Jul 10, 2023 · Models are interpretable when machine learning (ML) practitioners can readily understand the reasoning behind their predictions.
Jul 10, 2023 · Models are interpretable when machine learning (ML) practitioners can readily understand the reasoning behind their predictions.
Duration: 0:35
Posted: Jul 7, 2023
Posted: Jul 7, 2023
Jul 13, 2023 · From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey | Proceedings of the 2023 ACM Designing Interactive ...
From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey. N Ashtari, R Mullins, C Qian, J Wexler, I Tenney, M Pushkarna.
From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey. Narges Ashtari, Ryan Mullins, Crystal Qian, James Wexler, Ian ...
2020. From Discovery to Adoption: Understanding the ML Practitioners' Interpretability Journey. N Ashtari, R Mullins, C Qian, J Wexler, I Tenney, M Pushkarna.
Apr 1, 2024 · From Discovery to Adoption: Understanding the ML. Practitioners' Interpretability Journey. In Proceedings of the 2023 ACM Designing.
The advent of Large Language Models (LLMs) has transformed the landscape of artificial intelligence, providing unprecedented capabilities in natural language ...
The goal of mechanistic interpretability in deep learning is to make deep models more transparent and interpretable by understanding how and why model decisions ...