Summary of Gpd-1: Generative Pre-training For Driving, by Zixun Xie et al.
GPD-1: Generative Pre-training for Drivingby Zixun Xie, Sicheng Zuo, Wenzhao Zheng, Yunpeng Zhang, Dalong Du,…
Summary of Latentqa: Teaching Llms to Decode Activations Into Natural Language, by Alexander Pan and Lijie Chen and Jacob Steinhardt
LatentQA: Teaching LLMs to Decode Activations Into Natural Languageby Alexander Pan, Lijie Chen, Jacob SteinhardtFirst…
Summary of Alore: Efficient Visual Adaptation Via Aggregating Low Rank Experts, by Sinan Du et al.
ALoRE: Efficient Visual Adaptation via Aggregating Low Rank Expertsby Sinan Du, Guosheng Zhang, Keyao Wang,…
Summary of Forking Paths in Neural Text Generation, by Eric Bigelow et al.
Forking Paths in Neural Text Generationby Eric Bigelow, Ari Holtzman, Hidenori Tanaka, Tomer UllmanFirst submitted…
Summary of Distributed Gradient Descent with Many Local Steps in Overparameterized Models, by Heng Zhu et al.
Distributed Gradient Descent with Many Local Steps in Overparameterized Modelsby Heng Zhu, Harsh Vardhan, Arya…
Summary of Efficient Online Reinforcement Learning Fine-tuning Need Not Retain Offline Data, by Zhiyuan Zhou et al.
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline Databy Zhiyuan Zhou, Andy Peng, Qiyang…
Summary of Efficient Diversity-preserving Diffusion Alignment Via Gradient-informed Gflownets, by Zhen Liu et al.
Efficient Diversity-Preserving Diffusion Alignment via Gradient-Informed GFlowNetsby Zhen Liu, Tim Z. Xiao, Weiyang Liu, Yoshua…
Summary of Towards Graph Foundation Models: a Study on the Generalization Of Positional and Structural Encodings, by Billy Joe Franks et al.
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodingsby Billy…
Summary of Modula: Mixture Of Domain-specific and Universal Lora For Multi-task Learning, by Yufei Ma et al.
MoDULA: Mixture of Domain-Specific and Universal LoRA for Multi-Task Learningby Yufei Ma, Zihan Liang, Huangyu…
Summary of Crackess: a Self-prompting Crack Segmentation System For Edge Devices, by Yingchu Wang et al.
CrackESS: A Self-Prompting Crack Segmentation System for Edge Devicesby Yingchu Wang, Ji He, Shijie YuFirst…