A rising,
rotating column of hot air on Mars.
David McKay of "Information theory, inference and learning algorithms" fame writes
I briefly met David back at lunch in 2006 during a Bayesian conference in Paris. One thing for sure, he is impressively quick, witty and smart: The discussion went from how he built Dasher to figuring out how to go about detecting potential earth smashing meteorites by digitizing old astronomical photos in repositories around the world (this is still an awesome idea). The underlying algorithm is the one used by star trackers on satellites. However since each of the photographs are taken by different instruments there is an additional layer of parameters to guess before figuring out where the picture is pointing at. Obviously, the probabilities for such an event are rather small compared to other more highly probable catastrophic events. In effect, David produced the much needed:
David continues tweeting.In other news, here are some other blog entries (in no particular order):
John
Anand
- DIMACS Network Coding Workshop talks posted
- IHP “Nexus” Workshop on Privacy and Security: Day 1
- LabTV Profiles Are Up!
- Signal boost: IBM Social Good Fellowship for data science
- The Future of Real-Time SLAM and "Deep Learning vs SLAM"
- ICCV 2015: Twenty one hottest research papers
John
Dustin
- The Voronoi Means Conjecture
- The HRT Conjecture
- On the low-rank approach for semidefinite programs arising in synchronization and community detection
- Clustering noisy data with semidefinite relaxations
- Recent developments in equiangular tight frames
- Compressed Sensing and its Applications 2015
Fabian
Dirk
Joshua
Vladimir
Suresh
- Time to cluster !
- Who owns a review, and who's having the conversation ?
- ITA FTW: Bayesian surprise and eigenvectors in your meal.
- Making all my reviews public (and annotated): A question.
- On "the moral hazard of complexity-theoretic assumptions"
- White Elephant parties and fair division
- Fairness and The Good Wife
Bob
Afonso
- SDP for Community Detection with many communities
- Generalized Power Method for SO(2) Synchronization
- Below the Kesten-Stigum bound for Community Detection
- Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science
- 18.S096: Synchronization Problems and Alignment
- 18.S096: Compressed Sensing and Sparse Recovery
Off the convex path
- Semantic Word Embeddings
- Tensor Methods in Machine Learning
- Nature, Dynamical Systems and Optimization
- NIPS 2015 workshop on non-convex optimization
- Word Embeddings: Explaining their properties
- Evolution, Dynamical Systems and Markov Chains
- Stability as a foundation of machine learning
- Escaping from Saddle Points
- Saddles Again
- Markov Chains Through the Lens of Dynamical Systems: The Case of Evolution
Ben
On Reddit, there were two interesting AMAs
On Quora a few people asnwered quite a few interesting questions:
- What are the best tutorials/videos about Tensor Factorization/Tensor Decomposition?
- What are some introductory resources on tensors in machine learning?
- When would you recommend Tensor Factorization over Factorization Machines?
- What is the state of the art of Unsupervised Learning, and is human-like Unsupervised Learning possible in the near future?
- Do I need to use deep learning to build a product recommandation engine in ecommerce?
- What are the major factors that motivate us to use Neural networks over Kernel methods for large datasets in layman terms?
- Are stochastic variational approaches the way to do large scale Bayesian ML or do you see any hope of scaling up MCMC-based algorithms?
- Which is your favorite Machine Learning Algorithm?
- I have seen Alternating Least Squares (ALS), SVD and NMF (Non negative matrix factorization) used for recommendation systems. Apart from the optimization function how are the approaches different? In what situations would one be better than the other?
- What is the best way to learn a machine learning algorithm?
- How would you define "data science"?
- Can Deep learning techniques be useful for recommender systems?
- What are some of the best recommendation algorithms?
- What is the most important unresolved problem in machine learning?
Image Credit: NASA/JPL-Caltech
From its perch high on a ridge, NASA's Mars Exploration Rover Opportunity recorded this image of a Martian dust devil twisting through the valley below. The view looks back at the rover's tracks leading up the north-facing slope of "Knudsen Ridge," which forms part of the southern edge of "Marathon Valley."
Opportunity took the image using its navigation camera (Navcam) on March 31, 2016, during the 4,332nd Martian day, or sol, of the rover's work on Mars.
Dust devils were a common sight for Opportunity's twin rover, Spirit, in its outpost at Gusev Crater. Dust devils have been an uncommon sight for Opportunity though.
Just as on Earth, a dust devil is created by a rising, rotating column of hot air. When the column whirls fast enough, it picks up tiny grains of dust from the ground, making the vortex visible.
During the uphill drive to reach the top of Knudsen Ridge, Opportunity's tilt reached 32 degrees, the steepest ever for any rover on Mars.
NASA's Jet Propulsion Laboratory, a division of the California Institute of Technology in Pasadena, manages the Mars Exploration Rover Project for NASA's Science Mission Directorate, Washington.
For more information about Opportunity, visit http://www.nasa.gov/rovers and http://mars.nasa.gov/mer/.
From its perch high on a ridge, NASA's Mars Exploration Rover Opportunity recorded this image of a Martian dust devil twisting through the valley below. The view looks back at the rover's tracks leading up the north-facing slope of "Knudsen Ridge," which forms part of the southern edge of "Marathon Valley."
Opportunity took the image using its navigation camera (Navcam) on March 31, 2016, during the 4,332nd Martian day, or sol, of the rover's work on Mars.
Dust devils were a common sight for Opportunity's twin rover, Spirit, in its outpost at Gusev Crater. Dust devils have been an uncommon sight for Opportunity though.
Just as on Earth, a dust devil is created by a rising, rotating column of hot air. When the column whirls fast enough, it picks up tiny grains of dust from the ground, making the vortex visible.
During the uphill drive to reach the top of Knudsen Ridge, Opportunity's tilt reached 32 degrees, the steepest ever for any rover on Mars.
NASA's Jet Propulsion Laboratory, a division of the California Institute of Technology in Pasadena, manages the Mars Exploration Rover Project for NASA's Science Mission Directorate, Washington.
For more information about Opportunity, visit http://www.nasa.gov/rovers and http://mars.nasa.gov/mer/.
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