Search Results for author: Gaurav Pandey

Found 28 papers, 5 papers with code

Gaining Insights into Unrecognized User Utterances in Task-Oriented Dialog Systems

no code implementations11 Apr 2022 Ella Rabinovich, Matan Vetzler, David Boaz, Vineet Kumar, Gaurav Pandey, Ateret Anaby-Tavor

The rapidly growing market demand for dialogue agents capable of goal-oriented behavior has caused many tech-industry leaders to invest considerable efforts into task-oriented dialog systems.

Goal-Oriented Dialog

Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings

no code implementations6 Apr 2022 Gaurav Pandey, Danish Contractor, Sachindra Joshi

In this paper, we combine the best of both worlds by proposing a scalable model that can learn complex relationships between context-response pairs.

Variational Learning for Unsupervised Knowledge Grounded Dialogs

1 code implementation23 Nov 2021 Mayank Mishra, Dhiraj Madan, Gaurav Pandey, Danish Contractor

Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document.

Developing parsimonious ensembles using predictor diversity within a reinforcement learning framework

1 code implementation15 Feb 2021 Ana Stanescu, Gaurav Pandey

Heterogeneous ensembles that can aggregate an unrestricted number and variety of base predictors can effectively address challenging prediction problems.

reinforcement-learning Translation

Real Time Incremental Foveal Texture Mapping for Autonomous Vehicles

no code implementations16 Jan 2021 Ashish Kumar, James R. McBride, Gaurav Pandey

We propose an end-to-end real time framework to generate high resolution graphics grade textured 3D map of urban environment.

Autonomous Vehicles

Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

no code implementations Findings (EMNLP) 2021 Biswesh Mohapatra, Gaurav Pandey, Danish Contractor, Sachindra Joshi

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished.

Dialog Learning Language Modelling

Ford Highway Driving RTK Dataset: 30,000 km of North American Highways

no code implementations5 Oct 2020 Sarah E. Houts, Nahid Pervez, Umair Ibrahim, Gaurav Pandey, Tyler G. R. Reid

There is a growing need for vehicle positioning information to support Advanced Driver Assistance Systems (ADAS), Connectivity (V2X), and Autonomous Driving (AD) features.

Autonomous Driving

Unravelling the Architecture of Membrane Proteins with Conditional Random Fields

no code implementations6 Aug 2020 Lior Lukov, Sanjay Chawla, Wei Liu, Brett Church, Gaurav Pandey

In this paper, we will show that the recently introduced graphical model: Conditional Random Fields (CRF) provides a template to integrate micro-level information about biological entities into a mathematical model to understand their macro-level behavior.

SEIR and Regression Model based COVID-19 outbreak predictions in India

no code implementations1 Apr 2020 Gaurav Pandey, Poonam Chaudhary, Rajan Gupta, Saibal Pal

In this study, outbreak of this disease has been analysed for India till 30th March 2020 and predictions have been made for the number of cases for the next 2 weeks.

Aerial Imagery based LIDAR Localization for Autonomous Vehicles

no code implementations25 Mar 2020 Ankit Vora, Siddharth Agarwal, Gaurav Pandey, James McBride

This paper presents a localization technique using aerial imagery maps and LIDAR based ground reflectivity for autonomous vehicles in urban environments.

Autonomous Vehicles

Ford Multi-AV Seasonal Dataset

1 code implementation17 Mar 2020 Siddharth Agarwal, Ankit Vora, Gaurav Pandey, Wayne Williams, Helen Kourous, James McBride

This paper presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18.

Autonomous Vehicles POS

Mask & Focus: Conversation Modelling by Learning Concepts

no code implementations11 Feb 2020 Gaurav Pandey, Dinesh Raghu, Sachindra Joshi

The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts.

Machine Translation Response Generation

Deep-Geometric 6 DoF Localization from a Single Image in Topo-metric Maps

no code implementations4 Feb 2020 Tom Roussel, Punarjay Chakravarty, Gaurav Pandey, Tinne Tuytelaars, Luc Van Eycken

We describe a Deep-Geometric Localizer that is able to estimate the full 6 Degree of Freedom (DoF) global pose of the camera from a single image in a previously mapped environment.

Pose Estimation

Deep Discriminative Learning for Unsupervised Domain Adaptation

no code implementations17 Nov 2018 Rohith AP, Ambedkar Dukkipati, Gaurav Pandey

In contrast, we propose an approach that directly addresses the problem of learning a classifier in the unlabeled target domain.

General Classification Image Classification +2

Unsupervised Learning of Interpretable Dialog Models

no code implementations2 Nov 2018 Dhiraj Madan, Dinesh Raghu, Gaurav Pandey, Sachindra Joshi

However these states need to be handcrafted and annotated in the data.

Developing parsimonious ensembles using ensemble diversity within a reinforcement learning framework

no code implementations5 May 2018 Ana Stanescu, Gaurav Pandey

Ensemble selection is an especially promising approach here, not only for improving prediction performance, but also because of its ability to select a collectively predictive subset, often a relatively small one, of the base predictors.

reinforcement-learning

Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles

no code implementations28 Mar 2018 Juan Castorena, Gint Puskorius, Gaurav Pandey

This work proposes a novel motion guided method for target-less self-calibration of a LiDAR and camera and use the re-projection of LiDAR points onto the image reference frame for real-time depth upsampling.

Autonomous Vehicles Depth Estimation +2

Compact Environment-Invariant Codes for Robust Visual Place Recognition

no code implementations23 Sep 2017 Unnat Jain, Vinay P. Namboodiri, Gaurav Pandey

The modified system learns (in a supervised setting) compact binary codes from image feature descriptors.

Visual Place Recognition

Unsupervised feature learning with discriminative encoder

1 code implementation3 Sep 2017 Gaurav Pandey, Ambedkar Dukkipati

How can one use the same discriminative models for learning useful features in the absence of labels?

Learning to segment with image-level supervision

no code implementations3 May 2017 Gaurav Pandey, Ambedkar Dukkipati

In this paper, we propose a model that generates auxiliary labels for each image, while simultaneously forcing the output of the CNN to satisfy the mean-field constraints imposed by a conditional random field.

Semantic Segmentation

Discriminative Neural Topic Models

no code implementations24 Jan 2017 Gaurav Pandey, Ambedkar Dukkipati

We propose a neural network based approach for learning topics from text and image datasets.

Topic Models

On collapsed representation of hierarchical Completely Random Measures

no code implementations6 Sep 2015 Gaurav Pandey, Ambedkar Dukkipati

The aim of the paper is to provide an exact approach for generating a Poisson process sampled from a hierarchical CRM, without having to instantiate the infinitely many atoms of the random measures.

To go deep or wide in learning?

no code implementations23 Feb 2014 Gaurav Pandey, Ambedkar Dukkipati

To achieve acceptable performance for AI tasks, one can either use sophisticated feature extraction methods as the first layer in a two-layered supervised learning model, or learn the features directly using a deep (multi-layered) model.

A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

no code implementations19 Sep 2013 Sean Whalen, Gaurav Pandey

The combination of multiple classifiers using ensemble methods is increasingly important for making progress in a variety of difficult prediction problems.

Meta-Learning

Generative Maximum Entropy Learning for Multiclass Classification

no code implementations3 May 2012 Ambedkar Dukkipati, Gaurav Pandey, Debarghya Ghoshdastidar, Paramita Koley, D. M. V. Satya Sriram

In this paper, we introduce a maximum entropy classification method with feature selection for large dimensional data such as text datasets that is generative in nature.

Classification General Classification

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