yusuke1997/dblp-id-title huggingface dataset:
Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
Categorical representation learning: morphism is all you need
Deep(er) reconstruction of imaging Cherenkov detectors with swin transformers and normalizing flow models
Direct prediction of inelastic neutron scattering spectra from the crystal structure
Mic-hackathon 2024: hackathon on machine learning for electron and scanning probe microscopy
Predicting polarizabilities of silicon clusters using local chemical environments
Detecting model misspecification in cosmology with scale-dependent normalizing flows
Coincidence anomaly detection for unsupervised locating of edge localized modes in the DIII-D tokamak dataset
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Refereeing the referees: evaluating two-sample tests for validating generators in precision sciences
Ultra-low latency quantum-inspired machine learning predictors implemented on FPGA
From STEM-EDXS data to phase separation and quantification using physics-guided NMF
Intramolecular proton transfer reaction dynamics using machine-learned ab initio potential energy surfaces
NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signals
Data-driven discovery of Koopman eigenfunctions for control
Retrieving past quantum features with deep hybrid classical-quantum reservoir computing
The twin peaks of learning neural networks
Calibrated uncertainty for molecular property prediction using ensembles of message passing neural networks
Point cloud transformers applied to collider physics
Deep reinforcement learning for predicting kinetic pathways to surface reconstruction in a ternary alloy
Determining optical constants of 2D materials with neural networks from multi-angle reflectometry data
Res-CR-Net, a residual network with a novel architecture optimized for the semantic segmentation of microscopy images
Fast reconstruction of single-shot wide-angle diffraction images through deep learning
Variational autoencoders for at-source data reduction and anomaly detection in high energy particle detectors
Deep learning phase retrieval in x-ray single-particle imaging for biological macromolecules
Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
Estimating Gibbs free energies via isobaric-isothermal flows
A multifidelity approach to continual learning for physical systems
Deploying the Big Data Science Center at the Shanghai Synchrotron Radiation Facility: the first superfacility platform in China
Enhancing gravitational-wave science with machine learning
Quantum machine learning and quantum biomimetics: A perspective
On the expressivity of embedding quantum kernels
Exploring the potential of contemporary deep learning methods in purifying polluted information
Closed-loop control of a noisy qubit with reinforcement learning
Differentiable programming of isometric tensor networks
Data-driven acceleration of multi-physics simulations
Transfer learning and the early estimation of single-photon source quality using machine learning methods
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Improving epidemic testing and containment strategies using machine learning
Data efficiency and extrapolation trends in neural network interatomic potentials
A study of transfer learning in digital rock properties measurement
Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison
Mamba time series forecasting with uncertainty quantification
An automated approach for determining the number of components in non-negative matrix factorization with application to mutational signature learning
Magnetohydrodynamics with physics informed neural operators
Machine learning the derivative discontinuity of density-functional theory
Learning continuous scattering length density profiles from neutron reflectivities using convolutional neural networks *
Machine learning assisted quantum state estimation
Fast prediction of distances between synthetic routes with deep learning
Shape sensing of optical fiber Bragg gratings based on deep learning
Gradients should stay on path: better estimators of the reverse- and forward KL divergence for normalizing flows
Transfer learning from first-principles calculations to experiments with chemistry-informed domain transformation
Dynamics-based predictions of infinite-dimensional complex systems using dynamical system deep learning method
Is tokenization needed for masked particle modeling?
Designing quantum multi-category classifier from the perspective of brain processing information
CAD system for epileptic seizure detection from EEG through image processing and SURF-BOF technique
The MLIP package: moment tensor potentials with MPI and active learning
A quantum inspired approach to learning dynamical laws from data - block-sparsity and gauge-mediated weight sharing
Functional data learning using convolutional neural networks
The impact of cost function globality and locality in hybrid quantum neural networks on NISQ devices
Anomaly detection in gravitational waves data using convolutional autoencoders
Massively parallel fitting of Gaussian approximation potentials
Aspen Open Jets: unlocking LHC data for foundation models in particle physics
Adaptive learning rate clipping stabilizes learning
Neural network field theories: non-Gaussianity, actions, and locality
Mixed noise and posterior estimation with conditional deepGEM
Toward machine learning interatomic potentials for modeling uranium mononitride
Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery
Reducing training data needs with minimal multilevel machine learning (M3L)
Enhanced feature encoding and classification on distributed quantum hardware
On the capacity and superposition of minima in neural network loss function landscapes
Variational Monte Carlo approach to partial differential equations with neural networks
A combined modeling method for complex multi-fidelity data fusion
Beware of diffusion models for synthesizing medical images - a comparison with GANs in terms of memorizing brain MRI and chest x-ray images
Neural network representation of quantum systems
Machine learning-powered data cleaning for LEGEND: a semi-supervised approach using affinity propagation and support vector machines
DINAMO: Dynamic and INterpretable anomaly MOnitoring for large-scale particle physics experiments
Shallow recurrent decoder for reduced order modeling of E × B plasma dynamics
Machine learning of hidden variables in multiscale fluid simulation
Three-body renormalization group limit cycles based on unsupervised feature learning
Driving enhanced exciton transfer by automatic differentiation
Deep learning-guided surface characterization for autonomous hydrogen lithography
Enhanced deep learning-based water area segmentation for flood detection and monitoring
Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework
Accelerating cavity fault prediction using deep learning at Jefferson Laboratory
Machine learning of microscopic structure-dynamics relationships in complex molecular systems
Efficient autoencoder pipeline for discovering high entropy alloys with molecular dynamics data
Hierarchical Bayesian pharmacometrics analysis of Baclofen for alcohol use disorder
δARD loss for low-contrast medical image segmentation
Photonic indistinguishability characterization and optimization for cavity-based single-photon source
Machine learning based quantification of synchrotron radiation-induced x-ray fluorescence measurements - a case study
Randomized CP tensor decomposition
Interaction decompositions for tensor network regression
DIM: long-tailed object detection and instance segmentation via dynamic instance memory
Generalizability issues with deep learning models in medicine and their potential solutions: illustrated with cone-beam computed tomography (CBCT) to computed tomography (CT) image conversion
Nonlinear Gaussian process tomography with imposed non-negativity constraints on physical quantities for plasma diagnostics
Satellite image classification with neural quantum kernels
Uncertainty quantification with Bayesian higher order ReLU-KANs
AugLiChem: data augmentation library of chemical structures for machine learning
Unveiling the power of multimodal large language models for radio astronomical image understanding and question answering
Interpretable correlator Transformer for image-like quantum matter data
Online meta-learned gradient norms for active learning in science and technology
Comparing AI versus optimization workflows for simulation-based inference of spatial-stochastic systems
Evaluating cell growth and hypoxic regions of 3D spheroids via a machine learning approach
Tensor tree learns hidden relational structures in data to construct generative models
Neural networks as effective surrogate models of radio-frequency quadrupole particle accelerator simulations
Mud-Net: multi-domain deep unrolling network for simultaneous sparse-view and metal artifact reduction in computed tomography
Rapid parameter estimation of discrete decaying signals using autoencoder networks
Smart pixel sensors: towards on-sensor filtering of pixel clusters with deep learning
Robust quantum dots charge autotuning using neural network uncertainty
Paired autoencoders for likelihood-free estimation in inverse problems
Entangled q-convolutional neural nets
SMILES-X: autonomous molecular compounds characterization for small datasets without descriptors
Active robotic search for victims using ensemble deep learning techniques
Conditioning Boltzmann generators for rare event sampling
Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme
Optimizing ZX-diagrams with deep reinforcement learning
Explainable autoencoder for neutron star dense matter parameter estimation
Leveraging multi-task learning regressor chains for small and sparse tabular data in materials design
A deep neural network approach for parameterized PDEs and Bayesian inverse problems
Machine-learning approach for quantified resolvability enhancement of low-dose STEM data
Artificial neural network potentials for mechanics and fracture dynamics of two-dimensional crystals **
Variational quantum one-class classifier
Spatiotemporal forecasting of the edge localized modes in tokamak plasmas using neural networks
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
D-MOPH-25: diverse MOF-molecule pairs for Henry's constants prediction *
Topological machine learning for protein-nucleic acid binding affinity changes upon mutation
Deep multi-task mining Calabi-Yau four-folds
PyXtal_FF: a python library for automated force field generation
Unveiling the robustness of machine learning families
Manifold learning in atomistic simulations: a conceptual review
Multi-exit Kolmogorov-Arnold networks: enhancing accuracy and parsimony
Deep unsupervised clustering for prostate auto-segmentation with and without hydrogel spacer
Unsupervised machine learning of topological phase transitions from experimental data
A hybrid classical-quantum workflow for natural language processing
Discovering quantum circuit components with program synthesis
Feature space reduction method for ultrahigh-dimensional, multiclass data: random forest-based multiround screening (RFMS)
Simulation-based inference of single-molecule force spectroscopy
Reducing autocorrelation times in lattice simulations with generative adversarial networks
Tensor network for anomaly detection in the latent space of proton collision events at the LHC
Inverse Dirichlet weighting enables reliable training of physics informed neural networks
Deeply uncertain: comparing methods of uncertainty quantification in deep learning algorithms
Harnessing data augmentation to quantify uncertainty in the early estimation of single-photon source quality
Confined hydrogen atom: endohedrals H@C 36 and H@C 60
Prior guided deep difference meta-learner for fast adaptation to stylized segmentation
Defence against adversarial attacks using classical and quantum-enhanced Boltzmann machines †
LTAU-FF: Loss Trajectory Analysis for Uncertainty in atomistic Force Fields
Using convolutional neural networks for stereological characterization of 3D hetero-aggregates based on synthetic STEM data
Impact of data bias on machine learning for crystal compound synthesizability predictions
Training machine learning interatomic potentials for accurate phonon properties
Reply to Comment on 'Physics-based representations for machine learning properties of chemical reactions'
AB C MB : deep delensing assisted likelihood-free inference from CMB polarization maps
Quantum machine learning framework for virtual screening in drug discovery: a prospective quantum advantage
Looking at the posterior: accuracy and uncertainty of neural-network predictions
A multi-stage machine learning algorithm for estimating personal dose equivalent using thermoluminescent dosimeter
On learning higher-order cumulants in diffusion models
Bayesian model parameter learning in linear inverse problems: application in EEG focal source imaging
Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models
Robust detection of marine life with label-free image feature learning and probability calibration
Deep learning cosmic ray transport from density maps of simulated, turbulent gas
Efficient training of energy-based models via spin-glass control
Prediction of the morphological evolution of a splashing drop using an encoder-decoder
Online accelerator optimization with a machine learning-based stochastic algorithm
Machine learning enabled discovery of application dependent design principles for two-dimensional materials
Accelerating reaction rate predictions: a machine learning approach using a novel quantum chemical property for nucleophilic aromatic substitutions
A multi-objective permittivity optimization for object classification at the speed of light
A machine learning constitutive model for plasticity and strain hardening of polycrystalline metals based on data from micromechanical simulations
BiCrossNet: resource-efficient cross-view geolocalization with binary neural networks
Hadrons, better, faster, stronger
Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures
Data-driven prediction of the performance of enhanced surfaces from an extensive CFD-generated parametric search space
Photon detection probability prediction using one-dimensional generative neural network
Observation impact explanation in atmospheric state estimation using hierarchical message-passing graph neural networks *
Exploring the energy landscape of RBMs: reciprocal space insights into bosons, hierarchical learning and symmetry breaking
Plane-wave decomposition and randomised training; a novel path to generalised physics-informed neural networks for simple harmonic motion
Introducing Machine Learning: Science and Technology
Uncertainty quantification in graph neural networks with shallow ensembles
Alzheimer's disease detection and stage identification from magnetic resonance brain images using vision transformer
Transforming the bootstrap: using transformers to compute scattering amplitudes in planar N = 4 super Yang-Mills theory
Data-driven plasma modelling: surrogate collisional radiative models of fluorocarbon plasmas from deep generative autoencoders
Data-driven enhancement of cubic phase stability in mixed-cation perovskites
Automation of some macromolecular properties using a machine learning approach
Beyond dynamics: learning to discover conservation principles
How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?
Deep quantum graph dreaming: deciphering neural network insights into quantum experiments
Natural evolutionary strategies for variational quantum computation
MATSE - multi-fidelity augmented time-series emulation: galvanic corrosion applications
Rapid parameter estimation for merging massive black hole binaries using continuous normalizing flows
Normalizing flows as an enhanced sampling method for atomistic supercooled liquids
Reinforcement learning-based architecture search for quantum machine learning
Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model
Variational quantum reinforcement learning via evolutionary optimization
Spectral-bias and kernel-task alignment in physically informed neural networks
Deep learning via message passing algorithms based on belief propagation
Towards a variational Jordan-Lee-Preskill quantum algorithm
Regulating the development of accurate data-driven physics-informed deformation models
Inception networks, data augmentation and transfer learning in EEG-based photosensitivity diagnosis
Interpretable delta-learning of GW quasiparticle energies from GGA-DFT
K-means-driven Gaussian Process data collection for angle-resolved photoemission spectroscopy
Benchmarking universal machine learning interatomic potentials for rapid analysis of inelastic neutron scattering data *
Multi-target quantum compilation algorithm
QEMOS: a scalable quantum error mitigation method to overcome qubit sensitivity
Characterising the area under the curve loss function landscape
Machine learning inference of molecular dipole moment in liquid water
Physics-based representations for machine learning properties of chemical reactions
Easy representation of multivariate functions with low-dimensional terms via Gaussian process regression kernel design: applications to machine learning of potential energy surfaces and kinetic energy densities from sparse data
WaveFormer: transformer-based denoising method for gravitational-wave data
Multi-objective Bayesian optimization of eco-friendly gas mixtures for resistive plate chambers
Probe microscopy is all you need *
Site2Vec: a reference frame invariant algorithm for vector embedding of protein-ligand binding sites
Forecasting high-dimensional spatio-temporal systems from sparse measurements
Quantum machine learning of large datasets using randomized measurements
DAVI: Deep learning-based tool for alignment and single nucleotide variant identification
ML-based regionalization of climate variables to forecast seasonal precipitation for water resources management
A comparative study of different machine learning methods for dissipative quantum dynamics
Neural network analysis of neutron and x-ray reflectivity data: pathological cases, performance and perspectives
Classification of diffraction patterns in single particle imaging experiments performed at x-ray free-electron lasers using a convolutional neural network
Amortized simulation-based frequentist inference for tractable and intractable likelihoods
Extracting electron scattering cross sections from swarm data using deep neural networks
An open-source robust machine learning platform for real-time detection and classification of 2D material flakes
Machine-learning-assisted dual harmonic generation FROG for enhanced ultrafast pulse recovery
Super-resolving 3D nanostructures using artificially generated image data and spatial transport simulations
Repetitive readout enhanced by machine learning
Parameter uncertainties for imperfect surrogate models in the low-noise regime
Deep learning of crystalline defects from TEM images: a solution for the problem of 'never enough training data'
Plug & play directed evolution of proteins with gradient-based discrete MCMC
InfoCGAN classification of 2D square Ising configurations
Bayesian experimental design and parameter estimation for ultrafast spin dynamics
Coupled CANN-DEM simulation in solid mechanics
Deep kernel methods learn better: from cards to process optimization
Towards harmonization of SO(3)-equivariance and expressiveness: a hybrid deep learning framework for electronic-structure Hamiltonian prediction
Thermal damage detection of EAST internal component based on machine learning
Optimal data generation for machine learned interatomic potentials
Synthetic pre-training for neural-network interatomic potentials
NeuralNEB - neural networks can find reaction paths fast
Path-minimizing latent ODEs for improved extrapolation and inference
Graph prolongation convolutional networks: explicitly multiscale machine learning on graphs with applications to modeling of cytoskeleton
Stokesian processes : inferring Stokes flows using physics-informed Gaussian processes
A multiscale Bayesian approach to quantification and denoising of energy-dispersive x-ray data
Differentiable Earth mover's distance for data compression at the high-luminosity LHC
Robust simulation-based inference in cosmology with Bayesian neural networks
MS2OD: outlier detection using minimum spanning tree and medoid selection
Large-scale gene expression data clustering through incremental ensemble approach
Machine learning for detection of equivariant finite symmetry groups in dynamical systems
Machine-learning enhanced dark soliton detection in Bose-Einstein condensates
Stochastic resetting mitigates latent gradient bias of SGD from label noise
Improving early detection of gravitational waves from binary neutron stars using CNNs and FPGAs
Domain knowledge-based deterministic graph traversal method for white blood cell classification
Improved decision making with similarity based machine learning: applications in chemistry
SymbolNet: neural symbolic regression with adaptive dynamic pruning for compression
Forecasting high-impact research topics via machine learning on evolving knowledge graphs
Quantum machine learning in high energy physics
Transfer learning with generative models for object detection on limited datasets
Dynamic learning rate decay for stochastic variational inference
Fast jet tagging with MLP-Mixers on FPGAs
Particle-based fast jet simulation at the LHC with variational autoencoders
Supplementing recurrent neural networks with annealing to solve combinatorial optimization problems
A novel quintile multi-model ensemble approach for improving future extreme precipitation projections using XGBoost
Governing equation discovery based on causal graph for nonlinear dynamic systems
End-to-end simulation of particle physics events with flow matching and generator oversampling
Physics-informed neural networks for modeling astrophysical shocks
Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics
Merging automatic differentiation and the adjoint method for photonic inverse design
Randomized algorithms for fast computation of low rank tensor ring model
Configuration interaction guided sampling with interpretable restricted Boltzmann machine
Lightweight jet reconstruction and identification as an object detection task
Ultrafast jet classification at the HL-LHC
Neuromorphic, physics-informed spiking neural network for molecular dynamics
ATSFCNN: a novel attention-based triple-stream fused CNN model for hyperspectral image classification
Generalization of graph-based active learning relaxation strategies across materials
NeuralPDR: neural differential equations as surrogate models for photodissociation regions
DeepAstroUDA: semi-supervised universal domain adaptation for cross-survey galaxy morphology classification and anomaly detection
Integer linear programming for unsupervised training set selection in molecular machine learning
Accelerating data acquisition with FPGA-based edge machine learning: a case study with LCLS-II
Symbolic approximations to Ricci-flat metrics via extrinsic symmetries of Calabi-Yau hypersurfaces
Journey over destination: dynamic sensor placement enhances generalization
Neural networks for parameter estimation in geostatistical models with geometric anisotropies
Graph convolutional multi-mesh autoencoder for steady transonic aircraft aerodynamics
Mapping causal pathways with structural modes fingerprint for perovskite oxides