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Results for Bayesian deep learning

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11 papers · ranked by Valyu relevance

01
BbioRxiv8 Sep 2026
Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces
Yi Ren, Zimei Chen, Hayden Deans, Ying Nian Wu + 1 more
Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the…
72relevance
02
BbioRxiv8 Sep 2026
Biologically grounded locality priors close the data gap for vision transformers in neural prediction
Matteo Farina, Pietro Zamberlan, Arno Onken, Ulisse Ferrari
For datasets with thousands of neurons and images, vision transformers have proven successful at predicting neural responses to stimuli. However, they are expected to underperform in low-data regimes, where CNNs and Gaussian processes are considered more effective. We ask whether transformers can be made competitive…
71relevance
03
PPubMed5 Sep 2026
Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control
Hayato Idei, Keisuke Suzuki, Yuichi Yamashita
Mindfulness has established psychological benefits, such as stress reduction and emotional regulation; however, the underlying computational mechanisms, particularly in relation to mind-wandering, remain unclear. This study aimed to present a hierarchical recurrent neural network-based agent model grounded in the free…
64relevance
04
PPubMed5 Sep 2026
Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight
Yasin Topcu, Alper Adak, Halim Can Kayikci, Serkan Aydin + 6 more
Key message Tomato fruit weight is primarily controlled by stable genetic effects and domestication-/improvement-associated loci, including prominent chromosome 5 signals, and integrating SNP, INDEL, and SV diversity improves candidate-locus discovery, biological interpretation, and genomic prediction across…
63relevance
05
BbioRxiv8 Sep 2026
scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data
Jackie Rao, Muntadher Jihad, Giulia Biffi, Paul D.W. Kirk
Identifying cell types from single-cell RNA sequencing (scRNA-seq) data typically requires several separate and often uninterpretable steps: dimensionality reduction, batch-correction, clustering, marker-gene identification and the discovery of finer-grained structure. Here we introduce scFLAME (single-cell Factor…
61relevance
06
BbioRxiv5 Sep 2026
Forecasting viral evolution from phylogenetic trees
Ivan Specht, Soyoon Park, Seyone Chithrananda, Claudia L. Driscoll + 3 more
Viral mutation forecasting plays a key role in pandemic preparedness by enabling researchers to anticipate novel variants and design proactive interventions. Evolutionary histories, represented as phylogenetic trees, offer key insights into the emergence of past and present strains, yet their role in predicting future…
61relevance
07
PPubMed7 Sep 2026
Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data
Xiaoyue Hu, Yuhao Ma, Ruixing Ming, Heping Zhang + 1 more
Identifying essential biomarkers remains a core challenge in elucidating the pathogenic mechanisms and achieving precise diagnosis of complex diseases. Deep neural networks offer immense predictive power, yet their lack of interpretability severely limits downstream biological insight. Here, we introduce DeepVaris, an…
60relevance
08
PPubMed5 Sep 2026
Deep learning-based multi-class misbehavior classification in fog-enabled vehicular ad hoc networks
Hadeer Kamal, Amira Y. Haikal, Mahmoud M. Saafan
Traffic collisions and congestion represent significant challenges within intelligent transportation systems (ITS). Consequently, a vehicular ad-hoc network (VANET) has been established. Numerous architectures have been incorporated into VANETs to manage the extensive data generated by vehicles. Collaboration with fog…
57relevance
09
BbioRxiv7 Sep 2026
A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data
Zichao Jin, Jiaoru Wang, Wenjiang Huang, Jingcheng Zhang + 2 more
Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework.…
55relevance
10
PPubMed8 Sep 2026
Cross-docking and redocking reveal distinct determinants of success in physics-based and AI-driven binding pose prediction in protein–ligand complexes
Kapali Suri, Anshul Yadav, Abhishek Tripathi, N. Arul Murugan
Protein-ligand pose prediction is central to structure-based drug discovery, yet the relative performance of physics-based and AI-driven methods under realistic cross-docking conditions remains insufficiently characterized. Here, we compare physics-based docking methods (AutoDock4, AutoDock Vina, and DOCK 6) with…
53relevance
11
BbioRxiv7 Sep 2026
Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions
Priyanka Bhutada, Nitin Goyal, Tatsam K. Lakhankiya, Sai D. Narahari + 8 more
Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI…
52relevance

Results for Bayesian deep learning

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control

Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data

Forecasting viral evolution from phylogenetic trees

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

Deep learning-based multi-class misbehavior classification in fog-enabled vehicular ad hoc networks

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Cross-docking and redocking reveal distinct determinants of success in physics-based and AI-driven binding pose prediction in protein–ligand complexes

Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions

Concurrent model evidence computation and posterior sampling in continuous attractor network subspaces

Biologically grounded locality priors close the data gap for vision transformers in neural prediction

Awareness of being: a computational neurophenomenological model of mindfulness, mind-wandering, and meta-attentional control

Multi-variant GWAS and genomic prediction dissect the genetic architecture underlying tomato fruit weight

scFLAME: a unified generative model for interpretable clustering, hierarchical structure discovery and marker-gene identification in single-cell RNA-seq data

Forecasting viral evolution from phylogenetic trees

Interpretable deep neural network identifies robust biomarkers for diseases with mechanistic insights from omics data

Deep learning-based multi-class misbehavior classification in fog-enabled vehicular ad hoc networks

A physics-informed hybrid deep learning model for spatiotemporal rice disease prediction using multi-source data

Cross-docking and redocking reveal distinct determinants of success in physics-based and AI-driven binding pose prediction in protein–ligand complexes

Real Science Is Harder Than Benchmarks: Evaluating Advanced AI Frameworks on Published Studies. II. Antibody Properties, Lipid-RNA Interactions