2 citations · 5 across the 16 of their papers we have counts for
19 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
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…
Capability Self-Assessment in Large Language Models
Haoyan Yang, Reza Shirkavand, Yukai Jin +3
The ability to recognize one's own limitations and decide whether to solve a problem or seek help is fundamental to reliable intelligent systems. Yet modern large language models t…
Privacy-Preserving LLMs Routing
Xidong Wu, Yukuan Zhang, Yuqiong Ji +3
Large language model (LLM) routing has emerged as a critical strategy to balance model performance and cost-efficiency by dynamically selecting services from various model provider…
Semantics Meet Signals: Dual Codebook Representationl Learning for Generative Recommendation
Zheng Hui, Xiaokai Wei, Reza Shirkavand +4
Generative recommendation has recently emerged as a powerful paradigm that unifies retrieval and generation, representing items as discrete semantic tokens and enabling flexible se…
Rotate Both Ways: Time-and-Order RoPE for Generative Recommendation
Xiaokai Wei, Jiajun Wu, Daiyao Yi +2
Generative recommenders, typically transformer-based autoregressive models, predict the next item or action from a user's interaction history. Their effectiveness depends on how th…
The Layout Is the Model: On Action-Item Coupling in Generative Recommendation
Xiaokai Wei, Jiajun Wu, Daiyao Yi +2
Generative Recommendation (GR) models treat a user's interaction history as a sequence to be autoregressively predicted. When both items and actions (e.g., watch time, purchase, co…
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-…
ToMoE: Converting Dense Large Language Models to Mixture-of-Experts through Dynamic Structural Pruning
Shangqian Gao, Ting Hua, Reza Shirkavand +10
Large Language Models (LLMs) have demonstrated remarkable abilities in tackling a wide range of complex tasks. However, their huge computational and memory costs raise significant…
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…
Prediction of Post-Operative Renal and Pulmonary Complications Using Transformers
Reza Shirkavand, Fei Zhang, Heng Huang
Postoperative complications pose a significant challenge in the healthcare industry, resulting in elevated healthcare expenses and prolonged hospital stays, and in rare instances,…
Incomplete Multimodal Learning for Complex Brain Disorders Prediction
Reza Shirkavand, Liang Zhan, Heng Huang +2
Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex br…
Dementia Severity Classification under Small Sample Size and Weak Supervision in Thick Slice MRI
Reza Shirkavand, Sana Ayromlou, Soroush Farghadani +7
Early detection of dementia through specific biomarkers in MR images plays a critical role in developing support strategies proactively. Fazekas scale facilitates an accurate quant…