Thinking LLMs: General Instruction Following with Thought Generation (Oct 2024)
a prompting and fine-tuning method that enables LLMs to engage in a "thinking" process before generating responses
a prompting and fine-tuning method that enables LLMs to engage in a "thinking" process before generating responses
a comprehensive evaluation of o1-preview across many tasks and domains.
LLMs can help and also hinder learning outcomes
a paper that shows a model needs to see a concept exponentially more times to achieve linear improvements
VALL-E can generate speech in anyone's voice with only a 3-second sample of the speaker and some text
Notes from paper Large-scale Contrastive Language-Audio Pre-training with Feature Fusion and Keyword-to-Caption Augmentation by Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, Shlomo Dubnov
Notes from paper A Discriminative Feature Learning Approach for Deep Face Recognition by Yandong Wen, Kaipeng Zhang, Zhifeng Li, and Yu Qiao
Notes from paper ArcFace: Additive Angular Margin Loss for Deep Face Recognition by Jiankang Deng, Jia Guo, Niannan Xue, Stefanos Zafeiriou