I’m developing a tool to use machine learning and sentiment analysis to determine the usefulness of context when parsing sarcasm.

In order to evaluate the success of the tool, I’m looking for human responses to a few examples from SARC 2.0 (https://arxiv.org/abs/1704.05579), a corpus of Reddit comments.

If you can spare a few minutes to answer this Google form (no login required), please help me out!

kaggledatasets: Collection of Kaggle Datasets ready for everyone to use.

It is a Python package which will contain several high voted kaggle datasets made available in easy to use format with special support for frameworks like Tensorflow 2.0 and PyTorch.

Every beginner in ML/DL/Data Science struggles in the initial stages of Data Loading and Pre-processing. Kaggle being the gold mine of datasets, it’s necessary to make it easy to use for everyone.

For the folks who have used tf.datasets or torchvision datasets, this is something similar to that and only for Kaggle 🙂

And hold on, support for more datasets and more types is on the way. Feel free to contribute and add datasets. Also, looking for experienced developers who can contribute to the architecture of the project or provide reviews for improvement.

GitHub: https://github.com/kaggledatasets/kaggledatasets

Hello,

As a PhD student, I read quite a lot of papers, and sometimes I make short summaries with a simple latex template to get a better understanding and have clearer idea of the paper’s contributions. For a while I stored them in private Github repo, so I tought why note share them, some people might find them helpful.

PS: Sorry for the (sometimes frequent) spelling mistakes.

Here is the Github link: https://github.com/yassouali/ML_paper_notes

Hi everyone!

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Paper: https://arxiv.org/abs/1911.07532

Github, blog

Abstract: We extend the framework of graph neural networks (GNN) to continuous time. Graph neural ordinary differential equations (GDEs) are introduced as the counterpart to GNNs where the input–output relationship is determined by a continuum of GNN layers. The GDE framework is shown to be compatible with the majority of commonly used GNN models with minimal modification to the original formulations. We evaluate the effectiveness of GDEs on both static as well as dynamic datasets: results prove their general effectiveness even in cases where the data is not generated by continuous time processes.

It might be an overly-simplified view of the field, but it seems to me that a lot of the ML boom of this decade is due to the appearance of hardware+architectures that were able to tackle a set of problems which were easy in terms of data gathering and “pretty deterministic” (that is to say, based on our human abilities to tackle them, we can be pretty certain there are usually no latent variables which are necessary to solve the problem correctly), things like bounding boxes, image classification and translation.

On the other hand these new methods have hardly put a dent in how most people approach mostly “pretty non-deterministic” issues (e.g. stock trading or risk analysis), where practice and intuition shows that there’s simply not sufficient “easy” data that can make a reliable prediction.

It seems to me that most efforts right now are focused on either “productizing” the gains that were had on text and image problems (e.g. getting that 0.x% extra accuracy and 0.y% extra specificity that makes them practical to use in fields with low error margins) or getting algorithms that can better communicate the uncertainty of non-deterministic datasets (e.g. Bayesian/Probabilistic NNs).

However, it’s not obvious to me what the next set of problems similar to images and text will hit the chopping block, or if there is such a set of problems.

I’ve seen some interesting research (e.g. Alpha Fold) and some huge failures (e.g. that earthquake prediction publish in Nature that was worse than a linear regression) in the realm of scientific problems where we “seem to” have sufficient data but lack the mathematical frameworks to gain insights from the data. I think anything related to complex molecular dynamics in a “static” environment is a pretty good example, since in theory the starting state should allow us insight into any state at a later time T, but in practice this is often too computationally expensive and/or too complex to formalize in a way that is fitting for our current models. However, there doesn’t seem to be near that amount of adoption, excitement or novel ideas coming from this class of problems.

So I wonder, what would you guys think would be the next “category” of problems where, conceptually, ML techniques could be applied without too much of a data-gathering barrier, yet the hardware+knowledge combination of current humans is yet to evolve to a point where they are feasible.

Here’s some work we did as a course project. It’s an (unsuccessful) attempt at incorporating curiosity in world models. It’s a beginner’s work and any feedback is appreciated.

Paper: https://arxiv.org/abs/1911.06987

Abstract: Data augmentation methods are indispensable heuristics to boost the performance of deep neural networks, especially in image recognition tasks. Recently, several studies have shown that augmentation strategies found by search algorithms outperform hand-made strategies. Such methods employ black-box search algorithms over image transformations with continuous or discrete parameters and require a long time to obtain better strategies. In this paper, we propose a differentiable policy search pipeline for data augmentation, which is much faster than previous methods. We introduce approximate gradients for several transformation operations with discrete parameters as well as the differentiable mechanism for selecting operations. As the objective of training, we minimize the distance between the distributions of augmented data and the original data, which can be differentiated. We show that our method, Faster AutoAugment, achieves significantly faster searching than prior work without a performance drop.

I have a project that uses Variational Autoencoder for an apparel dataset that is grouped into five categories (say, (A B C D E).

My plan is the following.

1. Train a VAE model using the apparel dataset.
2. Use encoder on each data to produce latent code (e.g. my bottleneck/latent representation is of size 10 for example). Store to database
3. Use K-Means to cluster the data (using the latent code) in the database with n categories (five for example). Store cluster labels for each data in database.
4. Store cluster centroids in the database created from #3.
5. User interacts with a GUI that lets him use sliders to generate its own latent code (ten slides because #2 is 10). Decoder generates an image from the latent code given.
6. Click Recommend – enables the user two parts
   1. Get the product/apparel from database that is most similar. (1. Predict cluster. 2. Find the most similar in the cluster using distance metric on the latent codes stored).
   2. Recommend from other cluster. The idea is that if the user generates a topwear (e.g. a shirt), I would also generate from other clusters (for example, other cluster have bottomwear, shoes, etc.). This is my problem right here.

For clustering, I could just use a simple K-Means. I can get the cluster labels and the cluster centroids.

My idea for 6.2 (Recommendation):

• I’m not really sure but for sure, there is a relationship between the cluster centroid (cluster mean) and the most similar/generated latent code. Is dot product applicable to this?
• My idea is that if my user generated code (vector) is called X, the most similar as A1, cluster centroid for the predicted cluster as A0, cluster centroid for another cluster as B0.
• I could know the projection of X w.r.t A0 and then use this amount of projection (idk what it is called, or if there is such a concept), to B0 to find the most similar in cluster B which is B1.

IS this even possible? If yes, what is this called? If not, could you recommend a better recommendation system that revolves around the same concept?

For the bayes optimal Classifier, when deriving it, if you have a loss function with unequal penalties for two incorrect decisions:

L=10 when y=1 and f=0

L=1 when y=0 and f=1

L=0 when y=f

Where f is the classifier. How does one go about deriving a decision threshold for this problem?