Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Thomas J. Brock is a CFA and CPA with more ...

Vector Post-Training Quantization (VPTQ) is a novel Post-Training Quantization method that leverages Vector Quantization to high accuracy on LLMs at an extremely low bit-width (<2-bit). VPTQ can ...

[2025.09.25]: 🔥🔥🔥 We released a toolkit that tests the impact of numerical precision and enables deterministic LLM inference. This helps eliminate the training–inference mismatch in reinforcement ...

Abstract: Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This ...

Abstract: Adversarial examples (AEs) are typical model evasion attacks and security threats in deep neural networks (DNNs). One of the countermeasures is adversarial training (AT), and it trains DNNs ...