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Big Learning : Algorithms, Systems, and Tools
This workshop will address algorithms, systems, and real-world problem domains related to large-scale machine learning (“Big Learning”). With active research spanning machine learning, databases, parallel and distributed systems, parallel architectures, programming languages and abstractions, and even the sciences, Big Learning has attracted intense interest. This workshop will bring together experts across these diverse communities to discuss recent progress, share tools and software, identify pressing new challenges, and to exchange new ideas. Topics of interest include (but are not limited to):
- Big Data: Methods for managing large, unstructured, and/or streaming data; cleaning, visualization, interactive platforms for data understanding and interpretation; sketching and summarization techniques; sources of large datasets.
- Models & Algorithms: Machine learning algorithms for parallel, distributed, GPGPUs, or other novel architectures; theoretical analysis; distributed online algorithms; implementation and experimental evaluation; methods for distributed fault tolerance.
- Applications of Big Learning: Practical application studies and challenges of real-world system building; insights on end-users, common data characteristics (stream or batch); trade-offs between labeling strategies (e.g., curated or crowd-sourced).
- Tools, Software & Systems: Languages and libraries for large-scale parallel or distributed learning which leverage cloud computing, scalable storage (e.g. RDBMs, NoSQL, graph databases), and/or specialized hardware.
Big Data Meets Computer Vision: First International Workshop on Large Scale Visual Recognition and Retrieval
The emergence of “big data” has brought about a paradigm shift throughout computer science. Computer vision is no exception. The explosion of images and videos on the Internet and the availability of large amounts of annotated data have created unprecedented opportunities and fundamental challenges on scaling up computer vision.
Over the past few years, machine learning on big data has become a thriving field with a plethora of theories and tools developed. Meanwhile, large scale vision has also attracted increasing attention in the computer vision community. This workshop aims to bring closer researchers in large scale machine learning and large scale vision to foster cross-talk between the two fields. The goal is to encourage machine learning researchers to work on large scale vision problems, to inform computer vision researchers about new developments on large scale learning, and to identify unique challenges and opportunities.
This workshop will focus on two distinct yet closely related vision problems: recognition and retrieval. Both are inherently large scale. In particular, both must handle high dimensional features (hundreds of thousands to millions), a large variety of visual classes (tens of thousands to millions), and a large number of examples (millions to billions).
This workshop will consist of invited talks, panels, discussions, and paper submissions including, but not limited to, the following topics:
-- State of the field: What really defines large scale vision? How does it differ from traditional vision research? What are its unique challenges for large scale learning?
-- Indexing algorithms and data structures: How do we efficiently find similar features/images/classes from a large collection, a key operation in both recognition and retrieval?
-- Semi-supervised/unsupervised learning: Large scale data comes with different levels of supervision, ranging from fully labeled and quality controlled to completely unlabeled. How do we make use of such data?
-- Metric learning: Retrieval visually similar images/objects requires learning a similarity metric. How do we learn a good metric from a large amount of data?
-- Visual models and feature representations: What is a good feature representation? How do we model and represent images/videos to handle tens of thousands of fine-grained visual classes?
-- Exploiting semantic structures: How do we exploit the rich semantic relations between visual categories to handle a large number of classes?
-- Transfer learning: How do we handle new visual classes (objects/scenes/activities) after having learned a large number of them? How do we transfer knowledge using the semantic relations between classes?
-- Optimization techniques: How do we perform learning with training data that do not fit into memory? How do we parallelize learning?
-- Datasets issues: What is a good large scale dataset? How should we construct datasets? How do we avoid dataset bias?
-- Systems and infrastructure: How do we design and develop libraries and tools to facilitate large scale vision research? What infrastructure do we need?
The target audience of this workshop includes industry and academic researchers interested in machine learning, computer vision, multimedia, and related fields.
OpenCV using Python
Learn how to develop a machine learning application using Python and OpenCV. OpenCV is a cross-platform library of programming functions for real-time computer vision. While OpenCV is written primarily in C and C++, much of the API functionality can be accessed through wrappers in other languages, including Python. This tutorial will delve into using Python to develop computer vision application.
IPC–SMTA High-Reliability Cleaning and Conformal Coating Conference 2012
This event is focused on electronics assembly reliability and the influence of cleaning and coating on the production of reliable hardware.
“How clean is clean?” is even more challenging to answer: conductors and circuit traces are growing narrower and what is acceptably clean for one industry segment may be unacceptable in others.
The Website Is The App (And Vice Versa)
There are two basic models of media and entertainment companies using mobile web apps built on HMTL5. One model is to create a whole new experience using web technologies. This could be re-packaging existing content, giving new life to archives, or just experimenting with a new format or media product. In my experience, some music labels to this really well The other model is to take the entire media property and create a single, multi-use app where the website, the mobile app, and the tablet app are all one entity. Here, there are some great examples from the news media. The talk will examine case studies of both of these approaches and provide the pros and cons. But the main takeaway is that with HTML5, mobile website and mobile apps can be one-and-the-same.
Vator Splash NY 2012
Splash is Vator’s once-a-quarter evening event, celebrating entrepreneurship with seasoned entrepreneurs sharing lessons and advice, and 10 promising startups pitching onstage. The top 10 are chosen through an online competition. The event draws some 350 to 400 attendees in the entrepreneur community, from seasoned to emerging entrepreneurs, venture capitalists and media.
Building Meaningful Customer Experiences
A one-day workshop on "Building Meaningful Customer Experiences" by the design expert, Nathan Shedroff, author of multiple books including Making Meaning, Experience Design 1.1, Design is the Problem, Experience Design 1 Cards, and, Dictionary of Sustainable Management.
How to start a startup as a non-technical founder
Tech Startup from the Ground Up: Advice from a non-Technical Founder. This workshop will cover the following topics:
- Day Job to Dream Job: Quit and Commit
- Execute! Learn from the Honey Badger
- Structuring & Hiring: Startup Oxygen
- Fundraising: Kickstart, Crowd-source, Accelerate, and Call in the Angels
- Growing Your Business: If Plan A Fails, there are 25 more Letters
- Recommended Resources, Questions from Viewers