Search Results for author:
Found 71 papers, 26 papers with code
WARM: A Weakly (+Semi) Supervised Math Word Problem Solver
In order to address this challenge of equation annotation, we propose a weakly supervised model for solving MWPs by requiring only the final answer as supervision.
A Benchmark and Dataset for Post-OCR text correction in Sanskrit
Keeping this in mind, we release a multi-domain dataset, from areas as diverse as astronomy, medicine and mathematics, with some of them as old as 18 centuries.
Speeding up NAS with Adaptive Subset Selection
A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final performance.
Partitioned Gradient Matching-based Data Subset Selection for Compute-Efficient Robust ASR Training
Training state-of-the-art ASR systems such as RNN-T often has a high associated financial and environmental cost.
DICTDIS: Dictionary Constrained Disambiguation for Improved NMT
In this work we present DICTDIS, a lexically constrained NMT system that disambiguates between multiple candidate translations derived from dictionaries.
AutoML for Climate Change: A Call to Action
The challenge that climate change poses to humanity has spurred a rapidly developing field of artificial intelligence research focused on climate change applications.
CLINICAL: Targeted Active Learning for Imbalanced Medical Image Classification
It is often the case that a suboptimal performance is obtained on some classes due to the natural class imbalance issue that comes with medical data.
DIAGNOSE: Avoiding Out-of-distribution Data using Submodular Information Measures
Avoiding out-of-distribution (OOD) data is critical for training supervised machine learning models in the medical imaging domain.
Counting in the 2020s: Binned Representations and Inclusive Performance Measures for Deep Crowd Counting Approaches
This skew affects all stages within the pipelines of deep crowd counting approaches.
Investigating Modality Bias in Audio Visual Video Parsing
We focus on the audio-visual video parsing (AVVP) problem that involves detecting audio and visual event labels with temporal boundaries.
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Our central insight is that using an informative subset of the dataset for model training runs involved in hyper-parameter optimization, allows us to find the optimal hyper-parameter configuration significantly faster.
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets
This issue is further pronounced in SSL methods, as they would use this biased model to obtain psuedo-labels (on the unlabeled data) during training.
UDAAN: Machine Learning based Post-Editing tool for Document Translation
UDAAN has an end-to-end Machine Translation (MT) plus post-editing pipeline wherein users can upload a document to obtain raw MT output.
Submodlib: A Submodular Optimization Library
A recent work has also leveraged submodular functions to propose submodular information measures which have been found to be very useful in solving the problems of guided subset selection and guided summarization.
Adaptive Mixing of Auxiliary Losses in Supervised Learning
In several supervised learning scenarios, auxiliary losses are used in order to introduce additional information or constraints into the supervised learning objective.
Error Correction in ASR using Sequence-to-Sequence Models
Post-editing in Automatic Speech Recognition (ASR) entails automatically correcting common and systematic errors produced by the ASR system.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Targeted Subset Selection for Limited-data ASR Accent Adaptation
We propose a subset selection approach using the recently proposed submodular mutual information functions, in which we identify a diverse set of utterances that match the target accent.
Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming
These LFs, in turn, have been used to generate a large amount of additional noisy labeled data, in a paradigm that is now commonly referred to as data programming.
Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting
We analyze the performance of representative crowd counting approaches across standard datasets at per strata level and in aggregate.
SPEAR : Semi-supervised Data Programming in Python
SPEAR facilitates weak supervision in the form of heuristics (or rules) and association of noisy labels to the training dataset.
Training Data Subset Selection for Regression with Controlled Generalization Error
First, we represent this problem with simplified constraints using the dual of the original training problem and show that the objective of this new representation is a monotone and alpha-submodular function, for a wide variety of modeling choices.
Effective Evaluation of Deep Active Learning on Image Classification Tasks
Issues in the current literature include sometimes contradictory observations on the performance of different AL algorithms, unintended exclusion of important generalization approaches such as data augmentation and SGD for optimization, a lack of study of evaluation facets like the labeling efficiency of AL, and little or no clarity on the scenarios in which AL outperforms random sampling (RS).
Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights
In this work, we propose the first large scale study of automatic speech recognition (ASR) in Sanskrit, with an emphasis on the impact of unit selection in Sanskrit ASR.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Rule Augmented Unsupervised Constituency Parsing
We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
Submodular Mutual Information for Targeted Data Subset Selection
With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data.
A Weakly Supervised Model for Solving Math word Problems
In order to address this challenge of equation annotation, we propose a weakly supervised model for solving math word problems by requiring only the final answer as supervision.
Unsupervised Learning of Explainable Parse Trees for Improved Generalisation
In this work, we propose an attention mechanism over Tree-LSTMs to learn more meaningful and explainable parse tree structures.
Cross-Modal learning for Audio-Visual Video Parsing
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.
Ranked #1 on
Event Detection
on Audio Set
Select, Substitute, Search: A New Benchmark for Knowledge-Augmented Visual Question Answering
In response, we identify a key structural idiom in OKVQA , viz., S3 (select, substitute and search), and build a new data set and challenge around it.
Rudder: A Cross Lingual Video and Text Retrieval Dataset
Video retrieval using natural language queries requires learning semantically meaningful joint embeddings between the text and the audio-visual input.
Ranked #1 on
Video Retrieval
on Charades-STA
GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
We show rigorous theoretical and convergence guarantees of the proposed algorithm and, through our extensive experiments on real-world datasets, show the effectiveness of our proposed framework.
PRISM: A Rich Class of Parameterized Submodular Information Measures for Guided Subset Selection
Examples of such problems include: i)targeted learning, where the goal is to find subsets with rare classes or rare attributes on which the model is underperforming, and ii)guided summarization, where data (e. g., image collection, text, document or video) is summarized for quicker human consumption with specific additional user intent.
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
For long videos, human reference summaries necessary for supervised video summarization techniques are difficult to obtain.
Meta-Learning for Effective Multi-task and Multilingual Modelling
We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset.
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification
Such a joint learning is expected to provide a twofold advantage: i) the classifier generalizes better as it leverages the prior knowledge of existence of a hierarchy over the labels, and ii) in addition to the label co-occurrence information, the label-embedding may benefit from the manifold structure of the input datapoints, leading to embeddings that are more faithful to the label hierarchy.
Ranked #1 on
Multi-Label Text Classification
on RCV1
General Classification
Hierarchical Multi-label Classification
+1
GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning
Finally, we propose Glister-Active, an extension to batch active learning, and we empirically demonstrate the performance of Glister on a wide range of tasks including, (a) data selection to reduce training time, (b) robust learning under label noise and imbalance settings, and (c) batch-active learning with several deep and shallow models.
LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos
Analyzing the interactions between humans and objects from a video includes identification of the relationships between humans and the objects present in the video.
Human-Object Interaction Detection
Visual Relationship Detection
Vocabulary Matters: A Simple yet Effective Approach to Paragraph-level Question Generation
Question generation (QG) has recently attracted considerable attention.
A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures
We study submodular information measures as a rich framework for generic, query-focused, privacy sensitive, and update summarization tasks.
Semi-Supervised Data Programming with Subset Selection
The first contribution of this work is an introduction of a framework, \model which is a semi-supervised data programming paradigm that learns a \emph{joint model} that effectively uses the rules/labelling functions along with semi-supervised loss functions on the feature space.
Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework
Thirdly, we demonstrate that in the presence of multiple ground truth summaries (due to the highly subjective nature of the task), learning from a single combined ground truth summary using a single loss function is not a good idea.
Data Programming using Continuous and Quality-Guided Labeling Functions
Scarcity of labeled data is a bottleneck for supervised learning models.
Question Generation from Paragraphs: A Tale of Two Hierarchical Models
Specifically, we propose (a) a novel hierarchical BiLSTM model with selective attention and (b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs.
Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text
The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.
Multi-Person 3D Human Pose Estimation from Monocular Images
Multi-person 3D human pose estimation from a single image is a challenging problem, especially for in-the-wild settings due to the lack of 3D annotated data.
Ranked #5 on
3D Multi-Person Pose Estimation
on MuPoTS-3D
3D Human Pose Estimation
3D Multi-Person Human Pose Estimation
ParaQG: A System for Generating Questions and Answers from Paragraphs
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications.
Tale of tails using rule augmented sequence labeling for event extraction
The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data.
Cross-Lingual Training for Automatic Question Generation
For a new language, such training instances are hard to obtain making the QG problem even more challenging.
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry.
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance
This paper addresses automatic summarization of videos in a unified manner.
A Framework towards Domain Specific Video Summarization
We propose a novel framework for domain specific video summarization.
Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text
The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.
Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data
We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples.
Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks
Supervised machine learning based state-of-the-art computer vision techniques are in general data hungry and pose the challenges of not having adequate computing resources and of high costs involved in human labeling efforts.
Automating Reading Comprehension by Generating Question and Answer Pairs
Neural network-based methods represent the state-of-the-art in question generation from text.
Learning Discriminative Relational Features for Sequence Labeling
For learning features derived from inputs at a particular sequence position, we propose a Hierarchical Kernels-based approach (referred to as Hierarchical Kernel Learning for Structured Output Spaces - StructHKL).
A Unified Multi-Faceted Video Summarization System
Most importantly, we also show that we can summarize hours of video data in a few seconds, and our system allows the user to generate summaries of various lengths and types interactively on the fly.
Efficient Reuse of Structured and Unstructured Resources for Ontology Population
A domain ontology for an organization, often consists of classes whose instances are either specific to, or independent of the organization.
