44 citations · 244 across the 30 of their papers we have counts for
10 papers · 1 filter
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Yonggang Jin, Chenxu Wang, Tianyu Zheng +5
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-ma…
HUB: Guiding Learned Optimizers with Continuous Prompt Tuning
Gaole Dai, Wei Wu, Ziyu Wang +3
Learned optimizers are a crucial component of meta-learning. Recent advancements in scalable learned optimizers have demonstrated their superior performance over hand-designed opti…
Towards Representation Identical Privacy-Preserving Graph Neural Network via Split Learning
Chuanqiang Shan, Huiyun Jiao, Jie Fu
In recent years, the fast rise in number of studies on graph neural network (GNN) has put it from the theories research to reality application stage. Despite the encouraging perfor…
Beyond Fully-Connected Layers with Quaternions: Parameterization of Hypercomplex Multiplications with Parameters
Aston Zhang, Yi Tay, Shuai Zhang +4
Recent works have demonstrated reasonable success of representation learning in hypercomplex space. Specifically, "fully-connected layers with Quaternions" (4D hypercomplex numbers…
Role-Wise Data Augmentation for Knowledge Distillation
Jie Fu, Xue Geng, Zhijian Duan +6
Knowledge Distillation (KD) is a common method for transferring the ``knowledge'' learned by one machine learning model (the \textit{teacher}) into another model (the \textit{stude…
Learning Sparse Mixture of Experts for Visual Question Answering
Vardaan Pahuja, Jie Fu, Christopher J. Pal
There has been a rapid progress in the task of Visual Question Answering with improved model architectures. Unfortunately, these models are usually computationally intensive due to…
Conditional Computation for Continual Learning
Min Lin, Jie Fu, Yoshua Bengio
Catastrophic forgetting of connectionist neural networks is caused by the global sharing of parameters among all training examples. In this study, we analyze parameter sharing unde…
Structure Learning for Neural Module Networks
Vardaan Pahuja, Jie Fu, Sarath Chandar +1
Neural Module Networks, originally proposed for the task of visual question answering, are a class of neural network architectures that involve human-specified neural modules, each…
Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks
Xue Geng, Jie Fu, Bin Zhao +4
This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.…
Deep Q-Networks for Accelerating the Training of Deep Neural Networks
Jie Fu
In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our ap…