Search Results for author:
Found 15 papers, 5 papers with code
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.
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.
Discovery of Single Independent Latent Variable
Latent variable discovery is a central problem in data analysis with a broad range of applications in applied science.
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.
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.
Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing
Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices.
Guidestar-free image-guided wavefront-shaping
Optical imaging through scattering media is a fundamental challenge in many applications.
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.
Diffusion-based nonlinear filtering for multimodal data fusion with application to sleep stage assessment
We show that without prior knowledge on the different modalities and on the measured system, our method gives rise to a data-driven representation that is well correlated with the underlying sleep process and is robust to noise and sensor-specific effects.
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).
