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- ShortScience.org is a platform for post-publication discussion aiming to improve accessibility and reproducibility of research ideas.
- The website has 1548 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
- Reading summaries of papers is useful to obtain the perspective and insight of another reader, why they liked or disliked it, and their attempt to demystify complicated sections.
- Also, writing summaries is a good exercise to understand the content of a paper because you are forced to challenge your assumptions when explaining it.
- Finally, you can keep up to date with the flood of research by reading the latest summaries on our Twitter and Facebook pages.

Learning to Estimate 3D Hand Pose from Single RGB Images

Zimmermann, Christian and Brox, Thomas

International Conference on Computer Vision - 2017 via Local Bibsonomy

Keywords: dblp

Zimmermann, Christian and Brox, Thomas

International Conference on Computer Vision - 2017 via Local Bibsonomy

Keywords: dblp

[link]
This paper estimate 3D hand shape from **single** RGB images based on deep learning. The overall pipeline is the following: https://i.imgur.com/H72P5ns.png 1. **Hand Segmentation** network is derived from this [paper](https://arxiv.org/pdf/1602.00134.pdf) but, in essence, any segmentation network would do the job. Hand image is cropped from the original image by utilizing segmentation mask and resized to a fixed size (256x256) with bilinear interpolation. 2. **Detecting hand keypoints**. 2D Keypoint detection is formulated as predicting score map for each hand joints (fixed size = 21). Encoder-decoder architecture is used. 3. **3D hand pose estimation**. https://i.imgur.com/uBheX3o.png - In this paper, the hand pose is represented as $w_i = (x_i, y_i, z_i)$, where $i$ is index for a particular hand joint. This representation is further normalized $w_i^{norm} = \frac{1}{s} \cdot w_i$, where $s = ||w_{k+1} - w_{k} ||$, and relative position to a reference joint $r$ (palm) is obtained as $w_i^{rel} = w_i^{norm} - w_r^{norm}$. - The network predicts coordinates within a canonical frame and additionally estimate the transformation into the canonical frame (as opposite to predicting absolute 3D coordinates). Therefore, the network predicts $w^{c^*} = R(w^{rel}) \cdot w^{rel}$ and $R(w^{rel}) = R_y \cdot R_{xz}$. Information whether left/right hand is the input is concatenated to flattened feature representation. The training loss is composed of a separate term for canonical coordinates and canonical transformation matrix L2 losses. Contribution: - Apparently, the first method to perform 3D hand shape estimation from a single RGB image rather than using both RGB and depth sensors; - Possible extension to sign language recognition problem by attaching classifier on predicted 3D poses. While this approach quite accurately predicts hand 3D poses among frames, they often fluctuate among frames. Probably several techniques (i.e. optical flow, RNN, post-processing smoothing) can be used for ensuring temporal consistency and make predictions more stable across frames. |

Self-Normalizing Neural Networks

Günter Klambauer and Thomas Unterthiner and Andreas Mayr and Sepp Hochreiter

arXiv e-Print archive - 2017 via Local arXiv

Keywords: cs.LG, stat.ML

**First published:** 2017/06/08 (4 years ago)

**Abstract:** Deep Learning has revolutionized vision via convolutional neural networks
(CNNs) and natural language processing via recurrent neural networks (RNNs).
However, success stories of Deep Learning with standard feed-forward neural
networks (FNNs) are rare. FNNs that perform well are typically shallow and,
therefore cannot exploit many levels of abstract representations. We introduce
self-normalizing neural networks (SNNs) to enable high-level abstract
representations. While batch normalization requires explicit normalization,
neuron activations of SNNs automatically converge towards zero mean and unit
variance. The activation function of SNNs are "scaled exponential linear units"
(SELUs), which induce self-normalizing properties. Using the Banach fixed-point
theorem, we prove that activations close to zero mean and unit variance that
are propagated through many network layers will converge towards zero mean and
unit variance -- even under the presence of noise and perturbations. This
convergence property of SNNs allows to (1) train deep networks with many
layers, (2) employ strong regularization, and (3) to make learning highly
robust. Furthermore, for activations not close to unit variance, we prove an
upper and lower bound on the variance, thus, vanishing and exploding gradients
are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning
repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with
standard FNNs and other machine learning methods such as random forests and
support vector machines. SNNs significantly outperformed all competing FNN
methods at 121 UCI tasks, outperformed all competing methods at the Tox21
dataset, and set a new record at an astronomy data set. The winning SNN
architectures are often very deep. Implementations are available at:
github.com/bioinf-jku/SNNs.
more
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Günter Klambauer and Thomas Unterthiner and Andreas Mayr and Sepp Hochreiter

arXiv e-Print archive - 2017 via Local arXiv

Keywords: cs.LG, stat.ML

[link]
_Objective:_ Design Feed-Forward Neural Network (fully connected) that can be trained even with very deep architectures. * _Dataset:_ [MNIST](yann.lecun.com/exdb/mnist/), [CIFAR10](https://www.cs.toronto.edu/%7Ekriz/cifar.html), [Tox21](https://tripod.nih.gov/tox21/challenge/) and [UCI tasks](https://archive.ics.uci.edu/ml/datasets/optical+recognition+of+handwritten+digits). * _Code:_ [here](https://github.com/bioinf-jku/SNNs) ## Inner-workings: They introduce a new activation functio the Scaled Exponential Linear Unit (SELU) which has the nice property of making neuron activations converge to a fixed point with zero-mean and unit-variance. They also demonstrate that upper and lower bounds and the variance and mean for very mild conditions which basically means that there will be no exploding or vanishing gradients. The activation function is: [![screen shot 2017-06-14 at 11 38 27 am](https://user-images.githubusercontent.com/17261080/27125901-1a4f7276-50f6-11e7-857d-ebad1ac94789.png)](https://user-images.githubusercontent.com/17261080/27125901-1a4f7276-50f6-11e7-857d-ebad1ac94789.png) With specific parameters for alpha and lambda to ensure the previous properties. The tensorflow impementation is: def selu(x): alpha = 1.6732632423543772848170429916717 scale = 1.0507009873554804934193349852946 return scale*np.where(x>=0.0, x, alpha*np.exp(x)-alpha) They also introduce a new dropout (alpha-dropout) to compensate for the fact that [![screen shot 2017-06-14 at 11 44 42 am](https://user-images.githubusercontent.com/17261080/27126174-e67d212c-50f6-11e7-8952-acad98b850be.png)](https://user-images.githubusercontent.com/17261080/27126174-e67d212c-50f6-11e7-8952-acad98b850be.png) ## Results: Batch norm becomes obsolete and they are also able to train deeper architectures. This becomes a good choice to replace shallow architectures where random forest or SVM used to be the best results. They outperform most other techniques on small datasets. [![screen shot 2017-06-14 at 11 36 30 am](https://user-images.githubusercontent.com/17261080/27125798-bd04c256-50f5-11e7-8a74-b3b6a3fe82ee.png)](https://user-images.githubusercontent.com/17261080/27125798-bd04c256-50f5-11e7-8a74-b3b6a3fe82ee.png) Might become a new standard for fully-connected activations in the future. |

Algorithms for Non-negative Matrix Factorization

Lee, Daniel D. and Seung, H. Sebastian

Neural Information Processing Systems Conference - 2000 via Local Bibsonomy

Keywords: dblp

Lee, Daniel D. and Seung, H. Sebastian

Neural Information Processing Systems Conference - 2000 via Local Bibsonomy

Keywords: dblp

[link]
We want to find two matrices $W$ and $H$ such that $V = WH$. Often a goal is to determine underlying patterns in the relationships between the concepts represented by each row and column. $W$ is some $m$ by $n$ matrix and we want the inner dimension of the factorization to be $r$. So $$\underbrace{V}_{m \times n} = \underbrace{W}_{m \times r} \underbrace{H}_{r \times n}$$ Let's consider an example matrix where of three customers (as rows) are associated with three movies (the columns) by a rating value. $$ V = \left[\begin{array}{c c c} 5 & 4 & 1 \\\\ 4 & 5 & 1 \\\\ 2 & 1 & 5 \end{array}\right] $$ We can decompose this into two matrices with $r = 1$. First lets do this without any non-negative constraint using an SVD reshaping matrices based on removing eigenvalues: $$ W = \left[\begin{array}{c c c} -0.656 \\\ -0.652 \\\ -0.379 \end{array}\right], H = \left[\begin{array}{c c c} -6.48 & -6.26 & -3.20\\\\ \end{array}\right] $$ We can also decompose this into two matrices with $r = 1$ subject to the constraint that $w_{ij} \ge 0$ and $h_{ij} \ge 0$. (Note: this is only possible when $v_{ij} \ge 0$): $$ W = \left[\begin{array}{c c c} 0.388 \\\\ 0.386 \\\\ 0.224 \end{array}\right], H = \left[\begin{array}{c c c} 11.22 & 10.57 & 5.41 \\\\ \end{array}\right] $$ Both of these $r=1$ factorizations reconstruct matrix $V$ with the same error. $$ V \approx WH = \left[\begin{array}{c c c} 4.36 & 4.11 & 2.10 \\\ 4.33 & 4.08 & 2.09 \\\ 2.52 & 2.37 & 1.21 \\\ \end{array}\right] $$ If they both yield the same reconstruction error then why is a non-negativity constraint useful? We can see above that it is easy to observe patterns in both factorizations such as similar customers and similar movies. `TODO: motivate why NMF is better` #### Paper Contribution This paper discusses two approaches for iteratively creating a non-negative $W$ and $H$ based on random initial matrices. The paper discusses a multiplicative update rule where the elements of $W$ and $H$ are iteratively transformed by scaling each value such that error is not increased. The multiplicative approach is discussed in contrast to an additive gradient decent based approach where small corrections are iteratively applied. The multiplicative approach can be reduced to this by setting the learning rate ($\eta$) to a ratio that represents the magnitude of the element in $H$ to the scaling factor of $W$ on $H$. ### Still a draft |

Deep Residual Learning for Image Recognition

He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian

arXiv e-Print archive - 2015 via Local Bibsonomy

Keywords: dblp

[link]
Deeper networks should never have a higher **training** error than smaller ones. In the worst case, the layers should "simply" learn identities. It seems as this is not so easy with conventional networks, as they get much worse with more layers. So the idea is to add identity functions which skip some layers. The network only has to learn the **residuals**. Advantages: * Learning the identity becomes learning 0 which is simpler * Loss in information flow in the forward pass is not a problem anymore * No vanishing / exploding gradient * Identities don't have parameters to be learned ## Evaluation The learning rate starts at 0.1 and is divided by 10 when the error plateaus. Weight decay of 0.0001 ($10^{-4}$), momentum of 0.9. They use mini-batches of size 128. * ImageNet ILSVRC 2015: 3.57% (ensemble) * CIFAR-10: 6.43% * MS COCO: 59.0% mAp@0.5 (ensemble) * PASCAL VOC 2007: 85.6% mAp@0.5 * PASCAL VOC 2012: 83.8% mAp@0.5 ## See also * [DenseNets](http://www.shortscience.org/paper?bibtexKey=journals/corr/1608.06993) |

Neural Turing Machines

Graves, Alex and Wayne, Greg and Danihelka, Ivo

arXiv e-Print archive - 2014 via Local Bibsonomy

Keywords: dblp

Graves, Alex and Wayne, Greg and Danihelka, Ivo

arXiv e-Print archive - 2014 via Local Bibsonomy

Keywords: dblp

[link]
TLDR; The authors propose Neural Turing Machines (NTMs). A NTM consists of a memory bank and a controller network. The controller network (LSTM or MLP in this paper) controls read/write heads by focusing their attention softly, using a distribution over all memory addresses. It can learn the parameters for two addressing mechanisms: Content-based addressing ("find similar items") and location-based addressing. NTMs can be trained end-to-end using gradient descent. The authors evaluate NTMs on program generations tasks and compare their performance against that of LSTMs. Tasks include copying, recall, prediction, and sorting binary vectors. While both LSTMs and NTMs seems to perform well on training data, only NTMs are able to generalize to longer sequences. #### Key Observations - Controller network tried with LSTM or MLP. Which one works better is task-dependent, but LSTM "cache" can be a bottleneck. - Controller size, number of read/write heads, and memory size are hyperparameters. - Monitoring the memory addressing shows that the NTM actually learns meaningful programs. - Number LSTM parameters grow quadratically with hidden unit size due to recurrent connection, not so for NTMs, leading to models with fewer parameters. - Example problems are very small, typically using sequences 8 bit vectors. #### Notes/Questions - At what length to NTMs stop to work? Would've liked to see where results get significantly worse. - Can we automatically transform fuzzy NTM programs into deterministic ones? |

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