3 citations · 5 across the 12 of their papers we have counts for
12 papers
Births are difficult to predict even with rich survey and full-population register data
Elizaveta Sivak, Emily M. Cantrell, Thomas Emery +109
Major life events have proven difficult to predict. Does this reflect limits of theory, data, and algorithms, or the large role of chance? We examine one outcome - having a child w…
Textual Supervision Enhances Geospatial Representations in Vision-Language Models
Marcelo Sartori Locatelli, Fernando Tonucci, Jea Kwon +5
Geospatial understanding is a critical yet underexplored dimension in the development of machine learning systems for tasks such as image geolocation and spatial reasoning. In this…
Machine Behavior in Relational Moral Dilemmas: Moral Rightness, Predicted Human Behavior, and Model Decisions
Jiseon Kim, Jea Kwon, Luiz Felipe Vecchietti +3
Human moral judgment is context-dependent and modulated by interpersonal relationships. As large language models (LLMs) increasingly function as decision-support systems, determini…
Dropouts in Confidence: Moral Uncertainty in Human-LLM Alignment
Jea Kwon, Luiz Felipe Vecchietti, Sungwon Park +1
Humans display significant uncertainty when confronted with moral dilemmas, yet the extent of such uncertainty in machines and AI agents remains underexplored. Recent studies have…
Exploring Persona-dependent LLM Alignment for the Moral Machine Experiment
Jiseon Kim, Jea Kwon, Luiz Felipe Vecchietti +2
Deploying large language models (LLMs) with agency in real-world applications raises critical questions about how these models will behave. In particular, how will their decisions…
Recent advances in interpretable machine learning using structure-based protein representations
Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev +5
Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure predict…
Robust Optimization in Protein Fitness Landscapes Using Reinforcement Learning in Latent Space
Minji Lee, Luiz Felipe Vecchietti, Hyunkyu Jung +3
Proteins are complex molecules responsible for different functions in nature. Enhancing the functionality of proteins and cellular fitness can significantly impact various industri…
Off-Policy Reinforcement Learning with Loss Function Weighted by Temporal Difference Error
Bumgeun Park, Taeyoung Kim, Woohyeon Moon +2
Training agents via off-policy deep reinforcement learning (RL) requires a large memory, named replay memory, that stores past experiences used for learning. These experiences are…
A Lightweight Domain Adaptive Absolute Pose Regressor Using Barlow Twins Objective
Praveen Kumar Rajendran, Quoc-Vinh Lai-Dang, Luiz Felipe Vecchietti +1
Identifying the camera pose for a given image is a challenging problem with applications in robotics, autonomous vehicles, and augmented/virtual reality. Lately, learning-based met…
RelMobNet: End-to-end relative camera pose estimation using a robust two-stage training
Praveen Kumar Rajendran, Sumit Mishra, Luiz Felipe Vecchietti +1
Relative camera pose estimation, i.e. estimating the translation and rotation vectors using a pair of images taken in different locations, is an important part of systems in augmen…
Sensing accident-prone features in urban scenes for proactive driving and accident prevention
Sumit Mishra, Praveen Kumar Rajendran, Luiz Felipe Vecchietti +1
In urban cities, visual information on and along roadways is likely to distract drivers and lead to missing traffic signs and other accident-prone (AP) features. To avoid accidents…
Two-stage training algorithm for AI robot soccer
Taeyoung Kim, Luiz Felipe Vecchietti, Kyujin Choi +2
In multi-agent reinforcement learning, the cooperative learning behavior of agents is very important. In the field of heterogeneous multi-agent reinforcement learning, cooperative…