Copertina

Adaptive teachers for amortized samplers

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Kim, Minsu, Choi, Sanghyeok, Yun, Taeyoung, Bengio, Emmanuel, Feng, Leo, Rector-Brooks, Jarrid, Ahn, Sungsoo, Park, Jinkyoo, Malkin, Nikolay, Bengio, Yoshua
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

Local Search GFlowNets
di: Kim, Minsu, et al.
Pubblicazione: (2023)

Improved off-policy training of diffusion samplers
di: Sendera, Marcin, et al.
Pubblicazione: (2024)

Active Attacks: Red-teaming LLMs via Adaptive Environments
di: Yun, Taeyoung, et al.
Pubblicazione: (2025)

Improved Off-policy Reinforcement Learning in Biological Sequence Design
di: Kim, Hyeonah, et al.
Pubblicazione: (2024)

Ant Colony Sampling with GFlowNets for Combinatorial Optimization
di: Kim, Minsu, et al.
Pubblicazione: (2024)

Learning to Scale Logits for Temperature-Conditional GFlowNets
di: Kim, Minsu, et al.
Pubblicazione: (2023)

Action abstractions for amortized sampling
di: Boussif, Oussama, et al.
Pubblicazione: (2024)

Improving and generalizing flow-based generative models with minibatch optimal transport
di: Tong, Alexander, et al.
Pubblicazione: (2023)

From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster training
di: Berner, Julius, et al.
Pubblicazione: (2025)

Machine learning and information theory concepts towards an AI Mathematician
di: Bengio, Yoshua, et al.
Pubblicazione: (2024)

Amortizing intractable inference in diffusion models for vision, language, and control
di: Venkatraman, Siddarth, et al.
Pubblicazione: (2024)

On Generalization for Generative Flow Networks
di: Krichel, Anas, et al.
Pubblicazione: (2024)

In-Context Parametric Inference: Point or Distribution Estimators?
di: Mittal, Sarthak, et al.
Pubblicazione: (2025)

Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
di: Kim, Hyeonah, et al.
Pubblicazione: (2023)

Decoupled Sequence and Structure Generation for Realistic Antibody Design
di: Kim, Nayoung, et al.
Pubblicazione: (2024)

Delta-AI: Local objectives for amortized inference in sparse graphical models
di: Falet, Jean-Pierre, et al.
Pubblicazione: (2023)

Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
di: Kim, Minsu, et al.
Pubblicazione: (2023)

Solving Bayesian inverse problems with diffusion priors and off-policy RL
di: Scimeca, Luca, et al.
Pubblicazione: (2025)

Genetic-guided GFlowNets for Sample Efficient Molecular Optimization
di: Kim, Hyeonah, et al.
Pubblicazione: (2024)

Offline Model-Based Optimization: Comprehensive Review
di: Kim, Minsu, et al.
Pubblicazione: (2025)

Discrete Probabilistic Inference as Control in Multi-path Environments
di: Deleu, Tristan, et al.
Pubblicazione: (2024)

Baking Symmetry into GFlowNets
di: Ma, George, et al.
Pubblicazione: (2024)

Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
di: Venkatraman, Siddarth, et al.
Pubblicazione: (2025)

MOFFlow: Flow Matching for Structure Prediction of Metal-Organic Frameworks
di: Kim, Nayoung, et al.
Pubblicazione: (2024)

Pessimistic Backward Policy for GFlowNets
di: Jang, Hyosoon, et al.
Pubblicazione: (2024)

On scalable and efficient training of diffusion samplers
di: Kim, Minkyu, et al.
Pubblicazione: (2025)

Latent Veracity Inference for Identifying Errors in Stepwise Reasoning
di: Kim, Minsu, et al.
Pubblicazione: (2025)

Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
di: Son, Jiwoo, et al.
Pubblicazione: (2023)

Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
di: Akhound-Sadegh, Tara, et al.
Pubblicazione: (2024)

Energy-based generator matching: A neural sampler for general state space
di: Woo, Dongyeop, et al.
Pubblicazione: (2025)

Adaptive Inference-Time Scaling via Cyclic Diffusion Search
di: Lee, Gyubin, et al.
Pubblicazione: (2025)

Diffusion Alignment as Variational Expectation-Maximization
di: Lee, Jaewoo, et al.
Pubblicazione: (2025)

Discrete, compositional, and symbolic representations through attractor dynamics
di: Nam, Andrew, et al.
Pubblicazione: (2023)

Solving Integer Linear Programming with Parallel Tempering
di: Sim, Kyuil, et al.
Pubblicazione: (2026)

torchgfn: A PyTorch GFlowNet library
di: Viviano, Joseph D., et al.
Pubblicazione: (2023)

Expected flow networks in stochastic environments and two-player zero-sum games
di: Jiralerspong, Marco, et al.
Pubblicazione: (2023)

GFlowNet Foundations
di: Bengio, Yoshua, et al.
Pubblicazione: (2021)

Learning Decision Trees as Amortized Structure Inference
di: Mahfoud, Mohammed, et al.
Pubblicazione: (2025)

Amortizing intractable inference in large language models
di: Hu, Edward J., et al.
Pubblicazione: (2023)

Noticing the Watcher: LLM Agents Can Infer CoT Monitoring from Blocking Feedback
di: Jiralerspong, Thomas, et al.
Pubblicazione: (2026)