CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.
Cross-attention is all you need: Adapt- ing pretrained transformers for machine translation
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
method 1
citation-polarity summary
roles
method 1polarities
background 1representative citing papers
GRaF learns a scene-independent latent RF radiance field from proximate transmitters via an interpolation theory, then uses neural ray tracing to synthesize spectra at new transmitter or receiver positions.
LLMs using in-context learning and fine-tuning on listener experiment data generate equalization settings that align better with population preferences than random sampling or static presets.
Proposes causal fingerprints via causality-decoupling in pre-trained diffusion residual latent space for improved source attribution across GANs and diffusion models.
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.
citing papers explorer
-
Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
CaTR applies value-decomposed RL with hierarchical conflict-aware observations to achieve better safety-efficiency trade-offs than planning, optimization, and standard RL baselines in a realistic airport taxiway simulation.
-
Generalizable Radio-Frequency Radiance Fields for Spatial Spectrum Synthesis
GRaF learns a scene-independent latent RF radiance field from proximate transmitters via an interpolation theory, then uses neural ray tracing to synthesize spectra at new transmitter or receiver positions.
-
One Prompt, Many Sounds: Modeling Listener Variability in LLM-Based Equalization
LLMs using in-context learning and fine-tuning on listener experiment data generate equalization settings that align better with population preferences than random sampling or static presets.
-
Causal Fingerprints of AI Generative Models
Proposes causal fingerprints via causality-decoupling in pre-trained diffusion residual latent space for improved source attribution across GANs and diffusion models.
-
Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey
A comprehensive survey of PEFT algorithms for large models, covering their performance, overhead, applications, and real-world system implementations.