43 citations · 97 across the 18 of their papers we have counts for
5 papers · 1 filter
Representation Learning via Variational Bayesian Networks
Oren Barkan, Avi Caciularu, Idan Rejwan +4
We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly…
Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps
Oren Barkan, Edan Hauon, Avi Caciularu +4
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks. As this revolution continues, the ability to explain model predictions has b…
MTAdam: Automatic Balancing of Multiple Training Loss Terms
Itzik Malkiel, Lior Wolf
When training neural models, it is common to combine multiple loss terms. The balancing of these terms requires considerable human effort and is computationally demanding. Moreover…
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
Oren Barkan, Noam Razin, Itzik Malkiel +3
Recent state-of-the-art natural language understanding models, such as BERT and XLNet, score a pair of sentences (A and B) using multiple cross-attention operations - a process in…
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
Itzik Malkiel, Sangtae Ahn, Valentina Taviani +3
Recent sparse MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much fas…