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The blog's standing answer to the accuracy-vs-latency tension is a two-stage fast/slow split. Distillation is a different answer: train one small model to mimic the ensemble. The KD loss, temperature, born-again nets, early exits for a variable latency budget, and the distill-to-FPGA pipeline — plus the measurements that would decide whether it beats the two-stage split.

Selection bias in the search is not the only way a regression fools you. When the regressor is correlated with the error, more data makes the estimate more confidently wrong. Instrumental variables applied to the one endogeneity problem this blog has already left open: permanent impact from net taker flow.

Fitting a self-exciting point process to real crypto trade tape: where the three numbers (mu, alpha, beta) come from, how to estimate the branching ratio n, whether the exponential kernel survives a goodness-of-fit test, and how much n moves when you change the estimation window.

Hamiltonian Neural Networks are provably stable — but stability is worthless if the conserved quantity does not exist. Learning a scalar H via autograd, symplectic integration, and the falsification test that decides whether a financial (q, p) pair is canonical at all.

Granger causality on crypto prices done two ways — differenced returns and Toda-Yamamoto on levels — with a correct Wald implementation, an effective-N corrected causality matrix, and a rolling-stability test of whether the lag is tradeable at all.

금융 시계열을 위한 커널 설계(모계 거칠기, 국지적 주기 구성, 스펙트럼 혼합) 및 검증 세트가 필요 없는 정규화 도구로서의 한계 가능성. 게다가 거래 가능하기 전에 측정해야 할 사항에 대한 정직한 목록도 있습니다.

연산자 학습은 포인트가 아닌 전체 기능 공간을 매핑합니다. 푸리에 신경 연산자가 주파수 공간에서 PDE 솔루션 연산자를 매개변수화하는 방법, 옵션 가격 책정을 위해 무엇을 구매하는지, 실제 하드웨어에서 여전히 측정이 필요한 주장은 무엇인지를 매핑합니다.