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. 2402.17092
  4. Cited By
An Innovative Information Theory-based Approach to Tackle and Enhance
  The Transparency in Phishing Detection

An Innovative Information Theory-based Approach to Tackle and Enhance The Transparency in Phishing Detection

27 February 2024
Van Nguyen
Tingmin Wu
Xingliang Yuan
M. Grobler
Surya Nepal
Carsten Rudolph
    AAML
ArXivPDFHTML

Papers citing "An Innovative Information Theory-based Approach to Tackle and Enhance The Transparency in Phishing Detection"

2 / 2 papers shown
Title
Have We Learned to Explain?: How Interpretability Methods Can Learn to
  Encode Predictions in their Interpretations
Have We Learned to Explain?: How Interpretability Methods Can Learn to Encode Predictions in their Interpretations
N. Jethani
Mukund Sudarshan
Yindalon Aphinyanagphongs
Rajesh Ranganath
FAtt
82
70
0
02 Mar 2021
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
285
9,138
0
06 Jun 2015
1