Artificial Intelligence has suddenly gone from the fringes of science to being everywhere. So how did we get here? And where's this all heading? In this new series of Science Friction, we're finding out.
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101 - The lottery ticket hypothesis, with Jonathan Frankle
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Kandungan disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh NLP Highlights and Allen Institute for Artificial Intelligence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode, Jonathan Frankle describes the lottery ticket hypothesis, a popular explanation of how over-parameterization helps in training neural networks. We discuss pruning methods used to uncover subnetworks (winning tickets) which were initialized in a particularly effective way. We also discuss patterns observed in pruned networks, stability of networks pruned at different time steps and transferring uncovered subnetworks across tasks, among other topics. A recent paper on the topic by Frankle and Carbin, ICLR 2019: https://arxiv.org/abs/1803.03635 Jonathan Frankle’s homepage: http://www.jfrankle.com/
…
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145 episod
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Manage episode 250344327 series 1452120
Kandungan disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh NLP Highlights and Allen Institute for Artificial Intelligence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode, Jonathan Frankle describes the lottery ticket hypothesis, a popular explanation of how over-parameterization helps in training neural networks. We discuss pruning methods used to uncover subnetworks (winning tickets) which were initialized in a particularly effective way. We also discuss patterns observed in pruned networks, stability of networks pruned at different time steps and transferring uncovered subnetworks across tasks, among other topics. A recent paper on the topic by Frankle and Carbin, ICLR 2019: https://arxiv.org/abs/1803.03635 Jonathan Frankle’s homepage: http://www.jfrankle.com/
…
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145 episod
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