Search Results for author:
Found 6 papers, 2 papers with code
LETS-GZSL: A Latent Embedding Model for Time Series Generalized Zero Shot Learning
One of the recent developments in deep learning is generalized zero-shot learning (GZSL), which aims to recognize objects from both seen and unseen classes, when only the labeled examples from seen classes are provided.
Similarity Learning based Few Shot Learning for ECG Time Series Classification
Using deep learning models to classify time series data generated from the Internet of Things (IoT) devices requires a large amount of labeled data.
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation
Most of the existing deep reinforcement learning (RL) approaches for session-based recommendations either rely on costly online interactions with real users, or rely on potentially biased rule-based or data-driven user-behavior models for learning.
NISER: Normalized Item and Session Representations to Handle Popularity Bias
The models using normalized item and session-graph representations perform significantly better: i. for the less popular long-tail items in the offline setting, and ii.
Ranked #2 on
Session-Based Recommendations
on Last.FM
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
We, therefore, conclude that pre-trained deep models like TimeNet and HealthNet allow leveraging the advantages of deep learning for clinical time series analysis tasks, while also minimize dependence on hand-crafted features, deal robustly with scarce labeled training data scenarios without overfitting, as well as reduce dependence on expertise and resources required to train deep networks from scratch.
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
We consider transferring the knowledge captured in an RNN trained on several source tasks simultaneously using a large labeled dataset to build the model for a target task with limited labeled data.
