Search Results for author:
Found 20 papers, 4 papers with code
Within-Between Lexical Relation Classification
We propose the novel \textit{Within-Between} Relation model for recognizing lexical-semantic relations between words.
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.
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.
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.
Multiscale Self Attentive Convolutions for Vision and Language Modeling
Self attention mechanisms have become a key building block in many state-of-the-art language understanding models.
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).
Bayesian Neural Word Embedding
Recently, several works in the domain of natural language processing presented successful methods for word embedding.
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.
Gaussian Process Regression for Out-of-Sample Extension
Many of the existing methods produce a low dimensional representation that attempts to describe the intrinsic geometric structure of the original data.
Adaptive Compressed Tomography Sensing
One of the main challenges in Computed Tomography (CT) is how to balance between the amount of radiation the patient is exposed to during scan time and the quality of the CT image.
