75 citations · 303 across the 30 of their papers we have counts for
4 papers · 1 filter
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement
Nicholas Lee, Thanakul Wattanawong, Sehoon Kim +6
Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still…
xT: Nested Tokenization for Larger Context in Large Images
Ritwik Gupta, Shufan Li, Tyler Zhu +3
Modern computer vision pipelines handle large images in one of two sub-optimal ways: down-sampling or cropping. These two methods incur significant losses in the amount of informat…
Do Vision and Language Encoders Represent the World Similarly?
Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali +4
Aligned text-image encoders such as CLIP have become the de facto model for vision-language tasks. Furthermore, modality-specific encoders achieve impressive performances in their…
DrNet: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning
Chen Zhao, Shuming Liu, Karttikeya Mangalam +5
Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning, which is highly memo…