Master's Theses & Technical Reports - Trevor Darrell
5th Year M.S.
Counting Counts: Overcoming Counting Challenges in Image Generation using Reinforcement Learning
Shaan Gill [2024]
Dropout Reduces Underfitting
Oscar Xu [2023]
Group Probability-Weighted Tree Sums for Interpretable Modeling of Heterogeneous Data
Keyan Abou-Nasseri [2022]
Object-Level Representation Learning for Natural and Medical Images
Akash Gokul [2022]
Vision and Language for Digital Forensics
Grace Luo [2022]
A Curiosity-Driven Approach for Generating Textual Descriptions of Environments
Xinyun Zhang [2021]
Approaching the Issue of Limited Annotation for Instance Segmentation
Vishnu Doppalapudi [2021]
Comparing Human and AI Behavior in 3D Navigation Environments
Jeffrey Liu [2021]
Language Guided Out-of-Distribution Detection
William Gan [2021]
A Scalable Synchronous System for Robust Real Time Human-Machine Collaboration
Christopher Powers [2020]
AI for HADR: Progress and Opportunities
Ross Luo [2020]
Addressing and Understanding Shortcomings in Vision and Language
Kaylee Burns [2019]
Combined Detection and Tracking Impacts on Vehicle Specific Data
David Zhang [2019]
The BDD-Nexar Collective: A Large-Scale, Crowsourced, Dataset of Driving Scenes
Vashisht Madhavan [2017]
M.S.
Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
Ritwik Gupta [2023]
Continual Learning with Neural Networks
Sayna Ebrahimi [2020]
Program Synthesis for Autonomous Driving Decisions
Yiteng Zhang [2020]
Learning to Segment Every Thing
Ronghang Hu [2019]
Applications of Machine Learning to Support Dementia Care through Commercially Available Off-the-Shelf Sensing
George Netscher [2016]
Visual Grasp Affordances From Appearance-Based Cues
Hyun Oh Song [2013]
The Berkeley 3D Object Dataset
Allison Janoch [2012]