8 citations · 11 across the 5 of their papers we have counts for
6 papers
MiCo: Mutual Information Coverage Optimization through Semantic Erasure Modeling for Efficient MLLM Inference
Tinghao Wang, Yichen Guo, Qizhe Zhang +11
Multimodal large language models (MLLMs) have demonstrated impressive performance in multimodal understanding, but processing large numbers of visual tokens results in high computa…
TOPS: First-Principles Visual Token Pruning via Constructing Token Optimal Preservation Sets for Efficient MLLM Inference
Tinghao Wang, Yichen Guo, Rui Huang +13
Multimodal large language models (MLLMs) have achieved strong multimodal reasoning capabilities, but their efficiency is limited by the large number of visual tokens, which introdu…
Producing Usable Taxonomies Cheaply and Rapidly at Pinterest Using Discovered Dynamic -Topics
Abhijit Mahabal, Jiyun Luo, Rui Huang +2
Creating a taxonomy of interests is expensive and human-effort intensive: not only do we need to identify nodes and interconnect them, in order to use the taxonomy, we must also co…
Efficient Knowledge Distillation from Model Checkpoints
Chaofei Wang, Qisen Yang, Rui Huang +2
Knowledge distillation is an effective approach to learn compact models (students) with the supervision of large and strong models (teachers). As empirically there exists a strong…
Toward Less Hidden Cost of Code Completion with Acceptance and Ranking Models
Jingxuan Li, Rui Huang, Wei Li +2
Code completion is widely used by software developers to provide coding suggestions given a partially written code snippet. Apart from the traditional code completion methods, whic…
Low-Resource Sequence Labeling via Unsupervised Multilingual Contextualized Representations
Zuyi Bao, Rui Huang, Chen Li +1
Previous work on cross-lingual sequence labeling tasks either requires parallel data or bridges the two languages through word-byword matching. Such requirements and assumptions ar…