347 citations · 795 across the 48 of their papers we have counts for
9 papers · 1 filter
MetaCI: Meta-Learning for Causal Inference in a Heterogeneous Population
Ankit Sharma, Garima Gupta, Ranjitha Prasad +3
Performing inference on data obtained through observational studies is becoming extremely relevant due to the widespread availability of data in fields such as healthcare, educatio…
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…
One-shot Information Extraction from Document Images using Neuro-Deductive Program Synthesis
Vishal Sunder, Ashwin Srinivasan, Lovekesh Vig +2
Our interest in this paper is in meeting a rapidly growing industrial demand for information extraction from images of documents such as invoices, bills, receipts etc. In practice…
Fast Online "Next Best Offers" using Deep Learning
Rekha Singhal, Gautam Shroff, Mukund Kumar +5
In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and…
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…