Slangvolution: A Causal Analysis of Semantic Change and Frequency Dynamics in Slang

1 code implementation • ACL 2022 • Daphna Keidar, Andreas Opedal, Zhijing Jin, Mrinmaya Sachan

We analyze the semantic change and frequency shift of slang words and compare them to those of standard, nonslang words.

Causal Discovery Causal Inference

Towards Automatic Bias Detection in Knowledge Graphs

1 code implementation • Findings (EMNLP) 2021 • Daphna Keidar, Mian Zhong, Ce Zhang, Yash Raj Shrestha, Bibek Paudel

With the recent surge in social applications relying on knowledge graphs, the need for techniques to ensure fairness in KG based methods is becoming increasingly evident.

Bias Detection Fairness +2

Recovering Barabási-Albert Parameters of Graphs through Disentanglement

1 code implementation • ICLR Workshop GTRL 2021 • Cristina Guzman, Daphna Keidar, Tristan Meynier, Andreas Opedal, Niklas Stoehr

We first learn the generative BA parameters in a supervised fashion using a Graph Neural Network (GNN) and a Random Forest Regressor, by minimizing the squared loss between the true generative parameters and the latent variables.

Disentanglement Graph Generation

Point of Care Image Analysis for COVID-19

no code implementations • 28 Oct 2020 • Daniel Yaron, Daphna Keidar, Elisha Goldstein, Yair Shachar, Ayelet Blass, Oz Frank, Nir Schipper, Nogah Shabshin, Ahuva Grubstein, Dror Suhami, Naama R. Bogot, Eyal Sela, Amiel A. Dror, Mordehay Vaturi, Federico Mento, Elena Torri, Riccardo Inchingolo, Andrea Smargiassi, Gino Soldati, Tiziano Perrone, Libertario Demi, Meirav Galun, Shai Bagon, Yishai M. Elyada, Yonina C. Eldar

Collaborating with several hospitals in Israel we collect a large dataset of CXRs and use this dataset to train a neural network obtaining above 90% detection rate for COVID-19.

COVID-19 Classification of X-ray Images Using Deep Neural Networks

no code implementations • 3 Oct 2020 • Elisha Goldstein, Daphna Keidar, Daniel Yaron, Yair Shachar, Ayelet Blass, Leonid Charbinsky, Israel Aharony, Liza Lifshitz, Dimitri Lumelsky, Ziv Neeman, Matti Mizrachi, Majd Hajouj, Nethanel Eizenbach, Eyal Sela, Chedva S Weiss, Philip Levin, Ofer Benjaminov, Gil N Bachar, Shlomit Tamir, Yael Rapson, Dror Suhami, Amiel A Dror, Naama R Bogot, Ahuva Grubstein, Nogah Shabshin, Yishai M Elyada, Yonina C Eldar

The purpose of this study is to create and evaluate a machine learning model for diagnosis of COVID-19, and to provide a tool for searching for similar patients according to their X-ray scans.

Data Augmentation General Classification