Information-Bottleneck

TL;DR for operators If a video-ranking model must compress a long sequence before deciding which moments deserve attention, the central design question is not simply how much information to remove. Compression can also change the relationships among temporal segments, and those relationships are part of what a ranking system uses to decide that one moment matters more than another. ...

TL;DR for operators Most quant teams already know the awkward truth: adding model capacity often makes the research backtest look smarter while making the deployed model less useful. The interesting part of Che Sun’s paper is not that it adds another neural network to asset pricing. We have enough of those. The useful move is more surgical: it asks the factor model to keep the information that helps explain returns and discard the information that merely helps memorise noisy firm characteristics.1 ...