71 citations · 168 across the 39 of their papers we have counts for
18 papers · 1 filter
DITTO: Data-efficient and Fair Targeted Subset Selection for ASR Accent Adaptation
Suraj Kothawade, Anmol Mekala, Chandra Sekhara D +4
State-of-the-art Automatic Speech Recognition (ASR) systems are known to exhibit disparate performance on varying speech accents. To improve performance on a specific target accent…
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
Ayush Maheshwari, Krishnateja Killamsetty, Ganesh Ramakrishnan +3
A critical bottleneck in supervised machine learning is the need for large amounts of labeled data which is expensive and time consuming to obtain. However, it has been shown that…
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting
Sravya Vardhani Shivapuja, Mansi Pradeep Khamkar, Divij Bajaj +2
Datasets for training crowd counting deep networks are typically heavy-tailed in count distribution and exhibit discontinuities across the count range. As a result, the de facto st…
SPEAR : Semi-supervised Data Programming in Python
Guttu Sai Abhishek, Harshad Ingole, Parth Laturia +4
We present SPEAR, an open-source python library for data programming with semi supervision. The package implements several recent data programming approaches including facility to…
Training Data Subset Selection for Regression with Controlled Generalization Error
Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan +1
Data subset selection from a large number of training instances has been a successful approach toward efficient and cost-effective machine learning. However, models trained on a sm…
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Nathan Beck, Durga Sivasubramanian, Apurva Dani +2
With the goal of making deep learning more label-efficient, a growing number of papers have been studying active learning (AL) for deep models. However, there are a number of issue…
Rule Augmented Unsupervised Constituency Parsing
Atul Sahay, Anshul Nasery, Ayush Maheshwari +2
Recently, unsupervised parsing of syntactic trees has gained considerable attention. A prototypical approach to such unsupervised parsing employs reinforcement learning and auto-en…
Submodular Mutual Information for Targeted Data Subset Selection
Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan +2
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data. We show that this problem can be effect…
Unsupervised Learning of Explainable Parse Trees for Improved Generalisation
Atul Sahay, Ayush Maheshwari, Ritesh Kumar +3
Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks…
WARM: A Weakly (+Semi) Supervised Model for Solving Math word Problems
Oishik Chatterjee, Isha Pandey, Aashish Waikar +2
Solving math word problems (MWPs) is an important and challenging problem in natural language processing. Existing approaches to solve MWPs require full supervision in the form of…
Cross-Modal learning for Audio-Visual Video Parsing
Jatin Lamba, Abhishek, Jayaprakash Akula +3
In this paper, we present a novel approach to the audio-visual video parsing (AVVP) task that demarcates events from a video separately for audio and visual modalities. The propose…
Rudder: A Cross Lingual Video and Text Retrieval Dataset
Jayaprakash A, Abhishek, Rishabh Dabral +2
Video retrieval using natural language queries requires learning semantically meaningful joint embeddings between the text and the audio-visual input. Often, such joint embeddings…
Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering
Aman Jain, Mayank Kothyari, Vishwajeet Kumar +3
Multimodal IR, spanning text corpus, knowledge graph and images, called outside knowledge visual question answering (OKVQA), is of much recent interest. However, the popular data s…
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection
Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan +2
With ever-increasing dataset sizes, subset selection techniques are becoming increasingly important for a plethora of tasks. It is often necessary to guide the subset selection to…
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan +2
The great success of modern machine learning models on large datasets is contingent on extensive computational resources with high financial and environmental costs. One way to add…
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
Vishal Kaushal, Suraj Kothawade, Anshul Tomar +2
Automatic video summarization is still an unsolved problem due to several challenges. The currently available datasets either have very short videos or have few long videos of only…
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan +1
We consider the problem of multi-label classification where the labels lie in a hierarchy. However, unlike most existing works in hierarchical multi-label classification, we do not…
Meta-Learning for Effective Multi-task and Multilingual Modelling
Ishan Tarunesh, Sushil Khyalia, Vishwajeet Kumar +2
Natural language processing (NLP) tasks (e.g. question-answering in English) benefit from knowledge of other tasks (e.g. named entity recognition in English) and knowledge of other…