Distributed Alignment Search: Identifying Causal Mechanisms Structure and Interpretation of Deep Networks Distributed Alignment Search: Identifying Causal Mechanisms October 31, 2024 • Lucas Laird The theory of causal abstraction provides a general framework for assessing the degree to which complex models (e.g. neural networks) implement a simpler interpretable causal model. The goal of causal abstraction is to identify high-level simple causal mechanisms that abstract away from the irrelevant low-level

Since people are talking about the NTK/GP hypothesis of neural nets again, I thought it might be worth bringing up some recent research in the area t

田中専務 拓海先生、最近部下から「不確実性をちゃんと扱えるRNNを使うべきだ」と言われまして、何をどう読めば良…

The text discusses the use of PCA and neural networks in trading, emphasizing the importance of stationarity and the challenges in achieving it. It highlights the non-linear nature of optimizers, the role of PCA in selecting predictors, and the limitations of current methods. The author also touches on the lack of a video explanation and the need for a better approach that doesn't rely on stationarity, suggesting that neural networks are more flexible in this regard

In this post, I’m going to show you how you can use a neural network from keras with the LIME algorithm implemented in the eli5 TextExplainer class. For this we will write a scikit-learn compatible wrapper for a keras bidirectional LSTM model. The wrapper will also handle the tokenization and the storage of the vocabulary

:speech_balloon: Speaker: Vincenzo Perri, Lisi Qarkaxhija, Albin Zehe, Andreas Hotho and Ingo Scholtes :classical_building: Affiliation: (1) Data Analytics Group, Department of Informatics(IfI), Universität Zürich, CH-8…

neural networks research group Implementing Evolutionary Optimization to Model Neural Functional Connectivity (2019) Computational models are crucial in understanding brain function. Their architecture is designed to replicate known brain structures, and the behavior that emerges is then compared to observed fMRI and other imaging techniques. As the models become more complex with more parameters, they can explain more of the observed phenomena, and may eventually be used for diagnosis and design of treat

Uncertainty quantification is a critical yet unsolved challenge for deep learning, especially for the time series imputation with irregularly sampled measurements. To tackle this problem, we propose a novel framework based on the principles of recurrent neural networks and neural stochastic differential equations for reconciling irregularly sampled measurements. We impute measurements at any arbitrary timescale and quantify the uncertainty in the imputations in a principled manner. Specifically, we derive a

Theory and Code

Self Aware Networks OCA First Draft.pdf2025-05-16 | Journal article | Author: Micah Blumberg DOI: 10.6084/m9.figshare.29085134SOURCE-WORK-ID: 29085134Contributors: Micah Blumberg Title: Self Aware Networks: OCA (First Draft) This manuscript, Self Aware Networks: OCA (First Draft), presents the earliest formal articulation of my Self Aware Networks (SAN) framework, a theory of predictive cognition, oscillatory computation, and autonomous network self-reference. The paper was originally published on Figshare