Copertina

Top-Down Bayesian Posterior Sampling for Sum-Product Networks

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Yokoi, Soma, Sato, Issei
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Power Distribution Bridges Sampling, Self-Reward RL, and Self-Distillation
di: Tomihari, Akiyoshi, et al.
Pubblicazione: (2026)

Can Test-time Computation Mitigate Reproduction Bias in Neural Symbolic Regression?
di: Sato, Shun, et al.
Pubblicazione: (2025)

Max-pooling Network Revisited: Analyzing the Role of Semantic Probability in Multiple Instance Learning for Hallucination Detection
di: Fujikawa, Shota, et al.
Pubblicazione: (2026)

End-to-End Training Induces Information Bottleneck through Layer-Role Differentiation: A Comparative Analysis with Layer-wise Training
di: Sakamoto, Keitaro, et al.
Pubblicazione: (2024)

Benign Overfitting in Token Selection of Attention Mechanism
di: Sakamoto, Keitaro, et al.
Pubblicazione: (2024)

Multiplicative Logit Adjustment Approximates Neural-Collapse-Aware Decision Boundary Adjustment
di: Hasegawa, Naoya, et al.
Pubblicazione: (2024)

On Expressive Power of Looped Transformers: Theoretical Analysis and Enhancement via Timestep Encoding
di: Xu, Kevin, et al.
Pubblicazione: (2024)

Understanding Linear Probing then Fine-tuning Language Models from NTK Perspective
di: Tomihari, Akiyoshi, et al.
Pubblicazione: (2024)

Explaining Grokking and Information Bottleneck through Neural Collapse Emergence
di: Sakamoto, Keitaro, et al.
Pubblicazione: (2025)

Exploring Weight Balancing on Long-Tailed Recognition Problem
di: Hasegawa, Naoya, et al.
Pubblicazione: (2023)

Bayesian Symbolic Regression via Posterior Sampling
di: Bomarito, Geoffrey F., et al.
Pubblicazione: (2025)

Rethinking Associative Memory Mechanism in Induction Head
di: Wang, Shuo, et al.
Pubblicazione: (2024)

On the Optimal Memorization Capacity of Transformers
di: Kajitsuka, Tokio, et al.
Pubblicazione: (2024)

Understanding Generalization in Physics Informed Models through Affine Variety Dimensions
di: Koshizuka, Takeshi, et al.
Pubblicazione: (2025)

To CoT or To Loop? A Formal Comparison Between Chain-of-Thought and Looped Transformers
di: Xu, Kevin, et al.
Pubblicazione: (2025)

Fix Initial Codes and Iteratively Refine Textual Directions Toward Safe Multi-Turn Code Correction
di: Tanaka, Yuto, et al.
Pubblicazione: (2026)

Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
di: Takeno, Shion, et al.
Pubblicazione: (2023)

A Formal Comparison Between Chain of Thought and Latent Thought
di: Xu, Kevin, et al.
Pubblicazione: (2025)

Gibbs Sampling the Posterior of Neural Networks
di: Piccioli, Giovanni, et al.
Pubblicazione: (2023)

Outlier-robust Diffusion Posterior Sampling for Bayesian Inverse Problems
di: Yang, Yiming, et al.
Pubblicazione: (2026)

Understanding Transformer Optimization via Gradient Heterogeneity
di: Tomihari, Akiyoshi, et al.
Pubblicazione: (2025)

Are Transformers with One Layer Self-Attention Using Low-Rank Weight Matrices Universal Approximators?
di: Kajitsuka, Tokio, et al.
Pubblicazione: (2023)

Optimizing Posterior Samples for Bayesian Optimization via Rootfinding
di: Adebiyi, Taiwo A., et al.
Pubblicazione: (2024)

Practical Bayesian Algorithm Execution via Posterior Sampling
di: Cheng, Chu Xin, et al.
Pubblicazione: (2024)

On the Convergence of Locally Adaptive and Scalable Diffusion-Based Sampling Methods for Deep Bayesian Neural Network Posteriors
di: Rensmeyer, Tim, et al.
Pubblicazione: (2024)

Regret Analysis of Posterior Sampling-Based Expected Improvement for Bayesian Optimization
di: Takeno, Shion, et al.
Pubblicazione: (2025)

On the Interplay of Priors and Overparametrization in Bayesian Neural Network Posteriors
di: Kobialka, Julius, et al.
Pubblicazione: (2026)

Provable Diffusion Posterior Sampling for Bayesian Inversion
di: Chang, Jinyuan, et al.
Pubblicazione: (2025)

Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
di: Roderick, Melrose, et al.
Pubblicazione: (2023)

Understanding the Expressivity and Trainability of Fourier Neural Operator: A Mean-Field Perspective
di: Koshizuka, Takeshi, et al.
Pubblicazione: (2023)

Calibrating the Predictions for Top-N Recommendations
di: Sato, Masahiro
Pubblicazione: (2024)

Sketched Sum-Product Networks for Joins
di: Tsan, Brian, et al.
Pubblicazione: (2025)

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics
di: Paulin, Daniel, et al.
Pubblicazione: (2024)

Forward Learning with Top-Down Feedback: Empirical and Analytical Characterization
di: Srinivasan, Ravi, et al.
Pubblicazione: (2023)

Posterior Sampling for Continuing Environments
di: Xu, Wanqiao, et al.
Pubblicazione: (2022)

Q-learning with Posterior Sampling
di: Agrawal, Priyank, et al.
Pubblicazione: (2025)

Productively Deploying Emerging Models on Emerging Platforms: A Top-Down Approach for Testing and Debugging
di: Feng, Siyuan, et al.
Pubblicazione: (2024)

Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling
di: Xu, Jian, et al.
Pubblicazione: (2024)

Pareto Set Identification With Posterior Sampling
di: Kone, Cyrille, et al.
Pubblicazione: (2024)

Conditional Flow Matching for Bayesian Posterior Inference
di: Zhai, Percy S., et al.
Pubblicazione: (2025)