1 citations · 2 across the 8 of their papers we have counts for
11 papers
Flash-BoN: Instant Drafts for Inference-Time Scaling in Diffusion Models
Ruchit Rawal, Reza Shirkavand, Sayak Paul +5
Inference-time scaling for text-to-image generation has progressed from simple Best-of- (BoN) sampling to guided search methods that verify and steer candidate trajectories at i…
VGA-Bench: A Unified Benchmark and Multi-Model Framework for Video Aesthetics and Generation Quality Evaluation
Longteng Jiang, DanDan Zheng, Qianqian Qiao +7
The rapid advancement of AIGC-based video generation has underscored the critical need for comprehensive evaluation frameworks that go beyond traditional generation quality metrics…
New Hybrid Fine-Tuning Paradigm for LLMs: Algorithm Design and Convergence Analysis Framework
Shaocong Ma, Peiran Yu, Heng Huang
Fine-tuning Large Language Models (LLMs) typically involves either full fine-tuning, which updates all model parameters, or Parameter-Efficient Fine-Tuning (PEFT), which adjusts a…
Catalog-Native LLM: Speaking Item-ID Dialect with Less Entanglement for Recommendation
Reza Shirkavand, Xiaokai Wei, Chen Wang +3
While collaborative filtering delivers predictive accuracy and efficiency, and Large Language Models (LLMs) enable expressive and generalizable reasoning, modern recommendation sys…
Cost-Aware Contrastive Routing for LLMs
Reza Shirkavand, Shangqian Gao, Peiran Yu +1
We study cost-aware routing for large language models across diverse and dynamic pools of models. Existing approaches often overlook prompt-specific context, rely on expensive mode…
ARGUS: Hallucination and Omission Evaluation in Video-LLMs
Ruchit Rawal, Reza Shirkavand, Heng Huang +2
Video large language models have not yet been widely deployed, largely due to their tendency to hallucinate. Typical benchmarks for Video-LLMs rely simply on multiple-choice questi…
Bilevel ZOFO: Efficient LLM Fine-Tuning and Meta-Training
Reza Shirkavand, Peiran Yu, Qi He +1
Fine-tuning pre-trained Large Language Models (LLMs) for downstream tasks using First-Order (FO) optimizers presents significant computational challenges. Parameter-Efficient Fine-…
Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
Reza Shirkavand, Peiran Yu, Shangqian Gao +3
Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably i…
From Pixels to Prose: A Large Dataset of Dense Image Captions
Vasu Singla, Kaiyu Yue, Sukriti Paul +7
Training large vision-language models requires extensive, high-quality image-text pairs. Existing web-scraped datasets, however, are noisy and lack detailed image descriptions. To…
Not All Prompts Are Made Equal: Prompt-based Pruning of Text-to-Image Diffusion Models
Alireza Ganjdanesh, Reza Shirkavand, Shangqian Gao +1
Text-to-image (T2I) diffusion models have demonstrated impressive image generation capabilities. Still, their computational intensity prohibits resource-constrained organizations f…
Deep Prompt Tuning for Graph Transformers
Reza Shirkavand, Heng Huang
Graph transformers have gained popularity in various graph-based tasks by addressing challenges faced by traditional Graph Neural Networks. However, the quadratic complexity of sel…