:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Boyao, Thomson, Alexander J., Nassif, Houssam, Engelhard, Matthew M., Page, David
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2305.17583
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Balancing Interpretability and Flexibility in Modeling Diagnostic Trajectories with an Embedded Neural Hawkes Process Model
di: Zhao, Yuankang, et al.
Pubblicazione: (2025)

Infinite hierarchical contrastive clustering for personal digital envirotyping
di: Huang, Ya-Yun, et al.
Pubblicazione: (2025)

Interval-Based AUC (iAUC): Extending ROC Analysis to Uncertainty-Aware Classification
di: Li, Yuqi, et al.
Pubblicazione: (2026)

Probabilistic Graphical Model using Graph Neural Networks for Bayesian Inversion of Discrete Structural Component States
di: Li, Teng, et al.
Pubblicazione: (2026)

Best of Three Worlds: Adaptive Experimentation for Digital Marketing in Practice
di: Fiez, Tanner, et al.
Pubblicazione: (2024)

Borrowing From the Future: Enhancing Early Risk Assessment through Contrastive Learning
di: Sun, Minghui, et al.
Pubblicazione: (2025)

CLEAR: Unlearning Spurious Style-Content Associations with Contrastive LEarning with Anti-contrastive Regularization
di: Sun, Minghui, et al.
Pubblicazione: (2025)

Rational Tuning of LLM Cascades via Probabilistic Modeling
di: Zellinger, Michael J., et al.
Pubblicazione: (2025)

Probabilistic Graphical Models: A Concise Tutorial
di: Maasch, Jacqueline, et al.
Pubblicazione: (2025)

How do Probabilistic Graphical Models and Graph Neural Networks Look at Network Data?
di: Lapenna, Michela, et al.
Pubblicazione: (2025)

MALADE: Orchestration of LLM-powered Agents with Retrieval Augmented Generation for Pharmacovigilance
di: Choi, Jihye, et al.
Pubblicazione: (2024)

Multimodal Training to Unimodal Deployment: Leveraging Unstructured Data During Training to Optimize Structured Data Only Deployment
di: Wang, Zigui, et al.
Pubblicazione: (2026)

Fixed Budget is No Harder Than Fixed Confidence in Best-Arm Identification up to Logarithmic Factors
di: Balagopalan, Kapilan, et al.
Pubblicazione: (2026)

Knowledge-Aided Semantic Communication Leveraging Probabilistic Graphical Modeling
di: Wan, Haowen, et al.
Pubblicazione: (2024)

Fast-PGM: Fast Probabilistic Graphical Model Learning and Inference
di: Jiang, Jiantong, et al.
Pubblicazione: (2024)

Addressing Class Imbalance with Probabilistic Graphical Models and Variational Inference
di: Lou, Yujia, et al.
Pubblicazione: (2025)

Probabilistic Multilabel Graphical Modelling of Motif Transformations in Symbolic Music
di: Taieb, Ron, et al.
Pubblicazione: (2026)

Probabilistic Functional Neural Networks
di: Wang, Haixu, et al.
Pubblicazione: (2025)

Learning to Guide Local Search for MPE Inference in Probabilistic Graphical Models
di: Malhotra, Brij, et al.
Pubblicazione: (2026)

Semiparametric Efficient Test for Interpretable Distributional Treatment Effects
di: Zenati, Houssam, et al.
Pubblicazione: (2026)

Federated Learning with Neural Graphical Models
di: Chajewska, Urszula, et al.
Pubblicazione: (2023)

Covariate-dependent Graphical Model Estimation via Neural Networks with Statistical Guarantees
di: Lin, Jiahe, et al.
Pubblicazione: (2025)

NEST: Nested Event Stream Transformer for Sequences of Multisets
di: Sun, Minghui, et al.
Pubblicazione: (2026)

SGM-PINN: Sampling Graphical Models for Faster Training of Physics-Informed Neural Networks
di: Anticev, John, et al.
Pubblicazione: (2024)

Flexible Infinite-Width Graph Convolutional Neural Networks
di: Anson, Ben, et al.
Pubblicazione: (2024)

Infinite Width Limits of Self Supervised Neural Networks
di: Fleissner, Maximilian, et al.
Pubblicazione: (2024)

Adaptive Variational Inference in Probabilistic Graphical Models: Beyond Bethe, Tree-Reweighted, and Convex Free Energies
di: Leisenberger, Harald, et al.
Pubblicazione: (2025)

Ranking-Aware Calibration for Reliable Multimodal Reinforcement Learning
di: Cui, Peng, et al.
Pubblicazione: (2026)

On the Relationship between Bayesian Networks and Probabilistic Structural Causal Models
di: Lucas, Peter J. F., et al.
Pubblicazione: (2026)

Learning to Solve the Constrained Most Probable Explanation Task in Probabilistic Graphical Models
di: Arya, Shivvrat, et al.
Pubblicazione: (2024)

Benchmarking Probabilistic Time Series Forecasting Models on Neural Activity
di: Lu, Ziyu, et al.
Pubblicazione: (2025)

Partially Stochastic Infinitely Deep Bayesian Neural Networks
di: Calvo-Ordonez, Sergio, et al.
Pubblicazione: (2024)

Predicting Biomedical Interactions with Probabilistic Model Selection for Graph Neural Networks
di: KC, Kishan, et al.
Pubblicazione: (2022)

Depth Separation in Norm-Bounded Infinite-Width Neural Networks
di: Parkinson, Suzanna, et al.
Pubblicazione: (2024)

Exact and Approximate MCMC for Doubly-intractable Probabilistic Graphical Models Leveraging the Underlying Independence Model
di: Chen, Yujie, et al.
Pubblicazione: (2025)

Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model
di: Wang, Xinjue, et al.
Pubblicazione: (2022)

Probabilistic Demand Forecasting with Graph Neural Networks
di: Kozodoi, Nikita, et al.
Pubblicazione: (2024)

Probabilistic Calibration by Design for Neural Network Regression
di: Dheur, Victor, et al.
Pubblicazione: (2024)

BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling
di: Huang, Hengguan, et al.
Pubblicazione: (2024)

Defending Large Language Models Against Attacks With Residual Stream Activation Analysis
di: Kawasaki, Amelia, et al.
Pubblicazione: (2024)