diff --git a/NASBench.ipynb b/NASBench.ipynb
index 2beef0c27..3a983cd3c 100644
--- a/NASBench.ipynb
+++ b/NASBench.ipynb
@@ -4,7 +4,6 @@
   "metadata": {
     "colab": {
       "name": "NASBench.ipynb",
-      "version": "0.3.2",
       "provenance": [],
       "collapsed_sections": [],
       "toc_visible": true
@@ -16,11 +15,11 @@
   },
   "cells": [
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "SRxqMakh3PRY",
         "colab_type": "text"
       },
-      "cell_type": "markdown",
       "source": [
         "Copyright 2019 Google LLC\n",
         "\n",
@@ -38,11 +37,11 @@
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "47ieDn-jNLYd",
         "colab_type": "text"
       },
-      "cell_type": "markdown",
       "source": [
         "# NASBench-101\n",
         "\n",
@@ -52,27 +51,31 @@
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "lBNMsBUS3SAq",
         "colab_type": "text"
       },
-      "cell_type": "markdown",
       "source": [
         "## Load NASBench library and dataset"
       ]
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "vl1oLYux3FhJ",
         "colab_type": "code",
-        "outputId": "8008287a-d070-4b32-a7af-bd7fc17f9da1",
+        "outputId": "260484df-3a86-48ef-88a4-f8e441d524bc",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 883
+          "height": 853
         }
       },
-      "cell_type": "code",
       "source": [
+        "# This code was written in TF 1.12 but should be supported all the way through\n",
+        "# TF 1.15. Untested in TF 2.0+.\n",
+        "%tensorflow_version 1.x\n",
+        "\n",
         "# Download the raw data (only 108 epoch data points, for full dataset,\n",
         "# uncomment the second line for nasbench_full.tfrecord).\n",
         "\n",
@@ -91,70 +94,71 @@
         "# Use nasbench_full.tfrecord for full dataset (run download command above).\n",
         "nasbench = api.NASBench('nasbench_only108.tfrecord')"
       ],
-      "execution_count": 4,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
             "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
             "                                 Dload  Upload   Total   Spent    Left  Speed\n",
-            "100  498M  100  498M    0     0   192M      0  0:00:02  0:00:02 --:--:--  192M\n",
+            "100  498M  100  498M    0     0  54.1M      0  0:00:09  0:00:09 --:--:-- 67.7M\n",
             "Cloning into 'nasbench'...\n",
-            "remote: Enumerating objects: 81, done.\u001b[K\n",
-            "remote: Counting objects: 100% (81/81), done.\u001b[K\n",
-            "remote: Compressing objects: 100% (44/44), done.\u001b[K\n",
-            "remote: Total 81 (delta 36), reused 79 (delta 36), pack-reused 0\u001b[K\n",
-            "Unpacking objects: 100% (81/81), done.\n",
+            "remote: Enumerating objects: 92, done.\u001b[K\n",
+            "remote: Total 92 (delta 0), reused 0 (delta 0), pack-reused 92\u001b[K\n",
+            "Unpacking objects: 100% (92/92), done.\n",
             "Processing ./nasbench\n",
-            "Requirement already satisfied: tensorflow>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from nasbench==1.0) (1.13.1)\n",
-            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.33.1)\n",
-            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.0)\n",
-            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.0.7)\n",
-            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.11.0)\n",
-            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.7.0)\n",
-            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.2.2)\n",
-            "Requirement already satisfied: tensorflow-estimator<1.14.0rc0,>=1.13.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.13.0)\n",
+            "Requirement already satisfied: tensorflow>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from nasbench==1.0) (1.15.0)\n",
+            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (3.1.0)\n",
             "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.1.0)\n",
-            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.14.6)\n",
-            "Collecting tensorboard<1.14.0,>=1.13.0 (from tensorflow>=1.12.0->nasbench==1.0)\n",
-            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/fa/7b/3ee06856ec30d5136cd2002408df1d111fcff269f3691147dbf3b8dc0ba2/tensorboard-1.13.0-py3-none-any.whl (3.2MB)\n",
-            "\u001b[K    100% |████████████████████████████████| 3.2MB 10.9MB/s \n",
-            "\u001b[?25hRequirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.0.9)\n",
-            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (3.6.1)\n",
-            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.7.1)\n",
-            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow>=1.12.0->nasbench==1.0) (2.8.0)\n",
-            "Requirement already satisfied: mock>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow>=1.12.0->nasbench==1.0) (2.0.0)\n",
-            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow>=1.12.0->nasbench==1.0) (0.14.1)\n",
-            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.14.0,>=1.13.0->tensorflow>=1.12.0->nasbench==1.0) (3.0.1)\n",
-            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow>=1.12.0->nasbench==1.0) (40.8.0)\n",
-            "Requirement already satisfied: pbr>=0.11 in /usr/local/lib/python3.6/dist-packages (from mock>=2.0.0->tensorflow-estimator<1.14.0rc0,>=1.13.0->tensorflow>=1.12.0->nasbench==1.0) (5.1.2)\n",
+            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.1.0)\n",
+            "Requirement already satisfied: tensorflow-estimator==1.15.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.1)\n",
+            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.8.0)\n",
+            "Requirement already satisfied: numpy<2.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.17.3)\n",
+            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.12.0)\n",
+            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.8.1)\n",
+            "Requirement already satisfied: keras-applications>=1.0.8 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.0.8)\n",
+            "Requirement already satisfied: gast==0.2.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.2.2)\n",
+            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (3.10.0)\n",
+            "Requirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.1.7)\n",
+            "Requirement already satisfied: tensorboard<1.16.0,>=1.15.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.0)\n",
+            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.15.0)\n",
+            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (0.33.6)\n",
+            "Requirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow>=1.12.0->nasbench==1.0) (1.11.2)\n",
+            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow>=1.12.0->nasbench==1.0) (2.8.0)\n",
+            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow>=1.12.0->nasbench==1.0) (41.4.0)\n",
+            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.12.0->nasbench==1.0) (0.16.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow>=1.12.0->nasbench==1.0) (3.1.1)\n",
             "Building wheels for collected packages: nasbench\n",
-            "  Building wheel for nasbench (setup.py) ... \u001b[?25ldone\n",
-            "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-t1ak51pu/wheels/4b/19/99/1d5fdfe30f8b16fab91e900808f4f7e5adc38e602c84970ad5\n",
+            "  Building wheel for nasbench (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for nasbench: filename=nasbench-1.0-cp36-none-any.whl size=46791 sha256=88bafd1b9d560fd0fe9b6f8ceac03001e0b42b4dc2718648550569f654f098b3\n",
+            "  Stored in directory: /tmp/pip-ephem-wheel-cache-blg0rruf/wheels/4b/19/99/1d5fdfe30f8b16fab91e900808f4f7e5adc38e602c84970ad5\n",
             "Successfully built nasbench\n",
-            "Installing collected packages: nasbench, tensorboard\n",
-            "  Found existing installation: tensorboard 1.12.2\n",
-            "    Uninstalling tensorboard-1.12.2:\n",
-            "      Successfully uninstalled tensorboard-1.12.2\n",
-            "Successfully installed nasbench-1.0 tensorboard-1.13.0\n",
+            "Installing collected packages: nasbench\n",
+            "Successfully installed nasbench-1.0\n",
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/lib/training_time.py:130: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/lib/training_time.py:174: The name tf.train.CheckpointSaverListener is deprecated. Please use tf.estimator.CheckpointSaverListener instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/lib/evaluate.py:30: The name tf.train.NanLossDuringTrainingError is deprecated. Please use tf.estimator.NanLossDuringTrainingError instead.\n",
+            "\n",
             "Loading dataset from file... This may take a few minutes...\n",
             "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/nasbench/api.py:146: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "Use eager execution and: \n",
             "`tf.data.TFRecordDataset(path)`\n",
-            "Loaded dataset in 56 seconds\n"
+            "Loaded dataset in 53 seconds\n"
           ],
           "name": "stdout"
         }
       ]
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "oFhFRmck7NzM",
         "colab_type": "code",
         "colab": {}
       },
-      "cell_type": "code",
       "source": [
         "# Standard imports\n",
         "import copy\n",
@@ -179,26 +183,26 @@
       "outputs": []
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "llC2AebQOWq9",
         "colab_type": "text"
       },
-      "cell_type": "markdown",
       "source": [
         "## Basic usage"
       ]
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "kZvm6i0VGP_M",
         "colab_type": "code",
+        "outputId": "2999b6a6-9f1d-4361-8be9-ee54afdb82be",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 271
-        },
-        "outputId": "02400c58-897e-4932-cbdb-69cada5f4686"
+          "height": 258
+        }
       },
-      "cell_type": "code",
       "source": [
         "# Query an Inception-like cell from the dataset.\n",
         "cell = api.ModelSpec(\n",
@@ -218,7 +222,7 @@
         "for k, v in data.items():\n",
         "  print('%s: %s' % (k, str(v)))"
       ],
-      "execution_count": 6,
+      "execution_count": 3,
       "outputs": [
         {
           "output_type": "stream",
@@ -232,32 +236,32 @@
             " [0 0 0 0 0 0 0]]\n",
             "module_operations: ['input', 'conv3x3-bn-relu', 'conv1x1-bn-relu', 'maxpool3x3', 'conv3x3-bn-relu', 'conv3x3-bn-relu', 'output']\n",
             "trainable_parameters: 2694282\n",
-            "training_time: 1154.361083984375\n",
+            "training_time: 1155.85302734375\n",
             "train_accuracy: 1.0\n",
-            "validation_accuracy: 0.9336938858032227\n",
-            "test_accuracy: 0.9286859035491943\n"
+            "validation_accuracy: 0.9376001358032227\n",
+            "test_accuracy: 0.9311898946762085\n"
           ],
           "name": "stdout"
         }
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "uXnVdG32Oe19",
         "colab_type": "text"
       },
-      "cell_type": "markdown",
       "source": [
         "## Example search experiment (random vs. evolution)"
       ]
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "Xtl_Aqr7OeOF",
         "colab_type": "code",
         "colab": {}
       },
-      "cell_type": "code",
       "source": [
         "def random_spec():\n",
         "  \"\"\"Returns a random valid spec.\"\"\"\n",
@@ -386,16 +390,16 @@
       "outputs": []
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "HMfF2zXxpQNA",
         "colab_type": "code",
+        "outputId": "edff2ca4-8a55-4537-838a-dedc7a9f360b",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 197
-        },
-        "outputId": "2eaa5578-6c15-47c3-c6e3-9bf4a8c1e84b"
+          "height": 187
+        }
       },
-      "cell_type": "code",
       "source": [
         "# Run random search and evolution search 10 times each. This should take a few\n",
         "# minutes to run. Note that each run would have taken days of compute to\n",
@@ -410,7 +414,7 @@
         "  times, best_valid, best_test = run_evolution_search()\n",
         "  evolution_data.append((times, best_valid, best_test))"
       ],
-      "execution_count": 12,
+      "execution_count": 5,
       "outputs": [
         {
           "output_type": "stream",
@@ -431,16 +435,16 @@
       ]
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "2d-yRmuhkz35",
         "colab_type": "code",
+        "outputId": "2ae12897-074f-4705-db94-6be1aa4208dc",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 368
-        },
-        "outputId": "6a035511-69a3-4516-e65b-dcf89a3f2364"
+          "height": 367
+        }
       },
-      "cell_type": "code",
       "source": [
         "plt.figure(figsize=(20, 5))\n",
         "\n",
@@ -467,7 +471,7 @@
         "plt.grid()\n",
         "plt.title('Evolution search trajectories (red=validation, blue=test)')"
       ],
-      "execution_count": 20,
+      "execution_count": 6,
       "outputs": [
         {
           "output_type": "execute_result",
@@ -479,12 +483,12 @@
           "metadata": {
             "tags": []
           },
-          "execution_count": 20
+          "execution_count": 6
         },
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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bNiil/JZSXgG68uAaZhO/DcMwDMPIPb29+r+8PL9yGIaRFczIMAzDMAwj9/gj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N2aeemvXrZpzOTtizB44+GubMOXB466pVjD/uuBwKll0KKb95m9fubrjvPpg7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\nq6n/D2wF67IP/o7nAAAAAElFTkSuQmCC\n",
             "text/plain": [
               "<Figure size 1440x360 with 2 Axes>"
             ]
@@ -496,16 +500,16 @@
       ]
     },
     {
+      "cell_type": "code",
       "metadata": {
         "id": "F9lB_qL2oz9M",
         "colab_type": "code",
+        "outputId": "2a9d25c8-3adb-456a-ae2d-3229a7ed8bfe",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 283
-        },
-        "outputId": "1fa08bff-0e52-4667-bd24-07dd95aeec5c"
+        }
       },
-      "cell_type": "code",
       "source": [
         "# Compare the mean test accuracy along with error bars.\n",
         "def plot_data(data, color, label, gran=10000, max_budget=5000000):\n",
@@ -532,7 +536,7 @@
         "      assert prev_time < cur and next_time >= cur\n",
         "\n",
         "      # Linearly interpolate the test between the two surrounding points\n",
-        "      cur_val = ((cur - prev_time) / (next_test - prev_time)) * (next_test - prev_test) + prev_test\n",
+        "      cur_val = ((cur - prev_time) / (next_time - prev_time)) * (next_test - prev_test) + prev_test\n",
         "      \n",
         "      all_vals.append(cur_val)\n",
         "      \n",
@@ -554,12 +558,12 @@
         "plt.ylabel('accuracy')\n",
         "plt.grid()"
       ],
-      "execution_count": 16,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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y98nACcBVZnZY3PZT4DJgfHzNyFcdREQkN/lsqRwPrHf3l9y9BrgbOLtJngnA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\nLv8cOL9pPuB84Odp6T+PaYc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v96k451w6PKikw2OKc86lJadBRdKJklZJWi1pj2fcSxoiaY6kJZLKJJUkpb8q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\nGw8qzjnnYuNBxTnnXGz+P/49ipLwK8GAAAAAAElFTkSuQmCC\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -571,11 +575,11 @@
       ]
     },
     {
+      "cell_type": "markdown",
       "metadata": {
         "id": "DPnRtp0zXUDh",
         "colab_type": "text"
       },
-      "cell_type": "markdown",
       "source": [
         "## More information\n",
         "\n",
@@ -583,5 +587,4 @@
       ]
     }
   ]
-}
-
+}
\ No newline at end of file
diff --git a/README.md b/README.md
index 65dd69bbd..5deee5fc8 100644
--- a/README.md
+++ b/README.md
@@ -16,15 +16,19 @@ Search](https://arxiv.org/abs/1902.09635)
 If you use this dataset, please cite:
 
 ```
-@ARTICLE{ying2019nasbench,
-         author = {{Ying}, Chris and {Klein}, Aaron and {Real}, Esteban and
-                  {Christiansen}, Eric and {Murphy}, Kevin and {Hutter}, Frank},
-         title = "{NAS-Bench-101: Towards Reproducible Neural Architecture Search}",
-         journal = {arXiv e-prints},
-         year = "2019",
-         month = "Feb",
-         eid = {arXiv:1902.09635}
-}
+@InProceedings{pmlr-v97-ying19a,
+    title =     {{NAS}-Bench-101: Towards Reproducible Neural Architecture Search},
+    author =    {Ying, Chris and Klein, Aaron and Christiansen, Eric and Real, Esteban and Murphy, Kevin and Hutter, Frank},
+    booktitle = {Proceedings of the 36th International Conference on Machine Learning},
+    pages =     {7105--7114},
+    year =      {2019},
+    editor =    {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
+    volume =    {97},
+    series =    {Proceedings of Machine Learning Research},
+    address =   {Long Beach, California, USA},
+    month =     {09--15 Jun},
+    publisher = {PMLR},
+    url =       {http://proceedings.mlr.press/v97/ying19a.html},
 ```
 
 ## Dataset overview