Asiful Arefeen
  • Bio
  • Publications
  • Talks
  • News
  • Awards
  • Projects
  • Teaching
  • Recent Talks
    • Counterfactual Explainable Recommendation
    • Adversarial Counterfactual Visual Explanations
    • CounterNet: End-to-End Training of Prediction Aware Counterfactual Explanations
    • Generating Interpretable Counterfactual Explanations By Implicit Minimization of Epistemic and Aleatoric Uncertainties
    • Counterfactual Explanations for Multivariate Time Series
    • Explaining Machine Learning Classifiers through Diverse Counterfactual Explanations
    • Forecasting the future clinical events of a patient through contrastive learning
    • Feature Importance Explanations for Temporal Black-Box Models
    • Entropy-based Logic Explanations of Neural Networks
    • Rep-Net: Efficient On-Device Learning via Feature Reprogramming
    • Training Generative Adversarial Networks with Limited Data
    • Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response
    • Augmented Experiment in Material Engineering Using Machine Learning
    • Computational Framework for Sequential Diet Recommendation: Integrating Linear Optimization and Clinical Domain Knowledge
    • Local Interpretable Model-Agnostic Explanations
    • Characterizing Decision Boundary for DNN on High Dimensional Data
    • Grad-CAM for Interpreting DNN Model Decisions
    • Boosting Lying Posture Classification with Transfer Learning
    • Optimizations in Sparse Coding
    • Recent Techniques in Semi-Supervised Learning
    • SNR- Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
    • Which Tasks Should Be Learned Together in Multi-task Learning?
    • Anywidth Network
    • Paper Review: Attention is All You Need.
    • Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
    • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
    • U-Net: Convolutional Networks for Biomedical Image Segmentation
    • Social and competition stress detection with wristband physiological signals
  • Projects
  • Projects
    • GlyMan
    • HydroGuard
    • Smart Home
    • ExAct
  • Teaching
    • BMI 310: App Development for Population Health
    • BMI 502: Foundations of BMI Methods
  • News
    • 🎉 Easily create your own simple yet highly customizable blog
    • 🧠 Sharpen your thinking with a second brain
    • 📈 Communicate your results effectively with the best data visualizations
    • 👩🏼‍🏫 Teach academic courses
    • ✅ Manage your projects
  • Awards
  • Publications
    • Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations
    • GlucoseAssist: Personalized Blood Glucose Level Predictions and Early Dysglycemia Detection
    • GlySim: Modeling and Simulating Glycemic Response for Behavioral Lifestyle Interventions
    • Inter-Beat Interval Estimation with Tiramisu Model: A Novel Approach with Reduced Error
    • Use of Machine Learning to Predict Medication Adherence in Individuals at Risk for Atherosclerotic Cardiovascular Disease
    • Computational Framework for Sequential Diet Recommendation: Integrating Linear Optimization and Clinical Domain Knowledge
    • Forewarning Postprandial Hyperglycemia with Interpretations using Machine Learning
  • Recent news

Which Tasks Should Be Learned Together in Multi-task Learning?

Jun 21, 2021·
Asiful Arefeen
· 0 min read
PDF Slides
Image credit:
Date
Jun 21, 2021 3:00 PM — 3:30 PM
Event
Weekly seminar
Location

Zoom

WSU, Pullman, WA 99163

Last updated on Jun 21, 2021
Seminar Deep Learning
← SNR- Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning Aug 16, 2021
Anywidth Network May 17, 2021 →

© 2024 AREFEEN

Published with Hugo Blox Builder — the free, open source website builder that empowers creators.