We present a tool for Interactive Visual Exploration of Latent Space (IVELS) for model selection.  Evaluating generative models of discrete sequences from a  continuous  latent  space  is  a  challenging  problem,  since  their  optimization involves multiple competing objective terms.  We introduce a model-selection pipeline  to  compare  and  filter  models  throughout  consecutive  stages  of  more complex and expensive metrics.We present the pipeline in an interactive visual tool to enable the exploration of the metrics, analysis of the learned latent space, and selection of the best model for a given task.  We focus specifically on the variational auto-encoder family in a case study of modeling peptide sequences, which are short sequences of amino acids.This task is especially interesting due to the presence of multiple attributes we want to model.We demonstrate how an interactive visual comparison can assist in evaluating how well an unsupervised auto-encoder meaningfully captures the attributes of interest in its latent space.

Wir präsentieren ein visuelles Werkzeug zur interaktiven Erkundung des latenten Raums eines Auto-Encoders für Peptidsequenzen und deren Attribute.


Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding.This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons.While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown.We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing.During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training.During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction.These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments.

Wir berichten über Experimente, die eindeutig belegen, dass sich ein Neuron während des Trainings und der Tests wie ein binärer Klassifikator verhält.


Automatic classification of objects is one of the most important tasks in engineeringand data mining applications.Although using more complex and advancedclassifiers can help to improve the accuracy of classification systems, it can bedone by analyzing data sets and their features for a particular problem.Featurecombination is the one which can improve the quality of the features.In this paper,a structure similar to Feed-Forward Neural Network (FFNN) is used to generate anoptimized linear or non-linear combination of features for classification.GeneticAlgorithm (GA) is applied to update weights and biases.Since nature of data setsand their features impact on the effectiveness of combination and classificationsystem, linear and non-linear activation functions (or transfer function) are usedto achieve more reliable system.Experiments of several UCI data sets and usingminimum distance classifier as a simple classifier indicate that proposed linear andnon-linear intelligent FFNN-based feature combination can present more reliableand promising results.By using such a feature combination method, there is noneed to use more powerful and complex classifier anymore.

Ein Verfahren zur Anreicherung und Kombination von Merkmalen zur Verbesserung der Klassifizierungsgenauigkeit.


Recent improvements to Generative Adversarial Networks (GANs) have made it possible to generate realistic images in high resolution based on natural language descriptions such as image captions.Furthermore, conditional GANs allow us to control the image generation process through labels or even natural language descriptions.However, fine-grained control of the image layout, i.e. where in the image specific objects should be located, is still difficult to achieve.This is especially true for images that should contain multiple distinct objects at different spatial locations.We introduce a new approach which allows us to control the location of arbitrarily many objects within an image by adding an object pathway to both the generator and the discriminator.Our approach does not need a detailed semantic layout but only bounding boxes and the respective labels of the desired objects are needed.The object pathway focuses solely on the individual objects and is iteratively applied at the locations specified by the bounding boxes.The global pathway focuses on the image background and the general image layout.We perform experiments on the Multi-MNIST, CLEVR, and the more complex MS-COCO data set.Our experiments show that through the use of the object pathway we can control object locations within images and can model complex scenes with multiple objects at various locations.We further show that the object pathway focuses on the individual objects and learns features relevant for these, while the global pathway focuses on global image characteristics and the image background.

Erweiterung der GAN-Architektur, um die Kontrolle über Standorte und Identitäten mehrerer Objekte in den erzeugten Bildern zu erhalten.


The demand for abstractive dialog summary is growing in real-world applications.For example, customer service center or hospitals would like to summarize customer service interaction and doctor-patient interaction.However, few researchers explored abstractive summarization on dialogs due to the lack of  suitable datasets.We propose an abstractive dialog summarization dataset based on MultiWOZ.If we directly apply previous state-of-the-art document summarization methods on dialogs, there are two significant drawbacks: the informative entities such as restaurant names are difficult to preserve, and the contents from different dialog domains are sometimes mismatched.To address these two drawbacks, we propose Scaffold Pointer Network (SPNet) to utilize the existing annotation on speaker role, semantic slot and dialog domain.SPNet incorporates these semantic scaffolds for dialog summarization.Since ROUGE cannot capture the two drawbacks mentioned, we also propose a new evaluation metric that considers critical informative entities in the text.On MultiWOZ, our proposed SPNet outperforms state-of-the-art abstractive summarization methods on all the automatic and human evaluation metrics.

Wir schlagen ein neuartiges End-to-End-Modell (SPNet) vor, um semantische Gerüste zur Verbesserung der abstrakten Dialogzusammenfassung einzubeziehen.


Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information.A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities.Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities, or evaluating the truth of a proposed triple.Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them.We propose a new algorithm, MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity.Since random walks are impractical in a setting with unknown destination and combinatorially many paths from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths.On a comprehensive evaluation on seven knowledge base datasets, we found MINERVA to be competitive with many current state-of-the-art methods.

Wir stellen einen RL-Agenten MINERVA vor, der lernt, einen Wissensgraphen abzuschreiten und Anfragen zu beantworten.


There are many differences between convolutional networks and the ventral visual streams of primates.For example, standard convolutional networks lack recurrent and lateral connections, cell dynamics, etc.However, their feedforward architectures are somewhat similar to the ventral stream, and warrant a more detailed comparison.A recent study found that the feedforward architecture of the visual cortex could be closely approximated as a convolutional network, but the resulting architecture differed from widely used deep networks in several ways.The same study also found, somewhat surprisingly, that training the ventral stream of this network for object recognition resulted in poor performance.This paper examines the performance of this network in more detail.In particular, I made a number of changes to the ventral-stream-based architecture, to make it more like a DenseNet, and tested performance at each step.I chose DenseNet because it has a high BrainScore, and because it has some cortex-like architectural features such as large in-degrees and long skip connections.Most of the changes (which made the cortex-like network more like DenseNet) improved performance.Further work is needed to better understand these results.One possibility is that details of the ventral-stream architecture may be ill-suited to feedforward computation, simple processing units, and/or backpropagation, which could suggest differences between the way high-performance deep networks and the brain approach core object recognition.

Eine Annäherung des ventralen Stroms von Primaten als Convolutional Network schneidet bei der Objekterkennung schlecht ab, und mehrere architektonische Merkmale tragen dazu bei.


In reinforcement learning, it is common to let an agent interact with its environment for a fixed amount of time before resetting the environment and repeating the process in a series of episodes.The task that the agent has to learn can either be to maximize its performance over(i) that fixed amount of time, or(ii) an indefinite period where the time limit is only used during training.In this paper, we investigate theoretically how time limits could effectively be handled in each of the two cases.In the first one, we argue that the terminations due to time limits are in fact part of the environment, and propose to include a notion of the remaining time as part of the agent's input.In the second case, the time limits are not part of the environment and are only used to facilitate learning.We argue that such terminations should not be treated as environmental ones and propose a method, specific to value-based algorithms, that incorporates this insight by continuing to bootstrap at the end of each partial episode.To illustrate the significance of our proposals, we perform several experiments on a range of environments from simple few-state transition graphs to complex control tasks, including novel and standard benchmark domains.Our results show that the proposed methods improve the performance and stability of existing reinforcement learning algorithms.

Wir betrachten das Problem des Lernens optimaler Strategien in zeitlich begrenzten und zeitlich nicht begrenzten Bereichen unter Verwendung zeitlich begrenzter Interaktionen.


Although stochastic gradient descent (SGD) is a driving force behind the recent success of deep learning, our understanding of its dynamics in a high-dimensional parameter space is limited.In recent years, some researchers have used the stochasticity of minibatch gradients, or the signal-to-noise ratio, to better characterize the learning dynamics of SGD.Inspired from these work, we here analyze SGD from a geometrical perspective by inspecting the stochasticity of the norms and directions of minibatch gradients.We propose a model of the directional concentration for minibatch gradients through von Mises-Fisher (VMF) distribution, and show that the directional uniformity of minibatch gradients increases over the course of SGD.We empirically verify our result using deep convolutional networks and observe a higher correlation between the gradient stochasticity and the proposed directional uniformity than that against the gradient norm stochasticity, suggesting that the directional statistics of minibatch gradients is a major factor behind SGD.

Eines der theoretischen Probleme beim Deep Learning.


Design of reliable systems must guarantee stability against input perturbations.In machine learning, such guarantee entails preventing overfitting and ensuring robustness of models against corruption of input data.In order to maximize stability, we analyze and develop a computationally efficient implementation of Jacobian regularization that increases classification margins of neural networks.The stabilizing effect of the Jacobian regularizer leads to significant improvements in robustness, as measured against both random and adversarial input perturbations, without severely degrading generalization properties on clean data.

Wir analysieren und entwickeln eine rechnerisch effiziente Implementierung der Jacobischen Regularisierung, die die Klassifizierungsmargen neuronaler Netze erhöht.


With the increasing demand to deploy convolutional neural networks (CNNs) on mobile platforms, the sparse kernel approach was proposed, which could save more parameters than the standard convolution while maintaining accuracy.However, despite the great potential, no prior research has pointed out how to craft an sparse kernel design with such potential (i.e., effective design), and all prior works just adopt simple combinations of existing sparse kernels such as group convolution.Meanwhile due to the large design space it is also impossible to try all combinations of existing sparse kernels.In this paper, we are the first in the field to consider how to craft an effective sparse kernel design by eliminating the large design space.Specifically, we present a sparse kernel scheme to illustrate how to reduce the space from three aspects.First, in terms of composition we remove designs composed of repeated layers.Second, to remove designs with large accuracy degradation, we find an unified property named~\emph{information field} behind various sparse kernel designs, which could directly indicate the final accuracy.Last, we remove designs in two cases where a better parameter efficiency could be achieved.Additionally, we provide detailed efficiency analysis on the final 4 designs in our scheme.Experimental results validate the idea of our scheme by showing that our scheme is able to find designs which are more efficient in using parameters and computation with similar or higher accuracy.

Wir sind die ersten auf diesem Gebiet, die zeigen, wie man einen effektiven Sparse-Kernel unter drei Aspekten entwickelt: Zusammensetzung, Leistung und Effizienz.


In weakly-supervised temporal action localization, previous works have failed to locate dense and integral regions for each entire action due to the overestimation of the most salient regions.To alleviate this issue, we propose a marginalized average attentional network (MAAN) to suppress the dominant response of the most salient regions in a principled manner.The MAAN employs a novel marginalized average aggregation (MAA) module and learns a set of latent discriminative probabilities in an end-to-end fashion. MAA samples multiple subsets from the video snippet features according to a set of latent discriminative probabilities and takes the expectation over all the averaged subset features. Theoretically, we prove that the MAA module with learned latent discriminative probabilities successfully reduces the difference in responses between the most salient regions and the others. Therefore, MAAN is able to generate better class activation sequences and identify dense and integral action regions in the videos. Moreover, we propose a fast algorithm to reduce the complexity of constructing MAA from $O(2^T)$ to $O(T^2)$.Extensive experiments on two large-scale video datasets show that our MAAN achieves a superior performance on weakly-supervised temporal action localization.

Ein neuartiges marginalisiertes durchschnittliches Aufmerksamkeitsnetz für schwach überwachte zeitliche Handlungslokalisierung.


Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attracted huge attentions in computer vision community.  It empirically shows the effectiveness of ConvNet structure for various image restoration applications.  However, why the DIP works so well is still unknown, and why convolution operation is essential for image reconstruction or enhancement is not very clear.In this study, we tackle these questions.The proposed approach is dividing the convolution into ``delay-embedding'' and ``transformation (\ie encoder-decoder)'', and proposing a simple, but essential, image/tensor modeling method which is closely related to dynamical systems and self-similarity.The proposed method named as manifold modeling in embedded space (MMES) is implemented by using a novel denoising-auto-encoder in combination with multi-way delay-embedding transform.In spite of its simplicity, the image/tensor completion and super-resolution results of MMES are quite similar even competitive to DIP in our extensive experiments, and these results would help us for reinterpreting/characterizing the DIP from a perspective of ``low-dimensional patch-manifold prior''.

Wir schlagen einen neuen Autocodierer vor, der mit einer Multiway-Delay-Embedding-Transformation ausgestattet ist, um ein tiefes Bild zu interpretieren.


Federated learning is a recent advance in privacy protection. In this context, a trusted curator aggregates parameters optimized in decentralized fashion by multiple clients.The resulting model is then distributed back to all clients, ultimately converging to a joint representative model without explicitly having to share the data. However, the protocol is vulnerable to differential attacks, which could originate from any party contributing during federated optimization.In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model. We tackle this problem and propose an algorithm for client sided differential privacy preserving federated optimization.The aim is to hide clients' contributions during training, balancing the trade-off between privacy loss and model performance. Empirical studies suggest that given a sufficiently large number of participating clients, our proposed procedure can maintain client-level differential privacy at only a minor cost in model performance.

Sicherstellung, dass die im Verbund erlernten Modelle die Beteiligung eines Kunden nicht offenbaren.


Employing deep neural networks as natural image priors to solve inverse problems either requires large amounts of data to sufficiently train expressive generative models or can succeed with no data via untrained neural networks.However, very few works have considered how to interpolate between these no- to high-data regimes.In particular, how can one use the availability of a small amount of data (even 5-25 examples) to one's advantage in solving these inverse problems and can a system's performance increase as the amount of data increases as well?In this work, we consider solving linear inverse problems when given a small number of examples of images that are drawn from the same distribution as the image of interest.Comparing to untrained neural networks that use no data, we show how one can pre-train a neural network with a few given examples to improve reconstruction results in compressed sensing and semantic image recovery problems such as colorization.Our approach leads to improved reconstruction as the amount of available data increases and is on par with fully trained generative models, while requiring less than 1% of the data needed to train a generative model.

Wir zeigen, wie das Vortraining eines untrainierten neuronalen Netzes mit nur 5-25 Beispielen die Rekonstruktionsergebnisse bei komprimiertem Abfühlen und semantischen Wiederherstellungsproblemen wie der Kolorierung verbessern kann.


We propose Cooperative Training (CoT) for training generative models that measure a tractable density for discrete data.CoT coordinately trains a generator G and an auxiliary predictive mediator M. The training target of M is to estimate a mixture density of the learned distribution G and the target distribution P, and that of G is to minimize the Jensen-Shannon divergence estimated through M. CoT achieves independent success without the necessity of pre-training via Maximum Likelihood Estimation or involving high-variance algorithms like REINFORCE.This low-variance algorithm is theoretically proved to be superior for both sample generation and likelihood prediction.We also theoretically and empirically show the superiority of CoT over most previous algorithms in terms of generative quality and diversity, predictive generalization ability and computational cost.

Wir haben Cooperative Training vorgeschlagen, einen neuartigen Trainingsalgorithmus für die generative Modellierung diskreter Daten.


Intrinsic rewards in reinforcement learning provide a powerful algorithmic capability for agents to learn how to interact with their environment in a task-generic way.However, increased incentives for motivation can come at the cost of increased fragility to stochasticity.We introduce a method for computing an intrinsic reward for curiosity using metrics derived from sampling a latent variable model used to estimate dynamics.Ultimately, an estimate of the conditional probability of observed states is used as our intrinsic reward for curiosity.In our experiments, a video game agent uses our model to autonomously learn how to play Atari games using our curiosity reward in combination with extrinsic rewards from the game to achieve improved performance on games with sparse extrinsic rewards.When stochasticity is introduced in the environment, our method still demonstrates improved performance over the baseline.

Wir stellen eine Methode zur Berechnung einer intrinsischen Belohnung für Neugier vor, bei der Metriken verwendet werden, die aus dem Sampling eines latenten Variablenmodells abgeleitet werden, das zur Schätzung der Dynamik verwendet wird.


Word embedding is a powerful tool in natural language processing.In this paper we consider the problem of word embedding composition \--- given vector representations of two words, compute a vector for the entire phrase.We give a generative model that can capture specific syntactic relations between words.Under our model, we prove that the correlations between three words (measured by their PMI) form a tensor that has an approximate low rank Tucker decomposition.The result of the Tucker decomposition gives the word embeddings as well as a core tensor, which can be used to produce better compositions of the word embeddings.We also complement our theoretical results with experiments that verify our assumptions, and demonstrate the effectiveness of the new composition method.

Wir stellen ein generatives Modell für kompositionelle Worteinbettungen vor, das syntaktische Beziehungen erfasst, und bieten eine empirische Überprüfung und Bewertung.


Building deep reinforcement learning agents that can generalize and adapt to unseen environments remains a fundamental challenge for AI.This paper describes progresses on this challenge in the context of man-made environments, which are visually diverse but contain intrinsic semantic regularities.We propose a hybrid model-based and model-free approach, LEArning and Planning with Semantics (LEAPS), consisting of a multi-target sub-policy that acts on visual inputs, and a Bayesian model over semantic structures.When placed in an unseen environment, the agent plans with the semantic model to make high-level decisions, proposes the next sub-target for the sub-policy to execute, and updates the semantic model based on new observations.We perform experiments in visual navigation tasks using House3D, a 3D environment that contains diverse human-designed indoor scenes with real-world objects.LEAPS outperforms strong baselines that do not explicitly plan using the semantic content.

Wir schlagen einen hybriden modellbasierten und modellfreien Ansatz vor, der semantische Informationen nutzt, um die DRL-Verallgemeinerung in künstlichen Umgebungen zu verbessern.


In this paper, we focus on two challenges which offset the promise of sparse signal representation, sensing, and recovery.First, real-world signals can seldom be described as perfectly sparse vectors in a known basis, and traditionally used random measurement schemes are seldom optimal for sensing them.Second, existing signal recovery algorithms are usually not fast enough to make them applicable to real-time problems.In this paper, we address these two challenges by presenting a novel framework based on deep learning.For the first challenge, we cast the problem of finding informative measurements by using a maximum likelihood (ML) formulation and show how we can build a data-driven dimensionality reduction protocol for sensing signals using convolutional architectures.For the second challenge, we discuss and analyze a novel parallelization scheme and show it significantly speeds-up the signal recovery process.We demonstrate the significant improvement our method obtains over competing methods through a series of experiments.

Wir verwenden Deep Learning-Techniken, um das Problem der spärlichen Signaldarstellung und -wiederherstellung zu lösen.


To select effective actions in complex environments, intelligent agents need to generalize from past experience.World models can represent knowledge about the environment to facilitate such generalization.While learning world models from high-dimensional sensory inputs is becoming feasible through deep learning, there are many potential ways for deriving behaviors from them.We present Dreamer, a reinforcement learning agent that solves long-horizon tasks purely by latent imagination.We efficiently learn behaviors by backpropagating analytic gradients of learned state values through trajectories imagined in the compact state space of a learned world model.On 20 challenging visual control tasks, Dreamer exceeds existing approaches in data-efficiency, computation time, and final performance.

Wir stellen Dreamer vor, einen Agenten, der Verhaltensweisen mit langem Zeithorizont rein durch latente Vorstellungskraft unter Verwendung analytischer Wertgradienten erlernt.


Transfer reinforcement learning (RL) aims at improving learning efficiency of an agent by exploiting knowledge from other source agents trained on relevant tasks.However, it remains challenging to transfer knowledge between different environmental dynamics without having access to the source environments.In this work, we explore a new challenge in transfer RL, where only a set of source policies collected under unknown diverse dynamics is available for learning a target task efficiently.To address this problem, the proposed approach, MULTI-source POLicy AggRegation (MULTIPOLAR), comprises two key techniques.We learn to aggregate the actions provided by the source policies adaptively to maximize the target task performance.Meanwhile, we learn an auxiliary network that predicts residuals around the aggregated actions, which ensures the target policy's expressiveness even when some of the source policies perform poorly.We demonstrated the effectiveness of MULTIPOLAR through an extensive experimental evaluation across six simulated environments ranging from classic control problems to challenging robotics simulations, under both continuous and discrete action spaces.

Wir schlagen MULTIPOLAR vor, eine Transfer-RL-Methode, die eine Reihe von Quellrichtlinien, die unter unbekannten, unterschiedlichen Umweltdynamiken gesammelt wurden, nutzt, um effizient eine Zielrichtlinie in einer anderen Dynamik zu lernen.


Reinforcement learning algorithms rely on carefully engineered rewards from the environment that are extrinsic to the agent.However, annotating each environment with hand-designed, dense rewards is difficult and not scalable, motivating the need for developing reward functions that are intrinsic to the agent. Curiosity is such intrinsic reward function which uses prediction error as a reward signal.In this paper:(a) We perform the first large-scale study of purely curiosity-driven learning, i.e. {\em without any extrinsic rewards}, across $54$ standard benchmark environments, including the Atari game suite.Our results show surprisingly good performance as well as a high degree of alignment between the intrinsic curiosity objective and the hand-designed extrinsic rewards of many games.(b) We investigate the effect of using different feature spaces for computing prediction error and show that random features are sufficient for many popular RL game benchmarks, but learned features appear to generalize better (e.g. to novel game levels in Super Mario Bros.).(c) We demonstrate limitations of the prediction-based rewards in stochastic setups.Game-play videos and code are at https://doubleblindsupplementary.github.io/large-curiosity/.

Ein Agent, der nur mit Neugier und ohne extrinsische Belohnung trainiert wird, schneidet in 54 beliebten Umgebungen erstaunlich gut ab, darunter die Atari-Spiele, Mario usw.


This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustness).  Evaluated on these adversarially transformed examples, we demonstrate that adding regularization on top of standard or adversarial training reduces the relative error by 20% for CIFAR10 without increasing the computational cost.  This outperforms handcrafted networks that were explicitly designed to be spatial-equivariant.Furthermore, we observe for SVHN, known to have inherent variance in orientation, that robust training also improves standard accuracy on the test set.

Für räumliche Transformationen robuster Minimierer minimiert auch die Standardgenauigkeit; invarianzinduzierende Regularisierung führt zu besserer Robustheit als spezialisierte Architekturen.


We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes.To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `similar to,' or `smaller than' the other.Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably.We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance.Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.

Der Begriff des Ordnungslernens wird vorgeschlagen und auf Regressionsprobleme in der Computer Vision angewendet.


We study how the topology of a data set comprising two components representing two classes of objects in a binary classification problem changes as it passes through the layers of a well-trained neural network, i.e., one with perfect accuracy on training set and a generalization error of less than 1%.The goal is to shed light on two well-known mysteries in deep neural networks:(i) a nonsmooth activation function like ReLU outperforms a smooth one like hyperbolic tangent;(ii) successful neural network architectures rely on having many layers, despite the fact that a shallow network is able to approximate any function arbitrary well.We performed extensive experiments on persistent homology of a range of point cloud data sets.The results consistently demonstrate the following: (1) Neural networks operate by changing topology, transforming a topologically complicated data set into a topologically simple one as it passes through the layers.No matter how complicated the topology of the data set we begin with, when passed through a well-trained neural network, the Betti numbers of both components invariably reduce to their lowest possible values: zeroth Betti number is one and all higher Betti numbers are zero.Furthermore, (2) the reduction in Betti numbers is significantly faster for ReLU activation compared to hyperbolic tangent activation --- consistent with the fact that the former define nonhomeomorphic maps (that change topology) whereas the latter define homeomorphic maps (that preserve topology). Lastly, (3) shallow and deep networks process the same data set differently --- a shallow network operates mainly through changing geometry and changes topology only in its final layers, a deep network spreads topological changes more evenly across all its layers.

Wir zeigen, dass neuronale Netze funktionieren, indem sie die Topologie eines Datensatzes verändern, und untersuchen, wie die Wahl der Architektur diese Veränderung bewirkt.


The convergence rate and final performance of common deep learning models have significantly benefited from recently proposed heuristics such as learning rate schedules, knowledge distillation, skip connections and normalization layers.In the absence of theoretical underpinnings, controlled experiments aimed at explaining the efficacy of these strategies can aid our understanding of deep learning landscapes and the training dynamics.Existing approaches for empirical analysis rely on tools of linear interpolation and visualizations with dimensionality reduction, each with their limitations.Instead, we revisit the empirical analysis of heuristics through the lens of recently proposed methods for loss surface and representation analysis, viz. mode connectivity and canonical correlation analysis (CCA), and hypothesize reasons why the heuristics succeed.In particular, we explore knowledge distillation and learning rate heuristics of (cosine) restarts and warmup using mode connectivity and CCA. Our empirical analysis suggests that: (a) the reasons often quoted for the success of cosine annealing are not evidenced in practice; (b) that the effect of learning rate warmup is to prevent the deeper layers from creating training instability; and(c) that the latent knowledge shared by the teacher is primarily disbursed in the deeper layers.

Wir verwenden die empirischen Instrumente der Moduskonnektivität und der SVCCA, um die Trainingsheuristiken für neuronale Netze zu untersuchen, d.h. die Wiederaufnahme der Lernrate, das Aufwärmen und die Wissensdestillation.


The increasing demand for neural networks (NNs) being employed on embedded devices has led to plenty of research investigating methods for training low precision NNs.While most methods involve a quantization step, we propose a principled Bayesian approach where we first infer a distribution over a discrete weight space from which we subsequently derive hardware-friendly low precision NNs.To this end, we introduce a probabilistic forward pass to approximate the intractable variational objective that allows us to optimize over discrete-valued weight distributions for NNs with sign activation functions.In our experiments, we show that our model achieves state of the art performance on several real world data sets.In addition, the resulting models exhibit a substantial amount of sparsity that can be utilized to further reduce the computational costs for inference.

Variationsinferenz zur Ableitung einer diskreten Verteilung, aus der ein neuronales Netz mit geringer Genauigkeit abgeleitet wird.


Many irregular domains such as social networks, financial transactions, neuron connections, and natural language structures are represented as graphs.In recent years, a variety of  graph neural networks (GNNs) have been successfully applied for representation learning and prediction on such graphs.However, in many of the applications, the underlying graph changes over time and existing GNNs are inadequate for handling such dynamic graphs.In this paper we propose a novel technique for learning embeddings of dynamic graphs based on a tensor algebra framework.Our method extends the popular graph convolutional network (GCN) for learning representations of dynamic graphs using the recently proposed tensor M-product technique.Theoretical results that establish the connection between the proposed tensor approach and spectral convolution of tensors are developed.Numerical experiments on real datasets demonstrate the usefulness of the proposed method for an edge classification task on dynamic graphs.

Wir schlagen eine neue tensorbasierte Methode für Graph Convolutional Networks auf dynamischen Graphen vor.


Our main motivation is to propose an efficient approach to generate novel multi-element stable chemical compounds that can be used in real world applications.This task can be formulated as a combinatorial problem, and it takes many hours of human experts to construct, and to evaluate new data.Unsupervised learning methods such as Generative Adversarial Networks (GANs) can be efficiently used to produce new data.  Cross-domain Generative Adversarial Networks were reported to achieve exciting results in image processing applications.However, in the domain of materials science, there is a need to synthesize data with higher order complexity compared to observed samples, and the state-of-the-art cross-domain GANs can not be adapted directly. In this contribution, we propose a novel GAN called CrystalGAN which generates new chemically stable crystallographic structures with increased domain complexity.We introduce an original architecture, we provide the corresponding loss functions, and we show that the CrystalGAN generates very reasonable data.We illustrate the efficiency of the proposed method on a real original problem of novel hydrides discovery that can be further used in development of hydrogen storage materials.

Generierung neuer chemischer Materialien mit Hilfe neuartiger domänenübergreifender GANs.


Given samples from a group of related regression tasks, a data-enriched model describes observations by a common and per-group individual parameters.In high-dimensional regime, each parameter has its own structure such as sparsity or group sparsity.In this paper, we consider the general form of data enrichment where data comes in a fixed but arbitrary number of tasks $G$ and any convex function, e.g., norm, can characterize the structure of both common and individual parameters. 	We propose an estimator for the high-dimensional data enriched model and investigate its statistical properties.  We delineate the sample complexity of our estimator and provide high probability non-asymptotic bound for estimation error of all parameters under a condition weaker than the state-of-the-art.We propose an iterative estimation algorithm with a geometric convergence rate.Overall, we present a first through statistical and computational analysis of inference in the data enriched model.

Wir stellen einen Schätzer und einen Schätzalgorithmus für eine Klasse von Multi-Task-Regressionsproblemen zur Verfügung und bieten statistische und rechnerische Analysen.


Autonomous vehicles are becoming more common in city transportation.  Companies will begin to find a need to teach these vehicles smart city fleet coordination.  Currently, simulation based modeling along with hand coded rules dictate the decision making of these autonomous vehicles.We believe that complex intelligent behavior can be learned by these agents through Reinforcement Learning.In this paper, we discuss our work for solving this system by adapting the Deep Q-Learning (DQN) model to the multi-agent setting.  Our approach applies deep reinforcement learning by combining convolutional neural networks with DQN to teach agents to fulfill customer demand in an environment that is partially observ-able to them.We also demonstrate how to utilize transfer learning to teach agents to balance multiple objectives such as navigating to a charging station when its en-ergy level is low.The two evaluations presented show that our solution has shown  hat we are successfully able to teach agents cooperation policies while balancing multiple objectives.

Nutzung von Deep Reinforcement Learning, um Agenten die Koordination von Mitfahrzentralen beizubringen.


Stability is a key aspect of data analysis.In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision.Scattering transforms construct deep convolutional representations which are certified stable to input deformations.This stability to deformations can be interpreted as stability with respect to changes in the metric structure of the domain. In this work, we show that scattering transforms can be generalized to non-Euclidean domains using diffusion wavelets, while preserving a notion of stability with respect to metric changes in the domain, measured with diffusion maps.The resulting representation is stable to metric perturbations of the domain while being able to capture ''high-frequency'' information, akin to the Euclidean Scattering.

Stabilität von Streutransformationsdarstellungen von Graphdaten gegenüber Verformungen des zugrunde liegenden Graphträgers.


We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it trains on a sequence of tasks, to learn a compact overlapping knowledge sharing structure among tasks.DEN is efficiently trained in an online manner by performing selective retraining, dynamically expands network capacity upon arrival of each task with only the necessary number of units, and effectively prevents semantic drift by splitting/duplicating units and timestamping them.We validate DEN on multiple public datasets in lifelong learning scenarios on multiple public datasets, on which it not only significantly outperforms existing lifelong learning methods for deep networks, but also achieves the same level of performance as the batch model with substantially fewer number of parameters.

Wir schlagen eine neuartige Deep-Network-Architektur vor, die dynamisch über ihre Netzwerkkapazität entscheiden kann, während sie in einem Szenario des lifelong Learnings trainiert.


This paper fosters the idea that deep learning methods can be sided to classicalvisual odometry pipelines to improve their accuracy and to produce uncertaintymodels to their estimations.We show that the biases inherent to the visual odom-etry process can be faithfully learnt and compensated for, and that a learning ar-chitecture associated to a probabilistic loss function can jointly estimate a fullcovariance matrix of the residual errors, defining a heteroscedastic error model.Experiments on autonomous driving image sequences and micro aerial vehiclescamera acquisitions assess the possibility to concurrently improve visual odome-try and estimate an error associated to its outputs.

In diesem Beitrag werden verschiedene Methoden der Verknüpfung von VO mit Deep Learning erörtert und eine gleichzeitige Vorhersage von Korrekturen und Unsicherheiten vorgeschlagen.


Building robust online content recommendation systems requires learning com- plex interactions between user preferences and content features.The field has evolved rapidly in recent years from traditional multi-arm bandit and collabora- tive filtering techniques, with new methods integrating Deep Learning models that enable to capture non-linear feature interactions.Despite progress, the dynamic nature of online recommendations still poses great challenges, such as finding the delicate balance between exploration and exploitation.In this paper we provide a novel method, Deep Density Networks (DDN) which deconvolves measurement and data uncertainty and predicts probability densities of CTR, enabling us to perform more efficient exploration of the feature space.We show the usefulness of using DDN online in a real world content recommendation system that serves billions of recommendations per day, and present online and offline results to eval- uate the benefit of using DDN.

Wir haben das Deep Density Network eingeführt, ein einheitliches DNN-Modell zur Abschätzung der Unsicherheit bei der Erkundung/Ausnutzung in Empfehlungssystemen.


While it is well-documented that climate change accepters and deniers have become increasingly polarized in the United States over time, there has been no large-scale examination of whether these individuals are prone to changing their opinions as a result of natural external occurrences.On the sub-population of Twitter users, we examine whether climate change sentiment changes in response to five separate natural disasters occurring in the U.S. in 2018.We begin by showing that tweets can be classified with over 75% accuracy as either accepting or denying climate change when using our methodology to compensate for limited labelled data; results are robust across several machine learning models and yield geographic-level results in line with prior research.We then apply RNNs to conduct a cohort-level analysis showing that the 2018 hurricanes yielded a statistically significant increase in average tweet sentiment affirming climate change.However, this effect does not hold for the 2018 blizzard and wildfires studied, implying that Twitter users' opinions on climate change are fairly ingrained on this subset of natural disasters.

Wir trainieren RNNs mit berühmten Twitter-Nutzern, um festzustellen, ob die allgemeine Twitter-Bevölkerung nach einer Naturkatastrophe eher an den Klimawandel glaubt.


We study the control of symmetric linear dynamical systems with unknown dynamics and a hidden state.Using a recent spectral filtering technique for concisely representing such systems in a linear basis, we formulate optimal control in this setting as a convex program.This approach eliminates the need to solve the non-convex problem of explicit identification of the system and its latent state, and allows for provable optimality guarantees for the control signal.We give the first efficient algorithm for finding the optimal control signal with an arbitrary time horizon T, with sample complexity (number of training rollouts) polynomial only in log(T) and other relevant parameters.

Unter Verwendung einer neuartigen Darstellung symmetrischer linearer dynamischer Systeme mit einem latenten Zustand formulieren wir die optimale Steuerung als konvexes Programm und liefern den ersten Polynomialzeit-Algorithmus, der die optimale Steuerung mit einer nur im Zeithorizont polylogarithmischen Stichprobenkomplexität löst.


Generative Adversarial Networks (GANs) have become the gold standard when it comes to learning generative models for high-dimensional distributions.Since their advent, numerous variations of GANs have been introduced in the literature, primarily focusing on utilization of novel loss functions, optimization/regularization strategies and network architectures.In this paper, we turn our attention to the generator and investigate the use of high-order polynomials as an alternative class of universal function approximators.Concretely, we propose PolyGAN, where we model the data generator by means of a high-order polynomial whose unknown parameters are naturally represented by high-order tensors.We introduce two tensor decompositions that significantly reduce the number of parameters and show how they can be efficiently implemented by hierarchical neural networks that only employ linear/convolutional blocks.We exhibit for the first time that by using our approach a GAN generator can approximate the data distribution without using any activation functions.Thorough experimental evaluation on both synthetic and real data (images and 3D point clouds) demonstrates the merits of PolyGAN against the state of the art.

Wir modellieren den Datengenerator (im GAN) durch ein Polynom hoher Ordnung, das durch Tensoren hoher Ordnung dargestellt wird.


Deep neural networks trained on large supervised datasets have led to impressive results in recent years.However, since well-annotated datasets can be prohibitively expensive and time-consuming to collect, recent work has explored the use of larger but noisy datasets that can be more easily obtained.In this paper, we investigate the behavior of deep neural networks on training sets with massively noisy labels.We show on multiple datasets such as MINST, CIFAR-10 and ImageNet that successful learning is possible even with an essentially arbitrary amount of noise.For example, on MNIST we find that accuracy of above 90 percent is still attainable even when the dataset has been diluted with 100 noisy examples for each clean example.Such behavior holds across multiple patterns of label noise, even when noisy labels are biased towards confusing classes.Further, we show how the required dataset size for successful training increases with higher label noise.Finally, we present simple actionable techniques for improving learning in the regime of high label noise.

Wir zeigen, dass tiefe neuronale Netze in der Lage sind, aus Daten zu lernen, die durch eine beliebige Menge an Störungen verwässert wurden.


In this paper, we propose to extend the recently introduced model-agnostic meta-learning algorithm (MAML, Finn et al., 2017) for low resource neural machine translation (NMT).We frame low-resource translation as a meta-learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language tasks.We use the universal lexical representation (Gu et al., 2018b) to overcome the input-output mismatch across different languages.We evaluate the proposed meta-learning strategy using eighteen European languages (Bg, Cs, Da, De, El, Es, Et, Fr, Hu, It, Lt, Nl, Pl, Pt, Sk, Sl, Sv and Ru) as source tasks and five diverse languages (Ro, Lv, Fi, Tr, and Ko) as target tasks.We show that the proposed approach significantly outperforms the multilingual, transfer learning based approach (Zoph et al., 2016) and enables us to train a competitive NMT system with only a fraction of training examples.For instance, the proposed approach can achieve as high as 22.04 BLEU on Romanian-English WMT’16 by seeing only 16,000 translated words (~600 parallel sentences).

Wir schlagen einen Meta-Lernansatz für neuronale maschinelle Übersetzung mit geringen Ressourcen vor, der schnell lernen kann, eine neue Sprache zu übersetzen.


This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection.We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection.We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results.To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.

Eine Methode zur aktiven Erkennung von Anomalien. Wir stellen eine neue Schicht vor, die an jedes Deep-Learning-Modell, das für die unbeaufsichtigte Erkennung von Anomalien entwickelt wurde, angehängt werden kann, um es in eine aktive Methode zu verwandeln.


In this paper, we ask for the main factors that determine a classifier's decision making and uncover such factors by studying latent codes produced by auto-encoding frameworks.To deliver an explanation of a classifier's behaviour, we propose a method that provides series of examples highlighting semantic differences between the classifier's decisions.We generate these examples through interpolations in latent space.We introduce and formalize the notion of a semantic stochastic path, as a suitable stochastic process defined in feature space via latent code interpolations.We then introduce the concept of semantic Lagrangians as a way to incorporate the desired classifier's behaviour and find that the solution of the associated variational problem allows for highlighting differences in the classifier decision.Very importantly, within our framework the classifier is used as a black-box, and only its evaluation is required.

Wir generieren Beispiele, um eine Klassifikatorentscheidung durch Interpolationen im latenten Raum zu erklären. Die Variationskosten des Autoencoders werden um ein Funktional des Klassifikators über den generierten Beispielpfad im Datenraum erweitert.


The soundness and optimality of a plan depends on the correctness of the domain model.In real-world applications, specifying complete domain models is difficult as the interactions between the agent and its environment can be quite complex.We propose a framework to learn a PPDDL representation of the model incrementally over multiple planning problems using only experiences from the current planning problem, which suits non-stationary environments.We introduce the novel concept of reliability as an intrinsic motivation for reinforcement learning, and as a means of learning from failure to prevent repeated instances of similar failures.Our motivation is to improve both learning efficiency and goal-directedness.We evaluate our work with experimental results for three planning domains.

Einführung eines Ansatzes, der es Agenten ermöglicht, PPDDL-Aktionsmodelle inkrementell über mehrere Planungsprobleme im Rahmen des Reinforcement Learnings zu lernen.


The field of Deep Reinforcement Learning (DRL) has recently seen a surge in the popularity of maximum entropy reinforcement learning algorithms.  Their popularity stems from the intuitive interpretation of the maximum entropy objective and their superior sample efficiency on standard benchmarks.In this paper, we seek to understand the primary contribution  of the entropy term to the performance of maximum entropy algorithms.For the Mujoco benchmark, we demonstrate that the entropy term in Soft Actor Critic (SAC) principally addresses the bounded nature of the action spaces.With this insight, we propose a simple normalization scheme which allows a streamlined algorithm without entropy maximization match the performance of SAC.Our experimental results demonstrate a need to revisit the benefits of entropy regularization in DRL.We also propose a simple non-uniform sampling method for selecting transitions from the replay buffer during training.  We further show that the streamlined algorithm with the simple non-uniform sampling scheme outperforms SAC and achieves state-of-the-art performance on challenging continuous control tasks.

Wir schlagen einen neuen DRL-Off-Policy-Algorithmus vor, der die beste Leistung erzielt.


Very recently, it comes to be a popular approach for answering open-domain questions by first searching question-related passages, then applying reading comprehension models to extract answers.Existing works usually extract answers from single passages independently, thus not fully make use of the multiple searched passages, especially for the some questions requiring several evidences, which can appear in different passages, to be answered.The above observations raise the problem of evidence aggregation from multiple passages.In this paper, we deal with this problem as answer re-ranking.Specifically, based on the answer candidates generated from the existing state-of-the-art QA model, we propose two different re-ranking methods, strength-based and coverage-based re-rankers, which make use of the aggregated evidences from different passages to help entail the ground-truth answer for the question.Our model achieved state-of-the-arts on three public open-domain QA datasets, Quasar-T, SearchQA and the open-domain version of TriviaQA, with about 8\% improvement on the former two datasets.

Wir schlagen eine Methode vor, die die Informationen aus mehreren Passagen für die Qualitätssicherung in einem offenen Bereich nutzen kann.


Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.Furthermore, social networks can be extracted from email corpora, tweets, or social media. When it comes to visualising these large corpora, either the textual content or the network graph are used.In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.

Algorithmus zur Dimensionalitätsreduzierung zur Visualisierung von Text mit Netzwerkinformationen, z. B. ein E-Mail-Korpus oder Ko-Autorenschaften.


Machine learned models exhibit bias, often because the datasets used to train them are biased.This presents a serious problem for the deployment of such technology, as the resulting models might perform poorly on populations that are minorities within the training set and ultimately present higher risks to them.We propose to use high-fidelity computer simulations to interrogate and diagnose biases within ML classifiers.We present a framework that leverages Bayesian parameter search to efficiently characterize the high dimensional feature space and more quickly identify weakness in performance.We apply our approach to an example domain, face detection, and show that it can be used to help identify demographic biases in commercial face application programming interfaces (APIs).

Wir stellen einen Rahmen vor, der hochrealistische Computersimulationen nutzt, um Verzerrungen in ML-Klassifikatoren zu untersuchen und zu diagnostizieren.


Point clouds are a flexible and ubiquitous way to represent 3D objects with arbitrary resolution and precision.Previous work has shown that adapting encoder networks to match the semantics of their input point clouds can significantly improve their effectiveness over naive feedforward alternatives.However, the vast majority of work on point-cloud decoders are still based on fully-connected networks that map shape representations to a fixed number of output points.In this work, we investigate decoder architectures that more closely match the semantics of variable sized point clouds.Specifically, we study sample-based point-cloud decoders that map a shape representation to a point feature distribution, allowing an arbitrary number of sampled features to be transformed into individual output points.We develop three sample-based decoder architectures and compare their performance to each other and show their improved effectiveness over feedforward architectures.In addition, we investigate the learned distributions to gain insight into the output transformation.Our work is available as an extensible software platform to reproduce these results and serve as a baseline for future work.

Wir präsentieren und bewerten Sampling-basierte Point Cloud Decoder, die den MLP-Ansatz durch eine bessere Anpassung der Semantik von Point Clouds übertreffen.


We present a deep reinforcement learning approach to minimizing the execution cost of neural network computation graphs in an optimizing compiler.Unlike earlier learning-based works that require training the optimizer on the same graph to be optimized, we propose a learning approach that trains an optimizer offline and then generalizes to previously unseen graphs without further training.This allows our approach to produce high-quality execution decisions on real-world TensorFlow graphs in seconds instead of hours.We consider two optimization tasks for computation graphs: minimizing running time and peak memory usage.In comparison to an extensive set of baselines, our approach achieves significant improvements over classical and other learning-based methods on these two tasks.

Wir verwenden Deep RL, um eine Strategie zu erlernen, die die Suche eines genetischen Algorithmus lenkt, um die Ausführungskosten von Berechnungsgraphen besser zu optimieren, und zeigen verbesserte Ergebnisse auf realen TensorFlow-Graphen.


Predictive coding theories suggest that the brain learns by predicting observations at various levels of abstraction.One of the most basic prediction tasks is view prediction: how would a given scene look from an alternative viewpoint?Humans excel at this task.Our ability to imagine and fill in missing visual information is tightly coupled with perception: we feel as if we see  the world in 3 dimensions, while in fact, information from only the front surface of the world hits our (2D) retinas.This paper explores the connection between view-predictive representation learning and its role in the development of 3D visual recognition.We propose inverse graphics networks, which take as input 2.5D video streams captured by a moving camera, and map to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera.The model can also project its 3D feature maps to novel viewpoints, to predict and match against target views.We propose contrastive prediction losses that can handle stochasticity of the visual input and can scale view-predictive learning to more photorealistic scenes than those considered in previous works.We show that the proposed  model learns 3D visual representations useful for (1) semi-supervised learning of 3D object detectors, and (2) unsupervised learning of 3D moving object detectors, by estimating  motion of the inferred 3D feature maps in videos of dynamic scenes.To the best of our knowledge, this is the first work that empirically shows view prediction to be a useful and scalable self-supervised task beneficial to 3D object detection.

Wir zeigen, dass mit dem richtigen Verlust und der richtigen Architektur die 3D-Objekterkennung durch vorausschauendes Lernen verbessert wird.


The modeling of style when synthesizing natural human speech from text has been the focus of significant attention.Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud.The synthesized audio waveform is expected to contain the verbal content of x_txt and the auditory style of x_aud.Unfortunately, modeling style in TTS is somewhat under-determined and training models with a reconstruction loss alone is insufficient to disentangle content and style from other factors of variation.In this work, we introduce an end-to-end TTS model that offers enhanced content-style disentanglement ability and controllability.We achieve this by combining a pairwise training procedure, an adversarial game, and a collaborative game into one training scheme.The adversarial game concentrates the true data distribution, and the collaborative game minimizes the distance between real samples and generated samples in both the original space and the latent space.As a result, the proposed model delivers a highly controllable generator, and a disentangled representation.Benefiting from the separate modeling of style and content, our model can generate human fidelity speech that satisfies the desired style conditions.Our model achieves start-of-the-art results across multiple tasks, including style transfer (content and style swapping), emotion modeling, and identity transfer (fitting a new speaker's voice).

Ein generatives adversariales Netzwerk für die Stilmodellierung in einem Text-to-Speech-System.


Empirical evidence suggests that neural networks with ReLU activations generalize better with over-parameterization.However, there is currently no theoretical analysis that explains this observation.In this work, we study a simplified learning task with over-parameterized convolutional networks that empirically exhibits the same qualitative phenomenon.  For this setting, we provide a theoretical analysis of the optimization and generalization performance of gradient descent.Specifically, we prove data-dependent sample complexity bounds which show that over-parameterization improves the generalization performance of gradient descent.

Wir zeigen in einer vereinfachten Lernaufgabe, dass Überparametrisierung die Generalisierung eines Convnets verbessert, das mit Gradientenabstieg trainiert wird.


We introduce a new and rigorously-formulated PAC-Bayes few-shot meta-learning algorithm that implicitly learns a model prior distribution of interest.Our proposed method extends the PAC-Bayes framework from a single task setting to the few-shot meta-learning setting to upper-bound generalisation errors on unseen tasks.We  also propose a generative-based approach to model the shared prior and task-specific posterior more expressively compared to the usual diagonal Gaussian assumption.We show that the models trained with our proposed meta-learning algorithm are well calibrated and accurate, with state-of-the-art calibration and classification results on mini-ImageNet benchmark, and competitive results in a multi-modal task-distribution regression.

Bayessches Meta-Lernen unter Verwendung des PAC-Bayes-Rahmens und impliziter Prioritätsverteilungen


As the area of Explainable AI (XAI), and Explainable AI Planning (XAIP), matures, the ability for agents to generate and curate explanations will likewise grow.We propose a new challenge area in the form of rebellious and deceptive explanations.We discuss how these explanations might be generated and then briefly discuss evaluation criteria.

Positionspapier mit Vorschlägen für rebellische und trügerische Erklärungen für Agenten.


We investigate a variant of variational autoencoders where there is a superstructure of discrete latent variables on top of the latent features.In general, our superstructure is a tree structure of multiple super latent variables and it is automatically learned from data.When there is only one latent variable in the superstructure, our model reduces to one that assumes the latent features to be generated from a Gaussian mixture model.We call our model the latent tree variational autoencoder (LTVAE).Whereas previous deep learning methods for clustering produce only one partition of data, LTVAE produces multiple partitions of data, each being given by one super latent variable.This is desirable because high dimensional data usually have many different natural facets and can be meaningfully partitioned in multiple ways.

Wir untersuchen eine Variante von Variations-Autoencodern, bei der eine Überstruktur von diskreten latenten Variablen über den latenten Merkmalen liegt.


Many practical robot locomotion tasks require agents to use control policies that can be parameterized by goals.Popular deep reinforcement learning approaches in this direction involve learning goal-conditioned policies or value functions, or Inverse Dynamics Models (IDMs).IDMs map an agent’s current state and desired goal to the required actions.We show that the key to achieving good performance with IDMs lies in learning the information shared between equivalent experiences, so that they can be generalized to unseen scenarios.We design a training process that guides the learning of latent representations to encode this shared information.Using a limited number of environment interactions, our agent is able to efficiently navigate to arbitrary points in the goal space.We demonstrate the effectiveness of our approach in high-dimensional locomotion environments such as the Mujoco Ant, PyBullet Humanoid, and PyBullet Minitaur.We provide quantitative and qualitative results to show that our method clearly outperforms competing baseline approaches.

Wir zeigen, dass der Schlüssel zur Erzielung einer guten Leistung mit IDMs im Erlernen latenter Repräsentationen liegt, um die zwischen gleichwertigen Erfahrungen geteilten Informationen zu kodieren, so dass sie auf ungesehene Szenarien verallgemeinert werden können.


In this paper, we first identify \textit{angle bias}, a simple but remarkable phenomenon that causes the vanishing gradient problem in a multilayer perceptron (MLP) with sigmoid activation functions.We then propose \textit{linearly constrained weights (LCW)} to reduce the angle bias in a neural network, so as to train the network under the constraints that the sum of the elements of each weight vector is zero.A reparameterization technique is presented to efficiently train a model with LCW by embedding the constraints on weight vectors into the structure of the network.Interestingly, batch normalization (Ioffe & Szegedy, 2015) can be viewed as a mechanism to correct angle bias.Preliminary experiments show that LCW helps train a 100-layered MLP more efficiently than does batch normalization.

Wir identifizieren die Winkelverzerrung, die das Problem des verschwindenden Gradienten in tiefen Netzen verursacht, und schlagen eine effiziente Methode zur Reduzierung der Verzerrung vor.


Markov Logic Networks (MLNs), which elegantly combine logic rules and probabilistic graphical models, can be used to address many knowledge graph problems.However, inference in MLN is computationally intensive, making the industrial-scale application of MLN very difficult.In recent years, graph neural networks (GNNs) have emerged as efficient and effective tools for large-scale graph problems.Nevertheless, GNNs do not explicitly incorporate prior logic rules into the models, and may require many labeled examples for a target task.In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.We propose a GNN variant, named ExpressGNN, which strikes a nice balance between the representation power and the simplicity of the model.Our extensive experiments on several benchmark datasets demonstrate that ExpressGNN leads to effective and efficient probabilistic logic reasoning.

Wir verwenden graphische neuronale Netze im Rahmen von Variational EM für effiziente Inferenz und Lernen von Markov Logic Networks.


Reinforcement learning (RL) methods achieved major advances in multiple tasks surpassing human performance.However, most of RL strategies show a certain degree of weakness and may become computationally intractable when dealing with high-dimensional and non-stationary environments.In this paper, we build a meta-reinforcement learning (MRL) method embedding an adaptive neural network (NN) controller for efficient policy iteration in changing task conditions.Our main goal is to extend RL application to the challenging task of urban autonomous driving in CARLA simulator.

Ein Meta-Reinforcement-Learning-Ansatz, in den ein neuronales Netz eingebettet ist, wird auf das autonome Fahren mit dem Carla-Simulator angewendet.


The information bottleneck principle is an elegant and useful approach to representation learning.In this paper, we investigate the problem of representation learning in the context of reinforcement learning using the information bottleneck framework, aiming at improving the sample efficiency of the learning algorithms.We analytically derive the optimal conditional distribution of the representation, and provide a variational lower bound.Then, we maximize this lower bound with the Stein variational (SV) gradient method. We incorporate this framework in the advantageous actor critic algorithm (A2C) and the proximal policy optimization algorithm (PPO).Our experimental results show that our framework can improve the sample efficiency of vanilla A2C and PPO significantly.Finally, we study the information-bottleneck (IB) perspective in deep RL with the algorithm called mutual information neural estimation(MINE).We experimentally verify that the information extraction-compression process also exists in deep RL and our framework is capable of accelerating this process.We also analyze the relationship between MINE and our method, through this relationship, we theoretically derive an algorithm to optimize our IB framework without constructing the lower bound.

Ableitung eines Rahmens für Informationsengpässe beim Reinforcement Learning und einiger einfacher relevanter Theorien und Werkzeuge.


A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly.Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities.We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework.We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment.We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance.Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previously-seen tasks to substantially improve their own learning efficiency.

Wir schlagen einen Ansatz für neuronale Module zum kontinuierlichen Lernen vor, der eine einheitliche visuelle Umgebung mit einem großen Aktionsraum verwendet.


Interpretability and small labelled datasets are key issues in the practical application of deep learning, particularly in areas such as medicine.In this paper, we present a semi-supervised technique that addresses both these issues simultaneously.We learn dense representations from large unlabelled image datasets, then use those representations to both learn classifiers from small labeled sets and generate visual rationales explaining the predictions.Using chest radiography diagnosis as a motivating application, we show our method has good generalization ability by learning to represent our chest radiography dataset while training a classifier on an separate set from a different institution.Our method identifies heart failure and other thoracic diseases.For each prediction, we generate visual rationales for positive classifications by optimizing a latent representation to minimize the probability of disease while constrained by a similarity measure in image space.Decoding the resultant latent representation produces an image without apparent disease.The difference between the original and the altered image forms an interpretable visual rationale for the algorithm's prediction.Our method simultaneously produces visual rationales that compare favourably to previous techniques and a classifier that outperforms the current state-of-the-art.

Wir schlagen eine Methode zur Verwendung von GANs vor, um qualitativ hochwertige visuelle Begründungen zur Erklärung von Modellvorhersagen zu erzeugen.


Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of objective functions.This paper investigates the potential benefits of using highly flexible search distributions in ES algorithms, in contrast to standard ones (typically Gaussians).We model such distributions with Generative Neural Networks (GNNs) and introduce a new ES algorithm that leverages their expressiveness to accelerate the stochastic search.Because it acts as a plug-in, our approach allows to augment virtually any standard ES algorithm with flexible search distributions.We demonstrate the empirical advantages of this method on a diversity of objective functions.

Wir schlagen einen neuen Algorithmus vor, der die Ausdruckskraft von Generativen Neuronalen Netzen nutzt, um die Algorithmen der Evolutionsstrategien zu verbessern.


We propose and evaluate new techniques for compressing and speeding up dense matrix multiplications as found in the fully connected and recurrent layers of neural networks for embedded large vocabulary continuous speech recognition (LVCSR).For compression, we introduce and study a trace norm regularization technique for training low rank factored versions of matrix multiplications.Compared to standard low rank training, we show that our method leads to good accuracy versus number of parameter trade-offs and can be used to speed up training of large models.For speedup, we enable faster inference on ARM processors through new open sourced kernels optimized for small batch sizes, resulting in 3x to 7x speed ups over the widely used gemmlowp library.Beyond LVCSR, we expect our techniques and kernels to be more generally applicable to embedded neural networks with large fully connected or recurrent layers.

Wir komprimieren und beschleunigen Spracherkennungsmodelle auf eingebetteten Geräten durch eine Spurnormregularisierungstechnik und optimierte Kernel.


Training activation quantized neural networks involves minimizing a piecewise constant training loss whose gradient vanishes almost everywhere, which is undesirable for the standard back-propagation or chain rule.An empirical way around this issue is to use a straight-through estimator (STE) (Bengio et al., 2013) in the backward pass only, so that the "gradient" through the modified chain rule becomes non-trivial.Since this unusual "gradient" is certainly not the gradient of loss function, the following question arises: why searching in its negative direction minimizes the training loss?In this paper, we provide the theoretical justification of the concept of STE by answering this question.We consider the problem of learning a two-linear-layer network with binarized ReLU activation and Gaussian input data.We shall refer to the unusual "gradient" given by the STE-modifed chain rule as coarse gradient.The choice of STE is not unique.We prove that if the STE is properly chosen, the expected coarse gradient correlates positively with the population gradient (not available for the training), and its negation is a descent direction for minimizing the population loss.We further show the associated coarse gradient descent algorithm converges to a critical point of the population loss minimization problem.  Moreover, we show that a poor choice of STE leads to instability of the training algorithm near certain local minima, which is verified with CIFAR-10 experiments.

Wir liefern eine theoretische Begründung für das Konzept des Straight-Through-Schätzers.


This paper presents GumbelClip, a set of modifications to the actor-critic algorithm, for off-policy reinforcement learning.GumbelClip uses the concepts of truncated importance sampling along with additive noise to produce a loss function enabling the use of off-policy samples.The modified algorithm achieves an increase in convergence speed and sample efficiency compared to on-policy algorithms and is competitive with existing off-policy policy gradient methods while being significantly simpler to implement.The effectiveness of GumbelClip is demonstrated against existing on-policy and off-policy actor-critic algorithms on a subset of the Atari domain.

Mit einer Reihe von Modifikationen unter 10 LOC an A2C erhält man einen Off-Policy-Actor-Critic, der A2C übertrifft und ähnlich gut abschneidet wie ACER. Die Modifikationen sind große Batchsizes, aggressives Clamping und Policy-Forcing mit Gumbel Noise.


In the past few years, various advancements have been made in generative models owing to the formulation of Generative Adversarial Networks (GANs).GANs have been shown to perform exceedingly well on a wide variety of tasks pertaining to image generation and style transfer.In the field of Natural Language Processing, word embeddings such as word2vec and GLoVe are state-of-the-art methods for applying neural network models on textual data.Attempts have been made for utilizing GANs with word embeddings for text generation.This work presents an approach to text generation using Skip-Thought sentence embeddings in conjunction with GANs based on gradient penalty functions and f-measures.The results of using sentence embeddings with GANs for generating text conditioned on input information are comparable to the approaches where word embeddings are used.

Generierung von Text unter Verwendung von Satzeinbettungen aus Skip-Thought Vektoren mit Hilfe von Generative Adversarial Networks.


Autoregressive recurrent neural decoders that generate sequences of tokens one-by-one and left-to-right are the workhorse of modern machine translation.In this work, we propose a new decoder architecture that can generate natural language sequences in an arbitrary order.Along with generating tokens from a given vocabulary, our model additionally learns to select the optimal position for each produced token.The proposed decoder architecture is fully compatible with the seq2seq framework and can be used as a drop-in replacement of any classical decoder.We demonstrate the performance of our new decoder on the IWSLT machine translation task as well as inspect and interpret the learned decoding patterns by analyzing how the model selects new positions for each subsequent token.

Neuer Out-of-Order Decoder für neuronale maschinelle Übersetzungen.


In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms.Although the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in polynomial time compared to the exponential time of the original NP-complete problem.Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting requires more global inference to oversee the whole graph.To tackle this problem, we propose a dynamic intermedium attention memory network (DIAMNet) which augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize different subgraph isomorphisms for the global counting.We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different models.Experimental results show that learning based subgraph isomorphism counting can help reduce the time complexity with acceptable accuracy.Our DIAMNet can further improve existing representation learning models for this more global problem.

In diesem Papier untersuchen wir ein neues Graphenlernproblem: Das Zählen von Untergraphen-Isomorphismen.


Domain adaptation is an open problem in deep reinforcement learning (RL).Often, agents are asked to perform in environments where data is difficult to obtain.In such settings, agents are trained in similar environments, such as simulators, and are then transferred to the original environment.The gap between visual observations of the source and target environments often causes the agent to fail in the target environment.We present a new RL agent, SADALA (Soft Attention DisentAngled representation Learning Agent).SADALA first learns a compressed state representation.It then jointly learns to ignore distracting features and solve the task presented.SADALA's separation of important and unimportant visual features leads to robust domain transfer.SADALA outperforms both prior disentangled-representation based RL and domain randomization approaches across RL environments (Visual Cartpole and DeepMind Lab).

Wir stellen einen Agenten vor, der eine Beta-Vae verwendet, um visuelle Merkmale zu extrahieren und einen Aufmerksamkeitsmechanismus, um irrelevante Merkmale aus visuellen Beobachtungen zu ignorieren, um einen robusten Transfer zwischen visuellen Domänen zu ermöglichen.


Robustness verification that aims to formally certify the prediction behavior of  neural networks has become an important tool for understanding the behavior of a given model and for obtaining safety guarantees.However, previous methods are usually limited to relatively simple neural networks.In this paper, we consider the robustness verification problem for Transformers.Transformers have complex self-attention layers that pose many challenges for verification, including cross-nonlinearity and cross-position dependency, which have not been discussed in previous work.We resolve these challenges and develop the first verification algorithm for Transformers.The certified robustness bounds computed by our method are significantly tighter than those by naive Interval Bound Propagation.These bounds also shed light on interpreting Transformers as they consistently reflect the importance of words in sentiment analysis.

Wir schlagen den ersten Algorithmus zur Überprüfung der Robustheit von Transformern vor.


In the last few years, deep learning has been tremendously successful in many applications.However, our theoretical understanding of deep learning, and thus the ability of providing principled improvements, seems to lag behind.A theoretical puzzle concerns the ability of deep networks to predict well despite their intriguing apparent lack of generalization: their classification accuracy on the training set is not a proxy for their performance on a test set.How is it possible that training performance is independent of testing performance?Do indeed deep networks require a drastically new theory of generalization?Or are there measurements based on the training data that are predictive of the network performance on future data?Here we show that when performance is measured appropriately, the training performance is in fact predictive of expected performance, consistently with classical machine learning theory.

Im Gegensatz zu früheren Annahmen ist die Trainingsleistung von tiefen Netzwerken, wenn sie angemessen gemessen wird, prädiktiv für die Testleistung, was der klassischen Theorie des maschinellen Lernens entspricht.


We propose a "plan online and learn offline" framework for the setting where an agent, with an internal model, needs to continually act and learn in the world.Our work builds on the synergistic relationship between local model-based control, global value function learning, and exploration.We study how local trajectory optimization can cope with approximation errors  in the value function, and can stabilize and accelerate value function learning.Conversely, we also study how approximate value functions can help reduce the planning horizon and allow for better policies beyond local solutions.Finally, we also demonstrate how trajectory optimization can be used to perform temporally coordinated exploration in conjunction with estimating uncertainty in value function approximation.This exploration is critical for fast and stable learning of the value function.Combining these components enable solutions to complex control tasks, like humanoid locomotion and dexterous in-hand manipulation, in the equivalent of a few minutes of experience in the real world.

Wir schlagen einen Rahmen vor, der die Planung für effizientes Erkunden und Lernen in komplexen Umgebungen beinhaltet.


Modern neural network architectures take advantage of increasingly deeper layers, and various advances in their structure to achieve better performance.While traditional explicit regularization techniques like dropout, weight decay, and data augmentation are still being used in these new models, little about the regularization and generalization effects of these new structures have been studied. Besides being deeper than their predecessors, could newer architectures like ResNet and DenseNet also benefit from their structures' implicit regularization properties? In this work, we investigate the skip connection's effect on network's generalization features.Through experiments, we show that certain neural network architectures contribute to their generalization abilities.Specifically, we study the effect that low-level features have on generalization performance when they are introduced to deeper layers in DenseNet, ResNet as well as networks with 'skip connections'.We show that these low-level representations do help with generalization in multiple settings when both the quality and quantity of training data is decreased.

Unser Beitrag analysiert die enorme Repräsentationskraft von Netzwerken, insbesondere mit "Skip Connections", die als Methode für eine bessere Generalisierung genutzt werden können.


One of the challenges in training generative models such as the variational auto encoder (VAE) is avoiding posterior collapse.When the generator has too much capacity, it is prone to ignoring latent code.This problem is exacerbated when the dataset is small, and the latent dimension is high.The root of the problem is the ELBO objective, specifically the Kullback–Leibler (KL) divergence term in objective function.This paper proposes a new  objective function to replace the KL term with one that emulates the maximum mean discrepancy (MMD) objective.It also introduces a new technique, named latent clipping, that is used to control distance between samples in latent space.A probabilistic autoencoder model, named $\mu$-VAE, is designed and trained on MNIST and MNIST Fashion datasets, using the new objective function and is shown to outperform models trained with ELBO and $\beta$-VAE objective.The $\mu$-VAE is less prone to posterior collapse, and can generate reconstructions and new samples in good quality.Latent representations learned by $\mu$-VAE are shown to be good and can be used for downstream tasks such as classification.

In dieser Arbeit wird eine neue Zielfunktion vorgeschlagen, die den Begriff KL durch eine Funktion ersetzt, die das Ziel der maximalen mittleren Diskrepanz (MMD) nachbildet.


Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system.However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significantly differ from training data.In this paper, we pose that this problem is due to the excessive influence that input complexity has in generative models' likelihoods.We report a set of experiments supporting this hypothesis, and use an estimate of input complexity to derive an efficient and parameter-free OOD score, which can be seen as a likelihood-ratio, akin to Bayesian model comparison.We find such score to perform comparably to, or even better than, existing OOD detection approaches under a wide range of data sets, models, model sizes, and complexity estimates.

Wir stellen fest, dass die Wahrscheinlichkeiten generativer Modelle übermäßig von der Komplexität der Eingaben beeinflusst werden, und schlagen einen Weg vor, dies zu kompensieren, wenn wir Eingaben außerhalb der Verteilung erkennen.


Several recently proposed stochastic optimization methods that have been successfully used in training deep networks such as RMSProp, Adam, Adadelta, Nadam are based on using gradient updates scaled by square roots of exponential moving averages of squared past gradients.In many applications, e.g. learning with large output spaces, it has been empirically observed that these algorithms fail to converge to an optimal solution (or a critical point in nonconvex settings).We show that one cause for such failures is the exponential moving average used in the algorithms.We provide an explicit example of a simple convex optimization setting where Adam does not converge to the optimal solution, and describe the precise problems with the previous analysis of Adam algorithm.Our analysis suggests that the convergence issues can be fixed by endowing such algorithms with ``long-term memory'' of past gradients, and propose new variants of the Adam algorithm which not only fix the convergence issues but often also lead to improved empirical performance.

Wir untersuchen die Konvergenz populärer Optimierungsalgorithmen wie Adam, RMSProp und schlagen neue Varianten dieser Methoden vor, die in konvexen Umgebungen nachweislich zur optimalen Lösung konvergieren.


Targeted clean-label poisoning is a type of adversarial attack on machine learning systems where the adversary injects a few correctly-labeled, minimally-perturbed samples into the training data thus causing the deployed model to misclassify a particular test sample during inference.Although defenses have been proposed for general poisoning attacks (those which aim to reduce overall test accuracy), no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and their realistic use cases.In this work, we propose a set of simple, yet highly-effective defenses against these attacks. We test our proposed approach against two recently published clean-label poisoning attacks, both of which use the CIFAR-10 dataset.After reproducing their experiments, we demonstrate that our defenses are able to detect over 99% of poisoning examples in both attacks and remove them without any compromise on model performance.Our simple defenses show that current clean-label poisoning attack strategies can be annulled, and serve as strong but simple-to-implement baseline defense for which to test future clean-label poisoning attacks.

Wir präsentieren wirksame Abwehrmaßnahmen gegen Clean-Label-Poisoning-Angriffe.


Abstract reasoning, particularly in the visual domain, is a complex human ability, but it remains a challenging problem for artificial neural learning systems.In this work we propose MXGNet, a multilayer graph neural network for multi-panel diagrammatic reasoning tasks.MXGNet combines three powerful concepts, namely, object-level representation, graph neural networks and multiplex graphs, for solving visual reasoning tasks.MXGNet first extracts object-level representations for each element in all panels of the diagrams, and then forms a multi-layer multiplex graph capturing multiple relations between objects across different diagram panels.MXGNet summarises the multiple graphs extracted from the diagrams of the task, and uses this summarisation to pick the most probable answer from the given candidates.We have tested MXGNet on two types of diagrammatic reasoning tasks, namely Diagram Syllogisms and Raven Progressive Matrices (RPM).For an Euler Diagram Syllogism task MXGNet achieves state-of-the-art accuracy of 99.8%.  For PGM and RAVEN, two comprehensive datasets for RPM reasoning, MXGNet outperforms the state-of-the-art models by a considerable margin.

MXGNet ist eine mehrschichtige, auf Multiplex-Graphen basierende Architektur, die gute Leistungen bei verschiedenen Diagramm-Aufgaben erzielt.


Semantic structure extraction for spreadsheets includes detecting table regions, recognizing structural components and classifying cell types.Automatic semantic structure extraction is key to automatic data transformation from various table structures into canonical schema so as to enable data analysis and knowledge discovery.However, they are challenged by the diverse table structures and the spatial-correlated semantics on cell grids.To learn spatial correlations and capture semantics on spreadsheets, we have developed a novel learning-based framework for spreadsheet semantic structure extraction.First, we propose a multi-task framework that learns table region, structural components and cell types jointly; second, we leverage the advances of the recent language model to capture semantics in each cell value; third, we build a large human-labeled dataset with broad coverage of table structures.Our evaluation shows that our proposed multi-task framework is highly effective that outperforms the results of training each task separately.

Wir schlagen ein neuartiges Multitasking-System vor, das die Erkennung von Tabellen, semantischen Komponenten und die Klassifizierung von Zelltypen für Tabellenkalkulationen mit vielversprechenden Ergebnissen erlernt.


Open-domain dialogue generation has gained increasing attention in Natural Language Processing.Comparing these methods requires a holistic means of dialogue evaluation.Human ratings are deemed as the gold standard.As human evaluation is inefficient and costly, an automated substitute is desirable.In this paper, we propose holistic evaluation metrics which capture both the quality and diversity of dialogues.Our metrics consists of (1) GPT-2 based context coherence between sentences in a dialogue, (2) GPT-2 based fluency in phrasing, and, (3) $n$-gram based diversity in responses to augmented queries.The empirical validity of our metrics is demonstrated by strong correlation with human judgments.We provide the associated code, datasets and human ratings.

Wir schlagen automatische Metriken zur ganzheitlichen Bewertung der Erzeugung offener Dialoge vor, die stark mit der menschlichen Bewertung korrelieren.


Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features.Recently, there has been an increasing interest in extending CNNs to the general spatial domain.Although various types of graph convolution and geometric convolution methods have been proposed, their connections to traditional 2D-convolution are not well-understood.In this paper, we show that depthwise separable convolution is a path to unify the two kinds of convolution methods in one mathematical view, based on which we derive a novel Depthwise Separable Graph Convolution that subsumes existing graph convolution methods as special cases of our formulation.Experiments show that the proposed approach consistently outperforms other graph convolution and geometric convolution baselines on benchmark datasets in multiple domains.

Wir entwickeln eine neuartige tiefenselektierbare Graphen Convolution (Depthwise Separable Graph Convolution, DSGC) für generische räumliche Daten, die in hohem Maße mit der tiefenselektierbaren Convolution kompatibel ist.