Recommendation has been a long-standing problem in many areas ranging from e-commerce to social websites. Most current studies focus only on traditional approaches such as content-based or collaborative filtering while there are relatively fewer studies in hybrid recommender systems. Due to the latest advances of deep learning achieved in different fields including computer vision and natural language processing, deep learning has also gained much attention in Recommendation Systems. There are several studies that utilize ID embeddings of users and items to implement collaborative filtering with deep neural networks. However, such studies do not take advantage of other categorical or continuous features of inputs. In this paper, we propose a new deep neural network architecture which consists of not only ID embeddings but also auxiliary information such as features of job postings and candidates for job recommendation system which is a reciprocal recommendation system. Experimental results on the dataset from a job-site show that the proposed method improves recommendation results over deep learning models utilizing ID embeddings.

Deep convolutional networks (convnets) show a remarkable ability to learn disentangled representations. In recent years, the generalization of deep learning to Lie groups beyond rigid motion in $\mathbb{R}^n$ has allowed to build convnets over datasets with non-trivial symmetries, such as patterns over the surface of a sphere. However, one limitation of this approach is the need to explicitly define the Lie group underlying the desired invariance property before training the convnet. Whereas rotations on the sphere have a well-known symmetry group ($\mathrm{SO}(3)$), the same cannot be said of many real-world factors of variability. For example, the disentanglement of pitch, intensity dynamics, and playing technique remains a challenging task in music information retrieval. This article proposes a machine learning method to discover a nonlinear transformation of the space $\mathbb{R}^n$ which maps a collection of $n$-dimensional vectors $(\boldsymbol{x}_i)_i$ onto a collection of target vectors $(\boldsymbol{y}_i)_i$. The key idea is to approximate every target $\boldsymbol{y}_i$ by a matrix--vector product of the form $\boldsymbol{\widetilde{y}}_i = \boldsymbol{\phi}(t_i) \boldsymbol{x}_i$, where the matrix $\boldsymbol{\phi}(t_i)$ belongs to a one-parameter subgroup of $\mathrm{GL}_n (\mathbb{R})$. Crucially, the value of the parameter $t_i \in \mathbb{R}$ may change between data pairs $(\boldsymbol{x}_i, \boldsymbol{y}_i)$ and does not need to be known in advance.

Machine learning has recently enabled large advances in artificial intelligence, but these results can be highly centralized. The large datasets required are generally proprietary; predictions are often sold on a per-query basis; and published models can quickly become out of date without effort to acquire more data and maintain them. Published proposals to provide models and data for free for certain tasks include Microsoft Research's Decentralized and Collaborative AI on Blockchain. The framework allows participants to collaboratively build a dataset and use smart contracts to share a continuously updated model on a public blockchain. The initial proposal gave an overview of the framework omitting many details of the models used and the incentive mechanisms in real world scenarios. For example, the Self-Assessment incentive mechanism proposed in their work could have problems such as participants losing deposits and the model becoming inaccurate over time if the proper parameters are not set when the framework is configured. In this work, we evaluate the use of several models and configurations in order to propose best practices when using the Self-Assessment incentive mechanism so that models can remain accurate and well-intended participants that submit correct data have the chance to profit. We have analyzed simulations for each of three models: Perceptron, Nave Bayes, and a Nearest Centroid Classifier, with three different datasets: predicting a sport with user activity from Endomondo, sentiment analysis on movie reviews from IMDB, and determining if a news article is fake. We compare several factors for each dataset when models are hosted in smart contracts on a public blockchain: their accuracy over time, balances of a good and bad user, and transaction costs (or gas) for deploying, updating, collecting refunds, and collecting rewards.

The data set would be astronomy sub-images that are either bad (edge of chip artifacts, bright star saturation and spikes, internal reflections, chip flaws) or good (populated with fuzzy-dot stars and galaxies and asteroids and stuff). Let's say the typical image is 512x512 but it varies a lot. Because the bad features tend to be big, I'd probably like to bin the images down to say 64x64 for compactness and speed. It has to run fast on tens of thousands of images. I'm sort of tempted by the solution of adopting PlaidML as my back end (if I understand what its role is), because it can compile the problem for many architectures, like CUDA, CPU-only, OpenCL.

Recently, I came across a Reddit thread on the different roles in data science and machine learning: data scientist, decision scientist, product data scientist, data engineer, machine learning engineer, machine learning tooling engineer, AI architect, etc. It's difficult to be effective when the data science process (problem framing, data engineering, ML, deployment/maintenance) is split across different people. It leads to coordination overhead, diffusion of responsibility, and lack of a big picture view. IMHO, I believe data scientists can be more effective by being end-to-end. Here, I'll discuss the benefits and counter-arguments, how to become end-to-end, and the experiences of Stitch Fix and Netflix. I find these definitions to be more prescriptive than I prefer. Instead, I have a simple (and pragmatic) definition: An end-to-end data scientist can identify and solve problems with data to deliver value.

Though the community continues to develop new algorithms, state-of-the-art results have stopped improving in the last couple of years. Since RL algorithms that use a tremendous amount of online data to learn from scratch are infeasible to apply in the real-world, much research has moved to fields such as Meta-RL, offline RL, and integrating RL with domain-knowledge, integrating RL and planning, etc. How do you unit test end-to-end ML pipelines?, by u/farmingvillein As perhaps a bit of tldr: once you've got the bare minimum data-replay testing in place ("yeah, it is probably working, because the results are pretty close to what they were before"), I'd encourage you to consider focusing your energy toward thinking of testing as outlier detection. Outliers, in real-world ML systems, tend to be harbingers of things that are wrong systematically, upstream data problems, and logic (pre-/post-processing) problems. How do you transition from a no name international college to FAIR/Brain?, by u/r-sync Coming from a no-name Indian engineering college with meh grades, you do have to get a bit creative, very persistent and build credibility for yourself. The examples above are one way to do so, but you can also maybe articulate your thoughts as really good blog posts and arxiv papers, or show great software engineering skills in open-source (i.e.

I have been on a 30-day challenge to improve my knowledge of Artificial Intelligence (AI), to understand how it works and how it impacts our lives, and this section talks about how not only have we already integrated it in our everyday lives, but in some cases already love it and depend on it. In this fifth section, we tackle "AI in Application." Exploring where AI is prevalent and the data that is being collected already is not surprising but it is humbling how much it has already penetrated our lives and how much we depend on it. Recently, a friend of mine named her baby Sirius. For those that love the Harry Potter books, the immediate connection is to Sirius Black, so of course being a Harry Potter fan I instantly loved it.

With data science and artificial intelligence evolving on a daily basis, the magnitude of information it generates can sometimes be challenging to keep pace with. And that's why all these data science news websites and blogs come with their newsletter that continually churns out relevant and significant information for readers. An excellent form of curated content, newsletters can be extremely informative and insightful for data science professionals, students as well as business leaders. These weekly newsletters provide updated trends of the industry, latest news, different methodologies as well as information on new technologies that can be an exciting learning resource for many. Further, with such a vast amount of information, it is critical for all to stay away from clickbait as well as fake news, and these newsletters can be the perfect rescue for the same.

The demand for Data Scientists, Machine Learning Engineers, and Data Engineers is unprecedented, as demonstrated by the ever-growing number of …

Abstract. We present modern machine learning, focusing on the state-of-the-art classification methods of deep networks and decision forests, …