Papers
47 papers found
Self-training Converts Weak Learners to Strong Learners in Mixture Models
Spencer Frei, Difan Zou, Zixiang Chen et al.
Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness
Avrim Blum, Omar Montasser, Greg Shakhnarovich et al.
Masked Generative Adversarial Networks are Data-Efficient Generation Learners
Jiaxing Huang, Kaiwen Cui, Dayan Guan et al.
Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners
Zhenhailong Wang, Manling Li, Ruochen Xu et al.
VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training
Zhan Tong, Yibing Song, Jue Wang et al.
Pure Transformers are Powerful Graph Learners
Jinwoo Kim, Dat Nguyen, Seonwoo Min et al.
Masked Autoencoders As Spatiotemporal Learners
Christoph Feichtenhofer, haoqi fan, Yanghao Li et al.
What is a Good Metric to Study Generalization of Minimax Learners?
Asuman Ozdaglar, Sarath Pattathil, Jiawei Zhang et al.
Vision Transformers Are Robust Learners
Sayak Paul, Pin-Yu Chen
DeepQR: Neural-Based Quality Ratings for Learnersourced Multiple-Choice Questions
Lin Ni, Qiming Bao, Xiaoxuan Li et al.
CLIP Models are Few-Shot Learners: Empirical Studies on VQA and Visual Entailment
Haoyu Song, Li Dong, Weinan Zhang et al.
Interpretability for Language Learners Using Example-Based Grammatical Error Correction
Masahiro Kaneko, Sho Takase, Ayana Niwa et al.
Automatic Gloss Dictionary for Sign Language Learners
Chenchen Xu, Dongxu Li, Hongdong Li et al.
Regret, stability & fairness in matching markets with bandit learners
Sarah H. Cen, Devavrat Shah
On characterizations of learnability with computable learners
Tom F. Sterkenburg
Robustly-reliable learners under poisoning attacks
Maria-Florina Balcan, Avrim Blum, Steve Hanneke et al.
Strategizing against Learners in Bayesian Games
Yishay Mansour, Mehryar Mohri, Jon Schneider et al.
Self-Supervised Models Are Continual Learners
Enrico Fini, Victor G. Turrisi da Costa, Xavier Alameda-Pineda et al.
Masked Autoencoders Are Scalable Vision Learners
Kaiming He, Xinlei Chen, Saining Xie et al.
Transformers As Meta-Learners for Implicit Neural Representations
Yinbo Chen, Xiaolong Wang
CoSCL: Cooperation of Small Continual Learners Is Stronger than a Big One
Liyuan Wang, Xingxing Zhang, Qian Li et al.
Frozen CLIP Models Are Efficient Video Learners
Ziyi Lin, Shijie Geng, Renrui Zhang et al.
Language Models of Code are Few-Shot Commonsense Learners
Aman Madaan, Shuyan Zhou, Uri Alon et al.
Making Pretrained Language Models Good Long-tailed Learners
Chen Zhang, Lei Ren, Jingang Wang et al.
Fine-tuned Language Models are Continual Learners
Thomas Scialom, Tuhin Chakrabarty, Smaranda Muresan