Research
43 articles
NewFCOS: Fully Convolutional One-Stage Object Detection
A Verbose Study Guide to arXiv:1904.01355
NewVL-Cache: A Technical Walkthrough
Sparsity- and Modality-Aware KV Cache Compression for Vision-Language Model Inference Acceleration
NewParameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment
A section-by-section walkthrough of arXiv:2312.12148v1
New3D Gaussian Splatting for Real-Time Radiance Field Rendering
A Deep Technical Guide to Kerbl et al. (SIGGRAPH 2023)
Segment Anything (SAM)
A researcher-oriented walkthrough of arXiv:2304.02643 (Kirillov et al., Meta AI / FAIR, 2023)
Visual Instruction Tuning (LLaVA)
Paper: Visual Instruction Tuning Authors: Haotian Liu¹, Chunyuan Li², Qingyang Wu³, Yong Jae Lee¹ ¹University of Wisconsin–Madison, ²Microsoft Research,…
Demystifying FLUX Architecture
A Reader’s Guide to arXiv:2507.09595v1
High-Resolution Image Synthesis with Latent Diffusion Models
A guide to Rombach et al. (2021), arXiv:2112.10752
Denoising Diffusion Implicit Models (DDIM): A Verbose Guide
Paper: Jiaming Song, Chenlin Meng, Stefano Ermon. Denoising Diffusion Implicit Models. ICLR 2021. arXiv:2010.02502
Denoising Diffusion Probabilistic Models
Paper: Jonathan Ho, Ajay Jain, Pieter Abbeel. Denoising Diffusion Probabilistic Models. NeurIPS 2020. arXiv: 2006.11239 · Code: hojonathanho/diffusion
Vision-Language-Action Models for Robotics: A Review Towards Real-World Applications
A Comprehensive Survey — IEEE Access 2025
Mathematics Behind Mamba Transformers: A Complete Guide
Mamba represents a breakthrough in sequence modeling that addresses the quadratic complexity limitation of traditional transformers. Built on State Space…
Complete Guide to Reinforcement Learning
Reinforcement Learning (RL) is a paradigm of machine learning where an agent learns to make decisions by interacting with an environment to maximize cumulative…
ControlNet: Revolutionizing AI Image Generation with Precise Control
ControlNet represents a groundbreaking advancement in the field of AI-generated imagery, providing unprecedented control over the output of diffusion models…
Stable Diffusion: A Complete Guide to Text-to-Image Generation
Stable Diffusion represents a watershed moment in artificial intelligence and creative technology. Released in August 2022 by Stability AI in collaboration…
DenseNet: Densely Connected Convolutional Networks
DenseNet (Densely Connected Convolutional Networks) represents a significant advancement in deep learning architecture design, introduced by Gao Huang, Zhuang…
MobileNet: Efficient Neural Networks for Mobile Vision Applications
MobileNet represents a revolutionary approach to deep learning architecture design, specifically optimized for mobile and embedded vision applications.…
GShard: Scaling Giant Neural Networks with Conditional Computation
GShard represents a pivotal advancement in neural network scaling, introduced by Google Research in 2020. This innovative approach addresses one of the most…
Mixture of Experts: A Deep Overview
Mixture of Experts (MoE) represents a fundamental paradigm shift in machine learning architecture design, offering a scalable approach to building models that…
Switch Transformer: Scaling Neural Networks with Sparsity
The Switch Transformer represents a groundbreaking advancement in neural network architecture, introduced by Google Research in 2021. This innovative model…
The Mathematics Behind YOLO: A Deep Dive into Object Detection
You Only Look Once (YOLO) revolutionized object detection by treating it as a single regression problem, directly predicting bounding boxes and class…
YOLO (You Only Look Once): A Comprehensive Beginner’s Guide
In the rapidly evolving world of computer vision and artificial intelligence, few innovations have been as transformative as YOLO (You Only Look Once). This…
The Mathematics Behind Neural Architecture Search
Neural Architecture Search (NAS) represents one of the most sophisticated applications of automated machine learning, where algorithms autonomously design…
Neural Architecture Search: A Comprehensive Guide
Neural Architecture Search (NAS) represents a paradigm shift in deep learning, moving from manual architecture design to automated discovery of optimal neural…
The Mathematics Behind Convolutional Kolmogorov-Arnold Networks
Convolutional Kolmogorov-Arnold Networks (CKANs) represent a revolutionary approach to neural network architecture that combines the theoretical foundations of…
Convolutional Kolmogorov-Arnold Networks vs Convolutional Neural Networks: A Comprehensive Analysis
The landscape of deep learning has been revolutionized by Convolutional Neural Networks (CNNs), which have dominated computer vision tasks for over a decade.…
The Mathematics Behind Kolmogorov-Arnold Networks
Kolmogorov-Arnold Networks (KANs) represent a paradigm shift in neural network architecture, moving away from the traditional linear combinations of fixed…
Kolmogorov-Arnold Networks: Revolutionizing Neural Architecture Design
Kolmogorov-Arnold Networks (KANs) represent a paradigm shift in neural network architecture design, moving away from the traditional Multi-Layer Perceptron…
The Mathematics Behind Matryoshka Transformers
Matryoshka Transformers represent a significant advancement in adaptive neural network architectures, inspired by the Russian nesting dolls (Matryoshka dolls)…
Matryoshka Transformer for Vision Language Models
The Matryoshka Transformer represents a significant advancement in the architecture of vision language models (VLMs), drawing inspiration from the nested…
Attention Mechanisms: Transformers vs Convolutional Neural Networks
Attention mechanisms have revolutionized deep learning by enabling models to focus on relevant parts of the input data. While originally popularized in…
Hugging Face Accelerate vs PyTorch Lightning Fabric: A Deep Dive Comparison
When you’re working with deep learning models that need to scale across multiple GPUs or even multiple machines, you’ll quickly encounter the complexity of…
Self-Supervised Learning: Training AI Without Labels
Machine learning has traditionally relied on vast amounts of labeled data to train models effectively. However, acquiring high-quality labeled datasets is…
Vision Transformers (ViT): A Simple Guide
Vision Transformers (ViTs) represent a paradigm shift in computer vision, adapting the transformer architecture that revolutionized natural language processing…
DINOv2: A Deep Dive into Architecture and Training
In 2023, Meta AI Research unveiled DINOv2 (Self-Distillation with No Labels v2), a breakthrough in self-supervised visual learning that produces remarkably…
DINO: Emerging Properties in Self-Supervised Vision Transformers
In 2021, Facebook AI Research (now Meta AI) introduced DINO (Self-Distillation with No Labels), a groundbreaking approach to self-supervised learning in…
