5 citations · 7 across the 5 of their papers we have counts for
5 papers
PIT: Optimization of Dynamic Sparse Deep Learning Models via Permutation Invariant Transformation
Ningxin Zheng, Huiqiang Jiang, Quanlu Zhang +8
Dynamic sparsity, where the sparsity patterns are unknown until runtime, poses a significant challenge to deep learning. The state-of-the-art sparsity-aware deep learning solutions…
Optimization for Amortized Inverse Problems
Tianci Liu, Tong Yang, Quan Zhang +1
Incorporating a deep generative model as the prior distribution in inverse problems has established substantial success in reconstructing images from corrupted observations. Notwit…
Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training
Cong Guo, Yuxian Qiu, Jingwen Leng +6
An activation function is an element-wise mathematical function and plays a crucial role in deep neural networks (DNN). Many novel and sophisticated activation functions have been…
Learning to Rank Ace Neural Architectures via Normalized Discounted Cumulative Gain
Yuge Zhang, Quanlu Zhang, Li Lyna Zhang +4
One of the key challenges in Neural Architecture Search (NAS) is to efficiently rank the performances of architectures. The mainstream assessment of performance rankers uses rankin…
Quantum-Classical Machine learning by Hybrid Tensor Networks
Ding Liu, Jiaqi Yao, Zekun Yao +1
Tensor networks (TN) have found a wide use in machine learning, and in particular, TN and deep learning bear striking similarities. In this work, we propose the quantum-classical h…