|
Workshop
|
Fri 13:07
|
Pervasive Label Errors in Test Sets Destabilize Machine Learning Benchmarks
Curtis G Northcutt
|
|
|
Poster
|
Mon 17:00
|
PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Yuliang Zou · Zizhao Zhang · Han Zhang · Chun-Liang Li · Xiao Bian · Jia-Bin Huang · Tomas Pfister
|
|
|
Poster
|
Thu 17:00
|
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning
Mamshad Nayeem Rizve · Kevin Duarte · Yogesh S Rawat · Mubarak Shah
|
|
|
Poster
|
Wed 17:00
|
Beyond Categorical Label Representations for Image Classification
Boyuan Chen · Yu Li · Sunand Raghupathi · Hod Lipson
|
|
|
Poster
|
Wed 17:00
|
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling
Benedikt Boecking · Willie Neiswanger · Eric P Xing · Artur Dubrawski
|
|
|
Poster
|
Tue 1:00
|
Learning Better Structured Representations Using Low-rank Adaptive Label Smoothing
Asish Ghoshal · Xilun Chen · Sonal Gupta · Luke Zettlemoyer · Yashar Mehdad
|
|
|
Spotlight
|
Wed 21:45
|
Behavioral Cloning from Noisy Demonstrations
Fumihiro Sasaki · Ryota Yamashina
|
|
|
Poster
|
Tue 17:00
|
Behavioral Cloning from Noisy Demonstrations
Fumihiro Sasaki · Ryota Yamashina
|
|
|
Poster
|
Thu 17:00
|
GAN2GAN: Generative Noise Learning for Blind Denoising with Single Noisy Images
Sungmin Cha · Taeeon Park · Byeongjoon Kim · Jongduk Baek · Taesup Moon
|
|
|
Spotlight
|
Wed 5:15
|
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Taiji Suzuki · Akiyama Shunta
|
|
|
Poster
|
Mon 17:00
|
Benefit of deep learning with non-convex noisy gradient descent: Provable excess risk bound and superiority to kernel methods
Taiji Suzuki · Akiyama Shunta
|
|
