Search Results for author:
Found 8 papers, 5 papers with code
Auto-Debias: Debiasing Masked Language Models with Automated Biased Prompts
Specifically, we propose a variant of the beam search method to automatically search for biased prompts such that the cloze-style completions are the most different with respect to different demographic groups.
A Recursively Recurrent Neural Network (R2N2) Architecture for Learning Iterative Algorithms
Meta-learning of numerical algorithms for a given task consist of the data-driven identification and adaptation of an algorithmic structure and the associated hyperparameters.
Explainable Action Advising for Multi-Agent Reinforcement Learning
This allows the student to self-reflect on what it has learned, enabling advice generalization and leading to improved sample efficiency and learning performance - even in environments where the teacher is sub-optimal.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
CELLS: A Parallel Corpus for Biomedical Lay Language Generation
Recent lay language generation systems have used Transformer models trained on a parallel corpus to increase health information accessibility.
Personalized Algorithm Generation: A Case Study in Learning ODE Integrators
As a case study, we develop a machine learning approach that automatically learns effective solvers for initial value problems in the form of ordinary differential equations (ODEs), based on the Runge-Kutta (RK) integrator architecture.
Automated Lay Language Summarization of Biomedical Scientific Reviews
Health literacy has emerged as a crucial factor in making appropriate health decisions and ensuring treatment outcomes.
Learning Modulated Loss for Rotated Object Detection
Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) to describe the rotated bounding box and l1-loss as the loss function.
Ranked #37 on
Object Detection In Aerial Images
on DOTA
Surface Defect Saliency of Magnetic Tile
Vision-based detection on surface defects has long postulated in the magnetic tile automation process.
Ranked #1 on
Anomaly Detection
on Surface Defect Saliency of Magnetic Tile
(Segmentation AUROC metric, using extra
training data)
