Search Results for author:
Found 7 papers, 3 papers with code
M3C: A Framework towards Convergent, Flexible, and Unsupervised Learning of Mixture Graph Matching and Clustering
Existing graph matching methods typically assume that there are similar structures between graphs and they are matchable.
Learning Universe Model for Partial Matching Networks over Multiple Graphs
We consider the general setting for partial matching of two or multiple graphs, in the sense that not necessarily all the nodes in one graph can find their correspondences in another graph and vice versa.
Spanning Tree-based Graph Generation for Molecules
In this paper, we explore the problem of generating molecules using deep neural networks, which has recently gained much interest in chemistry.
Molecule Optimization by Explainable Evolution
Optimizing molecules for desired properties is a fundamental yet challenging task in chemistry, material science and drug discovery.
APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
However, training this quantization-aware accuracy predictor requires collecting a large number of quantized <model, accuracy> pairs, which involves quantization-aware finetuning and thus is highly time-consuming.
Once for All: Train One Network and Specialize it for Efficient Deployment
Most of the traditional approaches either manually design or use neural architecture search (NAS) to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.
Once-for-All: Train One Network and Specialize it for Efficient Deployment
On diverse edge devices, OFA consistently outperforms state-of-the-art (SOTA) NAS methods (up to 4. 0% ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1. 5x faster than MobileNetV3, 2. 6x faster than EfficientNet w. r. t measured latency) while reducing many orders of magnitude GPU hours and $CO_2$ emission.
Ranked #76 on
Neural Architecture Search
on ImageNet
