Summary of Rethinking Graph Transformer Architecture Design For Node Classification, by Jiajun Zhou et al.
Rethinking Graph Transformer Architecture Design for Node Classificationby Jiajun Zhou, Xuanze Chen, Chenxuan Xie, Yu…
Summary of Tree Of Attributes Prompt Learning For Vision-language Models, by Tong Ding et al.
Tree of Attributes Prompt Learning for Vision-Language Modelsby Tong Ding, Wanhua Li, Zhongqi Miao, Hanspeter…
Summary of Towards Understanding Why Fixmatch Generalizes Better Than Supervised Learning, by Jingyang Li et al.
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learningby Jingyang Li, Jiachun Pan, Vincent Y.…
Summary of Classifying Healthy and Defective Fruits with a Multi-input Architecture and Cnn Models, by Luis Chuquimarca et al.
Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Modelsby Luis Chuquimarca, Boris…
Summary of Statistical Properties Of Deep Neural Networks with Dependent Data, by Chad Brown
Statistical Properties of Deep Neural Networks with Dependent Databy Chad BrownFirst submitted to arxiv on:…
Summary of A Bilevel Optimization Framework For Imbalanced Data Classification, by Karen Medlin et al.
A Bilevel Optimization Framework for Imbalanced Data Classificationby Karen Medlin, Sven Leyffer, Krishnan RaghavanFirst submitted…
Summary of Superficial Safety Alignment Hypothesis, by Jianwei Li and Jung-eun Kim
Superficial Safety Alignment Hypothesisby Jianwei Li, Jung-Eun KimFirst submitted to arxiv on: 7 Oct 2024CategoriesMain:…
Summary of Come: Test-time Adaption by Conservatively Minimizing Entropy, By Qingyang Zhang et al.
COME: Test-time adaption by Conservatively Minimizing Entropyby Qingyang Zhang, Yatao Bian, Xinke Kong, Peilin Zhao,…
Summary of Towards Calibrated Losses For Adversarial Robust Reject Option Classification, by Vrund Shah et al.
Towards Calibrated Losses for Adversarial Robust Reject Option Classificationby Vrund Shah, Tejas Chaudhari, Naresh ManwaniFirst…
Summary of Dynamical Loss Functions Shape Landscape Topography and Improve Learning in Artificial Neural Networks, by Eduardo Lavin and Miguel Ruiz-garcia
Dynamical loss functions shape landscape topography and improve learning in artificial neural networksby Eduardo Lavin,…