December finds in #arxiv
Repost from my googleplus stream
Computer Vision
Non-Local means is a local image denoising algorithm
Paper shows that non-local mean weights are not identify patches globally point in the images, but are susceptible to aperture problem:
http://en.wikipedia.org/wiki/Optical_flow#Estimation That’s why short radius NLM could be better then large radius NLM. Small radius cutoff play the role of regularizer, similar to the Total Variation in Horn-Shunk Optical flow.
http://en.wikipedia.org/wiki/Horn%E2%80%93Schunck_method (my comment – TV-L1 is generally better than TV-L2 in Horn-Schunk)
http://arxiv.org/abs/1311.3768
Deep Learning
Do Deep Nets Really Need to be Deep?
Authors state that shallow neural nets can in fact achieve similar performance to deep convolutional nets. The problem though is, that they had to be initialized or preconditioned – they can not be trained using existing algorithms.
And for that initialization they need deep nets. Authors hypothesize that there should be algorithms that allow training of those shallow nets to reach the same performance as deep nets.
http://arxiv.org/abs/1312.6184
Intriguing properties of neural networks
The linear combination of deep-level nodes produce the same results as the the original nodes. That suggest that nodes the spaces itself rather it’s representation keep information for deep levels.
The input-output mapping also discontinuous – small perturbations cause misclassification. Those perturbation are not dependent on the training, only on input of classification. (My comment – sparse coding is generally not smooth on input, another argument that sparse coding is part of internal mechanics of deep learning)
http://arxiv.org/abs/1312.6199
From Maxout to Channel-Out: Encoding Information on Sparse Pathways
This paper start with observation that max-out is a form of sparse coding: only one of the input pathway is chosen for father processing. From this inferred development of that principle:
remove “middle” layer which “choose” maximum input, and transfer maximal input at once into next level – make choice function index-aware. Some other choice function beside the max is considered, but max still seems the best
Piecewise-constant choice function make interesting reference to previous paper (discontinuity of input-output mapping)
http://arxiv.org/abs/1312.1909
Unsupervised Feature Learning by Deep Sparse Coding
This, for a difference is not about convolutional network.
Instead SIFT(or similar) descriptors are used to produce bag-of-words, sparse coding is used with max-out, and manifold learning applied to it. (http://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction)
http://arxiv.org/abs/1312.5783
Generative NeuroEvolution for Deep Learning
I’m generally wary of evolutionary methods, but this looks kind of interesting – it’s based on compositional pattern producing network (CPPN)- encoding geometric pattern as composition of simple functions.
This CPPN is used to encode connectivity pattern of ANN (Convolutional newtwork most used). Thus complete process is the combination of ANN training and evolutionary CPPN training
http://arxiv.org/abs/1312.5355
Some Improvements on Deep Convolutional Neural Network Based Image Classification, Unsupervised feature learning by augmenting single images
Botht papers seems about the same subject – squeeze more out of labeled images by applying a lot of transformation to them(Some of those transformations are implemented in cuda-convnet BTW)
http://arxiv.org/abs/1312.5402, http://arxiv.org/abs/1312.5242
Exact solutions to the nonlinear dynamics of learning in deep linear neural networks
Analytical exploration of toy 3-layer model *without_ actual non-linear neurons. Model completely linear to input (polynomial to weights). Nevertheless it show some interesting properties, like step in learning curve
http://arxiv.org/abs/1312.6120
Optimization
Distributed Interior-point Method for Loosely Coupled Problems
Mixing together all my favorite methods: Interior point, Newton, ADMM(Split-Bregman) into one algorithm and make a distribute implementation of it.
Mixing Newton and ADMM, ADMM and Interior point looks risky to me, though with a lot of subiterations it may work(that’s why it’s distributed – require a lot of calculating power)
Also I’m not sure about convergene of the combined algorithm – each step’s convergence is proven, but I’m not sure the same could be applyed to the combination.
Newton and ADMM have kind of contradicting optimal conditions – smoothness vs piecewise linearity. Would like to see more research on this…
http://arxiv.org/abs/1312.5440
Total variation regularization for manifold-valued data
Proximal mapping and soft thresholding for manifolds – analog of ADMM for manifolds.
http://arxiv.org/abs/1312.7710
just interesting stuff
Coping with Physical Attacks on Random Network Structures
Include finding vulnerable spots and results of random attacks
(My comment – shouldn’t it be connected to precolation theory?)
http://arxiv.org/abs/1312.6189
Finds in arxiv, october
This is duplication of my ongoing G+ series of post on interesting for me papers in arxiv. Older posts are not here but in my G+ thread.
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)
http://arxiv.org/abs/1309.2094
Stochastic gradient descent and the randomized Kaczmarz algorithm
Hybrid of randomized Kaczmarz and stochastic gradient descent – into my “to read” pile
http://arxiv.org/abs/1310.5715
Trust–Region Problems with Linear Inequality Constraints: Exact SDP Relaxation, Global Optimality and Robust Optimization
“Extended” trust region for linear inequalities constrains
http://arxiv.org/abs/1309.3000
Conic Geometric Programming
Unifing framwork for conic and geometric programming
http://arxiv.org/abs/1310.0899
http://en.wikipedia.org/wiki/Geometric_programming
http://en.wikipedia.org/wiki/Conic_programming
Gauge optimization, duality, and applications
Another big paper about different, not Lagrange duality, introduced by Freund (1987)
http://arxiv.org/abs/1310.2639
Color Bregman TV
mu parameters in split bregman made adaptive, to exploit coherence of edges in different color channels
http://arxiv.org/abs/1310.3146
Iteration Complexity Analysis of Block Coordinate Descent Methods
Some convergence analysis for BCD and projected gradient BCD
http://arxiv.org/abs/1310.6957
Successive Nonnegative Projection Algorithm for Robust Nonnegative Blind Source Separation
Nonnegative matrix factorization
http://arxiv.org/abs/1310.7529
Scaling SVM and Least Absolute Deviations via Exact Data Reduction
SVN for large-scale problems
http://arxiv.org/abs/1310.7048
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.
http://arxiv.org/abs/1310.3447
*Deep learning*
Deep and Wide Multiscale Recursive Networks for Robust Image Labeling
_Open source_ matlab/c package coming soon(not yet available)
http://arxiv.org/abs/1310.0354
Improvements to deep convolutional neural networks for LVCSR
convolutional networks, droput for speech recognition,
http://arxiv.org/abs/1309.1501v1
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.
http://arxiv.org/abs/1310.1531
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.
http://arxiv.org/abs/1301.7115
Randomized co-training: from cortical neurons to machine learning and back again
“Selectron” instead of perception – neurons are “specializing” with weights.
http://arxiv.org/abs/1310.6536
Provable Bounds for Learning Some Deep Representations
http://arxiv.org/abs/1310.6343
Citation:”The current paper presents both an interesting family of denoising autoencoders as
*Computer vision*
Online Unsupervised Feature Learning for Visual Tracking
Sparse representation, overcomplete dictionary
http://arxiv.org/abs/1310.1690
From Shading to Local Shape
Shape restoration from local shading – could be very useful in low-feature environment.
http://arxiv.org/abs/1310.2916
Fast 3D Salient Region Detection in Medical Images using GPUs
Finding interest point in 3D images
http://arxiv.org/abs/1310.6736
Object Recognition System Design in Computer Vision: a Universal Approach
Grid-based universal framework for object detection/classification
http://arxiv.org/abs/1310.7170
Gaming :)
Lyapunov-based Low-thrust Optimal Orbit Transfer: An approach in Cartesian coordinates
For space sim enthusiast :)
http://arxiv.org/abs/1310.4201
Mirror Image 