241 citations · 461 across the 16 of their papers we have counts for
5 papers · 1 filter
Large-scale Dataset Pruning with Dynamic Uncertainty
Muyang He, Shuo Yang, Tiejun Huang +1
The state of the art of many learning tasks, e.g., image classification, is advanced by collecting larger datasets and then training larger models on them. As the outcome, the incr…
Reinforcement Learning Friendly Vision-Language Model for Minecraft
Haobin Jiang, Junpeng Yue, Hao Luo +2
One of the essential missions in the AI research community is to build an autonomous embodied agent that can achieve high-level performance across a wide spectrum of tasks. However…
Kernel Quantization for Efficient Network Compression
Zhongzhi Yu, Yemin Shi, Tiejun Huang +1
This paper presents a novel network compression framework Kernel Quantization (KQ), targeting to efficiently convert any pre-trained full-precision convolutional neural network (CN…
Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning
Limeng Qiao, Yemin Shi, Jia Li +3
Few-shot learning, which aims at extracting new concepts rapidly from extremely few examples of novel classes, has been featured into the meta-learning paradigm recently. Yet, the…
Graph Convolutional Reinforcement Learning
Jiechuan Jiang, Chen Dun, Tiejun Huang +1
Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly…