Interpretabilidade versus Desempenho: Ceticismo e AI-Winter

Neste post do Michael Elad que é editor chefe da SIAM da publicação Journal on Imaging Sciences ele faz uma série de reflexões bem ponderadas de como os métodos de Deep Learning estão resolvendo problemas reais e alcançando um alto grau de visibilidade mesmo com métodos não tão elegantes dentro da perspectiva matemática.

Ele coloca o ponto principal de que, no que tange o processamento de imagens, a academia teve sempre um lugar de destaque em uma abordagem na qual a interpretabilidade e o entendimento do modelos sempre teve precedência em relação aos resultados alcançados.

Isso fica claro no parágrafo abaixo:

A series of papers during the early 2000s suggested the successful application of this architecture, leading to state-of-the-art results in practically any assigned task. Key aspects in these contributions included the following: the use of many network layers, which explains the term “deep learning;” a huge amount of data on which to train; massive computations typically run on computer clusters or graphic processing units; and wise optimization algorithms that employ effective initializations and gradual stochastic gradient learning. Unfortunately, all of these great empirical achievements were obtained with hardly any theoretical understanding of the underlying paradigm. Moreover, the optimization employed in the learning process is highly non-convex and intractable from a theoretical viewpoint.

No final ele coloca uma visão sobre pragmatismo e agenda acadêmica:

Should we be happy about this trend? Well, if we are in the business of solving practical problems such as noise removal, the answer must be positive. Right? Therefore, a company seeking such a solution should be satisfied. But what about us scientists? What is the true objective behind the vast effort that we invested in the image denoising problem? Yes, we do aim for effective noise-removal algorithms, but this constitutes a small fraction of our motivation, as we have a much wider and deeper agenda. Researchers in our field aim to understand the data on which we operate. This is done by modeling information in order to decipher its true dimensionality and manifested phenomena. Such models serve denoising and other problems in image processing, but far more than that, they allow identifying new ways to extract knowledge from the data and enable new horizons.

Isso lembra uma passagem minha na RCB Investimentos quando eu trabalhava com o grande Renato Toledo no mercado de NPL em que ele me ensinou que bons modelos têm um alto grau de interpretabilidade e simplicidade, no qual esse fator deve ser o barômetro da tomada de decisão, dado que um modelo cujo a sua incerteza (ou erro) seja conhecido é melhor do que um modelo que ninguém sabe o que está acontecendo (Nota pessoal: Quem me conhece sabe que eu tenho uma frase sobre isso que é: se você não entende a dinâmica do modelo quando ele funciona, nunca vai saber o que deu errado quando ele falhar.)

Contudo é inegável que as redes Deep Learning estão resolvendo, ao meu ver, uma demanda reprimida de problemas que já existiam e que os métodos computacionais não conseguiam resolver de forma fácil, como reconhecimento facial, classificação de imagens, tradução, e problemas estruturados como fraude (a Fast.AI está fazendo um ótimo trabalho de clarificar isso).

Em que pese o fato dos pesquisadores de DL terem hardware infinito a preços módicos, o fato brutal é que esse campo de pesquisa durante aproximadamente 30 anos engoliu uma pílula bem amarga de ceticismo proveniente da própria academia: seja em colocar esse método em uma esfera de alto ceticismo levando a sua quase total extinção, ou mesmo com alguns jornals implicitamente não aceitarem trabalhos de DL; enquanto matemáticos estavam ganhando prêmios e tendo um alto nível de visibilidade por causa da acurácia dos seus métodos ao invés de uma pretensa ideia de que o mundo gostava da interpretabilidade de seus métodos.

Duas grandes questões estão em tela que são: 1) Será que os matemáticos e comunidades que estão chocadas com esse fenômeno podem aguentar o mesmo que a comunidade de Redes Neurais aguentou por mais de 30 anos? e 2) E em caso de um Math-Winter, a comunidade matemática consegue suportar uma potencial marginalização de sua pesquisa?

É esperar e ver.

 

Interpretabilidade versus Desempenho: Ceticismo e AI-Winter

Redes Neurais e Redes Neurais Profundas

Como o assunto está muito quente foi feito até um livro online (com códigos e tudo mais):

Neural Networks and Deep Learning is a free online book. The book will teach you about:

Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data

Deep learning, a powerful set of techniques for learning in neural networks

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

The book is currently an incomplete beta draft. More chapters will be added over the coming months. For now, you can:

Read Chapter 1, which explains how neural networks can learn to recognize handwriting

Read Chapter 2, which explains backpropagation, the most important algorithm used to learn in neural networks.

Read Chapter 3, which explains many techniques which can be used to improve the performance of backpropagation.

Read Chapter 4, which explains why neural networks can compute any function.

Learn more about the approach taken in this book

Redes Neurais e Redes Neurais Profundas

Aplicações de Deep Learning e desafios e Big Data Analytics

Uma coisa interessante nesse artigo, foi que é um dos poucos que tem uma estratégia para Deep Learning não baseada em algoritmos, mas sim em indexação semântica. 

Abstract

Big Data Analytics and Deep Learning are two high-focus of data science. Big Data has become important as many organizations both public and private have been collecting massive amounts of domain-specific information, which can contain useful information about problems such as national intelligence, cyber security, fraud detection, marketing, and medical informatics. Companies such as Google and Microsoft are analyzing large volumes of data for business analysis and decisions, impacting existing and future technology. Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process. Complex abstractions are learnt at a given level based on relatively simpler abstractions formulated in the preceding level in the hierarchy. A key benefit of Deep Learning is the analysis and learning of massive amounts of unsupervised data, making it a valuable tool for Big Data Analytics where raw data is largely unlabeled and un-categorized. In the present study, we explore how Deep Learning can be utilized for addressing some important problems in Big Data Analytics, including extracting complex patterns from massive volumes of data, semantic indexing, data tagging, fast information retrieval, and simplifying discriminative tasks. We also investigate some aspects of Deep Learning research that need further exploration to incorporate specific challenges introduced by Big Data Analytics, including streaming data, high-dimensional data, scalability of models, and distributed computing. We conclude by presenting insights into relevant future works by posing some questions, including defining data sampling criteria, domain adaptation modeling, defining criteria for obtaining useful data abstractions, improving semantic indexing, semi-supervised learning, and active learning.

s40537-014-0007-7

Aplicações de Deep Learning e desafios e Big Data Analytics

Overview sobre Deep Neural Networks (Redes Neurais Artificiais em Profundidade)

No Random Ponderings tem um overview (que já é quase uma introdução completa) sobre Redes Neurais Artificiais em Profundidade.

Esse tema não é relativamente novo, mas tem tomado um grande espaço na literatura atual devido ao fato que essa técnica vem solucionando importantes problemas ligados à classificação, principalmente em relação à aproximação por imagens.

Essas redes nada mais são do que redes em que os parâmetros de entrada na rede são codificados em que não é somente o atributo é modelado, mas sub-conjuntos de atributo-valor são introduzidos na rede; e posteriormente entre 20-60 camadas escondidas (camadas intermediárias) são introduzidas para treinamento da rede e ajuste do calculo dos pesos.

Abaixo algumas heurísticas de como treinar as RNA’s em Profundidade:

  • Get the data: Make sure that you have a high-quality dataset of input-output examples that is large, representative, and has relatively clean labels. Learning is completely impossible without such a dataset.
  • Preprocessing: it is essential to center the data so that its mean is zero and so that the variance of each of its dimensions is one. Sometimes, when the input dimension varies by orders of magnitude, it is better to take the log(1 + x) of that dimension. Basically, it’s important to find a faithful encoding of the input with zero mean and sensibly bounded dimensions. Doing so makes learning work much better. This is the case because the weights are updated by the formula: change in wij \propto xidL/dyj (w denotes the weights from layer x to layer y, and L is the loss function). If the average value of the x’s is large (say, 100), then the weight updates will be very large and correlated, which makes learning bad and slow. Keeping things zero-mean and with small variance simply makes everything work much better.
  • Minibatches: Use minibatches. Modern computers cannot be efficient if you process one training case at a time. It is vastly more efficient to train the network on minibatches of 128 examples, because doing so will result in massively greater throughput. It would actually be nice to use minibatches of size 1, and they would probably result in improved performance and lower overfitting; but the benefit of doing so is outweighed the massive computational gains provided by minibatches. But don’t use very large minibatches because they tend to work less well and overfit more. So the practical recommendation is: use the smaller minibatch that runs efficiently on your machine.
  • Gradient normalization: Divide the gradient by minibatch size. This is a good idea because of the following pleasant property: you won’t need to change the learning rate (not too much, anyway), if you double the minibatch size (or halve it).
  • Learning rate schedule: Start with a normal-sized learning rate (LR) and reduce it towards the end.
    • A typical value of the LR is 0.1. Amazingly, 0.1 is a good value of the learning rate for a large number of neural networks problems. Learning rates frequently tend to be smaller but rarely much larger.
    • Use a validation set —- a subset of the training set on which we don’t train — to decide when to lower the learning rate and when to stop training (e.g., when error on the validation set starts to increase).
    • A practical suggestion for a learning rate schedule: if you see that you stopped making progress on the validation set, divide the LR by 2 (or by 5), and keep going. Eventually, the LR will become very small, at which point you will stop your training. Doing so helps ensure that you won’t be (over-)fitting the training data at the detriment of validation performance, which happens easily and often. Also, lowering the LR is important, and the above recipe provides a useful approach to controlling via the validation set.
  • But most importantly, worry about the Learning Rate. One useful idea used by some researchers (e.g., Alex Krizhevsky) is to monitor the ratio between the update norm and the weight norm. This ratio should be at around 10-3. If it is much smaller then learning will probably be too slow, and if it is much larger then learning will be unstable and will probably fail.
  • Weight initialization. Worry about the random initialization of the weights at the start of learning.
    • If you are lazy, it is usually enough to do something like 0.02 * randn(num_params). A value at this scale tends to work surprisingly well over many different problems. Of course, smaller (or larger) values are also worth trying.
    • If it doesn’t work well (say your neural network architecture is unusual and/or very deep), then you should initialize each weight matrix with the init_scale / sqrt(layer_width) * randn. In this case init_scale should be set to 0.1 or 1, or something like that.
    • Random initialization is super important for deep and recurrent nets. If you don’t get it right, then it’ll look like the network doesn’t learn anything at all. But we know that neural networks learn once the conditions are set.
    • Fun story: researchers believed, for many years, that SGD cannot train deep neural networks from random initializations. Every time they would try it, it wouldn’t work. Embarrassingly, they did not succeed because they used the “small random weights” for the initialization, which works great for shallow nets but simply doesn’t work for deep nets at all. When the nets are deep, the many weight matrices all multiply each other, so the effect of a suboptimal scale is amplified.
    • But if your net is shallow, you can afford to be less careful with the random initialization, since SGD will just find a way to fix it.

    You’re now informed. Worry and care about your initialization. Try many different kinds of initialization. This effort will pay off. If the net doesn’t work at all (i.e., never “gets off the ground”), keep applying pressure to the random initialization. It’s the right thing to do.

  • If you are training RNNs or LSTMs, use a hard constraint over the norm of the gradient (remember that the gradient has been divided by batch size). Something like 15 or 5 works well in practice in my own experiments. Take your gradient, divide it by the size of the minibatch, and check if its norm exceeds 15 (or 5). If it does, then shrink it until it is 15 (or 5). This one little trick plays a huge difference in the training of RNNs and LSTMs, where otherwise the exploding gradient can cause learning to fail and force you to use a puny learning rate like 1e-6 which is too small to be useful.
  • Numerical gradient checking: If you are not using Theano or Torch, you’ll be probably implementing your own gradients. It is easy to make a mistake when we implement a gradient, so it is absolutely critical to use numerical gradient checking. Doing so will give you a complete peace of mind and confidence in your code. You will know that you can invest effort in tuning the hyperparameters (such as the learning rate and the initialization) and be sure that your efforts are channeled in the right direction.
  • If you are using LSTMs and you want to train them on problems with very long range dependencies, you should initialize the biases of the forget gates of the LSTMs to large values. By default, the forget gates are the sigmoids of their total input, and when the weights are small, the forget gate is set to 0.5, which is adequate for some but not all problems. This is the one non-obvious caveat about the initialization of the LSTM.
  • Data augmentation: be creative, and find ways to algorithmically increase the number of training cases that are in your disposal. If you have images, then you should translate and rotate them; if you have speech, you should combine clean speech with all types of random noise; etc. Data augmentation is an art (unless you’re dealing with images). Use common sense.
  • Dropout. Dropout provides an easy way to improve performance. It’s trivial to implement and there’s little reason to not do it. Remember to tune the dropout probability, and to not forget to turn off Dropout and to multiply the weights by (namely by 1-dropout probability) at test time. Also, be sure to train the network for longer. Unlike normal training, where the validation error often starts increasing after prolonged training, dropout nets keep getting better and better the longer you train them. So be patient.
  • Ensembling. Train 10 neural networks and average their predictions. It’s a fairly trivial technique that results in easy, sizeable performance improvements. One may be mystified as to why averaging helps so much, but there is a simple reason for the effectiveness of averaging. Suppose that two classifiers have an error rate of 70%. Then, when they agree they are right. But when they disagree, one of them is often right, so now the average prediction will place much more weight on the correct answer. The effect will be especially strong whenever the network is confident when it’s right and unconfident when it’s wrong.

 

Overview sobre Deep Neural Networks (Redes Neurais Artificiais em Profundidade)