Deep Learning
LargeLanguageModelsareZero-ShotReasoners
Notably,chainofthought(CoT)prompting, a recent technique for eliciting complex multi-step reasoning through step-bystep answer examples, achieved the state-of-the-art performances in arithmetics and symbolic reasoning, difficultsystem-2 tasks that do not follow the standard scaling laws for LLMs. While these successes are often attributed to LLMs' ability forfew-shot learning, weshowthatLLMs aredecentzero-shotreasoners by simply adding "Let's think step by step" before each answer.
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.
Matrix Compression via Randomized Low Rank and Low Precision Factorization
Matrices are exceptionally useful in various fields of study as they provide a convenient framework to organize and manipulate data in a structured manner.
acaa23f71f963e96c8847585e71352d6-Paper.pdf
Pre-trained DNN!!-1Wavelet transform!X"#DNN attributions for wavelet coefficients"$
Inverse Preference Learning: Preference-based RL without a Reward Function
Reward functions are difficult to design and often hard to align with human intent.
8b2fc235787852ead92da2268cd9e90c-Paper-Conference.pdf
In recent years, deep learning has become a staple solution to different tasks, such as computer vision,bio-informatics,speechrecognition,andmanymore.
