Search Results for author:
Found 8 papers, 0 papers with code
CaM-Gen: Causally Aware Metric-Guided Text Generation
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
Deep Learning Based Named Entity Recognition Models for Recipes
Simultaneously, we systematically cleaned and analyzed ingredient phrases from RecipeDB, the gold-standard recipe data repository, and annotated them using the Stanford NER.
Prime+Probe 1, JavaScript 0: Overcoming Browser-based Side-Channel Defenses
To assess the effectiveness of this approach, in this work we seek to identify those JavaScript features which are essential for carrying out a cache-based attack.
Website Fingerprinting Attacks
Cryptography and Security
CaM-Gen:Causally-aware Metric-guided Text Generation
We leverage causal inference techniques to identify causally significant aspects of a text that lead to the target metric and then explicitly guide generative models towards these by a feedback mechanism.
RGait-NET: An Effective Network for Recovering Missing Information from Occluded Gait Cycles
Over the past decade, several computer vision-based gait recognition approaches have been proposed in which walking information corresponding to a complete gait cycle has been used to construct gait features for person identification.
Fiducia: A Personalized Food Recommender System for Zomato
This paper presents Fiducia, a food review system involving a pipeline which processes restaurant-related reviews obtained from Zomato (India's largest restaurant search and discovery service).
DyPerm: Maximizing Permanence for Dynamic Community Detection
In this paper, we propose DyPerm, the first dynamic community detection method which optimizes a novel community scoring metric, called permanence.
Social and Information Networks Physics and Society
Unsupervised Histopathology Image Synthesis
We propose a unified pipeline that: a) generates a set of initial synthetic histopathology images with paired information about the nuclei such as segmentation masks; b) refines the initial synthetic images through a Generative Adversarial Network (GAN) to reference styles; c) trains a task-specific CNN and boosts the performance of the task-specific CNN with on-the-fly generated adversarial examples.
