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View article: InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers
InterrogateLLM: Zero-Resource Hallucination Detection in LLM-Generated Answers Open
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. One critical factor hindering their wide…
View article: textual item similarty (video games and wines datasets)
textual item similarty (video games and wines datasets) Open
View article: textual item similarty (video games and wines datasets)
textual item similarty (video games and wines datasets) Open
View article: Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond
Efficient Discovery and Effective Evaluation of Visual Perceptual Similarity: A Benchmark and Beyond Open
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 objects with high perceptual visual similarity. Although be…
View article: Representation Learning via Variational Bayesian Networks
Representation Learning via Variational Bayesian Networks Open
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 t…
View article: GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models Open
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 recorded conversations can be an arduous and time-intensive…
View article: Interpreting BERT-based Text Similarity via Activation and Saliency Maps
Interpreting BERT-based Text Similarity via Activation and Saliency Maps Open
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. Despite significant progress in the field, the explanations f…
View article: MetricBERT: Text Representation Learning via Self-Supervised Triplet Training
MetricBERT: Text Representation Learning via Self-Supervised Triplet Training Open
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. We focus on downstream tasks of learning similarities for…
View article: Self-Supervised Transformers for fMRI representation
Self-Supervised Transformers for fMRI representation Open
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-supervised training is applied to a collection of fMRI scans, wh…
View article: Grad-SAM
Grad-SAM Open
Transformer-based language models significantly advanced the state-of-the-art\nin many linguistic tasks. As this revolution continues, the ability to explain\nmodel predictions has become a major area of interest for the NLP community. In\…
View article: Caption Enriched Samples for Improving Hateful Memes Detection
Caption Enriched Samples for Improving Hateful Memes Detection Open
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of p…
View article: GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps
GAM: Explainable Visual Similarity and Classification via Gradient Activation Maps Open
We present Gradient Activation Maps (GAM) - a machinery for explaining predictions made by visual similarity and classification models. By gleaning localized gradient and activation information from multiple network layers, GAM offers impr…
View article: Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation
Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation Open
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. However, these techniques sometimes …
View article: Adaptive Gradient Balancing for Undersampled MRI Reconstruction and\n Image-to-Image Translation
Adaptive Gradient Balancing for Undersampled MRI Reconstruction and\n Image-to-Image Translation Open
Recent accelerated MRI reconstruction models have used Deep Neural Networks\n(DNNs) to reconstruct relatively high-quality images from highly undersampled\nk-space data, enabling much faster MRI scanning. However, these techniques\nsometim…
View article: MTAdam: Automatic Balancing of Multiple Training Loss Terms
MTAdam: Automatic Balancing of Multiple Training Loss Terms Open
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, the optimal trade-off between the loss terms can change a…
View article: Caption Enriched Samples for Improving Hateful Memes Detection
Caption Enriched Samples for Improving Hateful Memes Detection Open
The recently introduced hateful meme challenge demonstrates the difficulty of determining whether a meme is hateful or not. Specifically, both unimodal language models and multimodal vision-language models cannot reach the human level of p…
View article: Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Self-Supervised Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference Open
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-document similarity ranking has been studied, most modern m…
View article: Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models
Maximal Multiverse Learning for Promoting Cross-Task Generalization of Fine-Tuned Language Models Open
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. We present a method that leverages the second phase to its fullest, by applying an …
View article: MTAdam: Automatic Balancing of Multiple Training Loss Terms
MTAdam: Automatic Balancing of Multiple Training Loss Terms Open
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, the optimal trade-off between the loss term can change as…
View article: Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding
Scalable Attentive Sentence Pair Modeling via Distilled Sentence Embedding Open
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 which each word in sentence A attends to all words in sente…
View article: Machine learning for nanophotonics
Machine learning for nanophotonics Open
View article: RecoBERT: A Catalog Language Model for Text-Based Recommendations
RecoBERT: A Catalog Language Model for Text-Based Recommendations Open
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 of language understanding tasks. However, it is yet unclear…
View article: Spectra2pix: Generating Nanostructure Images from Spectra
Spectra2pix: Generating Nanostructure Images from Spectra Open
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 limiting the reach and penetration of this groundbreaking app…
View article: MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language Models
MML: Maximal Multiverse Learning for Robust Fine-Tuning of Language Models Open
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 supervised task. More recently, many studies have been focused…
View article: Scalable Attentive Sentence-Pair Modeling via Distilled Sentence\n Embedding
Scalable Attentive Sentence-Pair Modeling via Distilled Sentence\n Embedding Open
Recent state-of-the-art natural language understanding models, such as BERT\nand XLNet, score a pair of sentences (A and B) using multiple cross-attention\noperations - a process in which each word in sentence A attends to all words in\nse…
View article: Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction
Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI Reconstruction Open
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. However, these techniques sometimes strug…
View article: Plasmonic nanostructure design and characterization via Deep Learning
Plasmonic nanostructure design and characterization via Deep Learning Open
View article: Deep Learning for Design and Retrieval of Nano-photonic Structures
Deep Learning for Design and Retrieval of Nano-photonic Structures Open
Our visual perception of our surroundings is ultimately limited by the diffraction limit, which stipulates that optical information smaller than roughly half the illumination wavelength is not retrievable. Over the past decades, many break…