1 citations · 3 across the 5 of their papers we have counts for
6 papers
Learning Robust Observable to Address Noise in Quantum Machine Learning
Bikram Khanal, Pablo Rivas
Quantum Machine Learning (QML) has emerged as a promising field that combines the power of quantum computing with the principles of machine learning. One of the significant challen…
Bottleneck-based Encoder-decoder ARchitecture (BEAR) for Learning Unbiased Consumer-to-Consumer Image Representations
Pablo Rivas, Gisela Bichler, Tomas Cerny +2
Unbiased representation learning is still an object of study under specific applications and contexts. Novel architectures are usually crafted to resolve particular problems using…
From Latent to Engine Manifolds: Analyzing ImageBind's Multimodal Embedding Space
Andrew Hamara, Pablo Rivas
This study investigates ImageBind's ability to generate meaningful fused multimodal embeddings for online auto parts listings. We propose a simplistic embedding fusion workflow tha…
On Adversarial Examples for Text Classification by Perturbing Latent Representations
Korn Sooksatra, Bikram Khanal, Pablo Rivas
Recently, with the advancement of deep learning, several applications in text classification have advanced significantly. However, this improvement comes with a cost because deep l…
Is ReLU Adversarially Robust?
Korn Sooksatra, Greg Hamerly, Pablo Rivas
The efficacy of deep learning models has been called into question by the presence of adversarial examples. Addressing the vulnerability of deep learning models to adversarial exam…
Combatting Human Trafficking in the Cyberspace: A Natural Language Processing-Based Methodology to Analyze the Language in Online Advertisements
Alejandro Rodriguez Perez, Pablo Rivas
This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodol…