diff --git a/examples/ncf/run_and_time.sh b/examples/ncf/run_and_time.sh
deleted file mode 100755
index 791248e4494ec03a7d351e3e52a212fbded4538d..0000000000000000000000000000000000000000
--- a/examples/ncf/run_and_time.sh
+++ /dev/null
@@ -1,55 +0,0 @@
-#!/bin/bash
-# runs benchmark and reports time to convergence
-# to use the script:
-#   run_and_time.sh <random seed 1-5>
-
-THRESHOLD=0.635
-BASEDIR=$(dirname -- "$0")
-
-# start timing
-start=$(date +%s)
-start_fmt=$(date +%Y-%m-%d\ %r)
-echo "STARTING TIMING RUN AT $start_fmt"
-
-# Get command line seed
-seed=${1:-1}
-
-echo "unzip ml-20m.zip"
-if unzip ml-20m.zip
-then
-    echo "Start processing ml-20m/ratings.csv"
-    t0=$(date +%s)
-	python $BASEDIR/convert.py ml-20m/ratings.csv ml-20m --negatives 999
-    t1=$(date +%s)
-	delta=$(( $t1 - $t0 ))
-    echo "Finish processing ml-20m/ratings.csv in $delta seconds"
-
-    echo "Start training"
-    t0=$(date +%s)
-	python $BASEDIR/ncf.py ml-20m -l 0.0005 -b 2048 --layers 256 256 128 64 -f 64 \
-		--seed $seed --threshold $THRESHOLD --processes 10
-    t1=$(date +%s)
-	delta=$(( $t1 - $t0 ))
-    echo "Finish training in $delta seconds"
-
-	# end timing
-	end=$(date +%s)
-	end_fmt=$(date +%Y-%m-%d\ %r)
-	echo "ENDING TIMING RUN AT $end_fmt"
-
-
-	# report result
-	result=$(( $end - $start ))
-	result_name="recommendation"
-
-
-	echo "RESULT,$result_name,$seed,$result,$USER,$start_fmt"
-else
-	echo "Problem unzipping ml-20.zip"
-	echo "Please run 'download_data.sh && verify_datset.sh' first"
-fi
-
-
-
-
-
diff --git a/examples/quantization/quant_aware_train/ncf_quant_aware_train_linear.yaml b/examples/quantization/quant_aware_train/ncf_quant_aware_train_linear.yaml
deleted file mode 100644
index f1c2d7c4d743fb89802087d8a778db5d1528dc54..0000000000000000000000000000000000000000
--- a/examples/quantization/quant_aware_train/ncf_quant_aware_train_linear.yaml
+++ /dev/null
@@ -1,29 +0,0 @@
-quantizers:
-  linear_quantizer:
-    class: NCFQuantAwareTrainQuantizer
-    bits_activations: 8
-    bits_weights: 8
-    bits_bias: 32
-    mode: 'SYMMETRIC'  # Can try "SYMMETRIC" as well
-    ema_decay: 0.999   # Decay value for exponential moving average tracking of activation ranges
-    per_channel_wts: True
-    overrides:
-      # We want to quantize the last FC layer prior to the sigmoid. So - We set up the quantizer to add fake-quantization
-      # layers after FC layers. But - here we override so that this doesn't actually happen in any of the early FC layers
-      mlp\.*:
-        bits_activations: null
-        bits_weights: 8
-        bits_bias: 32
-      final_concat:
-        bits_activations: null
-        bits_weights: null
-        bits_bias: null
-
-policies:
-    - quantizer:
-        instance_name: linear_quantizer
-      # For now putting a large range here, which should cover both training from scratch or resuming from some
-      # pre-trained checkpoint at some unknown epoch
-      starting_epoch: 0
-      ending_epoch: 300
-      frequency: 1
\ No newline at end of file