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A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
Machine Learning and Computer Vision Engineer - Technical Interview Questions
[AAAI2025] UniDet3D: Multi-dataset Indoor 3D Object Detection
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
Project Page for "LISA: Reasoning Segmentation via Large Language Model"
Code for benchmark comparing self-supervised and semi-supervised deep classifiers for medical images
Large Language-and-Vision Assistant for Biomedicine, built towards multimodal GPT-4 level capabilities.
M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models
This is the official repository of our paper "MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine“
SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model @ ICCV 2023 **AND** SLCA++: Unleash the Power of Sequential Fine-tuning for Continual Learning with Pre-tr…
Awesome Incremental Learning
Official repository for "CLIP model is an Efficient Continual Learner".
Simple and readable code for training and sampling from diffusion models
High-Resolution Image Synthesis with Latent Diffusion Models
PyTorch code for the CVPR'23 paper: "CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning"
PyTorch Implementation of Learning to Prompt (L2P) for Continual Learning @ CVPR22
(TPAMI 2024) A Survey on Open Vocabulary Learning
Semantic segmentation models with 500+ pretrained convolutional and transformer-based backbones.
We introduce a novel approach for parameter generation, named neural network parameter diffusion (p-diff), which employs a standard latent diffusion model to synthesize a new set of parameters
A curated list of practical guide resources of Medical LLMs (Medical LLMs Tree, Tables, and Papers)