A lightweight manifold constraint for PyTorch planners
LatentLinter is a lightweight PyTorch manifold constraint that penalizes out-of-distribution (OOD) latent states and stabilizes gradient planners.
LatentLinter is a lightweight PyTorch manifold constraint that penalizes out-of-distribution (OOD) latent states and stabilizes gradient planners.
A living resource for Vision-Language Models & multimodal learning
PyTorch implementations of research papers, aimed at deepening my understanding of the underlying concepts.
MATS, a behavioral audit for vision language models, identifies systematic failures in spatial consistency and suggests repair paths through activation patching.
Implementation of four self-supervised learning (SSL) algorithms; SimCLR, MoCo-v2, BYOL, and DINO on the CIFAR-10 dataset using a ResNet-18 backbone. All scripts are designed to run on Google Colab Free Tier (single GPU, ~12 GB RAM) with CIFAR-10.
Abstract PyTorch pipeline that uses a hypernetwork to generate task-specific LoRA adapters for Meta’s Segment Anything Model from natural language prompts, targeted segmentation on COCO instances and benchmarked mIoU via pycocotools. Links You can find the repo here.