Machine Learning

Single Cortical Neurons as Deep Artificial Neural Networks

September, 2021

Highlights

• Cortical neurons are well approximated by a deep neural network (DNN) with 5–8 layers

• DNN’s depth arises from the interaction between NMDA receptors and dendritic morphology

• Dendritic branches can be conceptualized as a set of spatiotemporal pattern detectors

• We provide a unified method to assess the computational complexity of any neuron type

Summary

Estimating individual-level optimal causal interventions combining causal models and machine learning models

DSA ADS Course - 2021

Causal Data Science, Machine Learning, Individual-level Optimal Causal Interventions, Causal Models

Discuss statistical causal inference methods.

Estimating individual-level optimal causal interventions combining causal models and machine learning models - 2021

Abstract

CausaLM: Causal Model Explanation Through Counterfactual Language Models

DSA ADS Course - 2021

Causal Data Science, Causal Inference, Linguistic Causation, CausaLM, Causal Model Explanation, Counterfactual Language Models, Machine Learning

Discuss causative linguistic expressions and causal model explanation through counterfactual language models.

CausaLM: Causal Model Explanation Through Counterfactual Language Models - 2021

Abstract

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions

DSA ADS Course - 2021

DoWhy, Causal Assumptions, Causal Inference, Causal Data Science, Machine Learning

Discuss successful application of causal inference techniques.  As computing systems are more frequently and more actively intervening in societally critical domains such as healthcare, education, and governance, it is critical to correctly predict and understand the causal effects of these interventions. Without an A/B test, conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for decision-making.

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