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Gold Blog, Jul 2017When not to use deep learning

Despite DL many successes, there are at least 4 situations where it is more of a hindrance, including low-budget problems, or when explaining models and features to general public is required.

Low-budget or low-commitment problems

Deep nets are very flexible models, with a multitude of architecture and node types, optimizers, and regularization strategies. Depending on the application, your model might have convolutional layers (how wide? with what pooling operation?) or recurrent structure (with or without gating?); it might be really deep (hourglass, siamese, or other of the many architectures?) or with just a few hidden layers (with how many units?); it might use rectifying linear units or other activation functions; it might or might not have dropout (in what layers? with what fraction?) and the weights should probably be regularized (l1, l2, or something weirder?). This is only a partial list, there are lots of other types of nodes, connections, and even loss functions out there to try. Those are a lot of hyperparameters to tweak and architectures to explore while even training one instance of large networks can be very time consuming.

Google recently boasted that its AutoML pipeline can automatically find the best architecture, which is very impressive, but still requires more than 800 GPUs churning full time for weeks, something out of reach for almost anyone else. The point is that training deep nets carries a big cost, in both computational and debugging time. Such expense doesn’t make sense for lots of day-to-day prediction problems and the ROI of tweaking a deep net to them, even when tweaking small networks, might be too low. Even when there’s plenty of budget and commitment, there’s no reason not to try alternative methods first even as a baseline. You might be pleasantly surprised that a linear SVM is really all you needed.

Interpreting and communicating model parameters/feature importance to a general audience

Deep nets are also notorious for being black boxes with high predictive power but low interpretability. Even though there’s been a lot of recent tools like saliency maps and activation differences that work great for some domains, they don’t transfer completely to all applications. Mainly, these tools work well when you want to make sure that the network is not deceiving you by memorizing the dataset or focusing on particular features that are spurious, but it is still difficult to interpret per-feature importance to the overall decision of the deep net. In this realm, nothing really beats linear models since the learned coefficients have a direct relationship to the response. This is especially crucial when communicating these interpretations to general audiences that need to make decisions based on them. Physicians for example need to incorporate all sorts of disparate data to elicit a diagnosis. The simpler and more direct relationship between a variable and an outcome, the better a physician will leverage it and not under/over-estimate it’s value. Further, there are cases where the accuracy of the model (typically where deep learning excels at) is not as important as interpretability. For example, a policy maker might want to know the effect some demographic variable has on e.g. mortality, and will likely be more interested in a direct approximation of this relationship than in the accuracy of the prediction. In both of these cases, deep learning is at a disadvantage compared to simpler, more penetrable methods.

Establishing causal mechanisms

The extreme case of model interpretability is when we are trying to establish a mechanistic model, that is, a model that actually captures the phenomena behind the data. Good examples include trying to guess whether two molecules (e.g. drugs, proteins, nucleic acids, etc.) interact in a particular cellular environment or hypothesizing if a particular marketing strategy is having an actual effect on sales. Nothing really beats old-style Bayesian methods informed by expert opinion in this realm; they are our best (if imperfect) way we have to represent and infer causality. Vicarious has some nice recent work illustrating why this more principled approach generalizes better than deep learning in video game tasks.

Learning from “unstructured” features

This one might be up for debate. I find that one area in which deep learning excels at is finding useful representations of the data for a particular task. A very good illustration of this is the aforementioned word embeddings. Natural language has a rich and complex structure that can be approximated with “context-aware” networks: each word can be represented in a vector that encodes the context in which it is mostly used. Using word embeddings learned in large corpora for NLP tasks can sometimes provide a boost in a particular task on another corpus. However, it might not be of any use if the corpus in question is completely unstructured. For example, say you are trying to classify objects by looking at unstructured lists of keywords. Since the keywords are not used in any particular structure (like in a sentence), it’s unlikely that word embeddings will help all that much. In this case, the data is truly a bag of words and such representations are likely sufficient for the task. A counter-argument to this might be that word embeddings are not really that expensive if you use pretrained ones and may capture keyword similarity better. However, I still would prefer to start with the bag of words representation and see if I can get good predictions. After all, each dimension of the bag of words is easier to interpret than the corresponding word embedding slot.

The future is deep

The deep learning field is hot, well-funded, and moves crazy fast. By the time you read a paper published in a conference, it’s likely there are two or three iterations on it that already deprecate it. This brings a big caveat to the points I’ve made above: deep learning might still be super useful for these scenarios in the near future. Tools for interpretation of deep learning models for images and discrete sequences are getting better. Recent software such as Edward marry Bayesian modeling and deep net frameworks, allowing for quantification of uncertainty of neural network parameters and easy Bayesian inference via probabilistic programming and automated variational inference. In the longer term, there might be a reduced modeling vocabulary that nails the salient properties that a deep net can have and thus reduce the parameter space of stuff that needs to be tried. So keep refreshing your arXiv feed, this post might be deprecated in a month or two.

dward marries probabilistic programming with tensorflow

Fig. Edward marries probabilistic programming with tensorflow, allowing for models that are both deep and Bayesian. Taken from Tran et al. ICLR 2017

Original. Reposted with permission.

Bio: Sergio Pablo Sanchez Cordero Gonzalez, usually goes by the name Pablo Cordero, and is currently a postdoc at UCSC’s systems biology group doing applied machine learning research in the context of cell biology and regenerative medicine, particularly looking at single-cell measurements