Search Results for author:
Found 19 papers, 9 papers with code
In Search of Truth: An Interrogation Approach to Hallucination Detection
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 to various reasons.
Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
Given an image of a certain object, the goal of VSD is to retrieve images of different objects with high perceptual visual similarity.
Representation Learning via Variational Bayesian Networks
We present Variational Bayesian Network (VBN) - a novel Bayesian entity representation learning model that utilizes hierarchical and relational side information and is particularly useful for modeling entities in the ``long-tail'', where the data is scarce.
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models
The online phase is applied to every call separately and scores the similarity between the transcripted conversation and the topic anchors found in the offline phase.
Interpreting BERT-based Text Similarity via Activation and Saliency Maps
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.
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
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
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks.
Self-Supervised Transformers for fMRI representation
We present TFF, which is a Transformer framework for the analysis of functional Magnetic Resonance Imaging (fMRI) data.
Caption Enriched Samples for Improving Hateful Memes Detection
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not.
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
The algorithmic advantages of GAM are explained in detail, and validated empirically, where it is shown that GAM outperforms its alternatives across various tasks and datasets.
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Hence, we introduce SDR, a self-supervised method for document similarity that can be applied to documents of arbitrary length.
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image Translation
Recent accelerated MRI reconstruction models have used Deep Neural Networks (DNNs) to reconstruct relatively high-quality images from highly undersampled k-space data, enabling much faster MRI scanning.
Generative Adversarial Network
Image-to-Image Translation
+2
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models
Language modeling with BERT consists of two phases of (i) unsupervised pre-training on unlabeled text, and (ii) fine-tuning for a specific supervised task.
RecoBERT: A Catalog Language Model for Text-Based Recommendations
In addition, we introduce a new language understanding task for wine recommendations using similarities based on professional wine reviews.
MTAdam: Automatic Balancing of Multiple Training Loss Terms
When training neural models, it is common to combine multiple loss terms.
Spectra2pix: Generating Nanostructure Images from Spectra
Our model architecture is not limited to a closed set of nanostructure shapes, and can be trained for the design of any geometry.
MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language Models
In this work, we present a method that leverages BERT's fine-tuning phase to its fullest, by applying an extensive number of parallel classifier heads, which are enforced to be orthogonal, while adaptively eliminating the weaker heads during training.
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence Embedding
In this paper, we introduce Distilled Sentence Embedding (DSE) - a model that is based on knowledge distillation from cross-attentive models, focusing on sentence-pair tasks.
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
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 faster MRI scanning.
