Summary of Revealing the Self: Brainwave-based Human Trait Identification, by Md Mirajul Islam et al.
Revealing the Self: Brainwave-Based Human Trait Identificationby Md Mirajul Islam, Md Nahiyan Uddin, Maoyejatun Hasana,…
Summary of Spectralkd: a Unified Framework For Interpreting and Distilling Vision Transformers Via Spectral Analysis, by Huiyuan Tian et al.
SpectralKD: A Unified Framework for Interpreting and Distilling Vision Transformers via Spectral Analysisby Huiyuan Tian,…
Summary of Ffcg: Effective and Fast Family Column Generation For Solving Large-scale Linear Program, by Yi-xiang Hu et al.
FFCG: Effective and Fast Family Column Generation for Solving Large-Scale Linear Programby Yi-Xiang Hu, Feng…
Summary of Dapointr: Domain Adaptive Point Transformer For Point Cloud Completion, by Yinghui Li et al.
DAPoinTr: Domain Adaptive Point Transformer for Point Cloud Completionby Yinghui Li, Qianyu Zhou, Jingyu Gong,…
Summary of Learning Monocular Depth From Events Via Egomotion Compensation, by Haitao Meng et al.
Learning Monocular Depth from Events via Egomotion Compensationby Haitao Meng, Chonghao Zhong, Sheng Tang, Lian…
Summary of Effective and Secure Federated Online Learning to Rank, by Shuyi Wang
Effective and secure federated online learning to rankby Shuyi WangFirst submitted to arxiv on: 26…
Summary of Improving Generative Pre-training: An In-depth Study Of Masked Image Modeling and Denoising Models, by Hyesong Choi et al.
Improving Generative Pre-Training: An In-depth Study of Masked Image Modeling and Denoising Modelsby Hyesong Choi,…
Summary of Assessing Pre-trained Models For Transfer Learning Through Distribution Of Spectral Components, by Tengxue Zhang et al.
Assessing Pre-Trained Models for Transfer Learning Through Distribution of Spectral Componentsby Tengxue Zhang, Yang Shu,…
Summary of Mopd: Mixture-of-prompts Distillation For Vision-language Models, by Yang Chen et al.
MoPD: Mixture-of-Prompts Distillation for Vision-Language Modelsby Yang Chen, Shuai Fu, Yu ZhangFirst submitted to arxiv…
Summary of Why Train Everything? Tint a Single Layer For Multi-task Model Merging, by Aecheon Jung et al.
Why Train Everything? Tint a Single Layer for Multi-task Model Mergingby Aecheon Jung, Seunghwan Lee,…