LatentHDR generates structurally consistent panoramic HDR images by producing one scene latent with a diffusion backbone then deterministically mapping it to multiple exposure latents via a lightweight conditional head.
Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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ARC-RL is a new suite of four MuJoCo continuous-control environments featuring game-inspired hexapod and quadruped morphologies, a single closed-form multi-component reward function, CPG demonstrators, and empirical comparisons of online and offline-to-online RL algorithms.
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.
SOL is a new hierarchical RL algorithm that reaches 35x higher throughput and outperforms flat agents when trained on 30 billion frames in NetHack while showing positive scaling.
RL policies decompose into information-regularized primitives that compete by requesting state information amounts, with the greediest one acting, yielding better generalization than flat or hierarchical baselines.
citing papers explorer
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LatentHDR: Decoupling Exposure from Diffusion via Conditional Latent-to-Latent Mapping for Text/Image-to-Panoramic HDR
LatentHDR generates structurally consistent panoramic HDR images by producing one scene latent with a diffusion backbone then deterministically mapping it to multiple exposure latents via a lightweight conditional head.
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Seeing enough: non-reference perceptual resolution selection for power-efficient client-side rendering
A neural network trained on full-reference perceptual quality labels predicts minimal sufficient resolution for rendered video to enable power-efficient client-side rendering.
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Scalable Option Learning in High-Throughput Environments
SOL is a new hierarchical RL algorithm that reaches 35x higher throughput and outperforms flat agents when trained on 30 billion frames in NetHack while showing positive scaling.
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Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives
RL policies decompose into information-regularized primitives that compete by requesting state information amounts, with the greediest one acting, yielding better generalization than flat or hierarchical baselines.