Summary of Primus: Pretraining Imu Encoders with Multimodal Self-supervision, by Arnav M. Das et al.
PRIMUS: Pretraining IMU Encoders with Multimodal Self-Supervisionby Arnav M. Das, Chi Ian Tang, Fahim Kawsar,…
Summary of Procedural Knowledge in Pretraining Drives Reasoning in Large Language Models, by Laura Ruis et al.
Procedural Knowledge in Pretraining Drives Reasoning in Large Language Modelsby Laura Ruis, Maximilian Mozes, Juhan…
Summary of Mplite: Multi-aspect Pretraining For Mining Clinical Health Records, by Eric Yang et al.
MPLite: Multi-Aspect Pretraining for Mining Clinical Health Recordsby Eric Yang, Pengfei Hu, Xiaoxue Han, Yue…
Summary of Geomclip: Contrastive Geometry-text Pre-training For Molecules, by Teng Xiao et al.
GeomCLIP: Contrastive Geometry-Text Pre-training for Moleculesby Teng Xiao, Chao Cui, Huaisheng Zhu, Vasant G. HonavarFirst…
Summary of Measuring Non-adversarial Reproduction Of Training Data in Large Language Models, by Michael Aerni et al.
Measuring Non-Adversarial Reproduction of Training Data in Large Language Modelsby Michael Aerni, Javier Rando, Edoardo…
Summary of Time-to-event Pretraining For 3d Medical Imaging, by Zepeng Huo et al.
Time-to-Event Pretraining for 3D Medical Imagingby Zepeng Huo, Jason Alan Fries, Alejandro Lozano, Jeya Maria…
Summary of The Limited Impact Of Medical Adaptation Of Large Language and Vision-language Models, by Daniel P. Jeong et al.
The Limited Impact of Medical Adaptation of Large Language and Vision-Language Modelsby Daniel P. Jeong,…
Summary of Sparse Upcycling: Inference Inefficient Finetuning, by Sasha Doubov et al.
Sparse Upcycling: Inference Inefficient Finetuningby Sasha Doubov, Nikhil Sardana, Vitaliy ChileyFirst submitted to arxiv on:…
Summary of Renaissance: Investigating the Pretraining Of Vision-language Encoders, by Clayton Fields et al.
Renaissance: Investigating the Pretraining of Vision-Language Encodersby Clayton Fields, Casey KenningtonFirst submitted to arxiv on:…
Summary of Q-sft: Q-learning For Language Models Via Supervised Fine-tuning, by Joey Hong et al.
Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuningby Joey Hong, Anca Dragan, Sergey LevineFirst submitted…