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Current evaluation metrics for trajectory inference can mislead researchers, but functional KL divergence offers a clearer, more reliable comparison of methods in sparse data conditions.
LIME leverages LLM-generated narratives to create a scalable surgical dataset, but SurgLIME's innovative approach ensures that noisy text doesn't compromise model performance.
DQPOPE achieves the same sample efficiency as traditional OPE methods while providing a comprehensive return distribution, leading to significantly more accurate policy evaluations.
Achieve flicker-free, scalable reconstruction of long dynamic videos by blending the best of stream and clip-based Gaussian Splatting.
Speculative decoding can be sped up by >2x without sacrificing accuracy by rescuing previously rejected tokens that are semantically valid but lexically different.
