yusuke1997/dblp-id-title huggingface dataset:
Self-supervised retinal thickness prediction enables deep learning from unlabelled data to boost classification of diabetic retinopathy
A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions
Accurate data-driven prediction does not mean high reproducibility
The democratization of global AI governance and the role of tech companies
Multi-purpose RNA language modelling with motif-aware pretraining and type-guided fine-tuning
Identifying important sensory feedback for learning locomotion skills
Deep learning prediction of glycopeptide tandem mass spectra powers glycoproteomics
A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories
Reusability report: Deep learning-based analysis of images and spectroscopy data with AtomAI
Learning with explainable trees
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
Author Correction: Improving de novo molecular design with curriculum learning
Artificial intelligence-powered electronic skin
An integrated iterative annotation technique for easing neural network training in medical image analysis
Integration of multiomics data with graph convolutional networks to identify new cancer genes and their associated molecular mechanisms
A web of tidings
Human-AI collaboration enables more empathic conversations in text-based peer-to-peer mental health support
Self-supervised learning of hologram reconstruction using physics consistency
A social path to human-like artificial intelligence
Protein function prediction as approximate semantic entailment
Results from the autoPET challenge on fully automated lesion segmentation in oncologic PET/CT imaging
Emergent behaviour and neural dynamics in artificial agents tracking odour plumes
Consumer protection requires artificial intelligence
Embedding gene sets in low-dimensional space
A prize for discoveries past, present and future
Uncertainty quantification via a memristor Bayesian deep neural network for risk-sensitive reinforcement learning
Cell type annotation of single-cell chromatin accessibility data via supervised Bayesian embedding
Ethical hazards of health data governance in the metaverse
A fast blind zero-shot denoiser
Accelerated rational PROTAC design via deep learning and molecular simulations
Towards a more inductive world for drug repurposing approaches
Responsible AI for conservation
Battling disinformation with cryptography
Publisher Correction: Leveraging implicit knowledge in neural networks for functional dissection and engineering of proteins
Network of evolvable neural units can learn synaptic learning rules and spiking dynamics
Federated machine learning in data-protection-compliant research
Language and culture internalization for human-like autotelic AI
LLM-based agentic systems in medicine and healthcare
State-specific protein-ligand complex structure prediction with a multiscale deep generative model
Capturing complex hand movements and object interactions using machine learning-powered stretchable smart textile gloves
A method for multiple-sequence-alignment-free protein structure prediction using a protein language model
Unassisted noise reduction of chemical reaction datasets
Search and rescue with airborne optical sectioning
A unified evolution-driven deep learning framework for virus variation driver prediction
Quantifying the spatial homogeneity of urban road networks via graph neural networks
Deep neural networks predict class I major histocompatibility complex epitope presentation and transfer learn neoepitope immunogenicity
Parameter-efficient fine-tuning of large-scale pre-trained language models
A multi-modal pre-training transformer for universal transfer learning in metal-organic frameworks
Contributions from the Catholic Church to ethical reflections in the digital era
Leveraging ancestral sequence reconstruction for protein representation learning
Addressing the harms of AI-generated inauthentic content
Author Correction: High-resolution radar road segmentation using weakly supervised learning
Out-of-the-box deep learning prediction of pharmaceutical properties by broadly learned knowledge-based molecular representations
Discussions of machine versus living intelligence need more clarity
An extension to the FDA approval process is needed to achieve AI equity
Deep transfer operator learning for partial differential equations under conditional shift
AI meets Angry Birds
Climbing Mount AI
A new age for content filters
What is in your LLM-based framework?
ARNLE model identifies prevalence potential of SARS-CoV-2 variants
Seeking a quantum advantage for machine learning
Synergy-based robotic quadruped leveraging passivity for natural intelligence and behavioural diversity
Deep neural networks with controlled variable selection for the identification of putative causal genetic variants
Deep reinforcement learning-designed radiofrequency waveform in MRI
CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
A portable three-degrees-of-freedom force feedback origami robot for human-robot interactions
Address behaviour vulnerabilities in the next generation of autonomous robots
Poisoning medical knowledge using large language models
Generation of 3D molecules in pockets via a language model
Deep learning supported discovery of biomarkers for clinical prognosis of liver cancer
Generating mutants of monotone affinity towards stronger protein complexes through adversarial learning
High-resolution real-space reconstruction of cryo-EM structures using a neural field network
Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning
A checklist for safe robot swarms
A year of racing ahead with AI and not breaking things
A definition, benchmark and database of AI for social good initiatives
Code-free deep learning for multi-modality medical image classification
Can AI invent?
Self-play reinforcement learning guides protein engineering
Joint variational autoencoders for multimodal imputation and embedding
The benefits, risks and bounds of personalizing the alignment of large language models to individuals
Materializing artificial intelligence
Wiring up recurrent neural networks
Interpretable socioeconomic status inference from aerial imagery through urban patterns
Transparency (in training data) is what we want
Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery
Advancing ethics review practices in AI research
A soft thumb-sized vision-based sensor with accurate all-round force perception
Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks
A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware
Predicting myocardial infarction through retinal scans and minimal personal information
Why the carbon footprint of generative large language models alone will not help us assess their sustainability
Accurate and robust protein sequence design with CarbonDesign
A neural speech decoding framework leveraging deep learning and speech synthesis
Reusability report: Designing organic photoelectronic molecules with descriptor conditional recurrent neural networks
A step forward in tracing and documenting dataset provenance
Deep-learning cardiac motion analysis for human survival prediction
Solving optimization tasks in condensed matter
A 'programming' framework for recurrent neural networks
Human action recognition with a large-scale brain-inspired photonic computer
Limitations in odour recognition and generalization in a neuromorphic olfactory circuit
Multimodal learning with graphs
Variable selection with false discovery rate control in deep neural networks
Inability of a graph neural network heuristic to outperform greedy algorithms in solving combinatorial optimization problems
Defining a role for AI ethics in national security
Learning spatiotemporal dynamics with a pretrained generative model
Reusability report: Uncovering associations in biomedical bipartite networks via a bilinear attention network with domain adaptation
From data chaos to precision medicine
Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings
Accelerating histopathology workflows with generative AI-based virtually multiplexed tumour profiling
Artificial intelligence cooperation to support the global response to COVID-19
Algorithmic fairness and bias mitigation for clinical machine learning with deep reinforcement learning
Machine learning could improve innovation policy
A collective AI via lifelong learning and sharing at the edge
Leveraging large language models for predictive chemistry
Federated disentangled representation learning for unsupervised brain anomaly detection
Intelligent problem-solving as integrated hierarchical reinforcement learning
An alternative to backpropagation through time
A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation
Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
A bioactivity foundation model using pairwise meta-learning
Of conferences and conversations in AI
Embodied intelligence weaves a better future
Estimation of causal effects of genes on complex traits using a Bayesian-network-based framework applied to GWAS data
Developing a brain atlas through deep learning
A statistical mechanics framework for Bayesian deep neural networks beyond the infinite-width limit
A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications
Evaluation of deep learning in non-coding RNA classification
Collaborative creativity in AI
A touch of virtual reality
Space missions out of this world with AI
A cautionary tale about the adoption of medical AI in Sweden
Resilient machines through adaptive morphology
Experimental discovery of structure-property relationships in ferroelectric materials via active learning
Potent antimalarial drugs with validated activities
Active mechanical haptics with high-fidelity perceptions for immersive virtual reality
A quantitative analysis of knowledge-learning preferences in large language models in molecular science
Biomonitoring and precision health in deep space supported by artificial intelligence
A transformer-based model to predict peptide-HLA class I binding and optimize mutated peptides for vaccine design
Prediction of drug combination effects with a minimal set of experiments
Ad hoc learning of peptide fragmentation from mass spectra enables an interpretable detection of phosphorylated and cross-linked peptides
A topology-based network tree for the prediction of protein-protein binding affinity changes following mutation
Deep learning based on parameterized physical forward model for adaptive holographic imaging with unpaired data
Ensemble deep learning in bioinformatics
Laplace neural operator for solving differential equations
Improving healthcare operations management with machine learning
Author Correction: Kernel approximation using analogue in-memory computing
FDA fosters innovative approaches in research, resources and collaboration
Principles alone cannot guarantee ethical AI
Encoding of tactile information in hand via skin-integrated wireless haptic interface
Modular deep learning enables automated identification of monoclonal cell lines
Enhanced spatio-temporal electric load forecasts using less data with active deep learning
Distinguishing two features of accountability for AI technologies
Improved fragment sampling for ab initio protein structure prediction using deep neural networks
Neural scaling of deep chemical models
The global landscape of AI ethics guidelines
Author Correction: A generalized-template-based graph neural network for accurate organic reactivity prediction
Deep learning-based prediction of the T cell receptor-antigen binding specificity
Application of variational graph encoders as an effective generalist algorithm in computer-aided drug design
A soft touch for robots
Publisher Correction: Advancing ethics review practices in AI research
A multi-modal deep language model for contaminant removal from metagenome-assembled genomes
DNA language model GROVER learns sequence context in the human genome
Clinical interpretation of an interpretable prognostic model for patients with COVID-19
Deep learning robotic guidance for autonomous vascular access
Learning as the unsupervised alignment of conceptual systems
ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling
A generalizable deep learning framework for inferring fine-scale germline mutation rate maps
The transformational role of GPU computing and deep learning in drug discovery
Trans-channel fluorescence learning improves high-content screening for Alzheimer's disease therapeutics
On the importance of ethnographic methods in AI research
Barriers to academic data science research in the new realm of algorithmic behaviour modification by digital platforms
A definition for robotics as an academic discipline
Evolutionary optimization of model merging recipes
A question of trust for AI research in medicine
Partial-convolution-implemented generative adversarial network for global oceanic data assimilation
Complex sequential understanding through the awareness of spatial and temporal concepts
Synthetic Lagrangian turbulence by generative diffusion models
Publisher Correction: Reconstructing growth and dynamic trajectories from single-cell transcriptomics data
Algorithmic recommendations, anyone?
Sparsity provides a competitive advantage
Learning fast in autonomous drone racing
A super-resolution strategy for mass spectrometry imaging via transfer learning
Deep domain adversarial neural network for the deconvolution of cell type mixtures in tissue proteome profiling
The promise of generative AI for suicide prevention in India
AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction
A human in the loop in surgery automation
Learning with known operators reduces maximum error bounds
Human ownership of artificial creativity
Anniversary AI reflections
Judging the creative prowess of AI
Bridging peptide presentation and T cell recognition with multi-task learning
Self-driving cars will take a while to get right
Machine learning solutions looking for PDE problems
Improving de novo molecular design with curriculum learning
Human-AI adaptive dynamics drives the emergence of information cocoons
Reusability report: Leveraging supervised learning to uncover phenotype-relevant biology from single-cell RNA sequencing data
Reusability report: Feature disentanglement in generating a three-dimensional structure from a two-dimensional slice with sliceGAN
What comparing deep neural networks can teach us about human vision
Increasing generality in machine learning through procedural content generation
Waking up to data challenges
Multi-objective goal-directed optimization of de novo stable organic radicals for aqueous redox flow batteries
Bridging the gap between chemical reaction pretraining and conditional molecule generation with a unified model
Systematic analysis of 32,111 AI model cards characterizes documentation practice in AI
Author Correction: Democratic classification of free-format survey responses with a network-based framework
Unsupervised data to content transformation with histogram-matching cycle-consistent generative adversarial networks
What large language models know and what people think they know
We may not cooperate with friendly machines
Knowledge graph-enhanced molecular contrastive learning with functional prompt
Blending neural operators and relaxation methods in PDE numerical solvers
Robotic body augmentation
Gene set inference from single-cell sequencing data using a hybrid of matrix factorization and variational autoencoders
AI's social sciences deficit
Embodied large language models enable robots to complete complex tasks in unpredictable environments
Modulating emotional states of rats through a rat-like robot with learned interaction patterns
Bio-robots step towards brain-body co-adaptation
Visual cognition in multimodal large language models
Optimizing the synergy between physics and machine learning
Harnessing the power of artificial intelligence to transform hearing healthcare and research
Bringing inclusivity to robotics with INBOTS
Activity-weight duality in feed-forward neural networks reveals two co-determinants for generalization
Geometric deep learning on molecular representations
Bringing robustness against adversarial attacks
Data-driven federated learning in drug discovery with knowledge distillation
Discovering neural policies to drive behaviour by integrating deep reinforcement learning agents with biological neural networks
Resource-efficient inference for particle physics
On board with COMET to improve omics prediction models
Explaining machine learning models with interactive natural language conversations using TalkToModel
Rethinking machine unlearning for large language models
Low-power image recognition
Picking the right robotics challenge
Author Correction: A 5′ UTR language model for decoding untranslated regions of mRNA and function predictions
Development of AI-assisted microscopy frameworks through realistic simulation with pySTED
Exploring the cloud of variable importance for the set of all good models
Development of the Senseiver for efficient field reconstruction from sparse observations
An embedded ethics approach for AI development
Why it is time to stop ostracizing social robots
Automated construction of cognitive maps with visual predictive coding
Deep learning for tomographic image reconstruction
Battery lifetime prediction across diverse ageing conditions with inter-cell deep learning
Replication of a mortality prediction model in Dutch patients with COVID-19
Bioinspired acousto-magnetic microswarm robots with upstream motility
Regulating AI in medicine in the United States and Europe
Will generative AI transform robotics?
Challenges, evaluation and opportunities for open-world learning
Super-resolution generative adversarial networks of randomly-seeded fields
Reusability report: Predicting spatiotemporal nonlinear dynamics in multimode fibre optics with a recurrent neural network
Reply to: Clinical interpretation of an interpretable prognostic model for patients with COVID-19
The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols
The carbon impact of artificial intelligence
Mitigating the missing-fragmentation problem in de novo peptide sequencing with a two-stage graph-based deep learning model
Return of cybernetics
Into the latent space
Robotics on a mission
Sequential memory improves sample and memory efficiency in episodic control
PocketFlow is a data-and-knowledge-driven structure-based molecular generative model
A framework for tool cognition in robots without prior tool learning or observation
Development of metaverse for intelligent healthcare
Evaluation of post-hoc interpretability methods in time-series classification
Estimating time with neural networks
Morphological and molecular breast cancer profiling through explainable machine learning
Evaluating deep learning for predicting epigenomic profiles
Benchmarking saliency methods for chest X-ray interpretation
Learning functional properties of proteins with language models
Contextual feature extraction hierarchies converge in large language models and the brain
Catching up with missing particles
No chemical killer AI (yet)
Large-scale automated investigation of free-falling paper shapes via iterative physical experimentation
Author Correction: Automatic strain sensor design via active learning and data augmentation for soft machines
Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework
An interpretable deep learning workflow for discovering subvisual abnormalities in CT scans of COVID-19 inpatients and survivors
Self-decoupling three-axis forces in a simple sensor
In situ training of feed-forward and recurrent convolutional memristor networks
A role for analogue memory in AI hardware
Weak signal extraction enabled by deep neural network denoising of diffraction data
A shallow convolutional neural network predicts prognosis of lung cancer patients in multi-institutional computed tomography image datasets
Deep learning for predicting rate-induced tipping
Lessons for artificial intelligence from the study of natural stupidity
A generalized-template-based graph neural network for accurate organic reactivity prediction
Inverse design of nanoporous crystalline reticular materials with deep generative models
External validation demonstrates limited clinical utility of the interpretable mortality prediction model for patients with COVID-19
Apply rich psychological terms in AI with care
Interpretable meta-score for model performance
AI, COVID-19 and the long haul
Multiple stakeholders drive diverse interpretability requirements for machine learning in healthcare
Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers
A typology for exploring the mitigation of shortcut behaviour
Bringing artificial intelligence to business management
Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks
Versatile modular neural locomotion control with fast learning
Road safety, health inequity and the imminence of autonomous vehicles
Author Correction: Autoencoders on field-programmable gate arrays for real-time, unsupervised new physics detection at 40 MHz at the Large Hadron Collider
Improved protein structure prediction by deep learning irrespective of co-evolution information
Modelling human tool use in robots
A voice for tech workers
Modern maxims for an AI oracle
Deep learning prediction of ribosome profiling with Translatomer reveals translational regulation and interprets disease variants
Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis
Materiality and risk in the age of pervasive AI sensors
Multimodal data fusion for cancer biomarker discovery with deep learning
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
Low-power object-detection challenge on unmanned aerial vehicles
The citizen at the centre of ethics
Simultaneous dimensionality reduction and integration for single-cell ATAC-seq data using deep learning
Predictive control of aerial swarms in cluttered environments
Real-world embodied AI through a morphologically adaptive quadruped robot
The need for reproducible research in soft robotics
Database-independent molecular formula annotation using Gibbs sampling through ZODIAC
Guidelines for ethical use and acknowledgement of large language models in academic writing
Clustering single-cell RNA-seq data with a model-based deep learning approach
Long short-term memory networks in memristor crossbar arrays
Don't pause giant AI for the wrong reasons
Clinically applicable deep learning framework for organs at risk delineation in CT images
Fast, scale-adaptive and uncertainty-aware downscaling of Earth system model fields with generative machine learning
Continuous improvement of self-driving cars using dynamic confidence-aware reinforcement learning
The perils of machine learning in designing new chemicals and materials
Variational neural annealing
Reply to: Modern graph neural networks do worse than classical greedy algorithms in solving combinatorial optimization problems like maximum independent set
Biological research and self-driving labs in deep space supported by artificial intelligence
Publisher Correction: Database-independent molecular formula annotation using Gibbs sampling through ZODIAC
Maximum diffusion reinforcement learning
Protein-protein contact prediction by geometric triangle-aware protein language models