Merge pull request #13525 from tensorflow:LakshmiKalaKadali-patch-4

PiperOrigin-RevId: 733182458

Author: tensorflower-gardener

Diff for: official/vision/ops/augment.py

@@ -2697,8 +2697,8 @@ def distort(self, images: tf.Tensor,
 
   @staticmethod
   def _sample_from_beta(alpha, beta, shape):
-    sample_alpha = tf.random.gamma(shape, 1., beta=alpha)
-    sample_beta = tf.random.gamma(shape, 1., beta=beta)
+    sample_alpha = tf.random.gamma(shape, alpha, beta=1.0)
+    sample_beta = tf.random.gamma(shape, beta, beta=1.0)
     return sample_alpha / (sample_alpha + sample_beta)
 
   def _cutmix(self, images: tf.Tensor,

Diff for: research/audioset/README.md

@@ -18,18 +18,18 @@ For more details about AudioSet and the various models we have trained, please
 visit the [AudioSet website](http://g.co/audioset) and read our papers:
 
 * Gemmeke, J. et. al.,
-  [AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html),
+  [AudioSet: An ontology and human-labelled dataset for audio events](https://research.google/pubs/audio-set-an-ontology-and-human-labeled-dataset-for-audio-events/),
   ICASSP 2017
 
 * Hershey, S. et. al.,
-  [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html),
+  [CNN Architectures for Large-Scale Audio Classification](https://research.google/pubs/cnn-architectures-for-large-scale-audio-classification/),
   ICASSP 2017
 
 If you use any of our pre-trained models in your published research, we ask that
-you cite [CNN Architectures for Large-Scale Audio Classification](https://research.google.com/pubs/pub45611.html).
+you cite [CNN Architectures for Large-Scale Audio Classification](https://research.google/pubs/cnn-architectures-for-large-scale-audio-classification/).
 If you use the AudioSet dataset or the released embeddings of AudioSet segments,
 please cite
-[AudioSet: An ontology and human-labelled dataset for audio events](https://research.google.com/pubs/pub45857.html).
+[AudioSet: An ontology and human-labelled dataset for audio events](https://research.google/pubs/audio-set-an-ontology-and-human-labeled-dataset-for-audio-events/).
 
 ## Contact