Popular GitHub Repositories for Mlps

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

Javascript/WebAssembly deep learning library for MLPs and convolutional neural networks

Experiments of coordinate MLPs

[ICLR 2022] Code for Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation (GLNN)

A GPT, made only of MLPs, in Jax

Vision Transformers Needs Registers. And Gated MLPs. And +20M params. Tiny modality gap ensues!

Code for AAAI 2023 (Oral) paper "Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework"

My implementation of the gMLP model from the paper "Pay Attention to MLPs".

Code for ICML 2023 paper "Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs"

Implementation for paper MLP-Mixer: An all-MLP Architecture for Vision. MLP-Mixer, an architecture based exclusively on multi-layer perceptrons (MLPs). MLP-Mixer contains two types of layers: one with MLPs applied independently to image patches (i.e. "mixing" the per-location features), and one with MLPs applied across patches (i.e. "mixing" spatial information).

Implementation of the model "Hedgehog" from the paper: "The Hedgehog & the Porcupine: Expressive Linear Attentions with Softmax Mimicry"

Configurable MLPs built on JAX and Haiku

An implementation of gated MLPs in tinygrad, as an alternative to transformers.

Lossless is a Machine Learning library built for Golang, capable of handling MLPs, CNNs, and more soon.

[AAAI 2025] Representing Sounds as Neural Amplitude Fields: A Benchmark of Coordinate-MLPs and A Fourier Kolmogorov-Arnold Framework

This project applies Multi-Layer Perceptrons (MLPs) using PyTorch to predict patient no-shows for medical appointments. We preprocess the dataset, optimize MLP architectures, and enhance prediction accuracy through advanced techniques.

? This article explores the architecture and working mechanism of Vision-Language Models (VLMs) such as GPT-4V. It explains how these models process and fuse visual and textual inputs using encoders, embeddings, and attention mechanisms.