Understanding why modeling high-dimensional data requires more than just throwing parameters at the problem
Deep learning has achieved remarkable success in recent years, from image recognition to natural language processing. But beneath these achievements lies a fundamental challenge that’s often overlooked: how do we effectively model the rich, complex distributions found in real-world data?
Beyond Simple Classification
Most people think of machine learning as classification — showing a model thousands of cat photos so it can identify cats in new images. Classification is powerful, but it’s also forgiving. When classifying an object in a photo, the algorithm can ignore the background, lighting conditions, or other irrelevant details. It distills complex, high-dimensional input into a simple categorical output.
But what if we need our models to do more? What if we want them to:
- Generate new, realistic images from scratch
- Restore damaged photographs by filling in missing pixels
- Estimate how likely a particular data point is under the true distribution
- Complete missing information in datasets
