Yaoming95 / CitationRankerLinks
This code helps to retrieve all papers from conferences and rank them by the number of (Google Scholar) citations.
☆12Updated 3 years ago
Alternatives and similar repositories for CitationRanker
Users that are interested in CitationRanker are comparing it to the libraries listed below
Sorting:
- A high-performance distributed deep learning system targeting large-scale and automated distributed training. If you have any interests, …☆112Updated last year
- ☆12Updated 3 years ago
- Graphiler is a compiler stack built on top of DGL and TorchScript which compiles GNNs defined using user-defined functions (UDFs) into ef…☆61Updated 2 years ago
- Deep learning images developed from nvidia/cuda-cudnn-devel-ubuntu.☆23Updated 2 years ago
- ☆14Updated 5 years ago
- ☆45Updated 2 years ago
- SCR: Training Graph Neural Networks with Consistency Regularization☆37Updated 2 years ago
- My paper/code reading notes in Chinese☆46Updated last year
- ☆74Updated 2 years ago
- ☆18Updated 4 years ago
- MRT: Tracing the Evolution of Scientific Publications (TKDE 2021)☆17Updated 2 years ago
- ATC23 AE☆45Updated 2 years ago
- A plug-in of Microsoft DeepSpeed to fix the bug of DeepSpeed pipeline☆26Updated 4 years ago
- Latex双栏中文模板,修改自ACL会议模板☆57Updated 5 years ago
- ☆75Updated 2 years ago
- Inference framework for MoE layers based on TensorRT with Python binding☆41Updated 4 years ago
- ☆24Updated 3 years ago
- A simple calculation for LLM MFU.☆38Updated 3 months ago
- Python package for rematerialization-aware gradient checkpointing☆24Updated last year
- InsNet Runs Instance-dependent Neural Networks with Padding-free Dynamic Batching.☆66Updated 3 years ago
- Dynamic Tensor Rematerialization prototype (modified PyTorch) and simulator. Paper: https://arxiv.org/abs/2006.09616☆132Updated last year
- ☆13Updated 4 years ago
- ☆12Updated last year
- ☆48Updated 8 months ago
- Graph Partitioning for Large-scale Graph Datasets☆97Updated 3 years ago
- Differentiable Product Quantization for End-to-End Embedding Compression.