Cover Image

Probabilistic Learning and Generation in Deep Sequence Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Chen, Wenlong
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

On the Efficient Marginalization of Probabilistic Sequence Models
by: Boyd, Alex
Published: (2024)

Bayesian Computation in Deep Learning
by: Chen, Wenlong, et al.
Published: (2025)

Probabilistic Modeling for Sequences of Sets in Continuous-Time
by: Chang, Yuxin, et al.
Published: (2023)

Deep Neural Operator Learning for Probabilistic Models
by: Bayraktar, Erhan, et al.
Published: (2025)

Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
by: Khot, Ayush, et al.
Published: (2024)

Memory by Design: Probabilistic Sequence Layers
by: Dowling, Matthew, et al.
Published: (2026)

Calibrated Probabilistic Forecasts for Arbitrary Sequences
by: Marx, Charles, et al.
Published: (2024)

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
by: Papež, Milan, et al.
Published: (2025)

Generative AI-enhanced Probabilistic Multi-Fidelity Surrogate Modeling Via Transfer Learning
by: Zeng, Jice, et al.
Published: (2026)

Modes of Sequence Models and Learning Coefficients
by: Chen, Zhongtian, et al.
Published: (2025)

Compositional Deep Probabilistic Models of DNA Encoded Libraries
by: Chen, Benson, et al.
Published: (2023)

Learning Evolving Latent Strategies for Multi-Agent Language Systems without Model Fine-Tuning
by: Tang, Wenlong
Published: (2025)

Deep Generative Spatiotemporal Engression for Probabilistic Forecasting of Epidemics
by: Pathak, Rajdeep, et al.
Published: (2026)

Deep Sequence-to-Sequence Models for GNSS Spoofing Detection
by: Zelinka, Jan, et al.
Published: (2025)

Conditionally Whitened Generative Models for Probabilistic Time Series Forecasting
by: Yang, Yanfeng, et al.
Published: (2025)

A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation
by: Hosseini, Ryien, et al.
Published: (2024)

Decentralized Federated Learning of Probabilistic Generative Classifiers
by: Pérez, Aritz, et al.
Published: (2025)

Probabilistic Robustness in Deep Learning: A Concise yet Comprehensive Guide
by: Zhao, Xingyu
Published: (2025)

Conditional Sequence Modeling for Safe Reinforcement Learning
by: Bai, Wensong, et al.
Published: (2026)

Multistage Collaborative Knowledge Distillation from a Large Language Model for Semi-Supervised Sequence Generation
by: Zhao, Jiachen, et al.
Published: (2023)

Wormhole: Concept-Aware Deep Representation Learning for Co-Evolving Sequences
by: Xu, Kunpeng, et al.
Published: (2024)

Stochastic Thermodynamics of Learning Parametric Probabilistic Models
by: Parsi, Shervin Sadat
Published: (2023)

A Probabilistic Model for Non-Contrastive Learning
by: Fleissner, Maximilian, et al.
Published: (2025)

Exploring Probabilistic Models for Semi-supervised Learning
by: Wang, Jianfeng
Published: (2024)

PipelineRL: Faster On-policy Reinforcement Learning for Long Sequence Generation
by: Piché, Alexandre, et al.
Published: (2025)

Contextual Plackett-Luce: An Efficient Neural Model for Probabilistic Sequence Selection under Ambiguity
by: Mizrachi, Noam, et al.
Published: (2026)

DeepDFA: Automata Learning through Neural Probabilistic Relaxations
by: Umili, Elena, et al.
Published: (2024)

Neural Sequence-to-Sequence Modeling with Attention by Leveraging Deep Learning Architectures for Enhanced Contextual Understanding in Abstractive Text Summarization
by: Challagundla, Bhavith Chandra, et al.
Published: (2024)

Deep Dynamic Probabilistic Canonical Correlation Analysis
by: Tang, Shiqin, et al.
Published: (2025)

Calibrated Value-Aware Model Learning with Probabilistic Environment Models
by: Voelcker, Claas, et al.
Published: (2025)

EnTransformer: A Deep Generative Transformer for Multivariate Probabilistic Forecasting
by: Pathak, Rajdeep, et al.
Published: (2026)

Randomness Helps Rigor: A Probabilistic Learning Rate Scheduler Bridging Theory and Deep Learning Practice
by: Devapriya, Dahlia, et al.
Published: (2024)

Contrastive Deep Learning for Variant Detection in Wastewater Genomic Sequencing
by: Chinda, Adele, et al.
Published: (2025)

Data-Efficient Task Generalization via Probabilistic Model-based Meta Reinforcement Learning
by: Bhardwaj, Arjun, et al.
Published: (2023)

On Discriminative Probabilistic Modeling for Self-Supervised Representation Learning
by: Wang, Bokun, et al.
Published: (2024)

Stochastic Deep Learning: A Probabilistic Framework for Modeling Uncertainty in Structured Temporal Data
by: Rice, James
Published: (2026)

Calibrating Transformers via Sparse Gaussian Processes
by: Chen, Wenlong, et al.
Published: (2023)

Probabilistic Modeling of Latent Agentic Substructures in Deep Neural Networks
by: Lee, Su Hyeong, et al.
Published: (2025)

Deep Continuous-Time State-Space Models for Marked Event Sequences
by: Chang, Yuxin, et al.
Published: (2024)

Hierarchical Multi-Label Generation with Probabilistic Level-Constraint
by: Chen, Linqing, et al.
Published: (2025)