1 citations · 1 across the 6 of their papers we have counts for
6 papers
Can Heterogeneous Language Models Be Fused?
Shilian Chen, Jie Zhou, Qin Chen +4
Model merging aims to integrate multiple expert models into a single model that inherits their complementary strengths without incurring the inference-time cost of ensembling. Rece…
RecGen3D: Reconstruction-Guided 3D Generation in a Shared Canonical Space
Zhisheng Huang, Jiahao Chen, Cheng Lin +10
Sparse-view 3D modeling represents a fundamental tension between reconstruction fidelity and generative plausibility. While feed-forward reconstruction excels in efficiency and inp…
Unraveling the Localized Latents: Learning Stratified Manifold Structures in LLM Embedding Space with Sparse Mixture-of-Experts
Xin Li, Anand Sarwate
However, real-world data often exhibit complex local structures that can be challenging for single-model approaches with a smooth global manifold in the embedding space to unravel.…
LSR-Adapt: Ultra-Efficient Parameter Tuning with Matrix Low Separation Rank Kernel Adaptation
Xin Li, Anand Sarwate
Imposing an effective structural assumption on neural network weight matrices has been the major paradigm for designing Parameter-Efficient Fine-Tuning (PEFT) systems for adapting…
Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
Yongxin Zhu, Bocheng Li, Hang Zhang +3
Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typ…
LASERS: LAtent Space Encoding for Representations with Sparsity for Generative Modeling
Xin Li, Anand Sarwate
Learning compact and meaningful latent space representations has been shown to be very useful in generative modeling tasks for visual data. One particular example is applying Vecto…