Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch
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Updated
Sep 2, 2024 - Python
Implementation of Rotary Embeddings, from the Roformer paper, in Pytorch
Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022
[CVPR 2021] Adversarial Generation of Continuous Images
Learnable Fourier Features for Multi-Dimensional Spatial Positional Encoding
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces (CVPR 2023)
Continuous Augmented Positional Embeddings (CAPE) implementation for PyTorch
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Long-Range and Hierarchical Structures
[CVPR 2023] This is the official PyTorch implementation for "Dynamic Focus-aware Positional Queries for Semantic Segmentation".
Implementation of Rotary Embeddings, from the Roformer paper, in Tensorflow
A Basic Corpus Object , Giving Positional Encoding / Decoding . ,A Fully Loaded Corpus = Corpus > Document > Sentences > Clauses > Words
A PyTorch Implementation of PGL-SUM from "Combining Global and Local Attention with Positional Encoding for Video Summarization", Proc. IEEE ISM 2021
Developed the ViViT model for medical video classification, enhancing 3D organ image analysis using transformer-based architectures.
"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.
The Positional Encoder Decoder is a Visual Basic .NET class that provides functionality for encoding and decoding tokens and sentences using positional embeddings. It allows you to convert between string tokens and their corresponding embeddings, and vice versa.
Official code for NeurIPS 2023 paper "Laplacian Canonization: A Minimalist Approach to Sign and Basis Invariant Spectral Embedding".
Code implementation of Transformer Model in "Attention is All You Need" in PyTorch.
Robust Point Cloud Processing through Positional Embedding
Transformers - Basics
Code for "The Locality and Symmetry of Positional Encodings" EMNLP Findings
This is a novel Transformer network based approach to distinguish ChatGPT generated Text from Human text. The model was also deployed on local server using Flask where Docker was used to manage all dependencies.
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