Papers

Waste management is one of the most persistent challenges in modern urban life. As cities grow, so do our mountains of trash. The traditional way to deal with this—manual collection and sorting—is labor-intensive, costly, unhygienic, and often inefficient. What if we could bring intelligence to the very first step of waste management: the bin itself? A group of researchers set out to answer that question with SmartBin, a low-cost hardware solution that uses deep learning to automatically segregate garbage at the source. In their paper, “A deep learning approach based hardware solution to categorise garbage in environment”, they present a prototype that can distinguish between biodegradable and non-biodegradable waste, and physically sort it into separate compartments. ...

Every year, the world generates over 2 billion tons of municipal solid waste, and this number is projected to soar to 3.4 billion tons by 2050. A vast amount of this waste ends up in landfills, polluting the soil, water, and air. Recycling offers a powerful solution, but success depends on one crucial and often overlooked step: proper waste segregation. Traditionally, sorting waste into categories like paper, plastic, metal, and glass has been a manual, labor-intensive process. It’s slow, costly, and can be hazardous for workers. But what if a machine could sort garbage with the speed and accuracy of a human—possibly even better? ...

The world is facing a monumental waste problem. As cities expand and populations grow, so does the trash we produce. Projections suggest global waste could swell by 70%, reaching an astonishing 3.4 billion tons by 2050. At the heart of managing this crisis lies a deceptively simple task: sorting waste. Effective sorting is the first crucial step toward recycling, conserving resources, and sustaining a circular economy. But sorting isn’t easy. Traditional methods rely heavily on manual labor—slow, expensive, error-prone, and inadequate for the scale and complexity of today’s waste streams. This is where artificial intelligence (AI) —and specifically deep learning—has shown promise. Convolutional Neural Networks (CNNs) can classify waste images automatically, but they struggle with the messy reality of real-world trash: varied lighting conditions, visually similar materials, and imbalanced categories. ...

In the late 2000s, the internet was overflowing with images. Flickr had billions of photos, Google was indexing countless more, and social media was just beginning its visual explosion. For computer vision researchers, this digital flood was both a tantalizing opportunity and a monumental challenge. On one hand, more data meant the potential to train more powerful and robust models. On the other, this data was a chaotic, unlabeled mess. How could you possibly teach a computer to recognize a Siberian husky when your training data was a jumble of random images tagged “dog”? ...

In 2012, a deep convolutional neural network (CNN) named AlexNet stunned the world by winning the ImageNet Large Scale Visual Recognition Challenge with an error rate almost half that of the runner-up. It was a watershed moment that kicked off the modern deep learning revolution. But while the results were undeniable, these networks were still black boxes—we knew they worked, but not what was happening inside their millions of parameters. ...

If you’ve ever trained a large neural network, you’ve likely encountered its greatest nemesis: overfitting. You watch the training loss plummet, your model perfectly memorizing the training data, only to see its performance on unseen test data stagnate or even worsen. The model has learned the noise, not the signal. It’s like a student who memorizes the answers to a practice exam but fails the real test because they never learned the underlying concepts. ...

In 1998 a group from AT&T Labs and the Université de Montréal published a paper that became a landmark in machine learning and computer vision: “Gradient-Based Learning Applied to Document Recognition” (LeCun, Bottou, Bengio, Haffner). The paper did two things that matter even more today than they did then: It showed how convolutional neural networks (CNNs) can learn relevant visual features directly from pixels, removing the need for brittle, hand-crafted feature extractors. It introduced a principled way to build and train complex, multi-module systems end-to-end by representing intermediate hypotheses as graphs and backpropagating through them—what the authors call Graph Transformer Networks (GTNs). This article walks through the core ideas, the architectures, the training insights, and the practical systems in the paper. I’ll explain the concepts in a way that aims to be useful for both students and practitioners: not just what the authors did, but why it mattered and what to take away for modern work. ...

In the world of computer vision, object detection is a foundational task with applications ranging from autonomous driving to medical imaging. A persistent challenge in this field has been the trade-off between speed and accuracy. Highly precise models are often too slow for real-time scenarios, while faster models sometimes lack the accuracy needed for mission-critical applications. But what if you could have both? What if you could train a state-of-the-art detector not in a massive, expensive cloud infrastructure, but on a single consumer-grade GPU sitting in your lab or home office? ...

In the world of computer vision, the You Only Look Once (YOLO) family of models is legendary. Known for its blistering speed, YOLO redefined real-time object detection by framing it as a single regression problem. But the journey didn’t stop at one great idea. Following the success of YOLO and YOLOv2, the creators returned with a new iteration: YOLOv3. The 2018 paper YOLOv3: An Incremental Improvement is famous not just for its technical contributions but for its refreshingly candid and humorous tone. The authors don’t claim a massive breakthrough—instead, they present a series of thoughtful, practical updates that collectively create a significantly better detector. It’s a masterclass in engineering and the power of incremental progress. ...

Object detection has long been a cornerstone task in computer vision. We need models that can not only tell us what is in an image, but also where it is. For years, progress meant a trade-off: you could choose a model that was highly accurate or one fast enough for real-time applications—but rarely both. And even the best detectors were limited to a small vocabulary, trained on datasets with a few dozen or at most a few hundred object categories. ...

When you glance at a picture, you instantly recognize the objects within it. You can tell a dog from a bicycle, identify multiple people, and understand where they are in the scene. This ability is effortless for humans, but for computers, it has historically been a monumental challenge. The task, known as object detection, is a cornerstone of computer vision, unlocking capabilities ranging from self-driving cars to assistive technologies and robotics. ...

The rise of generative AI has been nothing short of explosive. Models like Stable Diffusion, Midjourney, and DALL·E can conjure breathtaking images from simple text prompts, democratizing artistic creation in ways we’ve never seen before. But this revolution comes with a controversial side: these powerful models are often trained on vast internet-sourced datasets without the explicit consent of original artists. This practice has sparked fierce debates about copyright, ownership, and the nature of creativity. ...

Sketches are the soul of visual art. Before an artist commits to a fully rendered painting, they start with a line drawing—a blueprint that captures the essential structure, layout, and proportions of the final piece. This process is intuitive and powerful because editing a sketch is far easier than making pixel-perfect adjustments to a finished color image. Despite the recent explosion in generative AI—particularly diffusion models capable of producing stunning photorealistic images from text—the world of automated sketch generation has been surprisingly quiet. Existing tools often fall short of what artists truly need: precise control over the final output. It’s one thing to generate “a cat sitting on a mat,” but it’s another to specify that the cat should be in the top-left corner, facing right, and be a certain size. ...

The world of Natural Language Processing (NLP) has been transformed by massive, pre-trained language models like GPT-3. These colossal models, trained on vast portions of the internet, can perform a stunning array of tasks right out of the box. But to unlock their full potential for a specific application—be it a customer service chatbot, a legal document summarizer, or a code generator—we need to adapt them. This process is called fine-tuning. ...

If you’ve ever trained a deep learning model, you’ve almost certainly encountered the Adam optimizer. Since its introduction in 2014, it has become one of the most popular—and often the default—optimization algorithms for training neural networks. But what exactly is Adam? How does it work, and why is it so effective? In this article, we’ll unpack the original paper that introduced Adam: Adam: A Method for Stochastic Optimization by Diederik P. Kingma and Jimmy Lei Ba. We’ll break down the core concepts, walk through the algorithm step-by-step, and explore the experiments that demonstrated its power. Whether you’re a student just starting in machine learning or a practitioner looking for a deeper understanding, this guide will demystify one of the most fundamental tools in the modern deep learning toolkit. ...

Imagine being able to direct a short film entirely from your laptop. You provide a photo of an actor, a script for their lines, and a description of the scene — and an AI model generates a high-quality video that brings your vision to life. This is the promise of Human-Centric Video Generation (HCVG), a rapidly evolving field that’s reshaping content creation. Traditionally, producing even a short video is a complex, expensive endeavor involving casting, location scouting, filming, and post-production. Generative AI aims to democratize this process by allowing creators to craft videos from simple multimodal inputs: text for describing scenes and actions, images for defining character identity, and audio for speech. ...

Imagine a robot that can understand your instructions, see the world around it, and perform complex tasks like: “Pick up the spoon, place it in the cup, then move the cup onto the plate.” This is the promise of Vision-Language-Action (VLA) models—the “brains” behind the next generation of general-purpose robots. Traditionally, building such models involves a brute-force approach: take a massive Vision-Language Model (VLM), pre-train it on colossal datasets of robot data, and fine-tune it for specific tasks. This works, but it has serious downsides: ...

Training large language models (LLMs) is a monumental task. But what happens after the initial pre-training? How do we refine these models—making them better at complex reasoning, following instructions, and avoiding harmful outputs? One of the most powerful techniques for this is Reinforcement Learning (RL), where a model learns through trial and error, much like a person mastering a new skill. However, applying RL to massive models comes with a hefty price tag. It often demands enormous, centralized GPU clusters where models are trained in lockstep. This approach is not only incredibly expensive but also poses significant technical challenges: communication bottlenecks, latency issues, and a reliance on highly specialized, homogeneous hardware. It’s a game largely reserved for a few big players with deep pockets. ...

From the chassis of our cars to the skeletons of our skyscrapers, steel is the unsung hero of the modern world. But not all steel is created equal. The quest for stronger yet more ductile materials has driven the development of Advanced High-Strength Steels (AHSSs). The secret to their remarkable performance often lies in a microscopic, lightning-fast rearrangement known as the Martensitic Transformation (MT). This process is like a rapid, disciplined dance of atoms, reshaping from one crystal structure to another without diffusion or change in composition. The result is martensite—an exceptionally hard and strong phase that strengthens many AHSSs. For materials scientists, controlling this transformation is the key to designing next-generation alloys. ...

For over 50 years, scientists have been grappling with one of the grandest challenges in biology: the protein folding problem. Proteins are the microscopic workhorses of life, responsible for everything from digesting your food to fighting off viruses. Their function is dictated by their intricate three-dimensional shapes. The challenge? To predict this 3D structure solely from a protein’s one-dimensional sequence of amino acids. Solving this would be revolutionary. While billions of protein sequences have been catalogued, determining their structures experimentally—using techniques like X-ray crystallography or cryo-electron microscopy—requires painstaking work that can take months or even years. This has created a vast “structure gap” in our biological knowledge. ...