Research

In the world of artificial intelligence, data is the fuel that powers machine learning models. But what if that fuel is contaminated? Mislabeled data, known as label noise, is a huge problem that can cause even the best algorithms to learn complete nonsense. The paper “ε-Softmax: Approximating One-Hot Vectors for Mitigating Label Noise,” accepted at the prestigious NeurIPS 2024 conference, offers an elegant solution. The Problem: When a Model Blindly Trusts Its Labels Let’s imagine we’re training a model to recognize animals. We show it a picture of a cute cat. In the traditional approach, we give it an absolutely certain piece of information, a so-called one-hot vector: ...

In the era dominated by language models and machine learning, the importance of structured data is growing rapidly: social networks, biological relationships, and business connections. This data is represented in the form of graphs, which are often not homogeneous: they contain nodes of different types (e.g., people, products, companies) and different types of edges (e.g., “purchased”, “recommended”, “works at”). Processing such heterogeneous graphs requires specialized methods. What are heterogeneous graphs? A heterogeneous graph is a structure in which: ...