receptive field
receptive field on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on receptive field in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around receptive field, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Your GNN is probably just an overcomplicated MLP (Tabular Leakage). We built SynthFin-AML to enforce strict causal boundaries. [P]
Standard graph neural network (GNN) evaluations often mask a critical flaw: temporal leakage. Our investigation into anti-money laundering models revealed widespread instances where GNNs effectively "look into the future" during training, leading to artificially inflated performance. To address this, we developed SynthFin-AML v10.0, a benchmark enforcing strict causal boundaries through a 3-snapshot architecture and distribution-aware data splitting. Initial results show GraphSAGE narrowly outperforms LightGBM, highlighting the value of graph structure when evaluated correctly. See "py-evoFE" for related work on automated feature engineering.
Mechanistic interpretability: a first paper on disentangling a convolutional neuron [R]
Recent independent research offers a novel approach to mechanistic interpretability, focusing on detailed analysis of individual neurons. This initial paper explores a 1x1 convolution within InceptionV1, revealing that the Hadamard product of a neuron’s receptive field and weight defines the patterns it detects. Through clustering these products, the study identifies monosemantic activations—cars, cats, dogs—and surprisingly, lesser-known activations like letters and faces. This technique illuminates a deliberate pattern within gradient descent, suggesting a nuanced organization of concepts. [https://pages.narang99.in/posts/2026-07-12-disentangling-mixed4