347 citations · 795 across the 48 of their papers we have counts for
25 papers · 1 filter
Calibrating Deep Neural Networks using Explicit Regularisation and Dynamic Data Pruning
Ramya Hebbalaguppe, Rishabh Patra, Tirtharaj Dash +2
Deep neural networks (DNN) are prone to miscalibrated predictions, often exhibiting a mismatch between the predicted output and the associated confidence scores. Contemporary model…
Continual Learning for Multivariate Time Series Tasks with Variable Input Dimensions
Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2
We consider a sequence of related multivariate time series learning tasks, such as predicting failures for different instances of a machine from time series of multi-sensor data, o…
Learning to Liquidate Forex: Optimal Stopping via Adaptive Top-K Regression
Diksha Garg, Pankaj Malhotra, Anil Bhatia +3
We consider learning a trading agent acting on behalf of the treasury of a firm earning revenue in a foreign currency (FC) and incurring expenses in the home currency (HC). The goa…
DRTCI: Learning Disentangled Representations for Temporal Causal Inference
Garima Gupta, Lovekesh Vig, Gautam Shroff
Medical professionals evaluating alternative treatment plans for a patient often encounter time varying confounders, or covariates that affect both the future treatment assignment…
Solving Visual Analogies Using Neural Algorithmic Reasoning
Atharv Sonwane, Gautam Shroff, Lovekesh Vig +2
We consider a class of visual analogical reasoning problems that involve discovering the sequence of transformations by which pairs of input/output images are related, so as to ana…
Using Program Synthesis and Inductive Logic Programming to solve Bongard Problems
Atharv Sonwane, Sharad Chitlangia, Tirtharaj Dash +3
The ability to recognise and make analogies is often used as a measure or test of human intelligence. The ability to solve Bongard problems is an example of such a test. It has als…
Forecasting Market Prices using DL with Data Augmentation and Meta-learning: ARIMA still wins!
Vedant Shah, Gautam Shroff
Deep-learning techniques have been successfully used for time-series forecasting and have often shown superior performance on many standard benchmark datasets as compared to tradit…
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation
Diksha Garg, Priyanka Gupta, Pankaj Malhotra +2
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…
CAMTA: Causal Attention Model for Multi-touch Attribution
Sachin Kumar, Garima Gupta, Ranjitha Prasad +3
Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of a…
Handling Variable-Dimensional Time Series with Graph Neural Networks
Vibhor Gupta, Jyoti Narwariya, Pankaj Malhotra +2
Several applications of Internet of Things (IoT) technology involve capturing data from multiple sensors resulting in multi-sensor time series. Existing neural networks based appro…
Graph Neural Networks for Leveraging Industrial Equipment Structure: An application to Remaining Useful Life Estimation
Jyoti Narwariya, Pankaj Malhotra, Vishnu TV +2
Automated equipment health monitoring from streaming multisensor time-series data can be used to enable condition-based maintenance, avoid sudden catastrophic failures, and ensure…
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…
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…
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…
Prosocial or Selfish? Agents with different behaviors for Contract Negotiation using Reinforcement Learning
Vishal Sunder, Lovekesh Vig, Arnab Chatterjee +1
We present an effective technique for training deep learning agents capable of negotiating on a set of clauses in a contract agreement using a simple communication protocol. We use…
Transfer Learning for Clinical Time Series Analysis using Recurrent Neural Networks
Priyanka Gupta, Pankaj Malhotra, Lovekesh Vig +1
Deep neural networks have shown promising results for various clinical prediction tasks such as diagnosis, mortality prediction, predicting duration of stay in hospital, etc. Howev…
Predicting Remaining Useful Life using Time Series Embeddings based on Recurrent Neural Networks
Narendhar Gugulothu, Vishnu TV, Pankaj Malhotra +3
We consider the problem of estimating the remaining useful life (RUL) of a system or a machine from sensor data. Many approaches for RUL estimation based on sensor data make assump…
TimeNet: Pre-trained deep recurrent neural network for time series classification
Pankaj Malhotra, Vishnu TV, Lovekesh Vig +2
Inspired by the tremendous success of deep Convolutional Neural Networks as generic feature extractors for images, we propose TimeNet: a deep recurrent neural network (RNN) trained…
Deep Convolutional Neural Networks for Pairwise Causality
Karamjit Singh, Garima Gupta, Lovekesh Vig +2
Discovering causal models from observational and interventional data is an important first step preceding what-if analysis or counterfactual reasoning. As has been shown before, th…
Multi-Sensor Prognostics using an Unsupervised Health Index based on LSTM Encoder-Decoder
Pankaj Malhotra, Vishnu TV, Anusha Ramakrishnan +4
Many approaches for estimation of Remaining Useful Life (RUL) of a machine, using its operational sensor data, make assumptions about how a system degrades or a fault evolves, e.g.…