# If you did not already know

Zero-Shot Knowledge Distillation
Knowledge distillation deals with the problem of training a smaller model (Student) from a high capacity source model (Teacher) so as to retain most of its performance. Existing approaches use either the training data or meta-data extracted from it in order to train the Student. However, accessing the dataset on which the Teacher has been trained may not always be feasible if the dataset is very large or it poses privacy or safety concerns (e.g., bio-metric or medical data). Hence, in this paper, we propose a novel data-free method to train the Student from the Teacher. Without even using any meta-data, we synthesize the Data Impressions from the complex Teacher model and utilize these as surrogates for the original training data samples to transfer its learning to Student via knowledge distillation. We, therefore, dub our method ‘Zero-Shot Knowledge Distillation’ and demonstrate that our framework results in competitive generalization performance as achieved by distillation using the actual training data samples on multiple benchmark datasets. …

InferLine
The dominant cost in production machine learning workloads is not training individual models but serving predictions from increasingly complex prediction pipelines spanning multiple models, machine learning frameworks, and parallel hardware accelerators. Due to the complex interaction between model configurations and parallel hardware, prediction pipelines are challenging to provision and costly to execute when serving interactive latency-sensitive applications. This challenge is exacerbated by the unpredictable dynamics of bursty workloads. In this paper we introduce InferLine, a system which efficiently provisions and executes ML inference pipelines subject to end-to-end latency constraints by proactively optimizing and reactively controlling per-model configuration in a fine-grained fashion. Unpredictable changes in the serving workload are dynamically and cost-optimally accommodated with minimal service level degradation. InferLine introduces (1) automated model profiling and pipeline lineage extraction, (2) a fine-grain, cost-minimizing pipeline configuration planner, and (3) a fine-grain reactive controller. InferLine is able to configure and deploy prediction pipelines across a wide range of workload patterns and latency goals. It outperforms coarse-grained configuration alternatives by up 7.6x in cost while achieving up to 32x lower SLO miss rate on real workloads and generalizes across state-of-the-art model serving frameworks. …

1-Nearest-Neighbor-Based Multiclass Learning
This paper deals with Nearest-Neighbor (NN) learning algorithms in metric spaces. Initiated by Fix and Hodges in 1951 , this seemingly simplistic learning paradigm remains competitive against more sophisticated methods and, in its celebrated k-NN version, has been placed on a solid theoretical foundation. Although the classic 1-NN is well known to be inconsistent in general, in recent years a series of papers has presented variations on the theme of a regularized 1-NN classifier, as an alternative to the Bayesconsistent k-NN. Gottlieb et al. showed that approximate nearest neighbor search can act as a regularizer, actually improving generalization performance rather than just injecting noise. In a follow-up work, showed that applying Structural Risk Minimization to (essentially) the margin-regularized datadependent bound in yields a strongly Bayes-consistent 1-NN classifier. A further development has seen margin-based regularization analyzed through the lens of sample compression: a near-optimal nearest neighbor condensing algorithm was presented and later extended to cover semimetric spaces ; an activized version also appeared. As detailed in , margin-regularized 1-NN methods enjoy a number of statistical and computational advantages over the traditional k-NN classifier. Salient among these are explicit data-dependent generalization bounds, and considerable runtime and memory savings. Sample compression affords additional advantages, in the form of tighter generalization bounds and increased efficiency in time and space. …

Non Metric Space (Approximate) Library (NMSLIB)
A Non-Metric Space Library (‘NMSLIB’ <https://…/nmslib> ) wrapper, which according to the authors ‘is an efficient cross-platform similarity search library and a toolkit for evaluation of similarity search methods. The goal of the ‘NMSLIB’ <https://…/nmslib> Library is to create an effective and comprehensive toolkit for searching in generic non-metric spaces. Being comprehensive is important, because no single method is likely to be sufficient in all cases. Also note that exact solutions are hardly efficient in high dimensions and/or non-metric spaces. Hence, the main focus is on approximate methods’. The wrapper also includes Approximate Kernel k-Nearest-Neighbor functions based on the ‘NMSLIB’ <https://…/nmslib> ‘Python’ Library. …

# If you did not already know

RobustSTL
Decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. Although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. In the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. Specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. Based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. This process is repeated until accurate decomposition is obtained. Experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions. …

Deterministic Stretchy Regression
An extension of the regularized least-squares in which the estimation parameters are stretchable is introduced and studied in this paper. The solution of this ridge regression with stretchable parameters is given in primal and dual spaces and in closed-form. Essentially, the proposed solution stretches the covariance computation by a power term, thereby compressing or amplifying the estimation parameters. To maintain the computation of power root terms within the real space, an input transformation is proposed. The results of an empirical evaluation in both synthetic and real-world data illustrate that the proposed method is effective for compressive learning with high-dimensional data. …

We propose to take a novel approach to robot system design where each building block of a larger system is represented as a differentiable program, i.e. a deep neural network. This representation allows for integrating algorithmic planning and deep learning in a principled manner, and thus combine the benefits of model-free and model-based methods. We apply the proposed approach to a challenging partially observable robot navigation task. The robot must navigate to a goal in a previously unseen 3-D environment without knowing its initial location, and instead relying on a 2-D floor map and visual observations from an onboard camera. We introduce the Navigation Networks (NavNets) that encode state estimation, planning and acting in a single, end-to-end trainable recurrent neural network. In preliminary simulation experiments we successfully trained navigation networks to solve the challenging partially observable navigation task. …

DOLORES
We introduce a new method DOLORES for learning knowledge graph embeddings that effectively captures contextual cues and dependencies among entities and relations. First, we note that short paths on knowledge graphs comprising of chains of entities and relations can encode valuable information regarding their contextual usage. We operationalize this notion by representing knowledge graphs not as a collection of triples but as a collection of entity-relation chains, and learn embeddings for entities and relations using deep neural models that capture such contextual usage. In particular, our model is based on Bi-Directional LSTMs and learn deep representations of entities and relations from constructed entity-relation chains. We show that these representations can very easily be incorporated into existing models to significantly advance the state of the art on several knowledge graph prediction tasks like link prediction, triple classification, and missing relation type prediction (in some cases by at least 9.5%). …

# If you did not already know

Partial Order Pruning
Achieving good speed and accuracy trade-off on target platform is very important in deploying deep neural networks. Most existing automatic architecture search approaches only pursue high performance but ignores such an important factor. In this work, we propose an algorithm ‘Partial Order Pruning’ to prune architecture search space with partial order assumption, quickly lift the boundary of speed/accuracy trade-off on target platform, and automatically search the architecture with the best speed and accuracy trade-off. Our algorithm explicitly take profile information about the inference speed on target platform into consideration. With the proposed algorithm, we present several ‘Dongfeng’ networks that provide high accuracy and fast inference speed on various application GPU platforms. By further searching decoder architecture, our DF-Seg real-time segmentation models yields state-of-the-art speed/accuracy trade-off on both embedded device and high-end GPU. …

Open-Domain Spoken Question Answering Dataset (ODSQA)
Reading comprehension by machine has been widely studied, but machine comprehension of spoken content is still a less investigated problem. In this paper, we release Open-Domain Spoken Question Answering Dataset (ODSQA) with more than three thousand questions. To the best of our knowledge, this is the largest real SQA dataset. On this dataset, we found that ASR errors have catastrophic impact on SQA. To mitigate the effect of ASR errors, subword units are involved, which brings consistent improvements over all the models. We further found that data augmentation on text-based QA training examples can improve SQA. …

Variational Walkback
We propose a novel method to directly learn a stochastic transition operator whose repeated application provides generated samples. Traditional undirected graphical models approach this problem indirectly by learning a Markov chain model whose stationary distribution obeys detailed balance with respect to a parameterized energy function. The energy function is then modified so the model and data distributions match, with no guarantee on the number of steps required for the Markov chain to converge. Moreover, the detailed balance condition is highly restrictive: energy based models corresponding to neural networks must have symmetric weights, unlike biological neural circuits. In contrast, we develop a method for directly learning arbitrarily parameterized transition operators capable of expressing non-equilibrium stationary distributions that violate detailed balance, thereby enabling us to learn more biologically plausible asymmetric neural networks and more general non-energy based dynamical systems. The proposed training objective, which we derive via principled variational methods, encourages the transition operator to ‘walk back’ in multi-step trajectories that start at data-points, as quickly as possible back to the original data points. We present a series of experimental results illustrating the soundness of the proposed approach, Variational Walkback (VW), on the MNIST, CIFAR-10, SVHN and CelebA datasets, demonstrating superior samples compared to earlier attempts to learn a transition operator. We also show that although each rapid training trajectory is limited to a finite but variable number of steps, our transition operator continues to generate good samples well past the length of such trajectories, thereby demonstrating the match of its non-equilibrium stationary distribution to the data distribution. Source Code: http://…/walkback_nips17

Deep Decoder
Deep neural networks, in particular convolutional neural networks, have become highly effective tools for compressing images and solving inverse problems including denoising, inpainting, and reconstruction from few and noisy measurements. This success can be attributed in part to their ability to represent and generate natural images well. Contrary to classical tools such as wavelets, image-generating deep neural networks have a large number of parameters—typically a multiple of their output dimension—and need to be trained on large datasets. In this paper, we propose an untrained simple image model, called the deep decoder, which is a deep neural network that can generate natural images from very few weight parameters. The deep decoder has a simple architecture with no convolutions and fewer weight parameters than the output dimensionality. This underparameterization enables the deep decoder to compress images into a concise set of network weights, which we show is on par with wavelet-based thresholding. Further, underparameterization provides a barrier to overfitting, allowing the deep decoder to have state-of-the-art performance for denoising. The deep decoder is simple in the sense that each layer has an identical structure that consists of only one upsampling unit, pixel-wise linear combination of channels, ReLU activation, and channelwise normalization. This simplicity makes the network amenable to theoretical analysis, and it sheds light on the aspects of neural networks that enable them to form effective signal representations. …

# If you did not already know

MatrixDS
Work on your own projects, collaborate with others, and share with the whole community on a secure cloud-based platform. …

MediChainTM
The set of distributed ledger architectures known as blockchain is best known for cryptocurrency applications such as Bitcoin and Ethereum. These permissionless block chains are showing the potential to be disruptive to the financial services industry. Their broader adoption is likely to be limited by the maximum block size, the cost of the Proof of Work consensus mechanism, and the increasing size of any given chain overwhelming most of the participating nodes. These factors have led to many cryptocurrency blockchains to become centralized in the nodes with enough computing power and storage to be a dominant miner and validator. Permissioned chains operate in trusted environments and can, therefore, avoid the computationally expensive consensus mechanisms. Permissioned chains are still susceptible to asset storage demands and non-standard user interfaces that will impede their adoption. This paper describes an approach to addressing these limitations: permissioned blockchain that uses off-chain storage of the data assets and this is accessed through a standard browser and mobile app. The implementation in the Hyperledger framework is described as is an example use of patient-centered health data management. …

Data Fusion
Data fusion is the process of integration of multiple data and knowledge representing the same real-world object into a consistent, accurate, and useful representation. Data fusion processes are often categorized as low, intermediate or high, depending on the processing stage at which fusion takes place. Low level data fusion combines several sources of raw data to produce new raw data. The expectation is that fused data is more informative and synthetic than the original inputs. For example, sensor fusion is also known as (multi-sensor) data fusion and is a subset of information fusion. …

PingAn
Geo-distributed data analysis in a cloud-edge system is emerging as a daily demand. Out of saving time in wide area data transfer, some tasks are dispersed to the edge clusters satisfied data locality. However, execution in the edge clusters is less well, due to limited resource, overload interference and cluster-level unreachable troubles, which obstructs the guarantee on the speed and completion of jobs. Synthesizing the impact of cluster heterogeneity and costly inter-cluster data fetch, we expect to make effective copies across clusters for tasks to provide both success and efficiency of the arriving jobs. To this end, we design PingAn, an online insurance algorithm making redundance across-cluster copies for tasks, promising $(1+\varepsilon)-speed \, o(\frac{1}{\varepsilon^2+\varepsilon})-competitive$ in sum of the job flowtimes. PingAn shares resource among a part of jobs with an adjustable $\varepsilon$ fraction to fit the system load condition and insures for tasks following efficiency-first reliability-aware principle to optimize the effect of copies on jobs’ performance. Trace-driven simulations demonstrate that PingAn can reduce the average job flowtimes by at least $14\%$ more than the state-of-the-art speculation mechanisms. We also build PingAn in Spark on Yarn System to verify its practicality and generality. Experiments show that PingAn can reduce the average job completion time by up to $40\%$ comparing to the default Spark execution. …

# If you did not already know

Net-Trim
We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network. The method is a convex post-processing module, which prunes (sparsifies) a trained network layer by layer, while preserving the internal responses. We present a comprehensive analysis of Net-Trim from both the algorithmic and sample complexity standpoints, centered on a fast, scalable convex optimization program. Our analysis includes consistency results between the initial and retrained models before and after Net-Trim application and guarantees on the number of training samples needed to discover a network that can be expressed using a certain number of nonzero terms. Specifically, if there is a set of weights that uses at most $s$ terms that can re-create the layer outputs from the layer inputs, we can find these weights from $\mathcal{O}(s\log N/s)$ samples, where $N$ is the input size. These theoretical results are similar to those for sparse regression using the Lasso, and our analysis uses some of the same recently-developed tools (namely recent results on the concentration of measure and convex analysis). Finally, we propose an algorithmic framework based on the alternating direction method of multipliers (ADMM), which allows a fast and simple implementation of Net-Trim for network pruning and compression. …

F^3T
Standard automatic methods for recognizing problematic code can be greatly improved via the incremental application of human+artificial expertise. In this approach, call $F^3T$, AI tools explore software to find commits that they guess is most problematic. Humans the apply their expertise to check that guess (perhaps resulting in the AI updating the support vectors within their SVM learner). We recommend this human+AI partnership, for several reasons. When a new domain is encountered, $F^3T$ can learn better ways to label which comments refer to real problems. Further, in studies with 9 open source software projects, $F^3T$’s incremental application of human+artificial intelligence is at least an order of magnitude cheaper to use than existing methods. Lastly, $F^3T$ is very effective. For the data sets explored here, when compared to standard methods, $F^3T$ improved $P_{opt}(20)$ and G-scores performance by 26\% and 48\% on median value. …

Turek-Fletcher Model
Model-averaging is commonly used as a means of allowing for model uncertainty in parameter estimation. In the frequentist framework, a model-averaged estimate of a parameter is the weighted mean of the estimates from each of the candidate models, the weights typically being chosen using an information criterion. Current methods for calculating a model-averaged confidence interval assume approximate normality of the model-averaged estimate, i.e., they are Wald intervals. As in the single-model setting, we might improve the coverage performance of this interval by a one-to-one transformation of the parameter, obtaining a Wald interval, and then back-transforming the endpoints. However, a transformation that works in the single-model setting may not when model-averaging, due to the weighting and the need to estimate the weights. In the single-model setting, a natural alternative is to use a profile likelihood interval, which generally provides better coverage than a Wald interval. We propose a method for model-averaging a set of single-model profile likelihood intervals, making use of the link between profile likelihood intervals and Bayesian credible intervals. We illustrate its use in an example involving negative binomial regression, and perform two simulation studies to compare its coverage properties with the existing Wald intervals. …

SpykeTorch
Application of deep convolutional spiking neural networks (SNNs) to artificial intelligence (AI) tasks has recently gained a lot of interest since SNNs are hardware-friendly and energy-efficient. Unlike the non-spiking counterparts, most of the existing SNN simulation frameworks are not practically efficient enough for large-scale AI tasks. In this paper, we introduce SpykeTorch, an open-source high-speed simulation framework based on PyTorch. This framework simulates convolutional SNNs with at most one spike per neuron and the rank-order encoding scheme. In terms of learning rules, both spike-timing-dependent plasticity (STDP) and reward-modulated STDP (R-STDP) are implemented, but other rules could be implemented easily. Apart from the aforementioned properties, SpykeTorch is highly generic and capable of reproducing the results of various studies. Computations in the proposed framework are tensor-based and totally done by PyTorch functions, which in turn brings the ability of just-in-time optimization for running on CPUs, GPUs, or Multi-GPU platforms. …

# If you did not already know

Architecture Search, Anneal and Prune (ASAP)
Automatic methods for Neural Architecture Search (NAS) have been shown to produce state-of-the-art network models, yet, their main drawback is the computational complexity of the search process. As some primal methods optimized over a discrete search space, thousands of days of GPU were required for convergence. A recent approach is based on constructing a differentiable search space that enables gradient-based optimization, thus reducing the search time to a few days. While successful, such methods still include some incontinuous steps, e.g., the pruning of many weak connections at once. In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations, thus the search converges to a single output network in a continuous manner. Experiments on several vision datasets demonstrate the effectiveness of our method with respect to the search cost, accuracy and the memory footprint of the achieved model. …

Knowledge of Preconditions Principle (KoP)
The Knowledge of Preconditions principle (KoP) is proposed as a widely applicable connection between knowledge and action in multi-agent systems. Roughly speaking, it asserts that if some condition is a necessary condition for performing a given action A, then knowing that this condition holds is also a necessary condition for performing A. Since the specifications of tasks often involve necessary conditions for actions, the KoP principle shows that such specifications induce knowledge preconditions for the actions. Distributed protocols or multi-agent plans that satisfy the specifications must ensure that this knowledge be attained, and that it is detected by the agents as a condition for action. The knowledge of preconditions principle is formalised in the runs and systems framework, and is proven to hold in a wide class of settings. Well-known connections between knowledge and coordinated action are extended and shown to derive directly from the KoP principle: a ‘common knowledge of preconditions’ principle is established showing that common knowledge is a necessary condition for performing simultaneous actions, and a ‘nested knowledge of preconditions’ principle is proven, showing that coordinating actions to be performed in linear temporal order requires a corresponding form of nested knowledge. …

Dynamic Emulation Algorithm (DEA)
We consider solution of stochastic storage problems through regression Monte Carlo (RMC) methods. Taking a statistical learning perspective, we develop the dynamic emulation algorithm (DEA) that unifies the different existing approaches in a single modular template. We then investigate the two central aspects of regression architecture and experimental design that constitute DEA. For the regression piece, we discuss various non-parametric approaches, in particular introducing the use of Gaussian process regression in the context of stochastic storage. For simulation design, we compare the performance of traditional design (grid discretization), against space-filling, and several adaptive alternatives. The overall DEA template is illustrated with multiple examples drawing from natural gas storage valuation and optimal control of back-up generator in a microgrid. …

Analysis-of-Marginal-Tail-Means (ATM)
This paper presents a novel method, called Analysis-of-marginal-Tail-Means (ATM), for parameter optimization over a large, discrete design space. The key advantage of ATM is that it offers effective and robust optimization performance for both smooth and rugged response surfaces, using only a small number of function evaluations. This method can therefore tackle a wide range of engineering problems, particularly in applications where the performance metric to optimize is ‘black-box’ and expensive to evaluate. The ATM framework unifies two parameter optimization methods in the literature: the Analysis-of-marginal-Means (AM) approach (Taguchi, 1986), and the Pick-the-Winner (PW) approach (Wu et al., 1990). In this paper, we show that by providing a continuum between AM and PW via the novel idea of marginal tail means, the proposed method offers a balance between three fundamental trade-offs. By adaptively tuning these trade-offs, ATM can then provide excellent optimization performance over a broad class of response surfaces using limited data. We illustrate the effectiveness of ATM using several numerical examples, and demonstrate how such a method can be used to solve two real-world engineering design problems. …

# If you did not already know

Textual Membership Queries
Human labeling of textual data can be very time-consuming and expensive, yet it is critical for the success of an automatic text classification system. In order to minimize human labeling efforts, we propose a novel active learning (AL) solution, that does not rely on existing sources of unlabeled data. It uses a small amount of labeled data as the core set for the synthesis of useful membership queries (MQs) – unlabeled instances synthesized by an algorithm for human labeling. Our solution uses modification operators, functions from the instance space to the instance space that change the input to some extent. We apply the operators on the core set, thus creating a set of new membership queries. Using this framework, we look at the instance space as a search space and apply search algorithms in order to create desirable MQs. We implement this framework in the textual domain. The implementation includes using methods such as WordNet and Word2vec, for replacing text fragments from a given sentence with semantically related ones. We test our framework on several text classification tasks and show improved classifier performance as more MQs are labeled and incorporated into the training set. To the best of our knowledge, this is the first work on membership queries in the textual domain. …

Cohen’s d
Cohen’s d is defined as the difference between two means divided by a standard deviation for the data. Cohen’s d is a measure of effect size for the difference of two means that takes the variance of the population into account. …

Digital Passport
In order to prevent deep neural networks from being infringed by unauthorized parties, we propose a generic solution which embeds a designated digital passport into a network, and subsequently, either paralyzes the network functionalities for unauthorized usages or maintain its functionalities in the presence of a verified passport. Such a desired network behavior is successfully demonstrated in a number of implementation schemes, which provide reliable, preventive and timely protections against tens of thousands of fake-passport deceptions. Extensive experiments also show that the deep neural network performance under unauthorized usages deteriorate significantly (e.g. with 33% to 82% reductions of CIFAR10 classification accuracies), while networks endorsed with valid passports remain intact. …

Syntree2Vec
Word embeddings aims to map sense of the words into a lower dimensional vector space in order to reason over them. Training embeddings on domain specific data helps express concepts more relevant to their use case but comes at a cost of accuracy when data is less. Our effort is to minimise this by infusing syntactic knowledge into the embeddings. We propose a graph based embedding algorithm inspired from node2vec. Experimental results have shown that our algorithm improves the syntactic strength and gives robust performance on meagre data. …

# If you did not already know

DocBERT
Pre-trained language representation models achieve remarkable state of the art across a wide range of tasks in natural language processing. One of the latest advancements is BERT, a deep pre-trained transformer that yields much better results than its predecessors do. Despite its burgeoning popularity, however, BERT has not yet been applied to document classification. This task deserves attention, since it contains a few nuances: first, modeling syntactic structure matters less for document classification than for other problems, such as natural language inference and sentiment classification. Second, documents often have multiple labels across dozens of classes, which is uncharacteristic of the tasks that BERT explores. In this paper, we describe fine-tuning BERT for document classification. We are the first to demonstrate the success of BERT on this task, achieving state of the art across four popular datasets. …

Anonymous Information Delivery (AID)
We introduce the problem of anonymous information delivery (AID), comprised of $K$ messages, a user, and $N$ servers (each holds $M$ messages) that wish to deliver one out of $K$ messages to the user anonymously, i.e., without revealing the delivered message index to the user. This AID problem may be viewed as the dual of the private information retrieval problem. The information theoretic capacity of AID, $C$, is defined as the maximum number of bits of the desired message that can be anonymously delivered per bit of total communication to the user. For the AID problem with $K$ messages, $N$ servers, $M$ messages stored per server, and $N \geq \lceil \frac{K}{M} \rceil$, we provide an achievable scheme of rate $1/\lceil \frac{K}{M} \rceil$ and an information theoretic converse of rate $M/K$, i.e., the AID capacity satisfies $1/\lceil \frac{K}{M} \rceil \leq C \leq M/K$. This settles the capacity of AID when $\frac{K}{M}$ is an integer. When $\frac{K}{M}$ is not an integer, we show that the converse rate of $M/K$ is achievable if $N \geq \frac{K}{\gcd(K,M)} – (\frac{M}{\gcd(K,M)}-1)(\lfloor \frac{K}{M} \rfloor -1)$, and the achievable rate of $1/\lceil \frac{K}{M} \rceil$ is optimal if $N = \lceil \frac{K}{M} \rceil$. Otherwise if $\lceil \frac{K}{M} \rceil < N < \frac{K}{\gcd(K,M)} – (\frac{M}{\gcd(K,M)}-1)(\lfloor \frac{K}{M} \rfloor -1)$, we give an improved achievable scheme and prove its optimality for several small settings. …

We present a simple neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations. Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-‘coordinate’ channels, and apply a fully convolutional network with 1×1 stride. This provides an architectural prior for dissociating positional from non-positional features in the latent distribution of VAEs, yet without providing any explicit supervision to this effect. We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space. It provides a particularly dramatic benefit when applied to datasets with small objects. We also emphasize a method for visualizing learned latent spaces that helped us diagnose our models and may prove useful for others aiming to assess data representations. Finally, we show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance. …

Multi-Kernel Correntropy (MKC)
As a novel similarity measure that is defined as the expectation of a kernel function between two random variables, correntropy has been successfully applied in robust machine learning and signal processing to combat large outliers. The kernel function in correntropy is usually a zero-mean Gaussian kernel. In a recent work, the concept of mixture correntropy (MC) was proposed to improve the learning performance, where the kernel function is a mixture Gaussian kernel, namely a linear combination of several zero-mean Gaussian kernels with different widths. In both correntropy and mixture correntropy, the center of the kernel function is, however, always located at zero. In the present work, to further improve the learning performance, we propose the concept of multi-kernel correntropy (MKC), in which each component of the mixture Gaussian kernel can be centered at a different location. The properties of the MKC are investigated and an efficient approach is proposed to determine the free parameters in MKC. Experimental results show that the learning algorithms under the maximum multi-kernel correntropy criterion (MMKCC) can outperform those under the original maximum correntropy criterion (MCC) and the maximum mixture correntropy criterion (MMCC). …

# If you did not already know

Probabilistic Soft Logic (PSL)
Probabilistic soft logic (PSL) is a machine learning framework for developing probabilistic models. PSL models are easy to use and fast. You can define models using a straightforward logical syntax and solve them with fast convex optimization. PSL has produced state-of-the-art results in many areas spanning natural language processing, social-network analysis, knowledge graphs, recommender system, and computational biology. The PSL framework is available as an Apache-licensed, open source project on GitHub with an active user group for support.
Inferring New Relationships using the Probabilistic Soft Logic

Probabilistic Partial Least Squares (PPLS)
With a rapid increase in volume and complexity of data sets there is a need for methods that can extract useful information in these data sets. Dimension reduction approaches such as Partial least squares (PLS) are increasingly being utilized for finding relationships between two data sets. However these methods often lack a probabilistic formulation, hampering development of more flexible models. Moreover dimension reduction methods in general suffer from identifiability problems, causing difficulties in combining and comparing results from multiple studies. We propose Probabilistic PLS (PPLS) as an extension of PLS to model the overlap between two data sets. The likelihood formulation provides opportunities to address issues typically present in data, such as missing entries and heterogeneity between subjects. We show that the PPLS parameters are identifiable up to sign. We derive Maximum Likelihood estimators that respect the identifiability conditions by using an EM algorithm with a constrained optimization in the M step. A simulation study is conducted and we observe a good performance of the PPLS estimates in various scenarios, when compared to PLS estimates. Most notably the estimates seem to be robust against departures from normality. To illustrate the PPLS model, we apply it to real IgG glycan data from two cohorts. We infer the contributions of each variable to the correlated part and observe very similar behavior across cohorts. …

Fabrik
We present Fabrik, an online neural network editor that provides tools to visualize, edit, and share neural networks from within a browser. Fabrik provides a simple and intuitive GUI to import neural networks written in popular deep learning frameworks such as Caffe, Keras, and TensorFlow, and allows users to interact with, build, and edit models via simple drag and drop. Fabrik is designed to be framework agnostic and support high interoperability, and can be used to export models back to any supported framework. Finally, it provides powerful collaborative features to enable users to iterate over model design remotely and at scale. …

Semi-supervised and unsupervised Generative Adversarial Networks (GAN)-based methods have been gaining popularity in anomaly detection task recently. However, GAN training is somewhat challenging and unstable. Inspired from previous work in GAN-based image generation, we introduce a GAN-based anomaly detection framework – Adversarial Dual Autoencoders (ADAE) – consists of two autoencoders as generator and discriminator to increase training stability. We also employ discriminator reconstruction error as anomaly score for better detection performance. Experiments across different datasets of varying complexity show strong evidence of a robust model that can be used in different scenarios, one of which is brain tumor detection. …