3 citations · 8 across the 12 of their papers we have counts for
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Learnable Chamfer Distance for Point Cloud Reconstruction
Tianxin Huang, Qingyao Liu, Xiangrui Zhao +2
As point clouds are 3D signals with permutation invariance, most existing works train their reconstruction networks by measuring shape differences with the average point-to-point d…
Unified Data-Free Compression: Pruning and Quantization without Fine-Tuning
Shipeng Bai, Jun Chen, Xintian Shen +2
Structured pruning and quantization are promising approaches for reducing the inference time and memory footprint of neural networks. However, most existing methods require the ori…
Decentralized Riemannian Conjugate Gradient Method on the Stiefel Manifold
Jun Chen, Haishan Ye, Mengmeng Wang +4
The conjugate gradient method is a crucial first-order optimization method that generally converges faster than the steepest descent method, and its computational cost is much lowe…
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
Jun Chen, Shipeng Bai, Tianxin Huang +3
Neural network quantization is a very promising solution in the field of model compression, but its resulting accuracy highly depends on a training/fine-tuning process and requires…
Learning Global-aware Kernel for Image Harmonization
Xintian Shen, Jiangning Zhang, Jun Chen +5
Image harmonization aims to solve the visual inconsistency problem in composited images by adaptively adjusting the foreground pixels with the background as references. Existing me…
Learning Discretized Neural Networks under Ricci Flow
Jun Chen, Hanwen Chen, Mengmeng Wang +3
In this paper, we study Discretized Neural Networks (DNNs) composed of low-precision weights and activations, which suffer from either infinite or zero gradients due to the non-dif…
M22: A Communication-Efficient Algorithm for Federated Learning Inspired by Rate-Distortion
Yangyi Liu, Stefano Rini, Sadaf Salehkalaibar +1
In federated learning (FL), the communication constraint between the remote learners and the Parameter Server (PS) is a crucial bottleneck. For this reason, model updates must be c…