The paper “Paying more attention to attention: improving the performance of convolutional neural networks via attention transfer” by Sergey Zagoruyko, Nikos Komodakis Université proposed a novel training methodology called attention transfer to improve the performance of convolutional neural…
The method described in the paper “Investigating molecular transport in the human brain from MRI with physics‑informed neural networks” by Bastian Zapf, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide, Kent‑Andre Mardal centres on…
Finding datasets that naturally feature missing sequences (where patients were simply not scanned with the full protocol) is rare because public repositories usually curate data to ensure completeness. However, several high-profile datasets are specifically designed…
The Expectation-Maximization (EM) algorithm is an iterative approach to estimate the parameters of probabilistic models, such as a Gaussian (normal) distribution, when the data is incomplete or has missing values. It alternates between two steps:…
The paper “ImputeFormer: Low Rankness-Induced Transformers for Generalizable Spatiotemporal Imputation,” from the KDD ’24 conference, addresses the pervasive issue of missing data in spatiotemporal datasets, particularly contrasting traditional low-rank models with modern deep learning methods like…
The paper Imputation Using Training Labels and Classification via Label Imputation introduces two novel machine learning algorithms designed to efficiently handle missing values, a common issue in practical datasets. The first approach, Classification Based on…
In this post, we will talk about feature-based knowledge distillation. One of the pioneering paper is “FitNets: Hints for Thin Deep Nets” by Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou, Antoine Chassang,Carlo Gatta & Yoshua…
This example demonstrates Knowledge Distillation, a technique where a small “student” model is trained to mimic a larger, pre-trained “teacher” model. Let’s have a brief introduction to Knowledge Distillation first. 🎓 What is Knowledge Distillation?…
Knowledge distillation and perceptual loss are distinct concepts in machine learning, but they can be used together effectively, especially in computer vision tasks. Here’s the simple breakdown: 🧠 What is Knowledge Distillation? Knowledge Distillation is…
Visual Studio Code is the most popular and recommended code editor for Rust development. The experience is excellent, but it requires one key extension to make it work. How to Set It Up (2 Steps)…
What is RUST? Rust is a modern systems programming language focused on three core goals: performance, memory safety, and concurrency. Think of it as having the raw speed and low-level control of languages like C…
The simplest way to think about it is: Think of an MRI as a high-resolution photograph of the brain, showing you exactly what it looks like. An fMRI is more like a video or a…
The application of artificial intelligence (AI) in healthcare, particularly in medical imaging, holds immense promise for improving the detection, diagnosis, and treatment of human diseases. However, the translation of AI models from research prototypes to…
Ice melts faster on metal because metal is an excellent thermal conductor. This means it quickly transfers heat from the surrounding environment (like air or a tabletop) into the ice. Materials like wood, plastic, or…
Here are some more examples of MCAR (recall that Missing completely at random (MCAR) data occurs when the probability of missing data on a variable is independent of any other measured variables and the underlying…
Speech-to-text technology, also known as automatic speech recognition (ASR), converts spoken language into written text. At its core, this technology analyzes sound waves and transforms them into their corresponding text equivalents. This process involves sophisticated…
An SDF, or Signed Distance Function (sometimes called a Signed Distance Field), is a mathematical way to represent a shape by measuring the distance from any point in space to the closest point on that…
In AI image generation, the ability to guide the creative process with precision is paramount. While text prompts provide a general direction, advanced techniques are needed for fine-grained control over elements like character poses, composition,…
Hierarchical classification is a method of assigning items to a category that is part of a larger, structured hierarchy. Unlike traditional “flat” classification where categories are independent, hierarchical classification considers the relationships between categories, organizing…
“Do you know what segmentation is?” Jon asked the magician. The magician replied: “It’s the process of dividing an image into meaningful regions” “like giving every pixel a role. So, you know the pixels that…
“The YOLO-scope! It stands for ‘You Only Look Once.” He aimed the YOLO-scope at a passing dragon. A neat little box appeared on the viewing screen around the creature, with a label that read: ‘Dragon…
Blaze was fascinated by the tiny details of the world below. He dreamt of a way to instantly recognize every flower, every rock, and every scurrying critter in the meadow when he flies. One day,…
“Most neural networks pick one filter size at a time. But why not do all of them at once?” Inception said. Give me a photo, I can zoom in on tiny details, look at medium-sized…
Here, we will use the LangGraph library to create a simple AI agent that can decide whether to answer a user’s question directly or use a “search” tool to find the answer first. The system…
LangGraph is a powerful, open-source framework for building and managing complex, stateful, and long-running AI agents. It provides a flexible and controllable way to create sophisticated AI workflows by representing them as graphs. At its…
Perceptual loss is a type of loss function used in AI, especially for tasks like creating or changing images. Instead of comparing two images pixel by pixel, it measures the difference between them based on…
Developed by João Moura, crewAI provides a structured environment for orchestrating autonomous AI agents, enabling them to collaborate and tackle complex tasks that would be challenging for a single AI model to handle alone. At…
This is a common dependency conflict. So, sometimes, when you successfully install crewAI, you may encounter problems when using it. To resolve this, you need to install the specific version of litellm that crewai requires.…
Running large language models locally on a laptop is becoming increasingly feasible, and Ollama makes it accessible. The key to a good experience is choosing a model that matches your laptop’s hardware, primarily its RAM…
pywin32 lets your Python scripts directly control the Windows operating system and its applications. It acts as a bridge, giving you access to the vast Windows Application Programming Interface (API) from within Python. 🤖 Think…
RAG stands for Retrieval-Augmented Generation. It’s a powerful technique used in artificial intelligence to make Large Language Models (LLMs) like me more accurate, up-to-date, and trustworthy. The Simple Analogy: An “Open-Book Exam” Think of a…
