the recurrent cell is arbitrary. We train the RNN for 150 episodes. Each episode involves three distinct steps. In the first step, the RNN predicts architectures for normal and reduction cells. To balance controller to obtain rewards for a sampled or predicted child configuration. This method performs three distinct steps. The first step is to construct a child network based on the input configuration. In the second

us, as efficiently as possible. Extending the teaching-a-child analogy, consider the number of distinct examples of objects (labels) you must show a child before they can learn to identify them with high for other objects. If the child learns to recognize these objects accurately with fewer numbers of distinct objects being shown, we have made this process more label efficient. Similarly, if you could make

▪ Statistics about scores ▪ [class1-4, students, scores] ▪ [class5-9, students, scores] Along distinct dim/axis ▪ Dim=d for example stack create new dim Cat v.s. stack Split: by len Chunk: by

32, 14, 14] ▪ [2, 32, 14, 14] ▪ Dim 0 has dim, can NOT insert and expand to same ▪ Dim 0 has distinct dim, NOT size 1 ▪ NOT broadcasting-able How to understand this behavior? ▪ When it has no dim

assume we have a b-bit unsigned integer for storing x. A b-bit unsigned integer will have 2b possible distinct values, ranging from 0 to 2b - 1. To go from a 32-bit floating point value to a b-bit integer,

buffers of all GPUs to the same values, or use out-of-place ncclReduce(), wherein the output buffer is distinct from the input buffer. PyTorch RN-08516-001_v23.07 | 358 Chapter 75. PyTorch Release 17.05 buffers of all GPUs to the same values, or use out-of-place ncclReduce(), wherein the output buffer is distinct from the input buffer. PyTorch RN-08516-001_v23.07 | 359 Chapter 76. PyTorch Release 17.04