Search Results for author: Premkumar Natarajan

Found 21 papers, 9 papers with code

Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations

no code implementations ACL (ECNLP) 2021 Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan

We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.

Entity Resolution

Societal Biases in Language Generation: Progress and Challenges

1 code implementation ACL 2021 Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

Technology for language generation has advanced rapidly, spurred by advancements in pre-training large models on massive amounts of data and the need for intelligent agents to communicate in a natural manner.

Fairness Text Generation

Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations

no code implementations6 Apr 2021 Ying Lin, Han Wang, Jiangning Chen, Tong Wang, Yue Liu, Heng Ji, Yang Liu, Premkumar Natarajan

For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.

Entity Resolution

Discourse-level Relation Extraction via Graph Pooling

no code implementations1 Jan 2021 I-Hung Hsu, Xiao Guo, Premkumar Natarajan, Nanyun Peng

The ability to capture complex linguistic structures and long-term dependencies among words in the passage is essential for discourse-level relation extraction (DRE) tasks.

Natural Language Understanding Relation Extraction

Class-agnostic Object Detection

no code implementations28 Nov 2020 Ayush Jaiswal, Yue Wu, Pradeep Natarajan, Premkumar Natarajan

Finally, we propose (1) baseline methods and (2) a new adversarial learning framework for class-agnostic detection that forces the model to exclude class-specific information from features used for predictions.

Class-agnostic Object Detection Visual Grounding

"Nice Try, Kiddo": Investigating Ad Hominems in Dialogue Responses

1 code implementation24 Oct 2020 Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

Ad hominem attacks are those that target some feature of a person's character instead of the position the person is maintaining.

Abusive Language

Discovery and Separation of Features for Invariant Representation Learning

no code implementations2 Dec 2019 Ayush Jaiswal, Rob Brekelmans, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan

Supervised machine learning models often associate irrelevant nuisance factors with the prediction target, which hurts generalization.

Representation Learning

Invariant Representations through Adversarial Forgetting

no code implementations11 Nov 2019 Ayush Jaiswal, Daniel Moyer, Greg Ver Steeg, Wael Abd-Almageed, Premkumar Natarajan

We propose a novel approach to achieving invariance for deep neural networks in the form of inducing amnesia to unwanted factors of data through a new adversarial forgetting mechanism.

The Woman Worked as a Babysitter: On Biases in Language Generation

1 code implementation IJCNLP 2019 Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, Nanyun Peng

We present a systematic study of biases in natural language generation (NLG) by analyzing text generated from prompts that contain mentions of different demographic groups.

Language Modelling Text Generation +1

NIESR: Nuisance Invariant End-to-end Speech Recognition

1 code implementation7 Jul 2019 I-Hung Hsu, Ayush Jaiswal, Premkumar Natarajan

Deep neural network models for speech recognition have achieved great success recently, but they can learn incorrect associations between the target and nuisance factors of speech (e. g., speaker identities, background noise, etc.

Speech Recognition

Unified Adversarial Invariance

no code implementations7 May 2019 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

We present a unified invariance framework for supervised neural networks that can induce independence to nuisance factors of data without using any nuisance annotations, but can additionally use labeled information about biasing factors to force their removal from the latent embedding for making fair predictions.

Disentanglement Fairness

QATM: Quality-Aware Template Matching For Deep Learning

2 code implementations CVPR 2019 Jiaxin Cheng, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

Finding a template in a search image is one of the core problems many computer vision, such as semantic image semantic, image-to-GPS verification \etc.

Image-To-Gps Verification Template Matching

AIRD: Adversarial Learning Framework for Image Repurposing Detection

1 code implementation CVPR 2019 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Iacopo Masi, Premkumar Natarajan

Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda.

Misinformation

Unsupervised Adversarial Invariance

no code implementations NeurIPS 2018 Ayush Jaiswal, Yue Wu, Wael Abd-Almageed, Premkumar Natarajan

Data representations that contain all the information about target variables but are invariant to nuisance factors benefit supervised learning algorithms by preventing them from learning associations between these factors and the targets, thus reducing overfitting.

Data Augmentation Disentanglement +3

CapsuleGAN: Generative Adversarial Capsule Network

1 code implementation17 Feb 2018 Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

We provide guidelines for designing CapsNet discriminators and the updated GAN objective function, which incorporates the CapsNet margin loss, for training CapsuleGAN models.

General Classification Semi-Supervised Image Classification

Bidirectional Conditional Generative Adversarial Networks

no code implementations20 Nov 2017 Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan

Conditional Generative Adversarial Networks (cGANs) are generative models that can produce data samples ($x$) conditioned on both latent variables ($z$) and known auxiliary information ($c$).

Multimedia Semantic Integrity Assessment Using Joint Embedding Of Images And Text

no code implementations6 Jul 2017 Ayush Jaiswal, Ekraam Sabir, Wael Abd-Almageed, Premkumar Natarajan

In this paper, we present a novel deep learning-based approach for assessing the semantic integrity of multimedia packages containing images and captions, using a reference set of multimedia packages.

Representation Learning

Learning Document Image Binarization from Data

no code implementations4 May 2015 Yue Wu, Stephen Rawls, Wael Abd-Almageed, Premkumar Natarajan

In this paper we present a fully trainable binarization solution for degraded document images.

Binarization

Blockwise SURE Shrinkage for Non-Local Means

no code implementations18 May 2013 Yue Wu, Brian Tracey, Premkumar Natarajan, Joseph P. Noonan

In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising.

Image Denoising SSIM

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