Finds in #arxiv :
*Optimization, numerical & convex, ML*
The Linearized Bregman Method via Split Feasibility Problems: Analysis and Generalizations
Reformulation of Split Bregman/ ADMM as split feasibility problem and algorithm/convergence for generalized split feasibility by Bregman projection. This general formulation include both Split Bregman and Kaczmarz (my comment – randomized Kaczmarz seems could be here too)
Stochastic gradient descent and the randomized Kaczmarz algorithm
Hybrid of randomized Kaczmarz and stochastic gradient descent – into my “to read” pile
Trust–Region Problems with Linear Inequality Constraints: Exact SDP Relaxation, Global Optimality and Robust Optimization
“Extended” trust region for linear inequalities constrains
Conic Geometric Programming
Unifing framwork for conic and geometric programming
Gauge optimization, duality, and applications
Another big paper about different, not Lagrange duality, introduced by Freund (1987)
Color Bregman TV
mu parameters in split bregman made adaptive, to exploit coherence of edges in different color channels
Iteration Complexity Analysis of Block Coordinate Descent Methods
Some convergence analysis for BCD and projected gradient BCD
Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation
Nonnegative matrix factorization
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
SVN for large-scale problems
Image Restoration using Total Variation with Overlapping Group Sparsity
While title is promising I have doubt about that paper. The method authors suggest is equivalent to adding averaging filter to TV-L1 under L1 norm. There is no comparison to just applying TV-L1 and smoothing filter interchangeably.The method author suggest is very costly, and using median filter instead of averaging would cost the same while obviously more robust.
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling
_Open source_ matlab/c package coming soon(not yet available)
Improvements to deep convolutional neural networks for LVCSR
convolutional networks, droput for speech recognition,
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Already discussed on G+ – open source framework in “learn one use everywhere” stile. Learning done off-line on GPU using ConvNet, and recognition is online in pure python.
Statistical mechanics of complex neural systems and high dimensional data
Big textbook-like overview paper on statistical mechanics of learning. I’ve put it in my “to read” pile.
Randomized co-training: from cortical neurons to machine learning and back again
“Selectron” instead of perception – neurons are “specializing” with weights.
Provable Bounds for Learning Some Deep Representations
Citation:”The current paper presents both an interesting family of denoising autoencoders as
Online Unsupervised Feature Learning for Visual Tracking
Sparse representation, overcomplete dictionary
From Shading to Local Shape
Shape restoration from local shading – could be very useful in low-feature environment.
Fast 3D Salient Region Detection in Medical Images using GPUs
Finding interest point in 3D images
Object Recognition System Design in Computer Vision: a Universal Approach
Grid-based universal framework for object detection/classification
Lyapunov-based Low-thrust Optimal Orbit Transfer: An approach in Cartesian coordinates
For space sim enthusiast :)
I have seen an excellent wlakthrough on building Alex Krizhevsky’s cuda-convnet for windows, but difference in configuration and installed packages could be tiresome. So here is complete build of convnet for windows 64:
It require having CUDA compute capability 2.0 or better GPU of cause, Windows 64bit, Visual Studio 64bits and Python 64bit with NumPy installed. The rest of libs and dlls are precomplied. In building it I’ve followed instructions by Yalong Bai (Wyvernbai) from http://www.asiteof.me/archives/50.
Read Readme.md before installation – you may (or may not) require PYTHONPATH environmental variable set.
On side note I’ve used WinPython for both libraries and running the package. WinPython seems a very nice package, which include Spyder IDE. I have some problems with Spyder though – sometimes it behave erratically during debugging/running the code. Could be my inexperience with Spyder though. Another nice package – PythonXY – regretfully can not be used – it has only 32 bit version and will not compile with/run 64 bit modules.