347 citations · 728 across the 17 of their papers we have counts for
6 papers · 1 filter
Meta-Learning for Few-Shot Time Series Classification
Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig +2
Deep neural networks (DNNs) have achieved state-of-the-art results on time series classification (TSC) tasks. In this work, we focus on leveraging DNNs in the often-encountered pra…
NISER: Normalized Item and Session Representations to Handle Popularity Bias
Priyanka Gupta, Diksha Garg, Pankaj Malhotra +2
The goal of session-based recommendation (SR) models is to utilize the information from past actions (e.g. item/product clicks) in a session to recommend items that a user is likel…
Meta-Learning for Black-box Optimization
Vishnu TV, Pankaj Malhotra, Jyoti Narwariya +2
Recently, neural networks trained as optimizers under the "learning to learn" or meta-learning framework have been shown to be effective for a broad range of optimization tasks inc…
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
Kathan Kashiparekh, Jyoti Narwariya, Pankaj Malhotra +2
Training deep neural networks often requires careful hyper-parameter tuning and significant computational resources. In this paper, we propose ConvTimeNet (CTN): an off-the-shelf d…
Transfer Learning for Clinical Time Series Analysis using Deep Neural Networks
Priyanka Gupta, Pankaj Malhotra, Jyoti Narwariya +2
Deep neural networks have shown promising results for various clinical prediction tasks. However, training deep networks such as those based on Recurrent Neural Networks (RNNs) req…
Data-driven Prognostics with Predictive Uncertainty Estimation using Ensemble of Deep Ordinal Regression Models
Vishnu TV, Diksha, Pankaj Malhotra +2
Prognostics or Remaining Useful Life (RUL) Estimation from multi-sensor time series data is useful to enable condition-based maintenance and ensure high operational availability of…