Neural Networks

Audit is a boring word until the model says something expensive. A credit model rejects an applicant. A visual inspection model flags a component. A traffic-sign classifier keeps its prediction under small pixel changes. The business question is not merely, “What did the model look at?” That is the demo-room version. The operational question is harder: which input features must remain fixed so that the model’s decision is guaranteed not to change under allowed perturbations? ...

Cores are usually discussed as a performance story. More cores, more parallelism, less latency, happier product manager. Security people, being paid to ruin everyone’s afternoon, usually hear something else: more switching activity, more leakage, more things an attacker can measure. This paper complicates that instinct in a useful way. In Influence of Parallelism in Vector-Multiplication Units on Correlation Power Analysis, Manuel Brosch, Matthias Probst, Stefan Kögler, and Georg Sigl study a very specific question: when a neural-network accelerator processes the same input value across multiple processing elements, each with a different secret weight, what happens to correlation power analysis?1 ...

Pruning Is a Game, and Most Weights Lose Pruning usually sounds like housekeeping. Train the model. Rank the weights. Remove the small ones. Fine-tune the survivor. Pretend the whole exercise was more scientific than it looked in the notebook. That workflow has worked well enough to become familiar. But familiarity is not explanation. It tells us how to remove model components after training; it says less about why some components become removable in the first place. The paper Pruning as a Game: Equilibrium-Driven Sparsification of Neural Networks asks a sharper question: what if pruning is not merely an external compression operation, but the outcome of competition inside the model?1 ...

Sound is usually treated as a room problem. Put speakers in a space, measure the space, tune the system, then hope the listener does not move too much. Very elegant. Also very inconvenient. Personal sound zones push this problem into sharper form. The goal is simple to describe: one listener hears a desired acoustic scene in a bright zone, while another nearby area stays quiet or hears something else in a dark zone. In practice, the system depends on loudspeaker pre-filters designed from measured acoustic transfer functions. Those measurements are not just “some room data.” They are tied to where microphones were placed. Change the target scene, change the microphone grid, or reduce the number of measurement points, and the old optimization pipeline starts wearing handcuffs. ...

Accuracy is a comforting number. That is precisely the problem. A neural network can score well on a test set and still be operationally suspicious. The labels may be corrupted. The input may be degraded. A small patch may have quietly hijacked part of the model’s learned behavior. The model may be confident, calibrated enough for a dashboard, and still untrustworthy in the one place where the business actually needs it to behave. ...

TL;DR for operators This paper is about risk estimation, not market prophecy. The neural network does not try to forecast returns, detect tomorrow’s winners, or become a portfolio manager with a hoodie and a GPU budget. It learns how to clean covariance information so that a global minimum-variance portfolio behaves better out of sample.1 ...