Search Results for author:
Found 22 papers, 6 papers with code
Mitigating Data Shift of Biomedical Research Articles for Information Retrieval and Semantic Indexing
Our results show that the model effectively leverages task similarity to improve the robustness to dataset shift.
Hate and Toxic Speech Detection in the Context of Covid-19 Pandemic using XAI: Ongoing Applied Research
As social distancing, self-quarantines, and travel restrictions have shifted a lot of pandemic conversations to social media so does the spread of hate speech.
Explainable Artificial Intelligence (XAI)
Feature Importance
+2
COVID-19 Surveillance through Twitter using Self-Supervised and Few Shot Learning
Public health surveillance and tracking virus via social media can be a useful digital tool for contact tracing and preventing the spread of the virus.
Image Safeguarding: Reasoning with Conditional Vision Language Model and Obfuscating Unsafe Content Counterfactually
This process involves addressing two key problems: (1) the reason for obfuscating unsafe images demands the platform to provide an accurate rationale that must be grounded in unsafe image-specific attributes, and (2) the unsafe regions in the image must be minimally obfuscated while still depicting the safe regions.
Large Language Model Lateral Spear Phishing: A Comparative Study in Large-Scale Organizational Settings
The critical threat of phishing emails has been further exacerbated by the potential of LLMs to generate highly targeted, personalized, and automated spear phishing attacks.
Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text
We first study the effectiveness of state-of-the-art approaches and find that they are severely limited against text produced by diverse generators and domains in the real world.
Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models
This imbalance between the lines needed for security measures and the functional code enforces the supervised fine-tuned model to prioritize generating functional code without adding proper security measures, which also benefits the model by resulting in minimal loss.
Seeing the roads through the trees: A benchmark for modeling spatial dependencies with aerial imagery
In this work we propose a road segmentation benchmark dataset, Chesapeake Roads Spatial Context (RSC), for evaluating the spatial long-range context understanding of geospatial machine learning models and show how commonly used semantic segmentation models can fail at this task.
Ranked #1 on
Road Segmentation
on ChesapeakeRSC
LLM-Powered Code Vulnerability Repair with Reinforcement Learning and Semantic Reward
In software development, the predominant emphasis on functionality often supersedes security concerns, a trend gaining momentum with AI-driven automation tools like GitHub Copilot.
Revisiting pre-trained remote sensing model benchmarks: resizing and normalization matters
Research in self-supervised learning (SSL) with natural images has progressed rapidly in recent years and is now increasingly being applied to and benchmarked with datasets containing remotely sensed imagery.
Single-View Height Estimation with Conditional Diffusion Probabilistic Models
Digital Surface Models (DSM) offer a wealth of height information for understanding the Earth's surface as well as monitoring the existence or change in natural and man-made structures.
ZRG: A Dataset for Multimodal 3D Residential Rooftop Understanding
A crucial part of any home is the roof over our heads to protect us from the elements.
An Unbiased Transformer Source Code Learning with Semantic Vulnerability Graph
We present a training process utilizing a semantic vulnerability graph (SVG) representation from source code, created by integrating edges from a sequential flow, control flow, and data flow, as well as a novel flow dubbed Poacher Flow (PF).
Supervising Remote Sensing Change Detection Models with 3D Surface Semantics
Remote sensing change detection, identifying changes between scenes of the same location, is an active area of research with a broad range of applications.
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts
To overcome this problem, we propose a Multimodal Variational Auto-Encoder (M-VAE) which can learn the shared latent space of image features and the semantic space.
Interpretable Self-supervised Multi-task Learning for COVID-19 Information Retrieval and Extraction
Our model outperforms baselines in IE and IR tasks, respectively by micro-f score of 0. 08 (LCA-F score of 0. 05), and MAP of 0. 05 on average.
Show Why the Answer is Correct! Towards Explainable AI using Compositional Temporal Attention
This results in achieving better understanding of complex questions and also provides reasoning as to why the module predicts a particular answer.
Adaptive Clustering of Robust Semantic Representations for Adversarial Image Purification
In order to evaluate the most adequate SRD, we rely on the distance between robust latent representations and semantic cluster distributions.
A Self-supervised Approach for Semantic Indexing in the Context of COVID-19 Pandemic
We present a case study on a novel dataset that is based on COVID-19 papers published and manually indexed in PubMed.
Learning from Few Samples: A Survey
Deep neural networks have been able to outperform humans in some cases like image recognition and image classification.
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses.
