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Ben talks with former podcast host and current state senator Mike Tipping about his work so far in the legislature. Depths of Wikipedia Instagram Ask a question or leave a comment for a future show at (207) 619-3182 or by writing to podcasts@mainebeacon.com Subscribe to the podcast using any podcasting app or subscribe on Apple… The post Podcast: A conversation with Sen. Mike Tipping about life on the inside first appeared on Maine Beacon.

Ben talks with former podcast host and current state senator Mike Tipping about his work so far in the legislature. Depths of Wikipedia Instagram Ask a question or leave a comment for a future show at (207) 619-3182 or by writing to podcasts@mainebeacon.com Subscribe to the podcast using any podcasting app or subscribe on Apple… The post <a href="https://mainebeacon.com/podcast-a-conversation-with-sen-mike-tipping/" rel="nofollow" target="_blank">Podcast: A conversation with Sen. Mike Tipping about life on the inside</a> first appeared on <a href="https://mainebeacon.com" rel="nofollow" target="_blank">Maine Beacon</a>.

Podcast: A conversation with Sen. Mike Tipping about life on the inside

Mike Tipping, Maine People's Alliance, looking ahead at the Democratic Primary by Cynthia Dill

Mike Tipping, Maine People's Alliance, looking ahead at the Democratic Primary

In season five episode three we chat about take a listener question about Five Papers for Mike Tipping, take a listener question on AIAI and chat with Eoin O'Mahony of UberHere are Neil's five papers. What are yours?Stochastic variational inference by Hoffman, Wang, Blei and Paisleyhttp://arxiv.org/abs/1206.7051A way of doing approximate inference for probabilistic models with potentially billions of data ... need I say more?Austerity in MCMC Land: Cutting the Metropolis Hastings by Korattikara, Chen and Wellinghttp://arxiv.org/abs/1304.5299Oh ... I do need to say more ... because these three are at it as well but from the sampling perspective. Probabilistic models for big data ... an idea so important it needed to be in the list twice. Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adamshttp://arxiv.org/abs/1206.2944This paper represents the rise in probabilistic numerics, I could also have chosen papers by Osborne, Hennig or others. There are too many papers out there already. Definitely an exciting area, be it optimisation, integration, differential equations. I chose this paper because it seems to have blown the field open to a wider audience, focussing as it did on deep learning as an application, so it let's me capture both an area of developing interest and an area that hits the national news.Kernel Bayes Rule by Fukumizu, Song, Grettonhttp://arxiv.org/abs/1009.5736One of the great things about ML is how we have different (and competing) philosophies operating under the same roof. But because we still talk to each other (and sometimes even listen to each other)  these ideas can merge to create new and interesting things. Kernel Bayes Rule makes the list.http://www.cs.toronto.edu/~hinton/absps/imagenet.pdfAn obvious choice, but you don't leave the Beatles off lists of great bands just because they are an obvious choice.See omnystudio.com/listener for privacy information. Hosted on Acast. See acast.com/privacy for more information.

In season five episode three we chat about take a listener question about Five Papers for Mike Tipping, take a listener question on<a href="https://www.whitehouse.gov/articles/accelerating-americas-leadership-in-artificial-intelligence/" rel="nofollow" target="_blank"> AIAI</a> and chat with <a href="https://www.linkedin.com/in/eoin-o-mahony-274b967/" rel="nofollow" target="_blank">Eoin</a> O'Mahony of UberHere are Neil's five papers. What are yours?Stochastic variational inference by Hoffman, Wang, Blei and Paisley<a href="http://arxiv.org/abs/1206.7051" rel="nofollow" target="_blank">http://arxiv.org/abs/1206.7051</a>A way of doing approximate inference for probabilistic models with potentially billions of data ... need I say more?Austerity in MCMC Land: Cutting the Metropolis Hastings by Korattikara, Chen and Welling<a href="http://arxiv.org/abs/1304.5299" rel="nofollow" target="_blank">http://arxiv.org/abs/1304.5299</a>Oh ... I do need to say more ... because these three are at it as well but from the sampling perspective. Probabilistic models for big data ... an idea so important it needed to be in the list twice. Practical Bayesian Optimization of Machine Learning Algorithms by Snoek, Larochelle and Adams<a href="http://arxiv.org/abs/1206.2944" rel="nofollow" target="_blank">http://arxiv.org/abs/1206.2944</a>This paper represents the rise in probabilistic numerics, I could also have chosen papers by Osborne, Hennig or others. There are too many papers out there already. Definitely an exciting area, be it optimisation, integration, differential equations. I chose this paper because it seems to have blown the field open to a wider audience, focussing as it did on deep learning as an application, so it let's me capture both an area of developing interest and an area that hits the national news.Kernel Bayes Rule by Fukumizu, Song, Gretton<a href="http://arxiv.org/abs/1009.5736" rel="nofollow" target="_blank">http://arxiv.org/abs/1009.5736</a>One of the great things about ML is how we have different (and competing) philosophies operating under the same roof. But because we still talk to each other (and sometimes even listen to each other)  these ideas can merge to create new and interesting things. Kernel Bayes Rule makes the list.<a href="http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf" rel="nofollow" target="_blank">http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf</a>An obvious choice, but you don't leave the Beatles off lists of great bands just because they are an obvious choice.See <a href="https://omnystudio.com/listener" rel="nofollow" target="_blank">omnystudio.com/listener</a> for privacy information. Hosted on Acast. See <a href="https://acast.com/privacy" rel="noopener noreferrer nofollow" target="_blank">acast.com/privacy</a> for more information.

Real World Real Time and Five Papers for Mike Tipping