Search Results for author:
Found 29 papers, 13 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.
DiffMoog: a Differentiable Modular Synthesizer for Sound Matching
We introduce an open-source platform that comprises DiffMoog and an end-to-end sound matching framework.
Visual Explanations via Iterated Integrated Attributions
We introduce Iterated Integrated Attributions (IIA) - a generic method for explaining the predictions of vision models.
Learning to Explain: A Model-Agnostic Framework for Explaining Black Box Models
We present Learning to Explain (LTX), a model-agnostic framework designed for providing post-hoc explanations for vision models.
Deep Integrated Explanations
This paper presents Deep Integrated Explanations (DIX) - a universal method for explaining vision models.
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.
Detecting Security Patches via Behavioral Data in Code Repositories
The absolute majority of software today is developed collaboratively using collaborative version control tools such as Git.
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.
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.
Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps
Transformer-based language models significantly advanced the state-of-the-art in many linguistic tasks.
Cold Item Integration in Deep Hybrid Recommenders via Tunable Stochastic Gates
Next, we propose a novel hybrid recommendation algorithm that bridges these two conflicting objectives and enables a harmonized balance between preserving high accuracy for warm items while effectively promoting completely cold items.
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.
Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks
We present a hierarchical architecture based on Recurrent Neural Networks (RNNs) for predicting disaggregated inflation components of the Consumer Price Index (CPI).
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.
Bayesian Hierarchical Words Representation Learning
BHWR facilitates Variational Bayes word representation learning combined with semantic taxonomy modeling via hierarchical priors.
Autoencoders
An autoencoder is a specific type of a neural network, which is mainly designed to encode the input into a compressed and meaningful representation, and then decode it back such that the reconstructed input is similar as possible to the original one.
Neural Attentive Multiview Machines
An important problem in multiview representation learning is finding the optimal combination of views with respect to the specific task at hand.
Attentive Item2Vec: Neural Attentive User Representations
However, it is possible that a certain early movie may become suddenly more relevant in the presence of a popular sequel movie.
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.
InverSynth: Deep Estimation of Synthesizer Parameter Configurations from Audio Signals
Sound synthesis is a complex field that requires domain expertise.
CB2CF: A Neural Multiview Content-to-Collaborative Filtering Model for Completely Cold Item Recommendations
In Recommender Systems research, algorithms are often characterized as either Collaborative Filtering (CF) or Content Based (CB).
The Bayesian Low-Rank Determinantal Point Process Mixture Model
Determinantal point processes (DPPs) are an elegant model for encoding probabilities over subsets, such as shopping baskets, of a ground set, such as an item catalog.
Item2Vec: Neural Item Embedding for Collaborative Filtering
Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities.
Low-Rank Factorization of Determinantal Point Processes for Recommendation
In this work we present a new method for learning the DPP kernel from observed data using a low-rank factorization of this kernel.
Scalable Bayesian Modelling of Paired Symbols
We present a novel, scalable and Bayesian approach to modelling the occurrence of pairs of symbols (i, j) drawn from a large vocabulary.
One-class Collaborative Filtering with Random Graphs: Annotated Version
The bane of one-class collaborative filtering is interpreting and modelling the latent signal from the missing class.
