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Found 12 papers, 4 papers with code
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
Specifically, both ShrinkCNN and ShrinkRNN are crafted within 1. 5 GPU hours, which is 7. 2x and 6. 7x faster than the crafting time of SOTA CNN and RNN models, respectively.
NASGEM: Neural Architecture Search via Graph Embedding Method
To preserve graph correlation information in encoding, we propose NASGEM which stands for Neural Architecture Search via Graph Embedding Method.
Automatic Routability Predictor Development Using Neural Architecture Search
The rise of machine learning technology inspires a boom of its applications in electronic design automation (EDA) and helps improve the degree of automation in chip designs.
Automated Mobile Attention KPConv Networks via A Wide & Deep Predictor
MAKPConv employs a depthwise kernel to reduce resource consumption and re-calibrates the contribution of kernel points towards each neighbor point via Neighbor-Kernel attention to improve representation power.
Towards Collaborative Intelligence: Routability Estimation based on Decentralized Private Data
To further strengthen the results, we co-design a customized ML model FLNet and its personalization under the decentralized training scenario.
NASRec: Weight Sharing Neural Architecture Search for Recommender Systems
To overcome the data multi-modality and architecture heterogeneity challenges in the recommendation domain, NASRec establishes a large supernet (i. e., search space) to search the full architectures.
PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud
In this work, we establish PIDS, a novel paradigm to jointly explore point interactions and point dimensions to serve semantic segmentation on point cloud data.
Ranked #6 on
Robust 3D Semantic Segmentation
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Neural Architecture Search
Robust 3D Semantic Segmentation
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LISSNAS: Locality-based Iterative Search Space Shrinkage for Neural Architecture Search
We propose LISSNAS, an automated algorithm that shrinks a large space into a diverse, small search space with SOTA search performance.
Farthest Greedy Path Sampling for Two-shot Recommender Search
FGPS enhances path diversity to facilitate more comprehensive supernet exploration, while emphasizing path quality to ensure the effective identification and utilization of promising architectures.
DistDNAS: Search Efficient Feature Interactions within 2 Hours
To optimize search efficiency, DistDNAS distributes the search and aggregates the choice of optimal interaction modules on varying data dates, achieving over 25x speed-up and reducing search cost from 2 days to 2 hours.
Peeking Behind the Curtains of Residual Learning
The utilization of residual learning has become widespread in deep and scalable neural nets.
