Anthony Bak, Principal Data Scientist at Ayasdi at MLconf SEA - 5/01/15
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The document discusses using topological data analysis (TDA) and shape as an organizing principle for data. It describes TDA as providing topological summaries that are invariant to coordinate changes and deformations, representing data in a compressed way while maintaining essential topological features. The document provides examples of using TDA for applications like disease state analysis, gene expression, predictive maintenance, customer churn analysis, and transaction fraud detection.
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Shape as Organizing Principle for Data
The document discusses using topological data analysis (TDA) to analyze complex data by focusing on the underlying shape or structure. TDA generates topological summaries that are invariant to coordinate changes and deformations, providing a compressed representation of the data. These topological summaries can help reduce bias and discover patterns in the data without imposing assumptions on what patterns should exist.
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Topology data analysis (TDA) is an unsupervised approach which may revolutionise the way data can be mined and eventually drive the new generation of analytical tools. The idea behind TDA is an attempt to "measure" shape of data and find compressed combinatorial representation of the shape. In ordinary topology, the combinatorial representations serve the purpose of providing the compressed representation of high dimensional data sets which retains information about the geometric relationships between data points. TDA can also be used as a very powerful clustering technique. Edward will present the comparison between TDA and other dimension reduction algorithms like PCA, LLE, Isomap, MDS, and Spectral Embedding.
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Josh Wills - Data Labeling as Religious Experience
The document discusses obtaining labeled data and introduces weak supervision as an alternative to full manual labeling. It notes that weak supervision uses labeling functions to generate noisy training labels at scale, which can then be combined using a generative model to infer true labels. The document also briefly mentions Snorkel, a system for creating labeling functions, and Snuba, its successor which focuses on scaling to very large datasets.
Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...
Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics
The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has enough idiosyncratic clues that it can be harnessed as a passive biometric. While there were many siloed attempts made by the machine learning community to model Bipedal Gait sensor data, these were done with small datasets oft collected in restricted academic environs. In this talk, we will introduce the ImageNet moment for human gait analysis by presenting 'Project GaitNet', the largest ever planet-sized motion sensor based human bipedal gait dataset ever curated. We’ll also present the associated state-of-the-art results in classifying humans harnessing novel deep neural architectures and the related success stories we have enjoyed in transfer-learning into disparate domains of human kinematics analysis.
Jekaterina Novikova - Machine Learning Methods in Detecting Alzheimer’s Disea...
Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language
Alzheimer's disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer's, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer's disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer's detection performance the most, due to their impact on the features of syntactic and lexical complexity.
Meghana Ravikumar - Optimized Image Classification on the Cheap
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
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
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
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...
This document discusses Netflix's global deep learning recommender system model. It describes how Netflix recommends content to over 150 million members across 190 countries using personalized recommendations. The system utilizes collaborative filtering techniques like soft clustering models to group users with similar tastes and generate weighted popularity votes. It also leverages topic models to model users' tastes as distributions over topics and content. The challenges of scaling these models globally to account for factors like country-specific catalogs and trends over time are discussed. The solution presented is to incrementally train the models by first censoring unavailable content and adding contextual variables, then periodically training warm start models with new embeddings and parameters to efficiently update the models at scale.
Vito Ostuni - The Voice: New Challenges in a Zero UI World
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.
Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...
The document discusses challenges related to building AI systems at scale using large, multi-modal datasets. It presents an approach for efficient classification of datasets with an extremely large number of classes. The key challenges are handling data scale, selecting relevant information, and ensuring safety. An objective function is designed for training tree-based classifiers that favors balanced, pure splits, leading to efficient trees with logarithmic depth and small error. This approach allows online training and can be used for classification or density estimation problems while learning representations.
Janani Kalyanam - Machine Learning to Detect Illegal Online Sales of Prescrip...
This document discusses using AI to detect illicit online sales of opioids through social media analysis. It provides background on laws targeting online drug sales without prescriptions. While policy guidelines aim to regulate this, internet effects remain inadequately addressed. The document then presents a pipeline using natural language processing and machine learning to analyze over 1 million tweets, isolate topics related to illicit online pharmacies, and identify characteristics of relevant tweets to build models that can automatically detect emerging bad actors selling drugs online. The goal is to analyze social media content quickly to help address this important problem.
Esperanza Lopez Aguilera - Using a Bayesian Neural Network in the Detection o...
This document discusses using a Bayesian neural network to classify light curves from the Transiting Exoplanet Survey Satellite (TESS) mission to identify exoplanet candidates. It describes challenges in classifying large numbers of light curves, and how a Bayesian neural network approach provides probabilistic predictions and confidence levels to help identify promising exoplanet candidates while avoiding many false positives seen in other methods. The Bayesian network achieved 91% accuracy and 83% precision in tests on simulated TESS data.
Neel Sundaresan - Teaching a machine to code
1. Recommend using the 'AdamOptimizer' class to optimize the loss since it is commonly used for training neural networks.
2. Suggest mapping the input data to floating point tensors using 'tf.cast()' for compatibility with TensorFlow operations.
3. Advise normalizing the input data to speed up training by using 'tf.keras.utils.normalize()'
Rishabh Mehrotra - Recommendations in a Marketplace: Personalizing Explainabl...
Recommendations in a Marketplace: Personalizing Explainable Recommendations with Multi-objective Contextual Bandits
Soumith Chintala - Increasing the Impact of AI Through Better Software
Soumith Chintala of Facebook AI presented on PyTorch, an open source machine learning framework. PyTorch allows users to define neural networks as Python programs and supports automatic differentiation to calculate gradients. Key features include GPU acceleration for tensors, distributed training across hundreds of GPUs, and TorchScript for optimizing Python models and deploying to C++. PyTorch aims to bridge the gap between research prototyping and production use through tools like TorchScript that transition eager Python code to a static graph mode optimized for deployment.
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Anthony Bak, Principal Data Scientist at Ayasdi at MLconf SEA - 5/01/15
- 1. Shape as Organizing Principle for Data MLConf Seattle 2015 Anthony Bak, Principal Data Scientist
- 2. The Data Problem: Complexity
- 4. Shape as Organizing Principle for Data
- 5. Shape as Organizing Principle
- 6. Reduce Bias, Discover Models TDA tells you the data you have, not the data you want to have.
- 24. Remember/Forget Use multiple lenses/metrics to get the complete picture Different lenses provide different summaries
- 26. Lenses: where do they come from? Mean/Max/Min Variance n-Moment Density … Statistics PCA/SVD Autoencoders Isomap/MDS/TS NE … Machine Learning Centrality Curvature Harmonic Cycles … Geometry
- 27. Why Topology?
- 28. Key Properties of TDA Deformation Invariance Compressed Representation Coordinate Freeness
- 29. Coordinate Invariance 1. Topology of shape doesn’t depend on the coordinates used to describe the shape 1. Different feature sets can describe the same phenomena 1. While processing data, we frequently alter coordinates: scaling, rotating, whitening You want to study properties of your data that are invariant under coordinate changes
- 30. Coordinate Invariance: Gene Expression NKI GSE230
- 31. Coordinate Invariance: Disease State
- 32. Deformation Invariance • Topological features don’t change when you stretch and distort the data Advantage: Makes problems easier Noise resistance Less pre-processing of data Robust (stable) data
- 37. Compressed Representation • Replace the metric space with a combinatorial summary: a simplicial complex. • Data becomes easier to manage, search, and query while maintaining essential features. • Leverages many known algorithms from graph theory, computational topology, computational geometry.
- 39. Baby Steps: PCA
- 40. PCA
- 41. PCA
- 42. Data Stories
- 46. Customer Churn
- 47. Customer Churn
- 51. We’re Hiring! http://www.ayasdi.com/company/careers/ Data Has Shape And Shape Has Meaning
Editor's Notes
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS
- NEED: COLOR BYS