Often we wish to transfer representational knowledge from one neural network to another. Examples include distilling a large network into a smaller one, transferring knowledge from one sensory modality to a second, or ensembling a collection of models into a single estimator. Knowledge distillation, the standard approach to these problems, minimizes the KL divergence between the probabilistic outputs of a teacher and student network. We demonstrate that this objective ignores important structural knowledge of the teacher network. This motivates an alternative objective by which we train a student to capture significantly more information in the teacher's representation of the data. We formulate this objective as contrastive learning. Experiments demonstrate that our resulting new objective outperforms knowledge distillation and other cutting-edge distillers on a variety of knowledge transfer tasks, including single model compression, ensemble distillation, and cross-modal transfer. Our method sets a new state-of-the-art in many transfer tasks, and sometimes even outperforms the teacher network when combined with knowledge distillation. Code: http://github.com/HobbitLong/RepDistiller.

Consequential decision-making incentivizes individuals to adapt their behavior to the specifics of the decision rule. A long line of work has therefore sought to understand and anticipate adaptation, both to prevent strategic individuals from "gaming" the decision rule and to explicitly motivate individuals to improve. In this work, we frame the problem of adaptation as performing interventions in a causal graph. With this causal perspective, we make several contributions. First, we articulate a formal distinction between gaming and improvement. Second, we formalize strategic classification in a new way that recognizes that the individual may improve, rather than only game. In this setting, we show that it is beneficial for the decision-maker to incentivize improvement. Third, we give a reduction from causal inference to designing incentivizes for improvement. This shows that designing good incentives, while desirable, is at least as hard as causal inference.

Andromeda is at the Berkman Klein Center; in the past she has written code for the MIT Libraries, the Wikimedia Foundation, bespoke knitting patterns (http://customfit.makewearlove.com) and library space usage analytics (http://measurethefuture.net/), among other things. Previously, she was a jack of all trades at the open-licensed-ebook startup Unglue.it; She has a BS in Mathematics from Harvey Mudd College, an MA in Classics from Tufts, and an MLS from Simmons. Andromeda is a 2010 LITA/Ex Libris Student Writing awardee, a 2011 ALA Emerging Leader, and a 2013 Library Journal Mover & Shaker. She is a former president of the Library & Information Technology Association, and a past listener contestant on Wait, Wait... Don't Tell Me!

Participants engage with academics and professionals in the field to learn about cutting-edge ideas, such as AI applied to biology and healthcare. Applications for the 2019 summer program are now closed! Artificial Intelligence (AI) is a branch of computer science that allows computers to make predictions and decisions, solve problems, and perform tasks. It's a technology that's rapidly changing the world and has impact across all disciplines and industries. AI has amazingly broad applications, and we need people with comparably broad set of experiences, worldviews, and identities working on AI.

Lyft's mission is to improve people's lives with the world's best transportation and it'll be a slow slog to get there with dispatchers manually matching riders with drivers. We need automated decision making, and we need to scale it in a way that optimizes both the user experience and the market efficiency. Complementing our Science roles, an engineer with a knack for practical machine learning and an eye for business impact can help independently build and productionize models that power product experiences that make for an enjoyable commute. A year and a half ago when we began scouting for this type of machine learning-savvy engineer --something we now call the machine learning Software Engineer (ML SWE) -- it wasn't something we knew much about. We looked at other companies' equivalent roles but they weren't exactly contextualized to Lyft's business setting. This need motivated an entirely new role that we set up and started hiring for.

It is the first graduate level, research-based AI university in the world. MBZUAI will enable graduate students, businesses, and governments to advance artificial intelligence. The university is named after His Highness Sheikh Mohamed bin Zayed Al Nahyan, Crown Prince of Abu Dhabi and Deputy Supreme Commander of the UAE Armed Forces, who has long advocated for the UAE's development of human capital through knowledge and scientific thinking to take the nation into the future. MBZUAI will introduce a new model of academia and research to the field of AI, providing students and faculty access to some of the world's most advanced AI systems to unleash its potential for economic and societal development. The announcement was made at a press conference at the University campus in Masdar City and was immediately followed by the first meeting of the MBZUAI Board of Trustees. Dr Sultan Ahmed Al Jaber, UAE Minister of State, who has been appointed Chair of the MBZUAI board of trustees and is spearheading the establishment of the University, said: "Mohamed bin Zayed University of Artificial Intelligence aligns with the vision of the UAE leadership that is based on sustainable development, progress and the overall well-being of humanity and underpinned by capacity-building and active participation in finding practical solutions based on innovation and state-of-the-art technology.

Ace Data Science Interviews: Data science interviews can be daunting if you don't know what to expect. You might feel you have all the knowledge and yet you keep getting rejected. This course will guide you on how to navigate data science interviews, lay down a comprehensive 7-step process, and help you land your dream data science role! Structured Thinking and Communication for Data Science Professionals (launching 15th May): Whether you are creating dashboards for your business customers or solving cutting-edge machine learning problems, structured thinking and communications is a must have skill for every data professional Up-Level your Data Science Resume: Crafting the perfect data science resume is critical to landing your first data science role. Learn the various aspects of designing a resume that will give you the best chance of landing that interview you've been looking for Ace Data Science Interviews: Data science interviews can be daunting if you don't know what to expect.

Techfest is the annual science and technology festival of Indian Institute of Technology Bombay.[1] It also refers to the independent body of students who organize this event along with many other social initiatives and outreach programs around the year. Techfest is known for hosting a variety of events that include competitions, exhibitions, lectures as well as workshops. Started in 1998 with the aim of providing a platform for the Indian student community to develop and showcase their technical prowess, it has now grown into Asia's Largest Science and Technology Festival[2] with a footfall of 1.75 lakhs in its latest edition.[3][4][5] The activities culminate in a grand three-day event in the campus of IIT Bombay which attracts people from all over the World, including students, academia, corporates and the general public.[6] The very first edition of Techfest was in 1998. The underlying spirit of Techfest was "to promote technology and scientific thinking and innovation" a motto that has been followed by every Techfest since. Techfest '98 also set the broad outlines of Techfest in the form of competitions, lectures, workshops, and exhibitions which went on to become a standard feature at every Techfest. Entrepreneurship also made an appearance in the 1999 and 2000 editions. Technoholix--Techfest in the Dark, showcasing technological entertainment at the end of each day as well as the hub of on the spot activities, made their debut during these years. Techfest 2001-2002 saw the incorporation of IIT Bombay's department oriented events like Yantriki, Chemsplash and Last Straw. Students from G H Raisoni College of Engineering got the Engineering Excellence Award for best design.

In this presentation, Kathrin Melcher, who works as a data scientist at KNIME, will give an overview of KNIME Software, including the open-source tool KNIME Analytics Platform for creating data science applications and services and also the different deployment options you have when using KNIME Server. While the structure is often similar--data collection, data transformation, model training, deployment--each project required its own special trick, whether this was a change in perspective or a particular technique to deal with the special case and business questions. You'll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join us to learn what's possible in data science. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany.