Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch

PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

[CVPR 2021] Adversarial Generation of Continuous Images

[CVPR 2023] This is the official PyTorch implementation for "Dynamic Focus-aware Positional Queries for Semantic Segmentation".

Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding

Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch

Developed the ViViT model for medical video classification, enhancing 3D organ image analysis using transformer-based architectures.

Trading Positional Complexity vs Deepness in Coordinate Networks

Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Long-Range and Hierarchical Structures

"Found in the Middle: How Language Models Use Long Contexts Better via Plug-and-Play Positional Encoding" Zhenyu Zhang, Runjin Chen, Shiwei Liu, Zhewei Yao, Olatunji Ruwase, Beidi Chen, Xiaoxia Wu, Zhangyang Wang.

Unofficial pytorch implementation of the paper "Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding", NeurIPS 2021.

Robust Point Cloud Processing through Positional Embedding

Implementation of Rotary Embeddings, from the Roformer paper, in Tensorflow

Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".

PyTorch implementation of "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin

The implementation of transformer as presented in the paper "Attention is all you need" from scratch.

Code for "The Locality and Symmetry of Positional Encodings" EMNLP Findings

完整的原版transformer程序，complete origin transformer program

This repository provides a complete workflow for text processing using Hugging Face Transformers and NLTK. It includes modules for sentence normalization, spelling correction, word embedding generation, positional encoding computation, and English-to-French translation