Search Results for author: Ganesh Ramakrishnan

Found 55 papers, 17 papers with code

Personalizing ASR with limited data using targeted subset selection

1 code implementation10 Oct 2021 Mayank Kothyari, Anmol Reddy Mekala, Rishabh Iyer, Ganesh Ramakrishnan, Preethi Jyothi

We study the task of personalizing ASR models to a target non-native speaker/accent while being constrained by a transcription budget on the duration of utterances selected from a large unlabelled corpus.

Learning to Robustly Aggregate Labeling Functions for Semi-supervised Data Programming

no code implementations23 Sep 2021 Ayush Maheshwari, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer, Marina Danilevsky, Lucian Popa

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.

Text Classification

Wisdom of (Binned) Crowds: A Bayesian Stratification Paradigm for Crowd Counting

1 code implementation19 Aug 2021 Sravya Vardhani Shivapuja, Mansi Pradeep Khamkar, Divij Bajaj, Ganesh Ramakrishnan, Ravi Kiran Sarvadevabhatla

We analyze the performance of representative crowd counting approaches across standard datasets at per strata level and in aggregate.

Crowd Counting

SPEAR : Semi-supervised Data Programming in Python

1 code implementation1 Aug 2021 Guttu Sai Abhishek, Harshad Ingole, Parth Laturia, Vineeth Dorna, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer

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

no code implementations23 Jun 2021 Durga Sivasubramanian, Rishabh Iyer, Ganesh Ramakrishnan, Abir De

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

1 code implementation16 Jun 2021 Nathan Beck, Durga Sivasubramanian, Apurva Dani, Ganesh Ramakrishnan, Rishabh Iyer

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).

Active Learning Classification +2

Automatic Speech Recognition in Sanskrit: A New Speech Corpus and Modelling Insights

1 code implementation2 Jun 2021 Devaraja Adiga, Rishabh Kumar, Amrith Krishna, Preethi Jyothi, Ganesh Ramakrishnan, Pawan Goyal

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 Language Modelling +1

Rule Augmented Unsupervised Constituency Parsing

2 code implementations21 May 2021 Atul Sahay, Anshul Nasery, Ayush Maheshwari, Ganesh Ramakrishnan, Rishabh Iyer

We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.

Constituency Parsing

Submodular Mutual Information for Targeted Data Subset Selection

no code implementations30 Apr 2021 Suraj Kothawade, Vishal Kaushal, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer

With the rapid growth of data, it is becoming increasingly difficult to train or improve deep learning models with the right subset of data.

Active Learning Image Classification

A Weakly Supervised Model for Solving Math word Problems

no code implementations14 Apr 2021 Oishik Chatterjee, Aashish Waikar, Vishwajeet Kumar, Ganesh Ramakrishnan, Kavi Arya

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.

Cross-Modal learning for Audio-Visual Video Parsing

1 code implementation3 Apr 2021 Jatin Lamba, abhishek, Jayaprakash Akula, Rishabh Dabral, Preethi Jyothi, Ganesh Ramakrishnan

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.

Event Detection Multiple Instance Learning

Rudder: A Cross Lingual Video and Text Retrieval Dataset

1 code implementation9 Mar 2021 Jayaprakash A, abhishek, Rishabh Dabral, Ganesh Ramakrishnan, Preethi Jyothi

Video retrieval using natural language queries requires learning semantically meaningful joint embeddings between the text and the audio-visual input.

Video-Text Retrieval

GRAD-MATCH: Gradient Matching based Data Subset Selection for Efficient Deep Model Training

1 code implementation27 Feb 2021 KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Abir De, Rishabh Iyer

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 Unified Framework of Parameterized Submodular Information Measures for Targeted Data Subset Selection and Summarization

no code implementations27 Feb 2021 Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff Bilmes, Rishabh Iyer

Second, we apply PRISM to a more nuanced targeted summarization (PRISM-TSUM) where data (e. g., image collections, text or videos) is summarized for quicker human consumption with additional user intent.

Image Classification

Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification

1 code implementation EACL 2021 Soumya Chatterjee, Ayush Maheshwari, Ganesh Ramakrishnan, Saketha Nath Jagaralpudi

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.

Classification General Classification +1

GLISTER: Generalization based Data Subset Selection for Efficient and Robust Learning

1 code implementation19 Dec 2020 KrishnaTeja Killamsetty, Durga Sivasubramanian, Ganesh Ramakrishnan, Rishabh Iyer

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.

Active Learning

LIGHTEN: Learning Interactions with Graph and Hierarchical TEmporal Networks for HOI in videos

1 code implementation17 Dec 2020 Sai Praneeth Reddy Sunkesula, Rishabh Dabral, Ganesh Ramakrishnan

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

A Unified Framework for Generic, Query-Focused, Privacy Preserving and Update Summarization using Submodular Information Measures

no code implementations12 Oct 2020 Vishal Kaushal, Suraj Kothawade, Ganesh Ramakrishnan, Jeff Bilmes, Himanshu Asnani, Rishabh Iyer

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

2 code implementations22 Aug 2020 Ayush Maheshwari, Oishik Chatterjee, KrishnaTeja Killamsetty, Ganesh Ramakrishnan, Rishabh Iyer

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.

Text Classification

Realistic Video Summarization through VISIOCITY: A New Benchmark and Evaluation Framework

no code implementations29 Jul 2020 Vishal Kaushal, Suraj Kothawade, Rishabh Iyer, Ganesh Ramakrishnan

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.

Video Summarization

Question Generation from Paragraphs: A Tale of Two Hierarchical Models

no code implementations8 Nov 2019 Vishwajeet Kumar, Raktim Chaki, Sai Teja Talluri, Ganesh Ramakrishnan, Yuan-Fang Li, Gholamreza Haffari

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.

Question Generation

Putting the Horse before the Cart: A Generator-Evaluator Framework for Question Generation from Text

no code implementations CONLL 2019 Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li

The \textit{generator} is a sequence-to-sequence model that incorporates the \textit{structure} and \textit{semantics} of the question being generated.

Question Generation

Multi-Person 3D Human Pose Estimation from Monocular Images

no code implementations24 Sep 2019 Rishabh Dabral, Nitesh B. Gundavarapu, Rahul Mitra, Abhishek Sharma, Ganesh Ramakrishnan, Arjun Jain

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.

3D Human Pose Estimation

Tale of tails using rule augmented sequence labeling for event extraction

no code implementations19 Aug 2019 Ayush Maheshwari, Hrishikesh Patel, Nandan Rathod, Ritesh Kumar, Ganesh Ramakrishnan, Pushpak Bhattacharyya

The problem of event extraction is a relatively difficult task for low resource languages due to the non-availability of sufficient annotated data.

Event Extraction

Putting the Horse Before the Cart:A Generator-Evaluator Framework for Question Generation from Text

no code implementations15 Aug 2018 Vishwajeet Kumar, Ganesh Ramakrishnan, Yuan-Fang Li

The {\it generator} is a sequence-to-sequence model that incorporates the {\it structure} and {\it semantics} of the question being generated.

Question Generation

Entity Resolution and Location Disambiguation in the Ancient Hindu Temples Domain using Web Data

no code implementations NAACL 2018 Ayush Maheshwari, Vishwajeet Kumar, Ganesh Ramakrishnan, J. Saketha Nath

We present a system for resolving entities and disambiguating locations based on publicly available web data in the domain of ancient Hindu Temples.

Entity Resolution

Learning From Less Data: Diversified Subset Selection and Active Learning in Image Classification Tasks

no code implementations28 May 2018 Vishal Kaushal, Anurag Sahoo, Khoshrav Doctor, Narasimha Raju, Suyash Shetty, Pankaj Singh, Rishabh Iyer, Ganesh Ramakrishnan

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.

Active Learning General Classification +3

Learning Discriminative Relational Features for Sequence Labeling

no code implementations7 May 2017 Naveen Nair, Ajay Nagesh, Ganesh Ramakrishnan

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).

Hierarchical structure

A Unified Multi-Faceted Video Summarization System

no code implementations4 Apr 2017 Anurag Sahoo, Vishal Kaushal, Khoshrav Doctor, Suyash Shetty, Rishabh Iyer, Ganesh Ramakrishnan

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.

Extractive Summarization Video Summarization

Efficient Reuse of Structured and Unstructured Resources for Ontology Population

no code implementations LREC 2014 Chetana Gavankar, Ashish Kulkarni, Ganesh Ramakrishnan

A domain ontology for an organization, often consists of classes whose instances are either specific to, or independent of the organization.

Information Retrieval

Cannot find the paper you are looking for? You can Submit a new open access paper.