Investopedia

Understanding Simple Random Sampling: Key Advantages and Limitations

Melissa Horton is a financial literacy professional. She has 10+ years of experience in the financial services and planning industry. Robert Kelly is managing director of XTS Energy LLC, and has more ...
GitHub

leeroopedia/workflow-openrlhf-openrlhf-rejection-sampling

What is this? Rejection sampling (also called best-of-N training or iterative conditional SFT) is a technique for aligning language models using a reward model without the complexity of reinforcement ...
IEEE

An Approximate Digital CIM Macro With Low-Power Multiply-Add Units and Dynamic Sparse-Adaptive Configuring for Edge AI Inference

Abstract: This letter presents an approximate digital compute-in-memory (CIM) macro for low-power edge AI inference. It introduces three hierarchical innovations: 1) novel fused approximate ...
IEEE

Deep Learning and Low-Discrepancy Sampling for Efficient Approximate Dynamic Programming

Approximate dynamic programming (ADP) is the standard technique to derive optimal policies in finite-horizon stochastic multistage optimal decision problems, with continuous state space. Yet, it ...
GitHub

Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling

This repository contains the implementation of our EMNLP 2025 paper Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling. We ...