6 papers
Large-Scale High-Quality 3D Gaussian Head Reconstruction from Multi-View Captures
Evangelos Ntavelis, Sean Wu, Mohamad Shahbazi +21
We propose HeadsUp, a scalable feed-forward method for reconstructing high-quality 3D Gaussian heads from large-scale multi-camera setups. Our method employs an efficient encoder-d…
Semantic Self-Distillation for Language Model Uncertainty
Edward Phillips, Sean Wu, Fredrik K. Gustafsson +2
Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the…
Ablate and Rescue: A Causal Analysis of Residual Stream Hyper-Connections
William Peng, Josheev Rai, Kevin Tseng +2
Multi-stream transformer architectures have recently been proposed as a promising direction for managing representation collapse and the vanishing gradient problem for residual con…
Geometric Uncertainty for Detecting and Correcting Hallucinations in LLMs
Edward Phillips, Sean Wu, Soheila Molaei +3
Large language models demonstrate impressive results across diverse tasks but are still known to hallucinate, generating linguistically plausible but incorrect answers to questions…
PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
Sean Wu, Shamik Basu, Tim Broedermann +2
We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF…
Interpolated-MLPs: Controllable Inductive Bias
Sean Wu, Jordan Hong, Keyu Bai +1
Due to their weak inductive bias, Multi-Layer Perceptrons (MLPs) have subpar performance at low-compute levels compared to standard architectures such as convolution-based networks…