Don’t Push Things Into Neural Corners

The last year was quite a ride with lots of new ideas, controversial discussions and real-world examples that large-scale machine learning is actually more than a flash in the pan. However, we still have the feeling that more basic research should be done. For instance, a while ago we stumbled about an excellent blog[1] that explains neural nets in a very descriptive but nevertheless still formal way. Since manifolds are a very powerful tool to explain what the representation that a net learned looks like, we tried to find similar, but introductory literature. It was a bit surprising that we did not find much. Maybe we didn’t try hard enough, but it’s more likely that relevant literature is buried somewhere and cannot be easily accessed through search engines, or, in the worst case, there is not much at all.

Nevertheless, the post inspired us to rethink the way how we tackle our current problems. For instance, we try to build a preference-model that ranks items according to the known preferences of users. But instead of simply classifying new items, we would like to capture latent topics in known item pairs to be able to perform something like a k-NN search at test time to better explain why a user might like an item or not. The reason why we are working on alternatives is that because the input space is very sparse and heterogeneous the generalization often fails. In other words, we get good scores for train/test but unseen items still land on the wrong side of the decision boundary way too often.

To get a better understanding of what’s going wrong, we worked on ways to visualize decision boundaries. But it is not hard to believe that a hyperplane is not always the best way to support fine-grained classification. A problem is that all +1 items are not really equal, like some image category, but might contain items from very different categories and the same is true for -1 items. Thus, it might be much easier for the network to learn a local neighborhood instead. As a baseline, we could use k-NN, but it is well known that the accuracy of the algorithm highly depends on the feature representation and since our input space consists of highly entangled data, it does not work in the original space.

As a result, we need a neural net to disentangle the representation with some loss function and then we can use k-NN to perform a prediction that is more robust compared to an end-to-end classification. For example, let’s assume that we trained a classification model and we get an item x that lies on the wrong side of the decision boundary. However, there is also a wrongly classified -positive- item x’ that is pretty close to x and the next negative x” item is farther away than x’: dist(x, x”) > dist(x, x’). Therefore, while the classification of x would be -1, the 1-NN model would still get it right because we consider the neighborhood of x.

Of course it’s not always that trivial, but instead of pushing every category into a single corner of the feature space, we can make the life of the neural net easier by just separating positive and negative items by a fixed margin. This is nothing more than the good old triplet loss, but we still have one challenge to address!

The problem is that not all +1 items come from the same distribution. Thus, if we just sample uniformly, we likely sample items with different topics and force them to be close together in the feature space. This still works, but at the end, we probably just learn a representation that is linearly separable again. So, the question is how to preserve the local neighborhood in each +1/-1 category? We could cheat by using tags or meta-data, or we could also extract topics with a NMF.

Of course the new expressive power won’t come for free. The beauty of a classifier is that we can make predictions but just feeding the item to the network and get the score for it. In case of a k-NN-based method, we also need to store labeled training data and perform a lookup each time a new item needs to be labeled. But depending on how much labeled training data is required for a good score, the overhead is often negligible, since we just need to store a matrix of the size (N x dim) and perform a single matrix multiplication, following by an argument sorting. And if all items have unit-norm, this corresponds to the cosine score.

Of course we are not done here, but we have a strong feeling that pursuing this way will lead somewhere, even if it takes a lot of steps until we arrive somewhere.



Challenges With Real-World Embeddings

To relate TV titles that come from the electronic program guide (EPG), we have decided to train an embedding that directly optimizes on “sentence-level” instead of just related words, like word2vec. That is in the spirit of StarSpace[arxiv:1709.03856] which is a fairly simple but approach but which is nevertheless a strong baseline.

The idea for the training is straightforward and uses the leave-out-out approach. We use either existing or manually annotated categories for a weak supervision. Then we sample N positive items from one category, and use N-1 items to predict the Nth item. Negative items are sampled from other categories. A title is encoded as the sum of all the embeddings of the words it contains: np.sum(E[word_id_list], 0) (bag-of-words). All vectors are normalized to lie on the unit-ball. Next, we combine all bag-of-words into bag-of-documents: np.mean(V, 0) where V is a matrix with #title rows and #dim columns.

The loss is the well-known triplet loss: np.maximum(0, margin – pos + neg), where pos =, nth_item) and neg =, neg_item) and margin is a hyper-parameter (0.2 by default). The idea is not to learn a hyperplane to separate classes, but to move related items closer and push unrelated items further away. The learning stops, when positive and negative pairs are separated by at least the given margin. The performance of such models largely depends on the sampling of positive and negative items
but this is not the concern of this post.

In contrast to titles from books, and to some degree movies, generic TV titles belonging to shows, reports or entertain, are very heterogeneous with lots of special characters and that can also include meta information. Therefore, we need a more sophisticated tokenizer to convert titles into “words”, but we also need to address the issue of rare “words”. The long-tail is always a problem for text, but in our case the domain is more like tweets with special emoticons and/or hashtags than traditional text. Throwing away
those “words” is no solution which is why we need to adjust the learning scheme.

In word2vec down-sampling of frequent words is used, but this does not really address the problem since we do not want to damp the learning signal for frequent “words”, but we want to boost the signal for rare “words”. That is why we decided to scale the gradients with the inverse frequency of the words. The procedure just requires a fixed lookup table: ID->WEIGHT, which is easy to implement.

The necessity of the procedure became obvious when we checked the result of our first model. We took an arbitrary title and used the cosine score to rank all other titles. The results looked promising, but from time to time there were outliers and we wanted to find out why. We started by removing single “words” from the offending title and repeated the ranking. We found out that the problem were often related to rare words that did not get much weight updates and thus, their position in the embedding space is something “arbitrary”. When the “word” was removed, the cosine score reduced dramatically. This also worked for other titles.

Thanks to PyTorch, the implementation of re-scaling the gradients was very easy:

def scale_grad_by_freq(parameters, lookup):
parameters = list(filter(lambda p: p.grad is not None, parameters))
 for p in parameters:
  g,grads =, g._values()
  for j, i in enumerate(g._indices().view(-1)): grads[j].mul_(lookup[i])

The multiplication is done in-place and thanks to the sparse flag of the PyTorch Embedding module, we only re-scale a small subset of all embeddings. With this minor modification, a loss that involves rare “words” leads to a stronger error signal which partly compensates the fact that those “words” get fewer updates. This is not the holy grail, but a good example that a deeper understanding of a problem can minimize the level of frustration and gives you more time to enhance or tune your model.

Backtrack But Not Backwards

A recent talk at NIPS 2017 underlined that despite all the progress we made in the last years, we still practically know very little about how things work internally. The thing is what do you do, when you encounter a strange problem while you train your model? Do you just switch to another optimizer/architecture/hyper-parameter, or do you try to find the root-cause of the problem? With all the nice and publicly available ML stuff out there, it is tempting to just try all these things and if one does not work, just try the next one. At the end of the day, your model might be powerful enough to solve the problem at hand, but it is also very likely that it is just a black-box you don’t fully understand and if the system stops working, you need to search for a new model again.

The talk also emphasized that we need more well-understood building-blocks which can be combined to tackle more complex problems, instead of just plugging “mythical” things into your networks which makes it “magically” work. In other words, we should focus more on basic experiments to better understand existing building blocks which includes to spend more time to prove why things work, instead of just saying they do, because the error rate goes down, but no one cares to explain exactly why.

This is kind of backtracking you do, when you are stuck, since your model won’t work. If you just switch your architecture, you won’t get any new insights and if the problem occurs again, you also need to switch again. The process to really understand what is going can be extremely painful and probably need lots of resources, but at the end it pays since you can use the knowledge to build better models and to focus on new problems instead of just plugging black-boxes together and hope that they eventually work.

Like a long journey, it starts with a single step and at the begin, there might be no light at the end of the tunnel, but if you don’t give up, you will figure out how to put the next piece of the puzzle eventually, and then the next one and so forth. At the end you will see much more of the whole picture even if it takes a very long time.

IdeaPad 720s – Machine Learning For The Road

Even if the GPU is a mobile version, the performance gain compared to a CPU is very much noticeable. As a result, it makes a lot of sense to have a dedicated GPU in your notebook if you buy a new one. This allows you to play with more complex models, or even to train them, while you are traveling, which might mean that you don’t have easy access to servers with GPU cards all the time. And even if you just want to use a pre-trained model for feature extraction, you can spare a lot of time by using the GPU.

There is always the option to use a gamers notebook, but in case you want a lightweight companion there are much fewer options. Our choice was the IdeaPad 720s with a 14′ screen, because it is lightweight, but still powerful with enough RAM, and a dedicated GeForce 940mx GPU that comes with non-shared memory. Without a doubt this is no high-end configuration, but the CUDA capabilities are sufficient to run older nets, or to design your own one, might it be a ConvNet or a RNN. Plus, with the huge SSD, you can train on pretty large training sets with better I/O performance than SATA disks.

So far for the theory, but now comes the reality. Especially for newer, or more exotic notebooks, installing Linux on them is not always trivial. To spare others the pain, we summarize the steps we did to get it running. It’s still not perfect, there are minor problems with the WLAN, but we used it for quite some hours now without any problems and successfully tested PyTorch + CUDA.

The first thing you have to do is to switch from “UEFI” to “Legacy Support” but that’s nothing new. You can enter the BIOS by pressing F2 during boot, or use the Novo “button” on the left side of the notebook. If this worked, you should shrink the NTFS volume which is pretty straightforward to make room for a real OS. Just half the size, so you got about ~250 GB for Linux. After all settings were adjusted, we can start with the Linux installation. Lubuntu seems a good choice since it is also lightweight, but comes with excellent support for detecting more exotic hardware. Make sure you have chosen the correct boot order, so you can boot from a USB stick/DVD drive.

Long story short, the installation failed miserable because the SSD drive was not recognized. But there is no time for panic! Thanks to the active community, we found the answer to that problem pretty fast. You have to switch the “SATA Controller Mode” from “RAID” to “AHCI” in the BIOS. With the new setting, it was possible to create a ext4+swap partition in the free space of the SSD. Then, the actual installation could be done without any problems. Merely the GRUB installation seems not optimal, since we get no boot screen and thus, we don’t know if our Windows partition was correctly recognized. From the grub config it does not seem so, but this is not our major concern, since our focus is a working Linux system with GPU support. So, we are blind at startup, but since Linux starts correctly we do not investigate this any further right now.

The next step is to get PyTorch working which was no problem at all. We used pip, python 2.7 + cuda 8.0 and it worked like a charm. Only torchvision failed, but we solved it by using “pip install –no-deps torchvision” since one dependency is still pytorch 0.1.2. A quick test with ipython confirmed that everything is okay and working. The last step is the installation of the CUDA toolkit which was also no problem thanks to the apt sources we just had to uncomment in the sources.list file. After “apt-get update” we installed the cuda toolkit packages and all its dependencies. Since CUDA requires a kernel module that is compiled at the end of the installation, a restart is required. To check if the setup was done correctly, start “nvidia-smi” -after reboot- and see if at the device is listed there.

After we got a prompt again, we downloaded a pre-trained network from the model collection and hacked some code to perform an image classification. Compared to the early days of ConvNets, even the CPU version was pretty “fast”. Next, we checked that cuda is correctly recognized by PyTorch and after that, we moved the model to the GPU and also the tensor we use for classification. As we mentioned at the begin of the post, the performance boost was pretty much visible and except for the first call that triggered some background activities to setup CUDA, everything went smooth.

Bottom line, here is the check list again to enjoy the notebook with Linux:
– Shrink the size of your NTFS volumne by 50%
– Switch from “UEFI” to “Legacy Support”
– Switch the “SATA Controller Mode” from “RAID” to “AHCI”
Since there is still room for improvements, we might create a successor blog post with additional details.

PyTorch 0.3 – An Early Xmas Gift

In this year, Santa is a little early but the gifts are nonetheless still impressive. In the last days, we hacked on a model to predict if a text sequence belongs to a certain type, or not, and we found out, that if we do not process the text from left to right but in reverse order, the model learns much better and faster. The idea is not new and was already introduced in neural translation models. Still, it’s amazing that such a little modification has such a huge impact.

Next, we heard about the new PyTorch release and that it might also bring gifts, for instance performance boosts along with other nice goodies. So, we updated, still from the source since the provided pre-compiled packages do not work for us, and then run a short test. The results were pretty stunning since now the required time for a cycle takes about 5 seconds less compared to version 0.2.0.

In total, from a machine learning perspective we are pretty happy with the gifts Santa brought for us and with the experiences we made so far, we encourage everybody to update their PyTorch version to also feel the machine learning Christmas spirit.

When The ML Devil Is A Cute Squirrel

We recently stumbled over a problem that is pretty straightforward: Based on a sub-title, we had to decide if the text describes a movie or some other type, like a series or a documentary. So, we started our favorite editor and began to hack a very basic recurrent neural network. Furthermore, since we wanted to ensure that we can use the net for all kind of new input, we decided to use a character-based net. That was the easy part. From a fairly recent paper, we used the heuristic to initialize all non-recurrent weights from U[-0.1, 0.1] and the recurrent weights using orthogonalization and Adam as our optimizer.

We are aware that heuristics do not always work, but we were pretty astonished that no learning at all occurred, not even a little. So, we used the default weight initialization from the framework and voilĂ , there was immediate progress. Just a slightly different weight initialization procedure and it work. Out of curiosity, we also tried different optimizers and we found out that Adam with the default settings, lr=0.001, was far from being optimal. For instance, when we used RMSprop with the same lr parameter, the error after an epoch was “much” lower and also the number of correctly classified items.

The lesson we learned -again- is that even with all the insights and tricks from the dozens of papers, lectures and tutorials, optimizing neural nets is still more of an art than science and there is no recipe one can always use to get a good model. This is why we strongly favor to do more basic research instead of beating state-of-the-art results, since this is the only way to get more insights how to actually solve the actual problem. To be fair, there are people doing exactly this and they also share their insights which is very valuable, but on the other and, there are lots of papers that hardly provide even all the details to repeat the experiments.

It boils down to the question, what you do if you are working on a challenging problem and you run into a dead end? To quote from the AMA of Schmidhuber how to recognize a promising ML student: “[..]run into a dead end, and backtrack. Another dead end, another backtrack. But they don’t give up.” But way too often it seems that if something is not working, the method is discarded and something new is tried without understanding the actual problem. If you do ML stuff in your spare time it’s understandable, that you want to make progress no matter how, but if you are a professional, deeper insights should be the way to go and not to get just something done, even if you don’t know why it works or how.

To sum it up, even if machine learning is very fascinating these days, especially with all the resources you can use, it is still a long way until we really understand what is going on under the hood. And as long as we do not stop to find this out, we will make continual progress even if the steps seems to be very tiny.

The Opposite of Something

When the size of the vocabulary is very large, learning embeddings with negative log-likelihood and thus, the softmax, can be pretty slow. That’s why negative sampling has been introduced. It’s very fast and can lead to competitive results while it utilizes much fewer resources. However, there is a serious drawback. Similar to the triplet loss, the performance largely depends on the generation of proper positive and negative pairs. This is nothing new, but has been recently confirmed again by an analysis reported in the StarSpace [arxiv:1709.03856] paper.

The problem is that selecting “trivial” negative candidates might result in no loss or a low loss, since those items are likely to be already well separated from the positive item. Furthermore, there is often no clear strategy what the inverse of something is. For instance, let’s assume that we have two positive items related to “cooking” and now we need one or more negative items as a contrastive force. The question is are items from “cars” better than from those in “news”? Are they more inverse? A solution could be to perform hard negative mining by finding items that clearly violate the margin and thus lead to a higher loss which means some learning occurs. But the procedure is computationally very expensive and not feasible if we have thousand or more of candidates.

So, if we restrict the norm of each embedding and not using a L2 weightdecay scheme that always pushes down the weights, the model will eventually “converge”, but we don’t know how many steps are required. In other words, often a straightforward (linear) model might suffice, but we should instead invest more time in finding clever ways to perform the positive and negative sampling step.

It is astonishing and a little sad that the issue did not find more attention in research and often, just trivial examples are given that cannot be used in real-world problems. Without a doubt the issue challenging, but since it can often decide about the performance of a model, it should be worth the time.