20 citations · 61 across the 32 of their papers we have counts for
10 papers · 1 filter
Event-assisted 12-stop HDR Imaging of Dynamic Scene
Shi Guo, Zixuan Chen, Ziran Zhang +3
High dynamic range (HDR) imaging is a crucial task in computational photography, which captures details across diverse lighting conditions. Traditional HDR fusion methods face limi…
TRecViT: A Recurrent Video Transformer
Viorica Pătrăucean, Xu Owen He, Joseph Heyward +10
We propose a novel block for \emph{causal} video modelling. It relies on a time-space-channel factorisation with dedicated blocks for each dimension: gated linear recurrent units (…
Language Model Embeddings Can Be Sufficient for Bayesian Optimization
Tung Nguyen, Qiuyi Zhang, Bangding Yang +6
Bayesian Optimization is ubiquitous in experimental design and black-box optimization for improving search efficiency. However, most existing approaches rely on regression models w…
MAC-VO: Metrics-aware Covariance for Learning-based Stereo Visual Odometry
Yuheng Qiu, Yutian Chen, Zihao Zhang +2
We propose the MAC-VO, a novel learning-based stereo VO that leverages the learned metrics-aware matching uncertainty for dual purposes: selecting keypoint and weighing the residua…
Position: Leverage Foundational Models for Black-Box Optimization
Xingyou Song, Yingtao Tian, Robert Tjarko Lange +3
Undeniably, Large Language Models (LLMs) have stirred an extraordinary wave of innovation in the machine learning research domain, resulting in substantial impact across diverse fi…
RecurrentGemma: Moving Past Transformers for Efficient Open Language Models
Aleksandar Botev, Soham De, Samuel L Smith +59
We introduce RecurrentGemma, a family of open language models which uses Google's novel Griffin architecture. Griffin combines linear recurrences with local attention to achieve ex…
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Soham De, Samuel L. Smith, Anushan Fernando +14
Recurrent neural networks (RNNs) have fast inference and scale efficiently on long sequences, but they are difficult to train and hard to scale. We propose Hawk, an RNN with gated…
OmniPred: Language Models as Universal Regressors
Xingyou Song, Oscar Li, Chansoo Lee +4
Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been restricted to methods which are only applic…
Event-Based Motion Magnification
Yutian Chen, Shi Guo, Fangzheng Yu +3
Detecting and magnifying imperceptible high-frequency motions in real-world scenarios has substantial implications for industrial and medical applications. These motions are charac…
GATS: Gather-Attend-Scatter
Konrad Zolna, Serkan Cabi, Yutian Chen +5
As the AI community increasingly adopts large-scale models, it is crucial to develop general and flexible tools to integrate them. We introduce Gather-Attend-Scatter (GATS), a nove…