Mathias Brandewinder, Software Engineer & Data Scientist, Clear Lines Consulting at MLconf NYC - 4/15/16

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Scripts that Scale with F# and mbrace.io: Nothing beats interactive scripting for productive data exploration and rapid prototyping: grab data, run code, and iterate based on feedback. However, that story starts to break down once you need to process large datasets or expensive computations. Your local machine becomes the bottleneck, and your are left with a slow and unresponsive environment. In this talk, we will demonstrate on live examples how you can have your cake and eat it, too, using mbrace.io, a free, open-source engine for scalable cloud programming. Using a simple programming model, you can keep working from your favorite scripting environment, and execute code interactively against a cluster on the Azure cloud. We will discuss the relevance of F# and mbrace in a data science and machine learning context, from parallelizing code and data processing in a functional style, to leveraging F# type providers to consume data or even run R packages.

Technology

Scripts that scale:
F#, Azure & mbrace.io

What the hell is F#? Why do I care?
•You already use many languages
•F# is probably not one of them
I have 2 goals today:
1) Show you why F# is awesome for ML,
2) In particular with mbrace.io + Azure

F# in general
•Functional-first
•Statically typed, lightweight syntax
•Open Source, from Microsoft Research
•Very active & engaged community
•In the ML family (not machine learning)
•First class .NET language

What are pain-points in ML?
•I need data!
•I need clean data!
•Damn, this is a lot of data!
•Damn, when will this computation finish!

F# is awesome at this
• I need data!
• I need clean data!
• Damn, this is a lot of data!
• Slow computations
• Type Providers
• Pipelines with |>
• mbrace.io / CloudFlow
• mbrace.io / cloud { }

Why functional?
•Functional ≈ immutable data + functions
•map ≈ opportunity for parallelism
1 2 3 4 5 6
2 3 4 5 6 7
f(x) = x + 1

Conclusion: F#
•Awesome scripting language
•Type providers
• focus on data, not on how to get it
•Natural data pipelines
•Functional helps exploit parallelism
•Code ready to ship in production

Conclusion: mbrace.io + Azure
•Simple, extensible model
•Data parallelism
•Compute parallelism
•Run anything, distributed
•Scale fast, for cheap, on Azure

Thank you 
•More on F# at fsharp.org
•Ping me as @brandewinder

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Optimized Image Classification on the Cheap In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of the dataset through image augmentation to boost the classifier’s performance. We will use Bayesian optimization to learn the hyperparameters associated with image transformations using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also focus on the features of these augmented images and the downstream implications for our image classifier. To both maximize model performance on a budget and explore the impact of optimization on these methods, we apply a particularly efficient implementation of Bayesian optimization to each of these architectures in this comparison. Our goal is to draw on a rigorous set of experimental results that can help us answer the question: how can resource-constrained teams make trade-offs between efficiency and effectiveness using pre-trained models?

Noam Finkelstein - The Importance of Modeling Data Collection

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The Importance of Modeling Data Collection Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have. In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set. My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.

June Andrews - The Uncanny Valley of ML

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The Uncanny Valley of ML Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.

Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks

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Deep Learning Architectures for Semantic Relation Detection Tasks Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.

Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...

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Building an Incrementally Trained, Local Taste Aware, Global Deep Learned Recommender System Model At Netflix, our main goal is to maximize our members’ enjoyment of the selected show by minimizing the amount of time it takes for them to find it. We try to achieve this goal by personalizing almost all the aspects of our product -- from what shows to recommend, to how to present these shows and construct their home-pages to what images to select per show, among many other things. Everything is recommendations for us and as an applied Machine Learning group, we spend our time building models for personalization that will eventually increase the joy and satisfaction of our members. In this talk we will primarily focus our attention on a) making a global deep learned recommender model that is regional tastes and popularity aware and b) adapting this model to changing taste preferences as well as dynamic catalog availability. We will first go through some standard recommender system models that use Matrix Factorization and Topic Models and then compare and contrast them with more powerful and higher capacity deep learning based models such as sequence models that use recurrent neural networks. We will show what it entails to build a global model that is aware of regional taste preferences and catalog availability. We will show how models that are built on simple Maximum Likelihood principle fail to do that. We will then describe one solution that we have employed in order to enable the global deep learned models to focus their attention on capturing regional taste preferences and changing catalog.In the latter half of the talk, we will discuss how we do incremental learning of deep learned recommender system models. Why do we need to do that ? Everything changes with time. Users’ tastes change with time. What’s available on Netflix and what’s popular also change over time. Therefore, updating or improving recommendation systems over time is necessary to bring more joy to users. In addition to how we apply incremental learning, we will discuss some of the challenges we face involving large-scale data preparation, infrastructure setup for incremental model training as well as pipeline scheduling. The incremental training enables us to serve fresher models trained on fresher and larger amounts of data. This helps our recommender system to nicely and quickly adapt to catalog and users’ taste changes, and improve overall performance.

Vito Ostuni - The Voice: New Challenges in a Zero UI World

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Vito Ostuni - The Voice: New Challenges in a Zero UI World The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query. We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.

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Mathias Brandewinder, Software Engineer & Data Scientist, Clear Lines Consulting at MLconf NYC - 4/15/16

1. Scripts that scale: F#, Azure & mbrace.io

2. What the hell is F#? Why do I care? •You already use many languages •F# is probably not one of them I have 2 goals today: 1) Show you why F# is awesome for ML, 2) In particular with mbrace.io + Azure

3. F# in general •Functional-first •Statically typed, lightweight syntax •Open Source, from Microsoft Research •Very active & engaged community •In the ML family (not machine learning) •First class .NET language

4. What are pain-points in ML? •I need data! •I need clean data! •Damn, this is a lot of data! •Damn, when will this computation finish!

5. F# is awesome at this • I need data! • I need clean data! • Damn, this is a lot of data! • Slow computations • Type Providers • Pipelines with |> • mbrace.io / CloudFlow • mbrace.io / cloud { }

6. Why functional? •Functional ≈ immutable data + functions •map ≈ opportunity for parallelism 1 2 3 4 5 6 2 3 4 5 6 7 f(x) = x + 1

7. Kaggle Home Depot competition

8. Conclusion: F# •Awesome scripting language •Type providers • focus on data, not on how to get it •Natural data pipelines •Functional helps exploit parallelism •Code ready to ship in production

9. Conclusion: mbrace.io + Azure •Simple, extensible model •Data parallelism •Compute parallelism •Run anything, distributed •Scale fast, for cheap, on Azure

10. Thank you  •More on F# at fsharp.org •Ping me as @brandewinder

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