14 citations · 21 across the 8 of their papers we have counts for
8 papers
Pruning via Causal Attribution Preserves Reasoning Performance in Large Language Models
Amogh Sheth, Biruk Assefa, Yi Wen Huang +2
Large language models (LLMs) excel at multi-step reasoning but incur substantial inference cost. We introduce Causal Attribution Pruning (CAP), a training-free method that identifi…
The MiniMax-M2 Series: Mini Activations Unleashing Max Real-World Intelligence
Aili Chen, Aonian Li, Baichuan Zhou +215
We introduce the MiniMax-M2 series, a family of Mixture-of-Experts language models built around the principle that mini activations can unleash maximum real-world intelligence. The…
Hierarchical Visual Categories Modeling: A Joint Representation Learning and Density Estimation Framework for Out-of-Distribution Detection
Jinglun Li, Xinyu Zhou, Pinxue Guo +4
Detecting out-of-distribution inputs for visual recognition models has become critical in safe deep learning. This paper proposes a novel hierarchical visual category modeling sche…
FormalGeo: An Extensible Formalized Framework for Olympiad Geometric Problem Solving
Xiaokai Zhang, Na Zhu, Yiming He +17
This is the first paper in a series of work we have accomplished over the past three years. In this paper, we have constructed a consistent formal plane geometry system. This will…
'Tax-free' 3DMM Conditional Face Generation
Yiwen Huang, Zhiqiu Yu, Xinjie Yi +2
3DMM conditioned face generation has gained traction due to its well-defined controllability; however, the trade-off is lower sample quality: Previous works such as DiscoFaceGAN an…
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression Recognition in Videos
Yan Wang, Yixuan Sun, Yiwen Huang +5
Current benchmarks for facial expression recognition (FER) mainly focus on static images, while there are limited datasets for FER in videos. It is still ambiguous to evaluate whet…
Sequentially Aggregated Convolutional Networks
Yiwen Huang, Rihui Wu, Pinglai Ou +1
Modern deep networks generally implement a certain form of shortcut connections to alleviate optimization difficulties. However, we observe that such network topology alters the na…
Densely Connected High Order Residual Network for Single Frame Image Super Resolution
Yiwen Huang, Ming Qin
Deep convolutional neural networks (DCNN) have been widely adopted for research on super resolution recently, however previous work focused mainly on stacking as many layers as pos…