26 citations · 29 across the 9 of their papers we have counts for
11 papers
Characterizing High Bandwidth Flash for LLM Serving
Zack Yu, Chloe Wong, Coleman Hooper +7
Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larger and contexts become longer, memory capacity and bandwidth…
ParallelBench: Understanding the Trade-offs of Parallel Decoding in Diffusion LLMs
Wonjun Kang, Kevin Galim, Seunghyuk Oh +8
While most autoregressive LLMs are constrained to one-by-one decoding, diffusion LLMs (dLLMs) have attracted growing interest for their potential to dramatically accelerate inferen…
XQuant: Breaking the Memory Wall for LLM Inference with KV Cache Rematerialization
Aditya Tomar, Coleman Hooper, Minjae Lee +7
Although LLM inference has emerged as a critical workload for many downstream applications, efficiently inferring LLMs is challenging due to the substantial memory footprint and ba…
UNCAGE: Contrastive Attention Guidance for Masked Generative Transformers in Text-to-Image Generation
Wonjun Kang, Byeongkeun Ahn, Minjae Lee +4
Text-to-image (T2I) generation has been actively studied using Diffusion Models and Autoregressive Models. Recently, Masked Generative Transformers have gained attention as an alte…
Generating Multi-Table Time Series EHR from Latent Space with Minimal Preprocessing
Eunbyeol Cho, Jiyoun Kim, Minjae Lee +2
Electronic Health Records (EHR) are time-series relational databases that record patient interactions and medical events over time, serving as a critical resource for healthcare re…
Draft-based Approximate Inference for LLMs
Kevin Galim, Ethan Ewer, Wonjun Kang +3
Optimizing inference for long-context large language models (LLMs) is increasingly important due to the quadratic compute and linear memory cost of Transformers. Existing approxima…
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models
Wonjun Kang, Kevin Galim, Yuchen Zeng +3
State Space Models (SSMs) have emerged as efficient alternatives to Transformers, mitigating their quadratic computational cost. However, the application of Parameter-Efficient Fin…
VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data
Thomas Zeng, Shuibai Zhang, Shutong Wu +13
Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However,…
Rediscovery of CNN's Versatility for Text-based Encoding of Raw Electronic Health Records
Eunbyeol Cho, Min Jae Lee, Kyunghoon Hur +3
Making the most use of abundant information in electronic health records (EHR) is rapidly becoming an important topic in the medical domain. Recent work presented a promising frame…
UniHPF : Universal Healthcare Predictive Framework with Zero Domain Knowledge
Kyunghoon Hur, Jungwoo Oh, Junu Kim +6
Despite the abundance of Electronic Healthcare Records (EHR), its heterogeneity restricts the utilization of medical data in building predictive models. To address this challenge,…
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source Learning
Kyunghoon Hur, Jungwoo Oh, Junu Kim +7
Despite the remarkable progress in the development of predictive models for healthcare, applying these algorithms on a large scale has been challenging. Algorithms trained on a par…