From Swin’s hierarchical design to DaViT’s dual attention, why does combining CNNs and transformers beat pure architectures Continue reading on Towards AI »
How are you, hacker? 🪐Want to know what’s trending right now?: The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here. ## Back to Basics: Database Design as Storytelling By @dataops [ 3 Min read ] Why great database design is really storytelling—and why ignoring relational fundamentals leads to poor performance AI can’t fix. Read More. The Long Now of the Web: Inside the Internet Archive’s […]
To get a first hand understanding of the impact of AI on the software development lifecycle (SDLC), I decided to run an experiment. I wanted to try and write a reasonably complex system from scratch using AI. I didn’t want a “Hello World” or another “To Do” app; I wanted something realistic, something that could be used at scale like we’d build in the enterprise world. The result is Under The Hedge—a fun project blending my passion for […]
Humans don’t start their thinking from scratch every second. As you read this essay, you understand each word based on your understanding of previous words. You don’t throw everything away and start thinking from scratch again. Your thoughts have persistence. Traditional neural networks can’t do this, and it seems like a major shortcoming. For example, imagine you want to classify what kind of event is happening at every point in a movie. It’s unclear how a traditional neural […]
Introduction Disclaimer: This article is a cross post from Pytorch Medium Blog Post. One of the most exciting advancements, that has pushed the frontier of the Artificial Intelligence (AI) in recent years, is Deep Reinforcement Learning (DRL). DRL belongs to the family of machine learning algorithms. It assumes that intelligent machines can learn from their actions similar to the way humans learn from experience. Over the recent years we could witness some impressive real-world applications of DRL. The […]
arXiv:2601.22178v1 Announce Type: new Abstract: Utility-driven mining is an essential task in data science, as it can provide deeper insight into the real world. High-utility sequential rule mining (HUSRM) aims at discovering sequential rules with high utility and high confidence. It can certainly provide reliable information for decision-making because it uses confidence as an evaluation metric, as well as some algorithms like HUSRM and US-Rule. However, in current rule-growth mining methods, the linkage between HUSRs and their generation […]
The life of a prescription at Amazon Pharmacy From pricing estimation and regulatory compliance to inventory management and chatbot assistants, machine learning models help Amazon Pharmacy customers stay healthy and save time and money. Conversational AI Alexandre Alves Anita Vila September 30, 01:32 PM October 02, 11:42 AM Pharmacies play a vital role in ensuring patients health, but the process of dispensing medications is far more complex than it may appear. At Amazon Pharmacy, we are using artificial […]
You’ve probably caught yourself copy-pasting the same instructions into AI chats over and over again: “Write in TypeScript,” “Don’t use third-party libraries,” “Add tests.” In programming, we would basically call this a violation of the DRY (Don’t Repeat Yourself) principle. So why do we keep doing it when working with neural networks? You’ve probably caught yourself copy-pasting the same instructions into AI chats over and over again: “Write in TypeScript,” “Don’t use third-party libraries,” “Add tests.” In programming, […]
Stochastic Gradient Descent (SGD) and its momentum variants form the backbone of deep learning optimization, yet the underlying dynamics of their gradient behavior remain insufficiently understood. In this work, we reinterpret gradient updates through the lens of signal processing and reveal that fixed momentum coefficients inherently distort the balance between bias and variance, leading to skewed or suboptimal parameter updates. To address this, we propose SGDF (SGD with Filter), an optimizer inspired by the principles of Optimal Linear […]
arXiv:2602.10204v1 Announce Type: new Abstract: We introduce MVN-Grad (Momentum on Variance-Normalized Gradients), an Adam-style optimizer that improves stability and performance by combining two complementary ideas: variance-based normalization and momentum applied after normalization. MVN-Grad scales each coordinate by an exponential moving average of gradient uncertainty and applies momentum to the resulting normalized gradients, eliminating the cross-time coupling between stale momentum and a stochastic normalizer present in standard Adam-type updates. We prove that this decoupling yields strictly smaller one-step conditional […]