From f806ca8e2793f7f1f0c1879ff9e968ea9037c746 Mon Sep 17 00:00:00 2001 From: GitHub Actions Date: Tue, 19 Nov 2024 16:54:54 +0000 Subject: [PATCH] site deploy Auto-generated via `{sandpaper}` Source : 910d3b2c2be08f9e29df09d082008368ba1321ae Branch : md-outputs Author : GitHub Actions Time : 2024-11-15 20:01:20 +0000 Message : markdown source builds Auto-generated via `{sandpaper}` Source : 41b056a06520c51f353a555b289c4e4df907db43 Branch : main Author : Chris Endemann Time : 2024-11-15 20:00:26 +0000 Message : Update 7c-OOD-detection-energy.md --- 2-model-eval-and-fairness.html | 10 +++++----- 5a-explainable-AI-method-overview.html | 2 +- aio.html | 12 ++++++------ index.html | 6 +++--- instructor/2-model-eval-and-fairness.html | 10 +++++----- instructor/5a-explainable-AI-method-overview.html | 2 +- instructor/aio.html | 12 ++++++------ instructor/index.html | 6 +++--- pkgdown.yml | 2 +- 9 files changed, 31 insertions(+), 31 deletions(-) diff --git a/2-model-eval-and-fairness.html b/2-model-eval-and-fairness.html index e5c7d410..237b3302 100644 --- a/2-model-eval-and-fairness.html +++ b/2-model-eval-and-fairness.html @@ -544,7 +544,7 @@

What accuracy metric to use?

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  1. It is best if all patients who need the screening get it, and there is little downside for doing screenings unnecessarily because the @@ -673,7 +673,7 @@

    Matching fairness terminology with definitions

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    A - 3, B - 2, C - 4, D - 1

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    Red-teaming large language models

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    Most publicly-available LLM providers set up guardrails to avoid propagating biases present in their training data. For instance, as of @@ -805,7 +805,7 @@

    Challenge

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    While the picture is of Barack Obama, the upsampled image shows a white face. Unblurred version of the pixelated picture of Obama. Instead of showing Obama, it shows a white man.

    @@ -906,7 +906,7 @@

    Pros and cons of preprocessing options

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    A downside of oversampling is that it may violate statistical assumptions about independence of samples. A downside of undersampling diff --git a/5a-explainable-AI-method-overview.html b/5a-explainable-AI-method-overview.html index 15c95a7e..29ab71c7 100644 --- a/5a-explainable-AI-method-overview.html +++ b/5a-explainable-AI-method-overview.html @@ -718,7 +718,7 @@

    Classifying explanation techniques

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    diff --git a/aio.html b/aio.html index dc7c779a..8f0df6c5 100644 --- a/aio.html +++ b/aio.html @@ -1307,7 +1307,7 @@

    What accuracy metric to use?

    -
    +
    1. It is best if all patients who need the screening get it, and @@ -1450,7 +1450,7 @@

      Matching fairness terminology with definitions

      -
      +

      A - 3, B - 2, C - 4, D - 1

      @@ -1536,7 +1536,7 @@

      Red-teaming large language models

      -
      +

      Most publicly-available LLM providers set up guardrails to avoid propagating biases present in their training data. For instance, as of @@ -1588,7 +1588,7 @@

      Challenge

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      +

      While the picture is of Barack Obama, the upsampled image shows a white face. Unblurred version of the pixelated picture of Obama. Instead of showing Obama, it shows a white man.

      @@ -1693,7 +1693,7 @@

      Pros and cons of preprocessing options

      -
      +

      A downside of oversampling is that it may violate statistical assumptions about independence of samples. A downside of undersampling @@ -3248,7 +3248,7 @@

      Classifying explanation techniques

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    Approach Post Hoc or Inherently Interpretable?
    diff --git a/index.html b/index.html index 6a3d039d..8146a092 100644 --- a/index.html +++ b/index.html @@ -476,7 +476,7 @@

    Windows

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    Checkout the [video tutorial][video-windows] or:

    1. Open [https://www.anaconda.com/products/distribution][anaconda-distribution] @@ -497,7 +497,7 @@

      MacOS

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      Checkout the [video tutorial][video-mac] or:

      1. Open [https://www.anaconda.com/products/distribution][anaconda-distribution] @@ -518,7 +518,7 @@

        Linux

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        Note that the following installation steps require you to work from the shell. If you run into any difficulties, please request help before diff --git a/instructor/2-model-eval-and-fairness.html b/instructor/2-model-eval-and-fairness.html index b49c3e39..2ff2de97 100644 --- a/instructor/2-model-eval-and-fairness.html +++ b/instructor/2-model-eval-and-fairness.html @@ -546,7 +546,7 @@

        What accuracy metric to use?

        -
        +
        1. It is best if all patients who need the screening get it, and there is little downside for doing screenings unnecessarily because the @@ -675,7 +675,7 @@

          Matching fairness terminology with definitions

          -
          +

          A - 3, B - 2, C - 4, D - 1

          @@ -756,7 +756,7 @@

          Red-teaming large language models

          -
          +

          Most publicly-available LLM providers set up guardrails to avoid propagating biases present in their training data. For instance, as of @@ -807,7 +807,7 @@

          Challenge

          -
          +

          While the picture is of Barack Obama, the upsampled image shows a white face. Unblurred version of the pixelated picture of Obama. Instead of showing Obama, it shows a white man.

          @@ -908,7 +908,7 @@

          Pros and cons of preprocessing options

          -
          +

          A downside of oversampling is that it may violate statistical assumptions about independence of samples. A downside of undersampling diff --git a/instructor/5a-explainable-AI-method-overview.html b/instructor/5a-explainable-AI-method-overview.html index 6fc7671d..550199fe 100644 --- a/instructor/5a-explainable-AI-method-overview.html +++ b/instructor/5a-explainable-AI-method-overview.html @@ -720,7 +720,7 @@

          Classifying explanation techniques

          -
          +
    diff --git a/instructor/aio.html b/instructor/aio.html index 84ef9a19..fbb52d3c 100644 --- a/instructor/aio.html +++ b/instructor/aio.html @@ -1485,7 +1485,7 @@

    What accuracy metric to use?

    -
    +
    1. It is best if all patients who need the screening get it, and @@ -1628,7 +1628,7 @@

      Matching fairness terminology with definitions

      -
      +

      A - 3, B - 2, C - 4, D - 1

      @@ -1714,7 +1714,7 @@

      Red-teaming large language models

      -
      +

      Most publicly-available LLM providers set up guardrails to avoid propagating biases present in their training data. For instance, as of @@ -1766,7 +1766,7 @@

      Challenge

      -
      +

      While the picture is of Barack Obama, the upsampled image shows a white face. Unblurred version of the pixelated picture of Obama. Instead of showing Obama, it shows a white man.

      @@ -1871,7 +1871,7 @@

      Pros and cons of preprocessing options

      -
      +

      A downside of oversampling is that it may violate statistical assumptions about independence of samples. A downside of undersampling @@ -3429,7 +3429,7 @@

      Classifying explanation techniques

      -
      +
    Approach Post Hoc or Inherently Interpretable?
    diff --git a/instructor/index.html b/instructor/index.html index 84ee452b..1c328b8a 100644 --- a/instructor/index.html +++ b/instructor/index.html @@ -700,7 +700,7 @@

    Windows

    -
    +

    Checkout the [video tutorial][video-windows] or:

    1. Open [https://www.anaconda.com/products/distribution][anaconda-distribution] @@ -721,7 +721,7 @@

      MacOS

      -
      +

      Checkout the [video tutorial][video-mac] or:

      1. Open [https://www.anaconda.com/products/distribution][anaconda-distribution] @@ -742,7 +742,7 @@

        Linux

        -
        +

        Note that the following installation steps require you to work from the shell. If you run into any difficulties, please request help before diff --git a/pkgdown.yml b/pkgdown.yml index acf3f8f8..5c0dce1c 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -2,4 +2,4 @@ pandoc: 3.1.11 pkgdown: 2.1.1 pkgdown_sha: ~ articles: {} -last_built: 2024-11-19T00:52Z +last_built: 2024-11-19T16:54Z