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Steven Lu

7 accepted papers

2026

MOMO: Mars Orbital MOdel Foundation Model for Mars Orbital Applications

CVPR 2026

We introduce MOMO, the first multi-sensor foundation model for Mars remote sensing. MOMO uses model merge to integrate representations learned independently from three key Martian sensors (HiRISE, CTX, and THEMIS), spanning resolutions from 0.25 m/pixel to 100 m/pixel. Central to our method is our n

Cited by 0SourcecodeScholar
2025

Mars-Bench: A Benchmark for Evaluating Foundation Models for Mars Science Tasks

NeurIPS 2025poster

Foundation models have enabled rapid progress across many specialized domains by leveraging large-scale pre-training on unlabeled data, demonstrating strong generalization to a variety of downstream tasks. While such models have gained significant attention in fields like Earth Observation, their ap…

Cited by 0SourcecodeScholar
2024

Academics Can Contribute to Domain-Specialized Language Models

EMNLP 2024main

Commercially available models dominate academic leaderboards. While impressive, this has concentrated research on creating and adapting general-purpose models to improve NLP leaderboard standings for large language models. However, leaderboards collect many individual tasks and general-purpose model…

2024

Interactive Mars Image Content-Based Search with Interpretable Machine Learning

AAAI 2024technical

The NASA Planetary Data System (PDS) hosts millions of images of planets, moons, and other bodies collected throughout many missions. The ever-expanding nature of data and user engagement demands an interpretable content classification system to support scientific discovery and individual curiosity.…

Cited by 0SourcePDFScholar
2023

Cosmic Microwave Background Recovery: A Graph-Based Bayesian Convolutional Network Approach

AAAI 2023technical

The cosmic microwave background (CMB) is a significant source of knowledge about the origin and evolution of our universe. However, observations of the CMB are contaminated by foreground emissions, obscuring the CMB signal and reducing its efficacy in constraining cosmological parameters. We employ…

Cited by 2SourcePDFScholar
2023

MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies

ACL 2023long

Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P – that is, minimizing the forward cross-entropy, which is equivalent to maximum likelihood estimation (MLE). We have observed that models trained in this way may…

2019

Video Object Segmentation using Teacher-Student Adaptation in a Human Robot Interaction (HRI) Setting

ICRA 2019poster

Video object segmentation is an essential task in robot manipulation to facilitate grasping and learning affordances. Incremental learning is important for robotics in unstructured environments. Inspired by the children learning process, human robot interaction (HRI) can be utilized to teach robots…

Cited by 108SourcecodeScholar