Usable implementation of Emerging Symbol Binding Network (ESBN), in Pytorch
Repositories
lucidrains repositories
Implementation of ETSformer, state of the art time-series Transformer, in Pytorch
Implementation of E(n)-Transformer, which incorporates attention mechanisms into Welling's E(n)-Equivariant Graph Neural Network
Efficiently discovering algorithms via LLMs with evolutionary search and reinforcement learning.
Implementation of the Transformer variant proposed in "Transformer Quality in Linear Time"
Implementation of Gradient Agreement Filtering, from Chaubard et al. of Stanford, but for single machine microbatches, in Pytorch
Exploration into the proposed architecture from Sapient Intelligence of Singapore 🇸🇬
Implementation of HS-TasNet, "Real-time Low-latency Music Source Separation using Hybrid Spectrogram-TasNet"
Implementation of Hierarchical Transformer Memory (HTM) for Pytorch
Implementation of the Hierarchical Latent Action Model, proposed by Hanjung Kim et al. of Yonsei University
Implementation of Humanoid Standing Up, from the paper "Learning Humanoid Standing-up Control across Diverse Postures" out of Shanghai, in Pytorch
Implementation of the Hybrid Perception Block and Dual-Pruned Self-Attention block from the ITTR paper for Image to Image Translation using Transformers
Implementation of the proposed LVMAE, from the paper, Extending Video Masked Autoencoders to 128 frames, in Pytorch
Implementation of MEGABYTE, Predicting Million-byte Sequences with Multiscale Transformers, in Pytorch
Pytorch implementation of MIMO, Controllable Character Video Synthesis with Spatial Decomposed Modeling, from Alibaba Intelligence Group
Implementation of MaMMUT, a simple vision-encoder text-decoder architecture for multimodal tasks from Google, in Pytorch
Implementation of Mega, the Single-head Attention with Multi-headed EMA architecture that currently holds SOTA on Long Range Arena
Implementation of NWT, audio-to-video generation, in Pytorch
Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).
Pytorch implementation of the PEER block from the paper, Mixture of A Million Experts, by Xu Owen He at Deepmind