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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 1567 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
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Recurrent Batch Normalization

Cooijmans, Tim and Ballas, Nicolas and Laurent, César and Courville, Aaron

arXiv e-Print archive - 2016 via Local Bibsonomy

Keywords: dblp

Cooijmans, Tim and Ballas, Nicolas and Laurent, César and Courville, Aaron

arXiv e-Print archive - 2016 via Local Bibsonomy

Keywords: dblp

[link]
This paper describes how to apply the idea of batch normalization (BN) successfully to recurrent neural networks, specifically to LSTM networks. The technique involves the 3 following ideas: **1) Careful initialization of the BN scaling parameter.** While standard practice is to initialize it to 1 (to have unit variance), they show that this situation creates problems with the gradient flow through time, which vanishes quickly. A value around 0.1 (used in the experiments) preserves gradient flow much better. **2) Separate BN for the "hiddens to hiddens pre-activation and for the "inputs to hiddens" pre-activation.** In other words, 2 separate BN operators are applied on each contributions to the pre-activation, before summing and passing through the tanh and sigmoid non-linearities. **3) Use of largest time-step BN statistics for longer test-time sequences.** Indeed, one issue with applying BN to RNNs is that if the input sequences have varying length, and if one uses per-time-step mean/variance statistics in the BN transformation (which is the natural thing to do), it hasn't been clear how do deal with the last time steps of longer sequences seen at test time, for which BN has no statistics from the training set. The paper shows evidence that the pre-activation statistics tend to gradually converge to stationary values over time steps, which supports the idea of simply using the training set's last time step statistics. Among these ideas, I believe the most impactful idea is 1). The papers mentions towards the end that improper initialization of the BN scaling parameter probably explains previous failed attempts to apply BN to recurrent networks. Experiments on 4 datasets confirms the method's success. **My two cents** This is an excellent development for LSTMs. BN has had an important impact on our success in training deep neural networks, and this approach might very well have a similar impact on the success of LSTMs in practice. |

RIN4 Functions with Plasma Membrane H+-ATPases to Regulate Stomatal Apertures during Pathogen Attack

Jun Liu and James M. Elmore and Anja T. Fuglsang and Michael G. Palmgren and Brian J. Staskawicz and Gitta Coaker

PLoS Biology - 2009 via Local CrossRef

Keywords:

Jun Liu and James M. Elmore and Anja T. Fuglsang and Michael G. Palmgren and Brian J. Staskawicz and Gitta Coaker

PLoS Biology - 2009 via Local CrossRef

Keywords:

[link]
Pathogen perception by the plant innate immune system is of central importance to plant survival and productivity. The Arabidopsis protein RIN4 is a negative regulator of plant immunity. In order to identify additional proteins involved in RIN4-mediated immune signal transduction, we purified components of the RIN4 protein complex. We identified six novel proteins that had not previously been implicated in RIN4 signaling, including the plasma membrane (PM) H+-ATPases AHA1 and/or AHA2. RIN4 interacts with AHA1 and AHA2 both in vitro and in vivo. RIN4 overexpression and knockout lines exhibit differential PM H+-ATPase activity. PM H+-ATPase activation induces stomatal opening, enabling bacteria to gain entry into the plant leaf; inactivation induces stomatal closure thus restricting bacterial invasion. The rin4 knockout line exhibited reduced PM H+-ATPase activity and, importantly, its stomata could not be re-opened by virulent Pseudomonas syringae. We also demonstrate that RIN4 is expressed in guard cells, highlighting the importance of this cell type in innate immunity. These results indicate that the Arabidopsis protein RIN4 functions with the PM H+-ATPase to regulate stomatal apertures, inhibiting the entry of bacterial pathogens into the plant leaf during infection. Author Summary Top Plants are continuously exposed to microorganisms. In order to resist infection, plants rely on their innate immune system to inhibit both pathogen entry and multiplication. We investigated the function of the Arabidopsis protein RIN4, which acts as a negative regulator of plant innate immunity. We biochemically identified six novel RIN4-associated proteins and characterized the association between RIN4 and the plasma membrane H+-ATPase pump. Our results indicate that RIN4 functions in concert with this pump to regulate leaf stomata during the innate immune response, when stomata close to block the entry of bacterial pathogens into the leaf interior. |

Near-optimal probabilistic RNA-seq quantification

Nicolas L Bray and Harold Pimentel and Páll Melsted and Lior Pachter

Nature Biotechnology - 2016 via Local CrossRef

Keywords:

Nicolas L Bray and Harold Pimentel and Páll Melsted and Lior Pachter

Nature Biotechnology - 2016 via Local CrossRef

Keywords:

[link]
This paper from 2016 introduced a new k-mer based method to estimate isoform abundance from RNA-Seq data called kallisto. The method provided a significant improvement in speed and memory usage compared to the previously used methods while yielding similar accuracy. In fact, kallisto is able to quantify expression in a matter of minutes instead of hours. The standard (previous) methods for quantifying expression rely on mapping, i.e. on the alignment of a transcriptome sequenced reads to a genome of reference. Reads are assigned to a position in the genome and the gene or isoform expression values are derived by counting the number of reads overlapping the features of interest. The idea behind kallisto is to rely on a pseudoalignment which does not attempt to identify the positions of the reads in the transcripts, only the potential transcripts of origin. Thus, it avoids doing an alignment of each read to a reference genome. In fact, kallisto only uses the transcriptome sequences (not the whole genome) in its first step which is the generation of the kallisto index. Kallisto builds a colored de Bruijn graph (T-DBG) from all the k-mers found in the transcriptome. Each node of the graph corresponds to a k-mer (a short sequence of k nucleotides) and retains the information about the transcripts in which they can be found in the form of a color. Linear stretches having the same coloring in the graph correspond to transcripts. Once the T-DBG is built, kallisto stores a hash table mapping each k-mer to its transcript(s) of origin along with the position within the transcript(s). This step is done only once and is dependent on a provided annotation file (containing the sequences of all the transcripts in the transcriptome). Then for a given sequenced sample, kallisto decomposes each read into its k-mers and uses those k-mers to find a path covering in the T-DBG. This path covering of the transcriptome graph, where a path corresponds to a transcript, generates k-compatibility classes for each k-mer, i.e. sets of potential transcripts of origin on the nodes. The potential transcripts of origin for a read can be obtained using the intersection of its k-mers k-compatibility classes. To make the pseudoalignment faster, kallisto removes redundant k-mers since neighboring k-mers often belong to the same transcripts. Figure1, from the paper, summarizes these different steps. https://i.imgur.com/eNH2kuO.png **Figure1**. Overview of kallisto. The input consists of a reference transcriptome and reads from an RNA-seq experiment. (a) An example of a read (in black) and three overlapping transcripts with exonic regions as shown. (b) An index is constructed by creating the transcriptome de Bruijn Graph (T-DBG) where nodes (v1, v2, v3, ... ) are k-mers, each transcript corresponds to a colored path as shown and the path cover of the transcriptome induces a k-compatibility class for each k-mer. (c) Conceptually, the k-mers of a read are hashed (black nodes) to find the k-compatibility class of a read. (d) Skipping (black dashed lines) uses the information stored in the T-DBG to skip k-mers that are redundant because they have the same k-compatibility class. (e) The k-compatibility class of the read is determined by taking the intersection of the k-compatibility classes of its constituent k-mers.[From Bray et al. Near-optimal probabilistic RNA-seq quantification, Nature Biotechnology, 2016.] Then, kallisto optimizes the following RNA-Seq likelihood function using the expectation-maximization (EM) algorithm. $$L(\alpha) \propto \prod_{f \in F} \sum_{t \in T} y_{f,t} \frac{\alpha_t}{l_t} = \prod_{e \in E}\left( \sum_{t \in e} \frac{\alpha_t}{l_t} \right )^{c_e}$$ In this function, $F$ is the set of fragments (or reads), $T$ is the set of transcripts, $l_t$ is the (effective) length of transcript $t$ and **y**$_{f,t}$ is a compatibility matrix defined as 1 if fragment $f$ is compatible with $t$ and 0 otherwise. The parameters $α_t$ are the probabilities of selecting reads from a transcript $t$. These $α_t$ are the parameters of interest since they represent the isoforms abundances or relative expressions. To make things faster, the compatibility matrix is collapsed (factorized) into equivalence classes. An equivalent class consists of all the reads compatible with the same subsets of transcripts. The EM algorithm is applied to equivalence classes (not to reads). Each $α_t$ will be optimized to maximise the likelihood of transcript abundances given observations of the equivalence classes. The speed of the method makes it possible to evaluate the uncertainty of the abundance estimates for each RNA-Seq sample using a bootstrap technique. For a given sample containing $N$ reads, a bootstrap sample is generated from the sampling of $N$ counts from a multinomial distribution over the equivalence classes derived from the original sample. The EM algorithm is applied on those sampled equivalence class counts to estimate transcript abundances. The bootstrap information is then used in downstream analyses such as determining which genes are differentially expressed. Practically, we can illustrate the different steps involved in kallisto using a small example. Starting from a tiny genome with 3 transcripts, assume that the RNA-Seq experiment produced 4 reads as depicted in the image below. https://i.imgur.com/5JDpQO8.png The first step is to build the T-DBG graph and the kallisto index. All transcript sequences are decomposed into k-mers (here k=5) to construct the colored de Bruijn graph. Not all nodes are represented in the following drawing. The idea is that each different transcript will lead to a different path in the graph. The strand is not taken into account, kallisto is strand-agnostic. https://i.imgur.com/4oW72z0.png Once the index is built, the four reads of the sequenced sample can be analysed. They are decomposed into k-mers (k=5 here too) and the pre-built index is used to determine the k-compatibility class of each k-mer. Then, the k-compatibility class of each read is computed. For example, for read 1, the intersection of all the k-compatibility classes of its k-mers suggests that it might come from transcript 1 or transcript 2. https://i.imgur.com/woektCH.png This is done for the four reads enabling the construction of the compatibility matrix **y**$_{f,t}$ which is part of the RNA-Seq likelihood function. In this equation, the $α_t$ are the parameters that we want to estimate. https://i.imgur.com/Hp5QJvH.png The EM algorithm being too slow to be applied on millions of reads, the compatibility matrix **y**$_{f,t}$ is factorized into equivalence classes and a count is computed for each class (how many reads are represented by this equivalence class). The EM algorithm uses this collapsed information to maximize the new equivalent RNA-Seq likelihood function and optimize the $α_t$. https://i.imgur.com/qzsEq8A.png The EM algorithm stops when for every transcript $t$, $α_tN$ > 0.01 changes less than 1%, where $N$ is the total number of reads. |

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Ioffe, Sergey and Szegedy, Christian

International Conference on Machine Learning - 2015 via Local Bibsonomy

Keywords: dblp

Ioffe, Sergey and Szegedy, Christian

International Conference on Machine Learning - 2015 via Local Bibsonomy

Keywords: dblp

[link]
The main contribution of this paper is introducing a new transformation that the authors call Batch Normalization (BN). The need for BN comes from the fact that during the training of deep neural networks (DNNs) the distribution of each layer’s input change. This phenomenon is called internal covariate shift (ICS). #### What is BN? Normalize each (scalar) feature independently with respect to the mean and variance of the mini batch. Scale and shift the normalized values with two new parameters (per activation) that will be learned. The BN consists of making normalization part of the model architecture. #### What do we gain? According to the author, the use of BN provides a great speed up in the training of DNNs. In particular, the gains are greater when it is combined with higher learning rates. In addition, BN works as a regularizer for the model which allows to use less dropout or less L2 normalization. Furthermore, since the distribution of the inputs is normalized, it also allows to use sigmoids as activation functions without the saturation problem. #### What follows? This seems to be specially promising for training recurrent neural networks (RNNs). The vanishing and exploding gradient problems \cite{journals/tnn/BengioSF94} have their origin in the iteration of transformation that scale up or down the activations in certain directions (eigenvectors). It seems that this regularization would be specially useful in this context since this would allow the gradient to flow more easily. When we unroll the RNNs, we usually have ultra deep networks. #### Like * Simple idea that seems to improve training. * Makes training faster. * Simple to implement. Probably. * You can be less careful with initialization. #### Dislike * Does not work with stochastic gradient descent (minibatch size = 1). * This could reduce the parallelism of the algorithm since now all the examples in a mini batch are tied. * Results on ensemble of networks for ImageNet makes it harder to evaluate the relevance of BN by itself. (Although they do mention the performance of a single model). |

Comparing Rewinding and Fine-tuning in Neural Network Pruning

Renda, Alex and Frankle, Jonathan and Carbin, Michael

International Conference on Learning Representations - 2020 via Local Bibsonomy

Keywords: dblp

Renda, Alex and Frankle, Jonathan and Carbin, Michael

International Conference on Learning Representations - 2020 via Local Bibsonomy

Keywords: dblp

[link]
This is an interestingly pragmatic paper that makes a super simple observation. Often, we may want a usable network with fewer parameters, to make our network more easily usable on small devices. It's been observed (by these same authors, in fact), that pruned networks can achieve comparable weights to their fully trained counterparts if you rewind and retrain from early in the training process, to compensate for the loss of the (not ultimately important) pruned weights. This observation has been dubbed the "Lottery Ticket Hypothesis", after the idea that there's some small effective subnetwork you can find if you sample enough networks. Given these two facts - the usefulness of pruning, and the success of weight rewinding - the authors explore the effectiveness of various ways to train after pruning. Current standard practice is to prune low-magnitude weights, and then continue training remaining weights from values they had at pruning time, keeping the final learning rate of the network constant. The authors find that: 1. Weight rewinding, where you rewind weights to *near* their starting value, and then retrain using the learning rates of early in training, outperforms fine tuning from the place weights were when you pruned but, also 2. Learning rate rewinding, where you keep weights as they are, but rewind learning rates to what they were early in training, are actually the most effective for a given amount of training time/search cost To me, this feels a little bit like burying the lede: the takeaway seems to be that when you prune, it's beneficial to make your network more "elastic" (in the metaphor-to-neuroscience sense) so it can more effectively learn to compensate for the removed neurons. So, what was really valuable in weight rewinding was the ability to "heat up" learning on a smaller set of weights, so they could adapt more quickly. And the fact that learning rate rewinding works better than weight rewinding suggests that there is value in the learned weights after all, that value is just outstripped by the benefit of rolling back to old learning rates. All in all, not a super radical conclusion, but a useful and practical one to have so clearly laid out in a paper. |

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