activity
20222026
most citedControlling Text-to-Image Diffusion by Orthogonal Finetuning

21 citations · 27 across the 12 of their papers we have counts for

collaborators

14 papers

cs.CV2026

WAMJET: A Harness for World Action Model Acceleration

Le Chen, Lixin Liu, Jan Schneider +4

World Action Models (WAMs) leverage pretrained video foundation models for robot manipulation, but their large backbones and video-action co-prediction are expensive. Although exis…

cs.CL2026

PaperMentor: A Human-Centered Multi-Agent Writing Tutor for AI Research Papers on Overleaf

Jiarui Liu, Terry Jingchen Zhang, Ryan Faulkner +17

Expert writing feedback from experienced researchers is critical for early-career scholars to improve their manuscripts, yet high-quality feedback often remains scarce because revi…

cs.LG2026

PEFT-Arena: Understanding Parameter-Efficient Finetuning from a Stability-Plasticity Perspective

Yangyi Huang, Ruotian Peng, Zeju Qiu +4

Parameter-efficient finetuning (PEFT) has become the standard approach for adapting large language models, yet evaluations largely emphasize downstream accuracy while overlooking t…

cs.LG2026

Pion: A Spectrum-Preserving Optimizer via Orthogonal Equivalence Transformation

Kexuan Shi, Hanxuan Li, Zeju Qiu +3

We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additive optimizers such as Adam a…

cs.LG2026

Rigidity-Aware Geometric Pretraining for Protein Design and Conformational Ensembles

Zhanghan Ni, Yanjing Li, Zeju Qiu +4

Generative models have recently advanced protein design by learning the statistical regularities of natural structures. However, current approaches face three ke…

cs.LG2026

POET-X: Memory-efficient LLM Training by Scaling Orthogonal Transformation

Zeju Qiu, Lixin Liu, Adrian Weller +2

Efficient and stable training of large language models (LLMs) remains a core challenge in modern machine learning systems. To address this challenge, Reparameterized Orthogonal Equ…

cs.CV2025

Symbolic Graphics Programming with Large Language Models

Yamei Chen, Haoquan Zhang, Yangyi Huang +4

Large language models (LLMs) excel at program synthesis, yet their ability to produce symbolic graphics programs (SGPs) that render into precise visual content remains underexplore…

cs.LG2025

Orthogonal Finetuning Made Scalable

Zeju Qiu, Weiyang Liu, Adrian Weller +1

Orthogonal finetuning (OFT) offers highly parameter-efficient adaptation while preventing catastrophic forgetting, but its high runtime and memory demands limit practical deploymen…

cs.LG2025

Reparameterized LLM Training via Orthogonal Equivalence Transformation

Zeju Qiu, Simon Buchholz, Tim Z. Xiao +3

While large language models (LLMs) are driving the rapid advancement of artificial intelligence, effectively and reliably training these large models remains one of the field's mos…

cs.LG2024★ 2 cited

Can Large Language Models Understand Symbolic Graphics Programs?

Zeju Qiu, Weiyang Liu, Haiwen Feng +7

Against the backdrop of enthusiasm for large language models (LLMs), there is a growing need to scientifically assess their capabilities and shortcomings. This is nontrivial in par…

cs.LG2023★ 2 cited

Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

Weiyang Liu, Zeju Qiu, Yao Feng +11

Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is…

cs.CV2023★ 21 cited

Controlling Text-to-Image Diffusion by Orthogonal Finetuning

Zeju Qiu, Weiyang Liu, Haiwen Feng +6

Large text-to-image diffusion models have impressive capabilities in generating photorealistic images from text prompts. How to effectively guide or control these powerful models t…

cs.LG2022★ 1 cited

Iterative Teaching by Data Hallucination

Zeju Qiu, Weiyang Liu, Tim Z. Xiao +5

We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of…

cs.CV2022★ 1 cited

3D Multi-Object Tracking with Differentiable Pose Estimation

Dominik Schmauser, Zeju Qiu, Norman Müller +1

We propose a novel approach for joint 3D multi-object tracking and reconstruction from RGB-D sequences in indoor environments. To this end, we detect and reconstruct objects in eac…