7 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…
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…