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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 1583 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.

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen

arXiv e-Print archive - 2018 via Local arXiv

Keywords: cs.CV

**First published:** 2018/01/13 (5 years ago)

**Abstract:** In this paper we describe a new mobile architecture, MobileNetV2, that
improves the state of the art performance of mobile models on multiple tasks
and benchmarks as well as across a spectrum of different model sizes. We also
describe efficient ways of applying these mobile models to object detection in
a novel framework we call SSDLite. Additionally, we demonstrate how to build
mobile semantic segmentation models through a reduced form of DeepLabv3 which
we call Mobile DeepLabv3.
The MobileNetV2 architecture is based on an inverted residual structure where
the input and output of the residual block are thin bottleneck layers opposite
to traditional residual models which use expanded representations in the input
an MobileNetV2 uses lightweight depthwise convolutions to filter features in
the intermediate expansion layer. Additionally, we find that it is important to
remove non-linearities in the narrow layers in order to maintain
representational power. We demonstrate that this improves performance and
provide an intuition that led to this design. Finally, our approach allows
decoupling of the input/output domains from the expressiveness of the
transformation, which provides a convenient framework for further analysis. We
measure our performance on Imagenet classification, COCO object detection, VOC
image segmentation. We evaluate the trade-offs between accuracy, and number of
operations measured by multiply-adds (MAdd), as well as the number of
parameters
more
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Mark Sandler and Andrew Howard and Menglong Zhu and Andrey Zhmoginov and Liang-Chieh Chen

arXiv e-Print archive - 2018 via Local arXiv

Keywords: cs.CV

[link]
This work expands on prior techniques for designing models that can both be stored using fewer parameters, and also execute using fewer operations and less memory, both of which are key desiderata for having trained machine learning models be usable on phones and other personal devices. The main contribution of the original MobileNets paper was to introduce the idea of using "factored" decompositions of Depthwise and Pointwise convolutions, which separate the procedures of "pull information from a spatial range" and "mix information across channels" into two distinct steps. In this paper, they continue to use this basic Depthwise infrastructure, but also add a new design element: the inverted-residual linear bottleneck. The reasoning behind this new layer type comes from the observation that, often, the set of relevant points in a high-dimensional space (such as the 'per-pixel' activations inside a conv net) actually lives on a lower-dimensional manifold. So, theoretically, and naively, one could just try to use lower dimensional internal representations to map the dimensionality of that assumed manifold. However, the authors argue that ReLU non-linearities kill information (because of the region where all inputs are mapped to zero), and so having layers contain only the number of dimensions needed for the manifold would mean that you end up with too-few dimensions after the ReLU information loss. However, you need to have non-linearities somewhere in the network in order to be able to learn complex, non-linear functions. So, the authors suggest a method to mostly use smaller-dimensional representations internally, but still maintain ReLus and the network's needed complexity. https://i.imgur.com/pN4d9Wi.png - A lower-dimensional output is "projected up" into a higher dimensional output - A ReLu is applied on this higher-dimensional layer - That layer is then projected down into a smaller-dimensional layer, which uses a linear activation to avoid information loss - A residual connection between the lower-dimensional output at the beginning and end of the expansion This way, we still maintain the network's non-linearity, but also replace some of the network's higher-dimensional layers with lower-dimensional linear ones |

Tumor Phylogeny Topology Inference via Deep Learning

Erfan Sadeqi Azer and Mohammad Haghir Ebrahimabadi and Salem Malikić and Roni Khardon and S. Cenk Sahinalp

bioRxiv: The preprint server for biology - 0 via Local CrossRef

Keywords:

Erfan Sadeqi Azer and Mohammad Haghir Ebrahimabadi and Salem Malikić and Roni Khardon and S. Cenk Sahinalp

bioRxiv: The preprint server for biology - 0 via Local CrossRef

Keywords:

[link]
A very simple (but impractical) discrete model of subclonal evolution would include the following events: * Division of a cell to create two cells: * **Mutation** at a location in the genome of the new cells * Cell death at a new timestep * Cell survival at a new timestep Because measurements of mutations are usually taken at one time point, this is taken to be at the end of a time series of these events, where a tiny of subset of cells are observed and a **genotype matrix** $A$ is produced, in which mutations and cells are arbitrarily indexed such that $A_{i,j} = 1$ if mutation $j$ exists in cell $i$. What this matrix allows us to see is the proportion of cells which *both have mutation $j$*. Unfortunately, I don't get to observe $A$, in practice $A$ has been corrupted by IID binary noise to produce $A'$. This paper focuses on difference inference problems given $A'$, including *inferring $A$*, which is referred to as **`noise_elimination`**. The other problems involve inferring only properties of the matrix $A$, which are referred to as: * **`noise_inference`**: predict whether matrix $A$ would satisfy the *three gametes rule*, which asks if a given genotype matrix *does not describe a branching phylogeny* because a cell has inherited mutations from two different cells (which is usually assumed to be impossible under the infinite sites assumption). This can be computed exactly from $A$. * **Branching Inference**: it's possible that all mutations are inherited between the cells observed; in which case there are *no branching events*. The paper states that this can be computed by searching over permutations of the rows and columns of $A$. The problem is to predict from $A'$ if this is the case. In both problems inferring properties of $A$, the authors use fully connected networks with two hidden layers on simulated datasets of matrices. For **`noise_elimination`**, computing $A$ given $A'$, the authors use a network developed for neural machine translation called a [pointer network][pointer]. They also find it necessary to map $A'$ to a matrix $A''$, turning every element in $A'$ to a fixed length row containing the location, mutation status and false positive/false negative rate. Unfortunately, reported results on real datasets are reported only for branching inference and are limited by the restriction on input dimension. The inferred branching probability reportedly matches that reported in the literature. [pointer]: https://arxiv.org/abs/1409.0473 |

Gaussian Processes in Machine Learning

Rasmussen, Carl Edward

Springer Advanced Lectures on Machine Learning - 2003 via Local Bibsonomy

Keywords: dblp

Rasmussen, Carl Edward

Springer Advanced Lectures on Machine Learning - 2003 via Local Bibsonomy

Keywords: dblp

[link]
In this tutorial paper, Carl E. Rasmussen gives an introduction to Gaussian Process Regression focusing on the definition, the hyperparameter learning and future research directions. A Gaussian Process is completely defined by its mean function $m(\pmb{x})$ and its covariance function (kernel) $k(\pmb{x},\pmb{x}')$. The mean function $m(\pmb{x})$ corresponds to the mean vector $\pmb{\mu}$ of a Gaussian distribution whereas the covariance function $k(\pmb{x}, \pmb{x}')$ corresponds to the covariance matrix $\pmb{\Sigma}$. Thus, a Gaussian Process $f \sim \mathcal{GP}\left(m(\pmb{x}), k(\pmb{x}, \pmb{x}')\right)$ is a generalization of a Gaussian distribution over vectors to a distribution over functions. A random function vector $\pmb{\mathrm{f}}$ can be generated by a Gaussian Process through the following procedure: 1. Compute the components $\mu_i$ of the mean vector $\pmb{\mu}$ for each input $\pmb{x}_i$ using the mean function $m(\pmb{x})$ 2. Compute the components $\Sigma_{ij}$ of the covariance matrix $\pmb{\Sigma}$ using the covariance function $k(\pmb{x}, \pmb{x}')$ 3. A function vector $\pmb{\mathrm{f}} = [f(\pmb{x}_1), \dots, f(\pmb{x}_n)]^T$ can be drawn from the Gaussian distribution $\pmb{\mathrm{f}} \sim \mathcal{N}\left(\pmb{\mu}, \pmb{\Sigma} \right)$ Applying this procedure to regression, means that the resulting function vector $\pmb{\mathrm{f}}$ shall be drawn in a way that a function vector $\pmb{\mathrm{f}}$ is rejected if it does not comply with the training data $\mathcal{D}$. This is achieved by conditioning the distribution on the training data $\mathcal{D}$ yielding the posterior Gaussian Process $f \rvert \mathcal{D} \sim \mathcal{GP}(m_D(\pmb{x}), k_D(\pmb{x},\pmb{x}'))$ for noise-free observations with the posterior mean function $m_D(\pmb{x}) = m(\pmb{x}) + \pmb{\Sigma}(\pmb{X},\pmb{x})^T \pmb{\Sigma}^{-1}(\pmb{\mathrm{f}} - \pmb{\mathrm{m}})$ and the posterior covariance function $k_D(\pmb{x},\pmb{x}')=k(\pmb{x},\pmb{x}') - \pmb{\Sigma}(\pmb{X}, \pmb{x}')$ with $\pmb{\Sigma}(\pmb{X},\pmb{x})$ being a vector of covariances between every training case of $\pmb{X}$ and $\pmb{x}$. Noisy observations $y(\pmb{x}) = f(\pmb{x}) + \epsilon$ with $\epsilon \sim \mathcal{N}(0,\sigma_n^2)$ can be taken into account with a second Gaussian Process with mean $m$ and covariance function $k$ resulting in $f \sim \mathcal{GP}(m,k)$ and $y \sim \mathcal{GP}(m, k + \sigma_n^2\delta_{ii'})$. The figure illustrates the cases of noisy observations (variance at training points) and of noise-free observationshttps://i.imgur.com/BWvsB7T.png (no variance at training points). In the Machine Learning perspective, the mean and the covariance function are parametrised by hyperparameters and provide thus a way to include prior knowledge e.g. knowing that the mean function is a second order polynomial. To find the optimal hyperparameters $\pmb{\theta}$, 1. determine the log marginal likelihood $L= \mathrm{log}(p(\pmb{y} \rvert \pmb{x}, \pmb{\theta}))$, 2. take the first partial derivatives of $L$ w.r.t. the hyperparameters, and 3. apply an optimization algorithm. It should be noted that a regularization term is not necessary for the log marginal likelihood $L$ because it already contains a complexity penalty term. Also, the tradeoff between data-fit and penalty is performed automatically. Gaussian Processes provide a very flexible way for finding a suitable regression model. However, they require the high computational complexity $\mathcal{O}(n^3)$ due to the inversion of the covariance matrix. In addition, the generalization of Gaussian Processes to non-Gaussian likelihoods remains complicated. |

Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables

Rakelly, Kate and Zhou, Aurick and Quillen, Deirdre and Finn, Chelsea and Levine, Sergey

arXiv e-Print archive - 2019 via Local Bibsonomy

Keywords: dblp

Rakelly, Kate and Zhou, Aurick and Quillen, Deirdre and Finn, Chelsea and Levine, Sergey

arXiv e-Print archive - 2019 via Local Bibsonomy

Keywords: dblp

[link]
Rakelly et al. propose a method to do off-policy meta reinforcement learning (rl). The method achieves a 20-100x improvement on sample efficiency compared to on-policy meta rl like MAML+TRPO. The key difficulty for offline meta rl arises from the meta-learning assumption, that meta-training and meta-test time match. However during test time the policy has to explore and sees as such on-policy data which is in contrast to the off-policy data that should be used at meta-training. The key contribution of PEARL is an algorithm that allows for online task inference in a latent variable at train and test time, which is used to train a Soft Actor Critic, a very sample efficient off-policy algorithm, with additional dependence of the latent variable. The implementation of Rakelly et al. proposes to capture knowledge about the current task in a latent stochastic variable Z. A inference network $q_{\Phi}(z \vert c)$ is used to predict the posterior over latents given context c of the current task in from of transition tuples $(s,a,r,s')$ and trained with an information bottleneck. Note that the task inference is done on samples according to a sampling strategy sampling more recent transitions. The latent z is used as an additional input to policy $\pi(a \vert s, z)$ and Q-function $Q(a,s,z)$ of a soft actor critic algorithm which is trained with offline data of the full replay buffer. https://i.imgur.com/wzlmlxU.png So the challenge of differing conditions at test and train times is resolved by sampling the content for the latent context variable at train time only from very recent transitions (which is almost on-policy) and at test time by construction on-policy. Sampling $z \sim q(z \vert c)$ at test time allows for posterior sampling of the latent variable, yielding efficient exploration. The experiments are performed across 6 Mujoco tasks with ProMP, MAML+TRPO and $RL^2$ with PPO as baselines. They show: - PEARL is 20-100x more sample-efficient - the posterior sampling of the latent context variable enables deep exploration that is crucial for sparse reward settings - the inference network could be also a RNN, however it is crucial to train it with uncorrelated transitions instead of trajectories that have high correlated transitions - using a deterministic latent variable, i.e. reducing $q_{\Phi}(z \vert c)$ to a point estimate, leaves the algorithm unable to solve sparse reward navigation tasks which is attributed to the lack of temporally extended exploration. The paper introduces an algorithm that allows to combine meta learning with an off-policy algorithm that dramatically increases the sample-efficiency compared to on-policy meta learning approaches. This increases the chance of seeing meta rl in any sort of real world applications. |

Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction

Kumar, Aviral and Fu, Justin and Soh, Matthew and Tucker, George and Levine, Sergey

Neural Information Processing Systems Conference - 2019 via Local Bibsonomy

Keywords: dblp

Kumar, Aviral and Fu, Justin and Soh, Matthew and Tucker, George and Levine, Sergey

Neural Information Processing Systems Conference - 2019 via Local Bibsonomy

Keywords: dblp

[link]
Kumar et al. propose an algorithm to learn in batch reinforcement learning (RL), a setting where an agent learns purely form a fixed batch of data, $B$, without any interactions with the environments. The data in the batch is collected according to a batch policy $\pi_b$. Whereas most previous methods (like BCQ) constrain the learned policy to stay close to the behavior policy, Kumar et al. propose bootstrapping error accumulation reduction (BEAR), which constrains the newly learned policy to place some probability mass on every non negligible action. The difference is illustrated in the picture from the BEAR blog post: https://i.imgur.com/zUw7XNt.png The behavior policy is in both images the dotted red line, the left image shows the policy matching where the algorithm is constrained to the purple choices, while the right image shows the support matching. **Theoretical Contribution:** The paper analysis formally how the use of out-of-distribution actions to compute the target in the Bellman equation influences the back-propagated error. Firstly a distribution constrained backup operator is defined as $T^{\Pi}Q(s,a) = \mathbb{E}[R(s,a) + \gamma \max_{\pi \in \Pi} \mathbb{E}_{P(s' \vert s,a)} V(s')]$ and $V(s) = \max_{\pi \in \Pi} \mathbb{E}_{\pi}[Q(s,a)]$ which considers only policies $\pi \in \Pi$. It is possible that the optimal policy $\pi^*$ is not contained in the policy set $\Pi$, thus there is a suboptimallity constant $\alpha (\Pi) = \max_{s,a} \vert \mathcal{T}^{\Pi}Q^{*}(s,a) - \mathcal{T}Q^{*}(s,a) ]\vert $ which captures how far $\pi^{*}$ is from $\Pi$. Letting $P^{\pi_i}$ be the transition-matrix when following policy $\pi_i$, $\rho_0$ the state marginal distribution of the training data in the batch and $\pi_1, \dots, \pi_k \in \Pi $. The error analysis relies upon a concentrability assumption $\rho_0 P^{\pi_1} \dots P^{\pi_k} \leq c(k)\mu(s)$, with $\mu(s)$ the state marginal. Note that $c(k)$ might be infinite if the support of $\Pi$ is not contained in the state marginal of the batch. Using the coefficients $c(k)$ a concentrability coefficient is defined as: $C(\Pi) = (1-\gamma)^2\sum_{k=1}^{\infty}k \gamma^{k-1}c(k).$ The concentrability takes values between 1 und $\infty$, where 1 corresponds to the case that the batch data were collected by $\pi$ and $\Pi = \{\pi\}$ and $\infty$ to cases where $\Pi$ has support outside of $\pi$. Combining this Kumar et a. get a bound of the Bellman error for distribution constrained value iteration with the constrained Bellman operator $T^{\Pi}$: $\lim_{k \rightarrow \infty} \mathbb{E}_{\rho_0}[\vert V^{\pi_k}(s)- V^{*}(s)] \leq \frac{\gamma}{(1-\gamma^2)} [C(\Pi) \mathbb{E}_{\mu}[\max_{\pi \in \Pi}\mathbb{E}_{\pi}[\delta(s,a)] + \frac{1-\gamma}{\gamma}\alpha(\Pi) ] ]$, where $\delta(s,a)$ is the Bellman error. This presents the inherent batch RL trade-off between keeping policies close to the behavior policy of the batch (captured by $C(\Pi)$ and keeping $\Pi$ sufficiently large (captured by $\alpha(\Pi)$). It is finally proposed to use support sets to construct $\Pi$, that is $\Pi_{\epsilon} = \{\pi \vert \pi(a \vert s)=0 \text{ whenever } \beta(a \vert s) < \epsilon \}$. This amounts to the set of all policies that place probability on all non-negligible actions of the behavior policy. For this particular choice of $\Pi = \Pi_{\epsilon}$ the concentrability coefficient can be bounded. **Algorithm**: The algorithm has an actor critic style, where the Q-value to update the policy is taken to be the minimum over the ensemble. The support constraint to place at least some probability mass on every non negligible action from the batch is enforced via sampled MMD. The proposed algorithm is a member of the policy regularized algorithms as the policy is updated to optimize: $\pi_{\Phi} = \max_{\pi} \mathbb{E}_{s \sim B} \mathbb{E}_{a \sim \pi(\cdot \vert s)} [min_{j = 1 \dots, k} Q_j(s,a)] s.t. \mathbb{E}_{s \sim B}[MMD(D(s), \pi(\cdot \vert s))] \leq \epsilon$ The Bellman target to update the Q-functions is computed as the convex combination of minimum and maximum of the ensemble. **Experiments** The experiments use the Mujoco environments Halfcheetah, Walker, Hopper and Ant. Three scenarios of batch collection, always consisting of 1Mio. samples, are considered: - completely random behavior policy - partially trained behavior policy - optimal policy as behavior policy The experiments confirm that BEAR outperforms other off-policy methods like BCQ or KL-control. The ablations show further that the choice of MMD is crucial as it is sometimes on par and sometimes substantially better than choosing KL-divergence. |

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