yusuke1997/dblp-id-title huggingface dataset:
A textile exomuscle that assists the shoulder during functional movements for everyday life
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
Predicting functional effect of missense variants using graph attention neural networks
Radiological tumour classification across imaging modality and histology
Author Correction: Supervised learning of high-confidence phenotypic subpopulations from single-cell data
Author Correction: Gradient-based learning drives robust representations in recurrent neural networks by balancing compression and expansion
Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis
The need for uncertainty quantification in machine-assisted medical decision making
Author Correction: A machine learning approach to leveraging electronic health records for enhanced omics analysis
A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
Evaluating protein binding interfaces with transformer networks
Origami for the everyday
A deep neural network for real-time optoacoustic image reconstruction with adjustable speed of sound
Understanding adversarial examples requires a theory of artefacts for deep learning
Causal inference and counterfactual prediction in machine learning for actionable healthcare
Predicting tumour mutational burden from histopathological images using multiscale deep learning
Lessons from infant learning for unsupervised machine learning
Generating three-dimensional structures from a two-dimensional slice with generative adversarial network-based dimensionality expansion
Exploring the limit of using a deep neural network on pileup data for germline variant calling
A deep learning framework for high-throughput mechanism-driven phenotype compound screening and its application to COVID-19 drug repurposing
Learning integral operators via neural integral equations
Robotics and the art of science
Enabling collaborative governance of medical AI
Fast and generalizable micromagnetic simulation with deep neural nets
Predicting unseen antibodies' neutralizability via adaptive graph neural networks
Deep learning-inferred multiplex immunofluorescence for immunohistochemical image quantification
Playing with symmetry with neural networks
Shared, but not up for grabs
An interpretable RNA foundation model for exploring functional RNA motifs in plants
Author Correction: Reconstructing quantum states with generative models
Predicting disease-associated mutation of metal-binding sites in proteins using a deep learning approach
Automated abnormality detection in lower extremity radiographs using deep learning
Improving the quality of machine learning in health applications and clinical research
Machine intelligence enabled radiomics
Using online verification to prevent autonomous vehicles from causing accidents
Predicting metabolomic profiles from microbial composition through neural ordinary differential equations
Coordinate-based neural representations for computational adaptive optics in widefield microscopy
Safe driving cars
Visual odometry with neuromorphic resonator networks
Meta-learning for T cell receptor binding specificity and beyond
Artificial intelligence in a crisis needs ethics with urgency
Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein
Enhancing hyperspectral imaging
Author Correction: A challenge for rounded evaluation of recommender systems
Learning interpretable representations of entanglement in quantum optics experiments using deep generative models
A teachable moment for dual-use
Diving into deep learning
Design of prime-editing guide RNAs with deep transfer learning
Consider laboratory aspects in developing patient prediction models
Behavioural evidence for a transparency-efficiency tradeoff in human-machine cooperation
Interpretable deep-learning models to help achieve the Sustainable Development Goals
Semantic segmentation of microscopic neuroanatomical data by combining topological priors with encoder-decoder deep networks
Gaussian embedding for large-scale gene set analysis
Publisher Correction: Dimensionality reduction to maximize prediction generalization capability
Estimating categorical counterfactuals via deep twin networks
Improving Wikipedia verifiability with AI
Epitope-anchored contrastive transfer learning for paired CD8+ T cell receptor-antigen recognition
Federated learning is not a cure-all for data ethics
Evaluating generalizability of artificial intelligence models for molecular datasets
Multi-animal 3D social pose estimation, identification and behaviour embedding with a few-shot learning framework
Labelling instructions matter in biomedical image analysis
Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens
Prediction of water stability of metal-organic frameworks using machine learning
AI reflections in 2020
Chemically programmable microrobots weaving a web from hormones
A temporally resolved DNA framework state machine in living cells
A universal information theoretic approach to the identification of stopwords
A large-scale audit of dataset licensing and attribution in AI
Machine learning to guide the use of adjuvant therapies for breast cancer
Deep learning prediction of patient response time course from early data via neural-pharmacokinetic/pharmacodynamic modelling
Learning biophysical determinants of cell fate with deep neural networks
Automating crystal-structure phase mapping by combining deep learning with constraint reasoning
From local explanations to global understanding with explainable AI for trees
Robot teams stay safe with blockchains
AI model transferability in healthcare: a sociotechnical perspective
Automatic strain sensor design via active learning and data augmentation for soft machines
A neuro-vector-symbolic architecture for solving Raven's progressive matrices
A comprehensive large-scale biomedical knowledge graph for AI-powered data-driven biomedical research
People have the AI power
Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions
Enabling the future of colonoscopy with intelligent and autonomous magnetic manipulation
Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media
Tandem mass spectrum prediction for small molecules using graph transformers
Clinical large language models with misplaced focus
Federated learning and Indigenous genomic data sovereignty
A match for virtual conferences
Taking robots shopping
Gazing into Clever Hans machines
A deep learning method for recovering missing signals in transcriptome-wide RNA structure profiles from probing experiments
Goals as reward-producing programs
Autonomous 3D positional control of a magnetic microrobot using reinforcement learning
The imperative of interpretable machines
Calibrated geometric deep learning improves kinase-drug binding predictions
Human autonomy in the age of artificial intelligence
The promise and perils of using artificial intelligence to fight corruption
Deep neural networks identify sequence context features predictive of transcription factor binding
Lessons from the first DBTex Challenge
Next chapter in artificial writing
Encoding physics to learn reaction-diffusion processes
Topological structure of complex predictions
Large language models associate Muslims with violence
Better models of human high-level visual cortex emerge from natural language supervision with a large and diverse dataset
Self-supervised deep learning for tracking degradation of perovskite light-emitting diodes with multispectral imaging
Neutralizing the impact of atmospheric turbulence on complex scene imaging via deep learning
Towards universal translation
Forecasting SARS-CoV-2 transmission and clinical risk at small spatial scales by the application of machine learning architectures to syndromic surveillance data
A Global South perspective for ethical algorithms and the State
Deep convolutional neural networks in the face of caricature
Global voxel transformer networks for augmented microscopy
Investigating machine moral judgement through the Delphi experiment
Foundation models in healthcare require rethinking reliability
Machine-learning-based dynamic-importance sampling for adaptive multiscale simulations
Out-of-distribution generalization from labelled and unlabelled gene expression data for drug response prediction
Unsupervised behaviour analysis and magnification (uBAM) using deep learning
Environmental robotics for a sustainable future in circular economies
The path of foundational research in robotics
Improving customer decisions in web-based e-commerce through guerrilla modding
Domesticating the techno-racial project
The new NeuroAI
Direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion
Morphological flexibility in robotic systems through physical polygon meshing
The neural resource allocation problem when enhancing human bodies with extra robotic limbs
Algorithms to live by
Publisher Correction: Pathologist-level interpretable whole-slide cancer diagnosis with deep learning
Back to the future with machine learning
Detecting multiple retinal diseases in ultra-widefield fundus imaging and data-driven identification of informative regions with deep learning
Origami-inspired miniature manipulator for teleoperated microsurgery
A new regulatory framework for algorithm-powered recommendation services in China
Efficient generation of protein pockets with PocketGen
Equivariant 3D-conditional diffusion model for molecular linker design
Ethical requirements for responsible research with hacked data
Moving towards genome-wide data integration for patient stratification with Integrate Any Omics
Finding a role for AI in the pandemic
Controllable protein design with language models
Engineering flexible machine learning systems by traversing functionally invariant paths
Validity of machine learning in biology and medicine increased through collaborations across fields of expertise
Lifelike agility and play in quadrupedal robots using reinforcement learning and generative pre-trained models
A challenge for rounded evaluation of recommender systems
Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning
A text-guided protein design framework
Reconstructing Kinetic Models for Dynamical Studies of Metabolism using Generative Adversarial Networks
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions
Reconstructing growth and dynamic trajectories from single-cell transcriptomics data
Reply to: Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
Talking artificial intelligence
Deceiving possibilities
Machine learning-enabled globally guaranteed evolutionary computation
Machine learning and computation-enabled intelligent sensor design
Deep learning of causal structures in high dimensions under data limitations
The only game in town
Combining automated microfluidic experimentation with machine learning for efficient polymerization design
Privacy debate obscures pandemic power shifts
Limited applicability of a COVID-19 specific mortality prediction rule to the intensive care setting
Machine learning pipeline for battery state-of-health estimation
Deep learning incorporating biologically inspired neural dynamics and in-memory computing
Physically constrained generative adversarial networks for improving precipitation fields from Earth system models
Deep learning decodes the principles of differential gene expression
Learning equilibria in symmetric auction games using artificial neural networks
Author Correction: Self-play reinforcement learning guides protein engineering
Interpreting neural networks for biological sequences by learning stochastic masks
Influencing human-AI interaction by priming beliefs about AI can increase perceived trustworthiness, empathy and effectiveness
Reconciling privacy and accuracy in AI for medical imaging
Deep-learning-based prediction of late age-related macular degeneration progression
A plea for caution and guidance about using AI in genomics
Improving image labelling quality
Direct-to-consumer medical machine learning and artificial intelligence applications
Deep recurrent optical flow learning for particle image velocimetry data
Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics
Dimensionality reduction to maximize prediction generalization capability
Publisher Correction: Closed-form continuous-time neural networks
Back to basics to open the black box
Neural Error Mitigation of Near-Term Quantum Simulations
Optimal transport for generating transition states in chemical reactions
An online platform for interactive feedback in biomedical machine learning
High-speed quadrupedal locomotion by imitation-relaxation reinforcement learning
Image prediction of disease progression for osteoarthritis by style-based manifold extrapolation
Reply to: Consider the laboratory aspects in developing patient prediction models
Learning MRI artefact removal with unpaired data
Generalizability of an acute kidney injury prediction model across health systems
Reusability report: Evaluating reproducibility and reusability of a fine-tuned model to predict drug response in cancer patient samples
Multiscale topology-enabled structure-to-sequence transformer for protein-ligand interaction predictions
Envisioning better benchmarks for machine learning PDE solvers
Quantum circuit optimization with AlphaTensor
Revisiting code reusability
Publisher Correction: Automating turbulence modelling by multi-agent reinforcement learning
Towards a personalized AI assistant to learn machine learning
An interpretable mortality prediction model for COVID-19 patients
A case-based interpretable deep learning model for classification of mass lesions in digital mammography
Rewiring art
Towards ethical and socio-legal governance in AI
Robots and the return to collaborative intelligence
Modality attention and sampling enables deep learning with heterogeneous marker combinations in fluorescence microscopy
Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning
The rise of robots in surgical environments during COVID-19
Mapping the space of chemical reactions using attention-based neural networks
A deep generative model enables automated structure elucidation of novel psychoactive substances
Adversarial interference and its mitigations in privacy-preserving collaborative machine learning
Li Yan et al. reply
Stretchable e-skin and transformer enable high-resolution morphological reconstruction for soft robots
Disentangling automatic and semi-automatic approaches to the optimization-based design of control software for robot swarms
Codon language embeddings provide strong signals for use in protein engineering
Publisher Correction: Pandemic data challenges
Large language models challenge the future of higher education
The Algonauts Project
Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network
Designing a strong test for measuring true common-sense reasoning
Why virtual creatures matter
Distributed constrained combinatorial optimization leveraging hypergraph neural networks
Sliding-attention transformer neural architecture for predicting T cell receptor-antigen-human leucocyte antigen binding
Categorizing robots by performance fitness into the tree of robots
Enhancing optical-flow-based control by learning visual appearance cues for flying robots
Life-threatening ventricular arrhythmia detection challenge in implantable cardioverter-defibrillators
Data-driven discovery of movement-linked heterogeneity in neurodegenerative diseases
AI protein shake-up
Echo state graph neural networks with analogue random resistive memory arrays
Ask this robot for a helping hand
Integrated structure prediction of protein-protein docking with experimental restraints using ColabDock
A challenge for the law and artificial intelligence
AI reflections in 2019
Neural network architecture search with AMBER
A deep generative model for molecule optimization via one fragment modification
Avoid diluting democracy by algorithms
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires
From algorithmic accountability to digital governance
Publisher Correction: Learning MRI artefact removal with unpaired data
Responsible artificial intelligence in agriculture requires systemic understanding of risks and externalities
Time to face decisions
A deep learning method that identifies cellular heterogeneity using nanoscale nuclear features
Augmenting large language models with chemistry tools
Prediction of robust scientific facts from literature
Beware of fictional AI narratives
AI for radiographic COVID-19 detection selects shortcuts over signal
Learning function from structure in neuromorphic networks
Designing clinically translatable artificial intelligence systems for high-dimensional medical imaging
Geometry-enhanced pretraining on interatomic potentials
A context-aware deconfounding autoencoder for robust prediction of personalized clinical drug response from cell-line compound screening
Reinforcement learning supercharges redox flow batteries
Intelligent feature engineering and ontological mapping of brain tumour histomorphologies by deep learning
AI in biomaterials discovery: generating self-assembling peptides with resource-efficient deep learning
AI on the beach
Let's talk about digital death
Benchmarking AI-powered docking methods from the perspective of virtual screening
Adversarial explanations for understanding image classification decisions and improved neural network robustness
C. Elegans inspires self-driving cars
Multiscale simulations of complex systems by learning their effective dynamics
Distributed sensing for fluid disturbance compensation and motion control of intelligent robots
Sparse learned kernels for interpretable and efficient medical time series processing
Machine learning for active matter
Research, reuse, repeat
Computationally instrument-resolution-independent de novo peptide sequencing for high-resolution devices
Successful implementation of the EU AI Act requires interdisciplinary efforts
Pick your AI poison
Geometric deep learning of particle motion by MAGIK
Finding key players in complex networks through deep reinforcement learning
Descent into the unknown
Fully portable and wireless universal brain-machine interfaces enabled by flexible scalp electronics and deep learning algorithm
Mining for informative signals in biological sequences
Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning
Integration of millions of transcriptomes using batch-aware triplet neural networks
Extrapolating heterogeneous time-series gene expression data using Sagittarius
Predicting cracks in metalloproteins
Predicting the state of charge and health of batteries using data-driven machine learning
Call to action for robotics
Lessons from a challenge on forecasting epileptic seizures from non-cerebral signals
The incentive gap in data work in the era of large models
Transformers and genome language models
An adaptive graph learning method for automated molecular interactions and properties predictions
Listening in to perceived speech with contrastive learning
Interpretable weather forecasting for worldwide stations with a unified deep model
Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
Pre-training with fractional denoising to enhance molecular property prediction
Physics-based machine learning for subcellular segmentation in living cells
Matching algorithms for blood donation
Reducing racial bias in AI models for clinical use requires a top-down intervention
A transformer-based weakly supervised computational pathology method for clinical-grade diagnosis and molecular marker discovery of gliomas
More than machines
Teaching recurrent neural networks to infer global temporal structure from local examples
Cognitive maps from predictive vision
Assessing antibody and nanobody nativeness for hit selection and humanization with AbNatiV
Factuality challenges in the era of large language models and opportunities for fact-checking
Incorporating neuro-inspired adaptability for continual learning in artificial intelligence
Reusability report: Learning the language of synthetic methods used in medicinal chemistry
Improving representations of genomic sequence motifs in convolutional networks with exponential activations
AI reality check
Quantum approximate Bayesian computation for NMR model inference
A critical problem in benchmarking and analysis of evolutionary computation methods
Expanding functional protein sequence spaces using generative adversarial networks
Deep learning STEM-EDX tomography of nanocrystals
Reusability report: exploring the utility of variational graph encoders for predicting molecular toxicity in drug design
Self-correcting quantum many-body control using reinforcement learning with tensor networks
Author Correction: Augmenting vascular disease diagnosis by vasculature-aware unsupervised learning
Hierarchical deep reinforcement learning reveals a modular mechanism of cell movement
Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set
Bridging the gap between machine confidence and human perceptions
Characterizing the interaction conformation between T-cell receptors and epitopes with deep learning
Linking the length scales
A neural network trained for prediction mimics diverse features of biological neurons and perception
The design space of E(3)-equivariant atom-centred interatomic potentials
Kernel approximation using analogue in-memory computing
A unified cross-attention model for predicting antigen binding specificity to both HLA and TCR molecules
Towards highly sensitive deep learning-based end-to-end database search for tandem mass spectrometry
Medical artificial intelligence is as much social as it is technological
Mitigating allocative tradeoffs and harms in an environmental justice data tool
Pan-Peptide Meta Learning for T-cell receptor-antigen binding recognition
Author Correction: Learnability can be undecidable
When and how convolutional neural networks generalize to out-of-distribution category-viewpoint combinations
Shortcut learning in deep neural networks
Recurrent graph optimal transport for learning 3D flow motion in particle tracking
Evolutionary training and abstraction yields algorithmic generalization of neural computers
Educational strategies to foster diversity and inclusion in machine intelligence
Transferring policy of deep reinforcement learning from simulation to reality for robotics
An open source machine learning framework for efficient and transparent systematic reviews
Geometry-enhanced molecular representation learning for property prediction
Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research
Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins
The synergy complement control approach for seamless limb-driven prostheses
Optimizing molecules using efficient queries from property evaluations
Let's go 2020
Deep learning-based robust positioning for all-weather autonomous driving
Deeper evaluation of a single-cell foundation model
InstaNovo enables diffusion-powered de novo peptide sequencing in large-scale proteomics experiments
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time
An analysis of gender bias studies in natural language processing
Computing with a camera
Foundation models and the privatization of public knowledge
A biological perspective on evolutionary computation