arXiv:2312.09323
Perspectives on the State and Future of Deep Learning -- 2023
원문 초록
The goal of this series is to chronicle opinions and issues in the field of machine learning as they stand today and as they change over time. The plan is to host this survey periodically until the AI singularity paperclip-frenzy-driven doomsday, keeping an updated list of topical questions and interviewing new community members for each edition. In this issue, we probed people's opinions on interpretable AI, the value of benchmarking in modern NLP, the state of progress towards understanding deep learning, and the future of academia.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
B-XAIC Dataset: Benchmarking Explainable AI for Graph Neural Networks Using Chemical Data
SenseShift6D: Multimodal RGB-D Benchmarking for Robust 6D Pose Estimation across Environment and Sensor Variations
DVFL-Net: A Lightweight Distilled Video Focal Modulation Network for Spatio-Temporal Action Recognition
DCR: Quantifying Data Contamination in LLMs Evaluation
FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning
DrafterBench: Benchmarking Large Language Models for Tasks Automation in Civil Engineering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.