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Hacks for Doing Black Magic of Deep Learning

Originally in the paper of S.Ioffe², it’s proposed to normalize features across the batch and turn activations toward the unit Gaussian distribution, to learn one, universal mean and variance, for the all data distribution(test data included).

al.³ stated, that it is connected with the fact that in case of the large batches, training tends to converge to sharp minimizers of the training function, and in case of smaller batches, to flat minimizers.

In recent years, with the increase of performance, the number of parameters in the neural networks increased drastically, and the design of the efficient and less costly neural networks turn out to be an issue of the day.

Let us suppose, we have a layer with fi -input filters fo -output filters kh -height of the kernel kw -width of the kernel In the case of the convolution, the number of parameters in the layer will be N

= kh * kw * fi + 1 * 1 * fo We are convolving each input filter one time, with the kernel (kh, kw), and then, convolving these intermediate filters with the kernel (1, 1), by the number of times of output filters.

Suppose we have following values for the layer fi = 128 fo = 256 kh = 3 kw = 3 Number of parameters in the convolutional layer will be 3

Now let’s suppose that we have other values for the layer fi = 128 fo = 256 kh = 1 kw = 1 Number of parameters in the convolutional layer will be 1

* 1* 128 + 1 * 1 * 256 = 32.896 So, as we can see, in the second case, instead of having a reduction, we increased the number of parameters.

Predicting What Songs Phish Will Play Next with Deep Learning

It’s pretty amazing that a statistical model can understand and explain some of these nuanced relationships that I have internalized for years — especially considering that it knows nothing about what these songs actually sound like.

As shown below, Phish played the majority of their shows in the early 90’s (128 shows in 1994!) when they had relatively few unique songs that were played (~375 of today’s >850), meaning the majority our training data is heavily skewed to learn patterns associated with those 375 songs (during Phish 1.0).

Using the newly trained neural network [artfully named TrAI], we can recursively make predictions to generate what Phish’s next setlist will be based on an input of the most recent 50 songs played.

Workshop on Theory of Deep Learning: Where next?

The workshop is organized by Sanjeev Arora (IAS/Princeton University), Joan Bruna (IAS/NYU), Rong Ge (IAS/Duke), Suriya Gunasekar(IAS/Toyota Technical Institute), Jason Lee (IAS/USC), Bin Yu (IAS/UC Berkeley) This workshop seeks to bring together deep learning practitioners and theorists to discuss progress that has been made on deep learning theory, and to identify promising avenues where theory is possible and useful.

(Speakers do not need to register)   Although there is no registration fee, because seating is limited both in the seminar room and in our dining hall, you are required to register if you wish to attend all or part of the workshop.  In addition, in order for our chef to prepare enough food, we need to have a headcount for the meals.  Please note the workshop attendees are expected to pay for their lunch.

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