ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2009.05094
  4. Cited By
Why I'm not Answering: Understanding Determinants of Classification of
  an Abstaining Classifier for Cancer Pathology Reports
v1v2v3v4v5 (latest)

Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports

10 September 2020
S. Dhaubhadel
J. Mohd-Yusof
K. Ganguly
Gopinath Chennupati
S. Thulasidasan
N. Hengartner
B. Mumphrey
E. Durbin
J. Doherty
Mireille Lemieux
Noah Schaefferkoetter
Georgia D. Tourassi
Linda Coyle
Lynne Penberthy
Benjamin H. McMahon
T. Bhattacharya
ArXiv (abs)PDFHTML

Papers citing "Why I'm not Answering: Understanding Determinants of Classification of an Abstaining Classifier for Cancer Pathology Reports"

13 / 13 papers shown
Title
Pushing the Limits of Semi-Supervised Learning for Automatic Speech
  Recognition
Pushing the Limits of Semi-Supervised Learning for Automatic Speech Recognition
Yu Zhang
James Qin
Daniel S. Park
Wei Han
Chung-Cheng Chiu
Ruoming Pang
Quoc V. Le
Yonghui Wu
VLMSSL
192
309
0
20 Oct 2020
Is Attention Interpretable?
Is Attention Interpretable?
Sofia Serrano
Noah A. Smith
108
684
0
09 Jun 2019
Combating Label Noise in Deep Learning Using Abstention
Combating Label Noise in Deep Learning Using Abstention
S. Thulasidasan
Tanmoy Bhattacharya
J. Bilmes
Gopinath Chennupati
J. Mohd-Yusof
NoLa
57
179
0
27 May 2019
Sanity Checks for Saliency Maps
Sanity Checks for Saliency Maps
Julius Adebayo
Justin Gilmer
M. Muelly
Ian Goodfellow
Moritz Hardt
Been Kim
FAttAAMLXAI
136
1,967
0
08 Oct 2018
Deep Learning: A Critical Appraisal
Deep Learning: A Critical Appraisal
G. Marcus
HAIVLM
127
1,041
0
02 Jan 2018
A Survey on Multi-Task Learning
A Survey on Multi-Task Learning
Yu Zhang
Qiang Yang
AIMat
605
2,230
0
25 Jul 2017
A Baseline for Detecting Misclassified and Out-of-Distribution Examples
  in Neural Networks
A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Dan Hendrycks
Kevin Gimpel
UQCV
158
3,454
0
07 Oct 2016
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse
  Time Attention Mechanism
RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism
Edward Choi
M. T. Bahadori
Joshua A. Kulas
A. Schuetz
Walter F. Stewart
Jimeng Sun
AI4TS
121
1,247
0
19 Aug 2016
Deep Neural Networks are Easily Fooled: High Confidence Predictions for
  Unrecognizable Images
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
Anh Totti Nguyen
J. Yosinski
Jeff Clune
AAML
169
3,271
0
05 Dec 2014
Convolutional Neural Networks for Sentence Classification
Convolutional Neural Networks for Sentence Classification
Yoon Kim
AILawVLM
626
13,427
0
25 Aug 2014
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview
Jürgen Schmidhuber
HAI
246
16,361
0
30 Apr 2014
Deep Inside Convolutional Networks: Visualising Image Classification
  Models and Saliency Maps
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Karen Simonyan
Andrea Vedaldi
Andrew Zisserman
FAtt
312
7,295
0
20 Dec 2013
Representation Learning: A Review and New Perspectives
Representation Learning: A Review and New Perspectives
Yoshua Bengio
Aaron Courville
Pascal Vincent
OODSSL
264
12,439
0
24 Jun 2012
1