analysis of two-layer thin films
The more the better or the less the better: LASSO versus random forest in forecasting seasonal precipitation for drought management
GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity
An information theoretic limit to data amplification
Learning curves for the multi-class teacher-student perceptron
Machine learning configuration-dependent friction tensors in Langevin heatbaths
Deep learning-driven glioblastoma diagnosis from histopathological images via single-cell segmentation and morphological analysis
Does this smell the same? Learning representations of olfactory mixtures using inductive biases
Efficient prediction of aerodynamic forces in rarefied flow using convolutional neural network based multi-process method
A comparison of deep learning models for proton background rejection with the AMS electromagnetic calorimeter
Into the latent space of capacitive sensors: interpolation and synthetic data generation using variational autoencoders
Robust and scalable uncertainty estimation with conformal prediction for machine-learned interatomic potentials
Physics-informed neural network for turbulent flow reconstruction in composite porous-fluid systems
A biology-informed similarity metric for simulated patches of human cell membrane
Lightweight ECG signal classification via linear law-based feature extraction
Modified independent component analysis for extracting Eigen-Modes of a quantum system
Aortic KD former: aortic multiclass segmentation using SegFormer via knowledge distillation
Encrypted machine learning of molecular quantum properties
Chemformer: a pre-trained transformer for computational chemistry
Generative models for fast simulation of Cherenkov detectors at the electron-ion collider
Learning and interpreting gravitational-wave features from CNNs with a random forest approach
Improving proton dose calculation accuracy by using deep learning
Supervised and unsupervised learning of (1+1) -dimensional even-offspring branching annihilating random walks
Machine-learning accelerated identification of exfoliable two-dimensional materials
Chemistry-informed macromolecule graph representation for similarity computation, unsupervised and supervised learning
ArtiSAN: navigating the complexity of material structures with deep reinforcement learning
Efficient generation of stable linear machine-learning force fields with uncertainty-aware active learning
Self-supervised learning strategies for jet physics
Concept graph embedding models for enhanced accuracy and interpretability
Perspective on artificial intelligence for accelerated materials design (AI4Mat) workshops in 2024
Qsun: an open-source platform towards practical quantum machine learning applications
Towards robust data-driven automated recovery of symbolic conservation laws from limited data
Enhancing particle string detection in electrorheological plasmas using asymmetrical kernel convolutional networks
Transforming two-dimensional tensor networks into quantum circuits for supervised learning
A general approach to maximise information density in neutron reflectometry analysis
MSMGE-CNN: a multi-scale multi-graph embedding convolutional neural network for motor related EEG decoding
Classical versus quantum models in machine learning: insights from a finance application
Symmetric tensor networks for generative modeling and constrained combinatorial optimization
Bridging the gap between high-level quantum chemical methods and deep learning models
Learning the dynamics of particle-based systems with Lagrangian graph neural networks
Machine learning visualization tool for exploring parameterized hydrodynamics *
Closed-form interpretation of neural network classifiers with symbolic gradients
Learning group invariant Calabi-Yau metrics by fundamental domain projections
Flow annealed importance sampling bootstrap meets differentiable particle physics
Infinite neural network quantum states: entanglement and training dynamics
Quantum generative learning for high-resolution medical image generation