64 citations · 135 across the 14 of their papers we have counts for
15 papers
Balancing Continuous Pre-Training and Instruction Fine-Tuning: Optimizing Instruction-Following in LLMs
Ishan Jindal, Chandana Badrinath, Pranjal Bharti +2
Large Language Models (LLMs) for public use require continuous pre-training to remain up-to-date with the latest data. The models also need to be fine-tuned with specific instructi…
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning Architecture
Bingsheng Yao, Ishan Jindal, Lucian Popa +8
Real-world domain experts (e.g., doctors) rarely annotate only a decision label in their day-to-day workflow without providing explanations. Yet, existing low-resource learning tec…
When to Use What: An In-Depth Comparative Empirical Analysis of OpenIE Systems for Downstream Applications
Kevin Pei, Ishan Jindal, Kevin Chen-Chuan Chang +2
Open Information Extraction (OpenIE) has been used in the pipelines of various NLP tasks. Unfortunately, there is no clear consensus on which models to use in which tasks. Muddying…
PriMeSRL-Eval: A Practical Quality Metric for Semantic Role Labeling Systems Evaluation
Ishan Jindal, Alexandre Rademaker, Khoi-Nguyen Tran +4
Semantic role labeling (SRL) identifies the predicate-argument structure in a sentence. This task is usually accomplished in four steps: predicate identification, predicate sense d…
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation
Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann +122
Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained…
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation
Ishan Jindal, Ranit Aharonov, Siddhartha Brahma +2
Deep neural models achieve some of the best results for semantic role labeling. Inspired by instance-based learning that utilizes nearest neighbors to handle low-frequency context-…
CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling
Ishan Jindal, Yunyao Li, Siddhartha Brahma +1
Semantic role labeling (SRL) identifies predicate-argument structure(s) in a given sentence. Although different languages have different argument annotations, polyglot training, th…
An Effective Label Noise Model for DNN Text Classification
Ishan Jindal, Daniel Pressel, Brian Lester +1
Because large, human-annotated datasets suffer from labeling errors, it is crucial to be able to train deep neural networks in the presence of label noise. While training image cla…
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining
Ishan Jindal, Zhiwei Qin, Xuewen Chen +2
In this paper, we develop a reinforcement learning (RL) based system to learn an effective policy for carpooling that maximizes transportation efficiency so that fewer cars are req…
Tensor Matched Kronecker-Structured Subspace Detection for Missing Information
Ishan Jindal, Matthew Nokleby
We consider the problem of detecting whether a tensor signal having many missing entities lies within a given low dimensional Kronecker-Structured (KS) subspace. This is a matched…
A Unified Neural Network Approach for Estimating Travel Time and Distance for a Taxi Trip
Ishan Jindal, Tony, Qin +3
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource m…
Learning Deep Networks from Noisy Labels with Dropout Regularization
Ishan Jindal, Matthew Nokleby, Xuewen Chen
Large datasets often have unreliable labels-such as those obtained from Amazon's Mechanical Turk or social media platforms-and classifiers trained on mislabeled datasets often exhi…
Classification and Representation via Separable Subspaces: Performance Limits and Algorithms
Ishan Jindal, Matthew Nokleby
We study the classification performance of Kronecker-structured models in two asymptotic regimes and developed an algorithm for separable, fast and compact K-S dictionary learning…
Effective Object Tracking in Unstructured Crowd Scenes
Ishan Jindal, Shanmuganathan Raman
In this paper, we are presenting a rotation variant Oriented Texture Curve (OTC) descriptor based mean shift algorithm for tracking an object in an unstructured crowd scene. The pr…
SA-CNN: Dynamic Scene Classification using Convolutional Neural Networks
Aalok Gangopadhyay, Shivam Mani Tripathi, Ishan Jindal +1
The task of classifying videos of natural dynamic scenes into appropriate classes has gained lot of attention in recent years. The problem especially becomes challenging when the c…