43 citations · 97 across the 18 of their papers we have counts for
19 papers
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers
Yakir Yehuda, Itzik Malkiel, Oren Barkan +3
Despite the many advances of Large Language Models (LLMs) and their unprecedented rapid evolution, their impact and integration into every facet of our daily lives is limited due t…
Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
Oren Barkan, Tal Reiss, Jonathan Weill +4
Visual similarities discovery (VSD) is an important task with broad e-commerce applications. Given an image of a certain object, the goal of VSD is to retrieve images of different…
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…
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models
Itzik Malkiel, Uri Alon, Yakir Yehuda +4
Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these…
Interpreting BERT-based Text Similarity via Activation and Saliency Maps
Itzik Malkiel, Dvir Ginzburg, Oren Barkan +3
Recently, there has been growing interest in the ability of Transformer-based models to produce meaningful embeddings of text with several applications, such as text similarity. De…
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
Itzik Malkiel, Dvir Ginzburg, Oren Barkan +3
We present MetricBERT, a BERT-based model that learns to embed text under a well-defined similarity metric while simultaneously adhering to the ``traditional'' masked-language task…
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…
Self-Supervised Transformers for fMRI representation
Itzik Malkiel, Gony Rosenman, Lior Wolf +1
We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data. TFF employs a two-phase training approach. First, self-super…
Caption Enriched Samples for Improving Hateful Memes Detection
Efrat Blaier, Itzik Malkiel, Lior Wolf
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimod…
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
Oren Barkan, Omri Armstrong, Amir Hertz +4
We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activatio…
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Dvir Ginzburg, Itzik Malkiel, Oren Barkan +2
We present a novel model for the problem of ranking a collection of documents according to their semantic similarity to a source (query) document. While the problem of document-to-…
Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation
Itzik Malkiel, Sangtae Ahn, Valentina Taviani +3
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling muc…
RecoBERT: A Catalog Language Model for Text-Based Recommendations
Itzik Malkiel, Oren Barkan, Avi Caciularu +3
Language models that utilize extensive self-supervised pre-training from unlabeled text, have recently shown to significantly advance the state-of-the-art performance in a variety…
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…
Spectra2pix: Generating Nanostructure Images from Spectra
Itzik Malkiel, Michael Mrejen, Lior Wolf +1
The design of the nanostructures that are used in the field of nano-photonics has remained complex, very often relying on the intuition and expertise of the designer, ultimately li…
MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language Models
Itzik Malkiel, Lior Wolf
Recent state-of-the-art language models utilize a two-phase training procedure comprised of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific sup…
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…
Deep Learning for Design and Retrieval of Nano-photonic Structures
Itzik Malkiel, Achiya Nagler, Michael Mrejen +3
Our visual perception of our surroundings is ultimately limited by the diffraction limit, which stipulates that optical information smaller than roughly half the illumination wavel…