Genre
Semantic Density: Uncertainty Quantification for Large Language Models through Confidence Measurement in Semantic Space
A new framework is proposed in this paper to address these issues.
InfoRM: Mitigating Reward Hacking in RLHF via Information-Theoretic Reward Modeling Y uchun Miao
With the advent of large language models (LLMs), reinforcement learning from human feedback (RLHF) has emerged as a pivotal technological paradigm to align models' behaviors with human values [
Kernel PCA for Out-of-Distribution Detection Kun Fang
Out-of-Distribution (OoD) detection is vital for the reliability of Deep Neural Networks (DNNs).
ChronoEpilogi: Scalable Time-Series Variable Selection with Multiple Solutions
Lasso performance, with the added benefit of providing multiple solutions.
Meta-Controller: Few-Shot Imitation of Unseen Embodiments and Tasks in Continuous Control
Robot embodiments vary widely in their morphological ( e.g., number and connectivity
The Benefits of Balance: From Information Projections to Variance Reduction
ERM combines two well-understood techniques: mini-batch sampling and gradient-based optimization using backpropagation.
Certified Adversarial Robustness via Randomized α-Smoothing for Regression Models
Certified adversarial robustness of large-scale deep networks has progressed substantially after the introduction of randomized smoothing.