10 citations · 26 across the 21 of their papers we have counts for
22 papers
Learning to Plan via Supervised Contrastive Learning and Strategic Interpolation: A Chess Case Study
Andrew Hamara, Greg Hamerly, Pablo Rivas +1
Modern chess engines achieve superhuman performance through deep tree search and regressive evaluation, while human players rely on intuition to select candidate moves followed by…
Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration
Tianqi Ding, Dawei Xiang, Pablo Rivas +1
Neural Radiance Fields (NeRF) have become a popular 3D reconstruction approach in recent years. While they produce high-quality results, they also demand lengthy training times, of…
A Framework for Evaluating Vision-Language Model Safety: Building Trust in AI for Public Sector Applications
Maisha Binte Rashid, Pablo Rivas
Vision-Language Models (VLMs) are increasingly deployed in public sector missions, necessitating robust evaluation of their safety and vulnerability to adversarial attacks. This pa…
Exploring Visual Embedding Spaces Induced by Vision Transformers for Online Auto Parts Marketplaces
Cameron Armijo, Pablo Rivas
This study examines the capabilities of the Vision Transformer (ViT) model in generating visual embeddings for images of auto parts sourced from online marketplaces, such as Craigs…
Data-Dependent Generalization Bounds for Parameterized Quantum Models Under Noise
Bikram Khanal, Pablo Rivas
Quantum machine learning offers a transformative approach to solving complex problems, but the inherent noise hinders its practical implementation in near-term quantum devices. Thi…
NLP-Guided Synthesis: Transitioning from Sequential Programs to Distributed Programs
Arun Sanjel, Bikram Khanal, Greg Speegle +1
As the need for large-scale data processing grows, distributed programming frameworks like PySpark have become increasingly popular. However, the task of converting traditional, se…
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…
Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey
Bikram Khanal, Pablo Rivas, Arun Sanjel +3
Despite the mounting anticipation for the quantum revolution, the success of Quantum Machine Learning (QML) in the Noisy Intermediate-Scale Quantum (NISQ) era hinges on a largely u…
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…
Image-Based Leopard Seal Recognition: Approaches and Challenges in Current Automated Systems
Jorge Yero Salazar, Pablo Rivas, Renato Borras-Chavez +1
This paper examines the challenges and advancements in recognizing seals within their natural habitats using conventional photography, underscored by the emergence of machine learn…
AI Safety in Practice: Enhancing Adversarial Robustness in Multimodal Image Captioning
Maisha Binte Rashid, Pablo Rivas
Multimodal machine learning models that combine visual and textual data are increasingly being deployed in critical applications, raising significant safety and security concerns d…
An Automated SQL Query Grading System Using An Attention-Based Convolutional Neural Network
Donald R. Schwartz, Pablo Rivas
Grading SQL queries can be a time-consuming, tedious and challenging task, especially as the number of student submissions increases. Several systems have been introduced in an att…
A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing
Nurul Rafi, Pablo Rivas
Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, sp…
Detecting Hallucinations in Large Language Model Generation: A Token Probability Approach
Ernesto Quevedo, Jorge Yero, Rachel Koerner +2
Concerns regarding the propensity of Large Language Models (LLMs) to produce inaccurate outputs, also known as hallucinations, have escalated. Detecting them is vital for ensuring…
Efficacy of ByT5 in Multilingual Translation of Biblical Texts for Underrepresented Languages
Corinne Aars, Lauren Adams, Xiaokan Tian +6
This study presents the development and evaluation of a ByT5-based multilingual translation model tailored for translating the Bible into underrepresented languages. Utilizing the…
On the Challenges of Creating Datasets for Analyzing Commercial Sex Advertisements to Assess Human Trafficking Risk and Organized Activity
Pablo Rivas, Tomas Cerny, Alejandro Rodriguez Perez +4
Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. Th…
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…
A Review on Machine Learning Algorithms for Dust Aerosol Detection using Satellite Data
Nurul Rafi, Pablo Rivas
Dust storms are associated with certain respiratory illnesses across different areas in the world. Researchers have devoted time and resources to study the elements surrounding dus…
A Modified Depolarization Approach for Efficient Quantum Machine Learning
Bikram Khanal, Pablo Rivas
Quantum Computing in the Noisy Intermediate-Scale Quantum (NISQ) era has shown promising applications in machine learning, optimization, and cryptography. Despite the progress, cha…
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…