Summary of Predicting Emergent Capabilities by Finetuning, By Charlie Snell et al.
Predicting Emergent Capabilities by Finetuningby Charlie Snell, Eric Wallace, Dan Klein, Sergey LevineFirst submitted to…
Summary of Vicon: a Foundation Model For Multi-physics Fluid Dynamics Via Vision In-context Operator Networks, by Yadi Cao et al.
VICON: A Foundation Model for Multi-Physics Fluid Dynamics via Vision In-Context Operator Networksby Yadi Cao,…
Summary of Soft-transformers For Continual Learning, by Haeyong Kang et al.
Soft-TransFormers for Continual Learningby Haeyong Kang, Chang D. YooFirst submitted to arxiv on: 25 Nov…
Summary of Boosting 3d Object Generation Through Pbr Materials, by Yitong Wang et al.
Boosting 3D Object Generation through PBR Materialsby Yitong Wang, Xudong Xu, Li Ma, Haoran Wang,…
Summary of Cautious Optimizers: Improving Training with One Line Of Code, by Kaizhao Liang et al.
Cautious Optimizers: Improving Training with One Line of Codeby Kaizhao Liang, Lizhang Chen, Bo Liu,…
Summary of Exploring the Generalization Capabilities Of Aid-based Bi-level Optimization, by Congliang Chen et al.
Exploring the Generalization Capabilities of AID-based Bi-level Optimizationby Congliang Chen, Li Shen, Zhiqiang Xu, Wei…
Summary of Very Basics Of Tensors with Graphical Notations: Unfolding, Calculations, and Decompositions, by Tatsuya Yokota
Very Basics of Tensors with Graphical Notations: Unfolding, Calculations, and Decompositionsby Tatsuya YokotaFirst submitted to…
Summary of Blendserve: Optimizing Offline Inference For Auto-regressive Large Models with Resource-aware Batching, by Yilong Zhao et al.
BlendServe: Optimizing Offline Inference for Auto-regressive Large Models with Resource-aware Batchingby Yilong Zhao, Shuo Yang,…
Summary of Ldacp: Long-delayed Ad Conversions Prediction Model For Bidding Strategy, by Peng Cui (1) et al.
LDACP: Long-Delayed Ad Conversions Prediction Model for Bidding Strategyby Peng Cui, Yiming Yang, Fusheng Jin,…
Summary of Fun-ad: Fully Unsupervised Learning For Anomaly Detection with Noisy Training Data, by Jiin Im et al.
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training Databy Jiin Im, Yongho Son,…