microsoft / ResiDual
ResiDual: Transformer with Dual Residual Connections, https://arxiv.org/abs/2304.14802
☆93Updated last year
Alternatives and similar repositories for ResiDual:
Users that are interested in ResiDual are comparing it to the libraries listed below
- Implementation of Gated State Spaces, from the paper "Long Range Language Modeling via Gated State Spaces", in Pytorch☆97Updated last year
- Griffin MQA + Hawk Linear RNN Hybrid☆85Updated 9 months ago
- Implementation of Infini-Transformer in Pytorch☆109Updated last month
- Standalone Product Key Memory module in Pytorch - for augmenting Transformer models☆78Updated 6 months ago
- Explorations into the recently proposed Taylor Series Linear Attention☆92Updated 6 months ago
- Implementation of Agent Attention in Pytorch☆89Updated 7 months ago
- Yet another random morning idea to be quickly tried and architecture shared if it works; to allow the transformer to pause for any amount…☆53Updated last year
- Code for the paper "The Impact of Positional Encoding on Length Generalization in Transformers", NeurIPS 2023☆130Updated 9 months ago
- Exploration into the proposed "Self Reasoning Tokens" by Felipe Bonetto☆55Updated 9 months ago
- Randomized Positional Encodings Boost Length Generalization of Transformers☆79Updated 11 months ago
- LayerNorm(SmallInit(Embedding)) in a Transformer to improve convergence☆59Updated 2 years ago
- Implementation of an Attention layer where each head can attend to more than just one token, using coordinate descent to pick topk☆46Updated last year
- [NeurIPS 2023 spotlight] Official implementation of HGRN in our NeurIPS 2023 paper - Hierarchically Gated Recurrent Neural Network for Se…☆62Updated 9 months ago
- Efficient Transformers with Dynamic Token Pooling☆58Updated last year
- Implementation of the Kalman Filtering Attention proposed in "Kalman Filtering Attention for User Behavior Modeling in CTR Prediction"☆57Updated last year
- Some personal experiments around routing tokens to different autoregressive attention, akin to mixture-of-experts☆115Updated 4 months ago
- Implementation of GateLoop Transformer in Pytorch and Jax☆87Updated 8 months ago
- ☆86Updated last year
- Implementation of Token Shift GPT - An autoregressive model that solely relies on shifting the sequence space for mixing☆48Updated 3 years ago
- [NeurIPS 2022] Your Transformer May Not be as Powerful as You Expect (official implementation)☆34Updated last year
- Implementation of the conditionally routed attention in the CoLT5 architecture, in Pytorch☆225Updated 5 months ago
- [NeurIPS 2023] Sparse Modular Activation for Efficient Sequence Modeling☆35Updated last year
- Official repository for the paper "Approximating Two-Layer Feedforward Networks for Efficient Transformers"☆36Updated last year
- Experiments around a simple idea for inducing multiple hierarchical predictive model within a GPT☆206Updated 5 months ago
- Exploring an idea where one forgets about efficiency and carries out attention across each edge of the nodes (tokens)☆44Updated this week
- [ICLR 2023] Official implementation of Transnormer in our ICLR 2023 paper - Toeplitz Neural Network for Sequence Modeling☆78Updated 9 months ago
- Why Do We Need Weight Decay in Modern Deep Learning? [NeurIPS 2024]☆60Updated 4 months ago
- PyTorch implementation of Soft MoE by Google Brain in "From Sparse to Soft Mixtures of Experts" (https://arxiv.org/pdf/2308.00951.pdf)☆71Updated last year
- Unofficial PyTorch implementation of "Step-unrolled Denoising Autoencoders for Text Generation"☆23Updated 2 years ago
- CUDA implementation of autoregressive linear attention, with all the latest research findings☆44Updated last year