Summary of Ugmae: a Unified Framework For Graph Masked Autoencoders, by Yijun Tian et al.
UGMAE: A Unified Framework for Graph Masked Autoencodersby Yijun Tian, Chuxu Zhang, Ziyi Kou, Zheyuan…
Summary of A Masked Language Model For Multi-source Ehr Trajectories Contextual Representation Learning, by Ali Amirahmadi (1) et al.
A Masked language model for multi-source EHR trajectories contextual representation learningby Ali Amirahmadi, Mattias Ohlsson,…
Summary of Linearizing Models For Efficient Yet Robust Private Inference, by Sreetama Sarkar et al.
Linearizing Models for Efficient yet Robust Private Inferenceby Sreetama Sarkar, Souvik Kundu, Peter A. BeerelFirst…
Summary of Efficientvit-sam: Accelerated Segment Anything Model Without Accuracy Loss, by Zhuoyang Zhang et al.
EfficientViT-SAM: Accelerated Segment Anything Model Without Accuracy Lossby Zhuoyang Zhang, Han Cai, Song HanFirst submitted…
Summary of Instancediffusion: Instance-level Control For Image Generation, by Xudong Wang et al.
InstanceDiffusion: Instance-level Control for Image Generationby Xudong Wang, Trevor Darrell, Sai Saketh Rambhatla, Rohit Girdhar,…
Summary of Viewfusion: Learning Composable Diffusion Models For Novel View Synthesis, by Bernard Spiegl et al.
ViewFusion: Learning Composable Diffusion Models for Novel View Synthesisby Bernard Spiegl, Andrea Perin, Stéphane Deny,…
Summary of Pre-training Protein Bi-level Representation Through Span Mask Strategy on 3d Protein Chains, by Jiale Zhao et al.
Pre-Training Protein Bi-level Representation Through Span Mask Strategy On 3D Protein Chainsby Jiale Zhao, Wanru…
Summary of Training-time Neuron Alignment Through Permutation Subspace For Improving Linear Mode Connectivity and Model Fusion, by Zexi Li et al.
Training-time Neuron Alignment through Permutation Subspace for Improving Linear Mode Connectivity and Model Fusionby Zexi…
Summary of No Free Prune: Information-theoretic Barriers to Pruning at Initialization, by Tanishq Kumar et al.
No Free Prune: Information-Theoretic Barriers to Pruning at Initializationby Tanishq Kumar, Kevin Luo, Mark SellkeFirst…
Summary of Theoretical Understanding Of In-context Learning in Shallow Transformers with Unstructured Data, by Yue Xing et al.
Theoretical Understanding of In-Context Learning in Shallow Transformers with Unstructured Databy Yue Xing, Xiaofeng Lin,…