Paper Rebuttal

Creator

Creator

Seonglae Cho

Created

Created

2025 Nov 12 11:8

Editor

Editor

Seonglae Cho

Edited

Edited

2025 Dec 3 0:57

Refs

Refs

Rebuttal by Authors

Official Comment by Authors

how

peer pressure

small table

followup reminder

We have addressed all reviewer concerns and added new experiments that further validate our conclusions.

update

Scaling In-the-Wild Training for Diffusion-based Illumination...

Diffusion-based image generators are becoming unique methods for illumination harmonization and editing. The current bottleneck in scaling up the training of diffusion-based illumination editing...

https://openreview.net/forum?id=u1cQYxRI1H

summary

Simplifying, Stabilizing and Scaling Continuous-time Consistency...

Consistency models (CMs) are a powerful class of diffusion-based generative models optimized for fast sampling. Most existing CMs are trained using discretized timesteps, which introduce additional...

https://openreview.net/forum?id=LyJi5ugyJx

thank when score is high

Special Unitary Parameterized Estimators of Rotation

This paper revisits the topic of rotation estimation through the lens of special unitary matrices. We begin by reformulating Wahba’s problem using $SU(2)$ to derive multiple solutions that yield...

https://openreview.net/forum?id=VaS6xcDrTb

examples

neurips

Quantifying Elicitation of Latent Capabilities in Language Models

Large language models often possess latent capabilities that lie dormant unless explicitly elicited, or surfaced, through fine-tuning or prompt engineering. Predicting, assessing, and understanding...

https://openreview.net/forum?id=Dkgx2pS4Ww

tmlr

Open Problems in Mechanistic Interpretability

Mechanistic interpretability aims to understand the computational mechanisms underlying neural networks' capabilities in order to accomplish concrete scientific and engineering goals. Progress in...

https://openreview.net/forum?id=91H76m9Z94

iclr

MaxInfoRL: Boosting exploration in reinforcement learning through...

Reinforcement learning (RL) algorithms aim to balance exploiting the current best strategy with exploring new options that could lead to higher rewards. Most common RL algorithms use undirected...

https://openreview.net/forum?id=R4q3cY3kQf

Recommendations

/////