Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL).In the tabular case, all provably efficient model-free algorithms rely on it.However, model-free deep RL algorithms do not use optimistic initialisation despite taking inspiration from these provably efficient tabular algorithms.In particular, in scenarios with only positive rewards, Q-values are initialised at their lowest possible values due to commonly used network initialisation schemes, a pessimistic initialisation.Merely initialising the network to output optimistic Q-values is not enough, since we cannot ensure that they remain optimistic for novel state-action pairs, which is crucial for exploration.We propose a simple count-based augmentation to pessimistically initialised Q-values that separates the source of optimism from the neural network.We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting.Our algorithm, Optimistic Pessimistically Initialised Q-Learning (OPIQ), augments the Q-value estimates of a DQN-based agent with count-derived bonuses to ensure optimism during both action selection and bootstrapping.We show that OPIQ outperforms non-optimistic DQN variants that utilise a pseudocount-based intrinsic motivation in hard exploration tasks, and that it predicts optimistic estimates for novel state-action pairs.

Wir ergänzen die Q-Wert-Schätzungen mit einem zählbasierten Bonus, der für Optimismus bei der Aktionsauswahl und beim Bootstrapping sorgt, selbst wenn die Q-Wert-Schätzungen pessimistisch sind.


Model-free deep reinforcement learning (RL) algorithms have been demonstrated on a range of challenging decision making and control tasks.However, these methods typically suffer from two major challenges: very high sample complexity and brittle convergence properties, which necessitate meticulous hyperparameter tuning.Both of these challenges severely limit the applicability of such methods to complex, real-world domains.In this paper, we propose soft actor-critic, an off-policy actor-critic deep RL algorithm based on the maximum entropy reinforcement learning framework.In this framework, the actor aims to maximize expected reward while also maximizing entropy - that is, succeed at the task while acting as randomly as possible.Prior deep RL methods based on this framework have been formulated as either off-policy Q-learning, or on-policy policy gradient methods.By combining off-policy updates with a stable stochastic actor-critic formulation, our method achieves state-of-the-art performance on a range of continuous control benchmark tasks, outperforming prior on-policy and off-policy methods.Furthermore, we demonstrate that, in contrast to other off-policy algorithms, our approach is very stable, achieving very similar performance across different random seeds.

Wir schlagen Soft Actor-Critic vor, einen Deep-RL-Algorithmus, der auf dem Maximum-Entropy Reinforcement Learning Framework basiert.


In many settings, it is desirable to learn decision-making and control policies through learning or from expert demonstrations.The most common approaches under this framework are Behaviour Cloning (BC), and Inverse Reinforcement Learning (IRL).Recent methods for IRL have demonstrated the capacity to learn effective policies with access to a very limited set of demonstrations, a scenario in which BC methods often fail.Unfortunately, directly comparing the algorithms for these methods does not provide adequate intuition for understanding this difference in performance.This is the motivating factor for our work.We begin by presenting $f$-MAX, a generalization of AIRL (Fu et al., 2018), a state-of-the-art IRL method.$f$-MAX provides grounds for more directly comparing the objectives for LfD.We demonstrate that $f$-MAX, and by inheritance AIRL, is a subset of the cost-regularized IRL framework laid out by Ho & Ermon (2016).We conclude by empirically evaluating the factors of difference between various LfD objectives in the continuous control domain.

Die Verteilungsanpassung durch Divergenzminimierung bietet eine gemeinsame Grundlage für den Vergleich von gegnerischen Maximum-Entropy Inverse Reinforcement Learning-Methoden mit Behaviour Cloning.


Value-based methods constitute a fundamental methodology in planning and deep reinforcement learning (RL).In this paper, we propose to exploit the underlying structures of the state-action value function, i.e., Q function, for both planning and deep RL.In particular, if the underlying system dynamics lead to some global structures of the Q function, one should be capable of inferring the function better by leveraging such structures.Specifically, we investigate the low-rank structure, which widely exists for big data matrices.We verify empirically the existence of low-rank Q functions in the context of control and deep RL tasks (Atari games).As our key contribution, by leveraging Matrix Estimation (ME) techniques, we propose a general framework to exploit the underlying low-rank structure in Q functions, leading to a more efficient planning procedure for classical control, and additionally, a simple scheme that can be applied to any value-based RL techniques to consistently achieve better performance on ''low-rank'' tasks.Extensive experiments on control tasks and Atari games confirm the efficacy of our approach.

Wir schlagen einen generischen Rahmen vor, der es ermöglicht, die Low-Rank-Struktur sowohl beim Planungs- als auch beim Deep Reinforcement Learning zu nutzen.


Learned representations of source code enable various software developer tools, e.g., to detect bugs or to predict program properties.At the core of code representations often are word embeddings of identifier names in source code, because identifiers account for the majority of source code vocabulary and convey important semantic information.Unfortunately, there currently is no generally accepted way of evaluating the quality of word embeddings of identifiers, and current evaluations are biased toward specific downstream tasks.This paper presents IdBench, the first benchmark for evaluating to what extent word embeddings of identifiers represent semantic relatedness and similarity.The benchmark is based on thousands of ratings gathered by surveying 500 software developers.We use IdBench to evaluate state-of-the-art embedding techniques proposed for natural language, an embedding technique specifically designed for source code, and lexical string distance functions, as these are often used in current developer tools.Our results show that the effectiveness of embeddings varies significantly across different embedding techniques and that the best available embeddings successfully represent semantic relatedness.On the downside, no existing embedding provides a satisfactory representation of semantic similarities, e.g., because embeddings consider identifiers with opposing meanings as similar, which may lead to fatal mistakes in downstream developer tools.IdBench provides a gold standard to guide the development of novel embeddings that address the current limitations.

Ein Benchmark zur Bewertung der neuronalen Einbettung von Bezeichnern im Quellcode.


Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget.This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions.First, we argue that the existing MMD loss function may discourage the learning of fine details in data as it attempts to contract the discriminator outputs of real data.To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.Second, inspired by the hinge loss, we propose a bounded Gaussian kernel to stabilize the training of MMD-GAN with the repulsive loss function.The proposed methods are applied to the unsupervised image generation tasks on CIFAR-10, STL-10, CelebA, and LSUN bedroom datasets.Results show that the repulsive loss function significantly improves over the MMD loss at no additional computational cost and outperforms other representative loss functions.The proposed methods achieve an FID score of 16.21 on the CIFAR-10 dataset using a single DCGAN network and spectral normalization.

Durch Umstellung der Terme in der maximalen mittleren Diskrepanz ergibt sich eine viel bessere Verlustfunktion für den Diskriminator generativer adversarischer Netze


Deep neural networks have shown incredible performance for inference tasks in a variety of domains.Unfortunately, most current deep networks are enormous cloud-based structures that require significant storage space, which limits scaling of deep learning as a service (DLaaS) and use for on-device augmented intelligence.  This paper finds algorithms that directly use lossless compressed representations of deep feedforward networks (with synaptic weights drawn from discrete sets), to perform inference without full decompression.The basic insight that allows less rate than naive approaches is the recognition that the bipartite graph layers of feedforward networks have a kind of permutation invariance to the labeling of nodes, in terms of inferential operation and that the inference operation depends locally on the edges directly connected to it.We also provide experimental results of our approach on the MNIST dataset.

In diesem Werk werden Algorithmen entwickelt, die direkt verlustfrei komprimierte Darstellungen von tiefen Feedforward-Netzen verwenden, um Inferenzen ohne vollständige Dekompression durchzuführen.


Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.One common way to tackle this issue has been to propose new formulations of the GAN objective.Yet, surprisingly few studies have looked at optimization methods designed for this adversarial training.In this work, we cast GAN optimization problems in the general variational inequality framework.Tapping into the mathematical programming literature, we counter some common misconceptions about the difficulties of saddle point optimization and propose to extend methods designed for variational inequalities to the training of GANs.We apply averaging, extrapolation and a computationally cheaper variant that we call extrapolation from the past to the stochastic gradient method (SGD) and Adam.

Wir gliedern GANs in den Rahmen der Variationsungleichung ein und importieren Techniken aus dieser Literatur, um GANs besser zu optimieren; wir geben algorithmische Erweiterungen an und testen empirisch ihre Leistung beim Training von GANs.


In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones.However, a critical challenge in meta-learning is the task heterogeneity which cannot be well handled by traditional globally shared meta-learning methods.In addition, current task-specific meta-learning methods may either suffer from hand-crafted structure design or lack the capability to capture complex relations between tasks.In this paper, motivated by the way of knowledge organization in knowledge bases, we propose an automated relational meta-learning (ARML) framework that automatically extracts the cross-task relations and constructs the meta-knowledge graph.When a new task arrives, it can quickly find the most relevant structure and tailor the learned structure knowledge to the meta-learner.As a result, the proposed framework not only addresses the challenge of task heterogeneity by a learned meta-knowledge graph, but also increases the model interpretability.We conduct extensive experiments on 2D toy regression and few-shot image classification and the results demonstrate the superiority of ARML over state-of-the-art baselines.

Lösung des Problems der Aufgabenheterogenität beim Meta-Lernen durch Einführung eines Meta-Wissensgraphen


In this paper, a deep boosting algorithm is developed tolearn more discriminative ensemble classifier by seamlessly combining a set of base deep CNNs (base experts)with diverse capabilities, e.g., these base deep CNNs aresequentially trained to recognize a set of object classes in an easy-to-hard way according to theirlearning complexities.Our experimental results have demonstratedthat our deep boosting algorithm can significantly improve theaccuracy rates on large-scale visual recognition.

Es wird ein Deep-Boosting-Algorithmus entwickelt, um durch die nahtlose Kombination einer Reihe von Deep-CNNs einen diskriminanteren Ensemble-Klassifikator zu lernen.


We present a method for translating music across musical instruments and styles.This method is based on unsupervised training of a multi-domain wavenet autoencoder, with a shared encoder and a domain-independent latent space that is trained end-to-end on waveforms.Employing a diverse training dataset and large net capacity, the single encoder allows us to translate also from musical domains that were not seen during training.We evaluate our method on a dataset collected from professional musicians, and achieve convincing translations.We also study the properties of the obtained translation and demonstrate translating even from a whistle, potentially enabling the creation of instrumental music by untrained humans.

Eine automatische Methode zur Umwandlung von Musik zwischen Instrumenten und Stilen


Most existing defenses against adversarial attacks only consider robustness to L_p-bounded distortions.In reality, the specific attack is rarely known in advance and adversaries are free to modify images in ways which lie outside any fixed distortion model; for example, adversarial rotations lie outside the set of L_p-bounded distortions.In this work, we advocate measuring robustness against a much broader range of unforeseen attacks, attacks whose precise form is unknown during defense design.We propose several new attacks and a methodology for evaluating a defense against a diverse range of unforeseen distortions.First, we construct novel adversarial JPEG, Fog, Gabor, and Snow distortions to simulate more diverse adversaries.We then introduce UAR, a summary metric that measures the robustness of a defense against a given distortion.  Using UAR to assess robustness against existing and novel attacks, we perform an extensive study of adversarial robustness.We find that evaluation against existing L_p attacks yields redundant information which does not generalize to other attacks; we instead recommend evaluating against our significantly more diverse set of attacks.We further find that adversarial training against either one or multiple distortions fails to confer robustness to attacks with other distortion types.  These results underscore the need to evaluate and study robustness against unforeseen distortions.

Wir schlagen mehrere neue Angriffe und eine Methodik zur Messung der Robustheit gegenüber unvorhergesehenen Angriffen vor.


Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificialintelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and others.In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.The data set of each use case contains real known benign and malicious activities samples.These use cases are part of Cybersecurity Data Mining Competition (CDMC) 2017.The efficient network architecture for DNNs is chosen by conducting various trails of experiments for network parameters and network structures.The experiments of such chosen efficient configurations of DNNs are run up to 1000 epochs with learning rate set in the range [0.01-0.5].Experiments of DNNs performed well in comparison to the classical machine learning algorithm in all cases of experiments of cyber security use cases.This is due to the fact that DNNs implicitly extract and build better features, identifies the characteristics of the data that lead to better accuracy.The best accuracy obtained by DNNs and XGBoost on Android malware classification 0.940 and 0.741, incident detection 1.00 and 0.997, and fraud detection 0.972 and 0.916 respectively.The accuracy obtained by DNNs varies -0.05%, +0.02%, -0.01% from the top scored system in CDMC 2017 tasks.

Deep-Net: Deep Neural Network für Anwendungsfälle der Cybersicherheit


In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations.Current approaches to decomposing demonstrations into primitives often assume manually defined primitives and bypass the difficulty of discovering these primitives.On the other hand, approaches in primitive discovery put restrictive assumptions on the complexity of a primitive, which limit applicability to narrow tasks.Our approach attempts to circumvent these challenges by jointly learning both the underlying motor primitives and recomposing these primitives to form the original demonstration.Through constraints on both the parsimony of primitive decomposition and the simplicity of a given primitive, we are able to learn a diverse set of motor primitives, as well as a coherent latent representation for these primitives.We demonstrate, both qualitatively and quantitatively, that our learned primitives capture semantically meaningful aspects of a demonstration.This allows us to compose these primitives in a hierarchical reinforcement learning setup to efficiently solve robotic manipulation tasks like reaching and pushing.

Wir lernen einen Raum motorischer Primitive aus unkommentierten Roboterdemonstrationen und zeigen, dass diese Primitive semantisch sinnvoll sind und für neue Roboteraufgaben zusammengestellt werden können.


Using modern deep learning models to make predictions on time series data from wearable sensors generally requires large amounts of labeled data.However, labeling these large datasets can be both cumbersome and costly.In this paper, we apply weak supervision to time series data, and programmatically label a dataset from sensors worn by patients with Parkinson's.We then built a LSTM model that predicts when these patients exhibit clinically relevant freezing behavior (inability to make effective forward stepping).We show that (1) when our model is trained using patient-specific data (prior sensor sessions), we come within 9% AUROC of a model trained using hand-labeled data and (2) when we assume no prior observations of subjects, our weakly supervised model matched performance with hand-labeled data.These results demonstrate that weak supervision may help reduce the need to painstakingly hand label time series training data.

Wir demonstrieren die Machbarkeit eines schwach überwachten Zeitreihenklassifizierungsansatzes für tragbare Sensordaten.


Learning semantic correspondence between the structured data (e.g., slot-value pairs) and associated texts is a core problem for many downstream NLP applications, e.g., data-to-text generation.Recent neural generation methods require to use large scale training data.However, the collected data-text pairs for training are usually loosely corresponded, where texts contain additional or contradicted information compare to its paired input.In this paper, we propose a local-to-global alignment (L2GA) framework to learn semantic correspondences from loosely related data-text pairs.First, a local alignment model based on multi-instance learning is applied to build the semantic correspondences within a data-text pair.Then, a global alignment model built on top of a memory guided conditional random field (CRF) layer is designed to exploit dependencies among alignments in the entire training corpus, where the memory is used to integrate the alignment clues provided by the local alignment model.Therefore, it is capable of inducing missing alignments for text spans that are not supported by its imperfect paired input.Experiments on recent restaurant dataset show that our proposed method can improve the alignment accuracy and as a by product, our method is also applicable to induce semantically equivalent training data-text pairs for neural generation models.

Wir schlagen ein lokal-globales Alignment-Framework vor, um semantische Korrespondenzen aus verrauschten Daten-Text-Paaren mit schwacher Überwachung zu lernen.


Imitation learning algorithms provide a simple and straightforward approach for training control policies via standard supervised learning methods.By maximizing the likelihood of good actions provided by an expert demonstrator, supervised imitation learning can produce effective policies without the algorithmic complexities and optimization challenges of reinforcement learning, at the cost of requiring an expert demonstrator -- typically a person -- to provide the demonstrations.In this paper, we ask: can we use imitation learning to train effective policies without any expert demonstrations?The key observation that makes this possible is that, in the multi-task setting, trajectories that are generated by a suboptimal policy can still serve as optimal examples for other tasks.In particular, in the setting where the tasks correspond to different goals, every trajectory is a successful demonstration for the state that it actually reaches.Informed by this observation, we propose a very simple algorithm for learning behaviors without any demonstrations, user-provided reward functions, or complex reinforcement learning methods.Our method simply maximizes the likelihood of actions the agent actually took in its own previous rollouts, conditioned on the goal being the state that it actually reached.Although related variants of this approach have been proposed previously in imitation learning settings with example demonstrations, we present the first instance of this approach as a method for learning goal-reaching policies entirely from scratch.We present a theoretical result linking self-supervised imitation learning and reinforcement learning, and empirical results showing that it performs competitively with more complex reinforcement learning methods on a range of challenging goal reaching problems.

Lernen, wie man Ziele von Grund auf erreicht, indem man Imitation Learning mit Datenumetikettierung einsetzt


Neural networks have recently shown excellent performance on numerous classi- fication tasks.These networks often have a large number of parameters and thus require much data to train.When the number of training data points is small, however, a network with high flexibility will quickly overfit the training data, resulting in a large model variance and a poor generalization performance.To address this problem, we propose a new ensemble learning method called InterBoost for small-sample image classification.In the training phase, InterBoost first randomly generates two complementary datasets to train two base networks of the same structure, separately, and then next two complementary datasets for further training the networks are generated through interaction (or information sharing) between the two base networks trained previously.This interactive training process continues iteratively until a stop criterion is met.In the testing phase, the outputs of the two networks are combined to obtain one final score for classification.Detailed analysis of the method is provided for an in-depth understanding of its mechanism.

In dieser Arbeit haben wir eine Ensemble-Methode namens InterBoost für das Training neuronaler Netze zur Klassifizierung kleiner Stichproben vorgeschlagen. Die Methode hat eine bessere Generalisierungsleistung als andere Ensemble-Methoden und reduziert die Varianz erheblich.


Interpreting neural networks is a crucial and challenging task in machine learning.In this paper, we develop a novel framework for detecting statistical interactions captured by a feedforward multilayer neural network by directly interpreting its learned weights.Depending on the desired interactions, our method can achieve significantly better or similar interaction detection performance compared to the state-of-the-art without searching an exponential solution space of possible interactions.We obtain this accuracy and efficiency by observing that interactions between input features are created by the non-additive effect of nonlinear activation functions, and that interacting paths are encoded in weight matrices.We demonstrate the performance of our method and the importance of discovered interactions via experimental results on both synthetic datasets and real-world application datasets.

Wir erkennen statistische Interaktionen, die von einem mehrschichtigen neuronalen Netz mit Vorwärtskopplung erfasst werden, indem wir seine gelernten Gewichte direkt interpretieren.


The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning.Despite its apparent successes in these settings, to the best of our knowledge there has been no systematic exploration of its capabilities on simple regression tasks.In this work we characterize these on the UCI datasets, a popular benchmark for Bayesian regression models, as well as on the recently introduced ''gap'' datasets, which are better tests of out-of-distribution uncertainty.We demonstrate that the neural linear model is a simple method that shows competitive performance on these tasks.

Wir vergleichen das neuronale lineare Modell mit den UCI- und UCI-"Gap"-Datensätzen.


The reproducibility of reinforcement-learning research has been highlighted as a key challenge area in the field.In this paper, we present a case study in reproducing the results of one groundbreaking algorithm, AlphaZero, a reinforcement learning system that learns how to play Go at a superhuman level given only the rules of the game.We describe Minigo, a reproduction of the AlphaZero system using publicly available Google Cloud Platform infrastructure and Google Cloud TPUs.The Minigo system includes both the central reinforcement learning loop as well as auxiliary monitoring and evaluation infrastructure.With ten days of training from scratch on 800 Cloud TPUs, Minigo can play evenly against LeelaZero and ELF OpenGo, two of the strongest publicly available Go AIs.We discuss the difficulties of scaling a reinforcement learning system and the monitoring systems required to understand the complex interplay of hyperparameter configurations.

Wir haben AlphaZero auf der Google Cloud Platform reproduziert


Generative adversarial networks (GANs) train implicit generative models through solving minimax problems.Such minimax problems are known as nonconvex- nonconcave, for which the dynamics of first-order methods are not well understood.In this paper, we consider GANs in the type of the integral probability metrics (IPMs) with the generator represented by an overparametrized neural network.When the discriminator is solved to approximate optimality in each iteration, we prove that stochastic gradient descent on a regularized IPM objective converges globally to a stationary point with a sublinear rate.Moreover, we prove that when the width of the generator network is sufficiently large and the discriminator function class has enough discriminative ability, the obtained stationary point corresponds to a generator that yields a distribution that is close to the distribution of the observed data in terms of the total variation.To the best of our knowledge, we seem to first establish both the global convergence and global optimality of training GANs when the generator is parametrized by a neural network.

Wir weisen globale Konvergenz zur Optimalität für IPM-basierte GANs nach, bei denen der Generator ein überparametrisiertes neuronales Netz ist.


We present network embedding algorithms that capture information about a node from the local distribution over node attributes around it, as observed over random walks following an approach similar to Skip-gram.Observations from neighborhoods of different sizes are either pooled (AE) or encoded distinctly in a multi-scale approach (MUSAE).  Capturing attribute-neighborhood relationships over multiple scales is useful for a diverse range of applications, including latent feature identification across disconnected networks with similar attributes.We prove theoretically that matrices of node-feature pointwise mutual information are implicitly factorized by the embeddings.Experiments show that our algorithms are robust, computationally efficient and outperform comparable models on social, web and citation network datasets.

Wir entwickeln effiziente Verfahren zur approximativen Einbettung von Netzwerken mit mehreren Skalen und nachweisbaren Eigenschaften.


Few-shot classiﬁcation aims to learn a classiﬁer to recognize unseen classes during training with limited labeled examples.While signiﬁcant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difﬁcult.In this paper, we present1) a consistent comparative analysis of several representative few-shot classiﬁcation algorithms, with results showing that deeper backbones signiﬁcantly reduce the gap across methods including the baseline,2) a slightly modiﬁed baseline method that surprisingly achieves competitive performance when compared with the state-of-the-art on both the mini-ImageNet and the CUB datasets, and3) a new experimental setting for evaluating the cross-domain generalization ability for few-shot classiﬁcation algorithms.Our results reveal that reducing intra-class variation is an important factor when the feature backbone is shallow, but not as critical when using deeper backbones.In a realistic, cross-domain evaluation setting, we show that a baseline method with a standard ﬁne-tuning practice compares favorably against other state-of-the-art few-shot learning algorithms.

Eine detaillierte empirische Studie über die Few-Shot Klassifizierung, die die Herausforderungen bei der Standardbewertung aufzeigt und eine neue Richtung angibt.


Temporal logics are useful for describing dynamic system behavior, and have been successfully used as a language for goal definitions during task planning.Prior works on inferring temporal logic specifications have focused on "summarizing" the input dataset -- i.e., finding specifications that are satisfied by all plan traces belonging to the given set.In this paper, we examine the problem of inferring specifications that describe temporal differences between two sets of plan traces.We formalize the concept of providing such contrastive explanations, then present a Bayesian probabilistic model for inferring contrastive explanations as linear temporal logic specifications.We demonstrate the efficacy, scalability, and robustness of our model for inferring correct specifications across various benchmark planning domains and for a simulated air combat mission.

Wir stellen ein Bayes'sches Inferenzmodell vor, um kontrastive Erklärungen (als LTL-Spezifikationen) abzuleiten, die beschreiben, wie sich zwei Sätze von Planspuren unterscheiden.


This work tackles the problem of characterizing and understanding the decision boundaries of neural networks with piece-wise linear non-linearity activations.We use tropical geometry, a new development in the area of algebraic geometry, to provide a characterization of the decision boundaries of a simple neural network of the form (Affine, ReLU, Affine).Specifically, we show that the decision boundaries are a subset of a tropical hypersurface, which is intimately related to a polytope formed by the convex hull of two zonotopes.The generators of the zonotopes are precise functions of the neural network parameters.We utilize this geometric characterization to shed light and new perspective on three tasks.In doing so, we propose a new tropical perspective for the lottery ticket hypothesis, where we see the effect of different initializations on the tropical geometric representation of the decision boundaries.Also, we leverage this characterization as a new set of tropical regularizers, which  deal directly  with the decision boundaries of a network.We investigate the use of these regularizers  in neural network pruning (removing network parameters that do not contribute to the tropical geometric representation of the decision boundaries) and in generating adversarial input attacks (with input perturbations explicitly perturbing the decision boundaries geometry to change the network prediction of the input).

Die tropische Geometrie kann genutzt werden, um die Entscheidungsgrenzen neuronaler Netze darzustellen und interessante Erkenntnisse zu gewinnen.


First-order methods such as stochastic gradient descent (SGD) are currently the standard algorithm for training deep neural networks.Second-order methods, despite their better convergence rate, are rarely used in practice due to the pro- hibitive computational cost in calculating the second-order information.In this paper, we propose a novel Gram-Gauss-Newton (GGN) algorithm to train deep neural networks for regression problems with square loss.Our method draws inspiration from the connection between neural network optimization and kernel regression of neural tangent kernel (NTK).Different from typical second-order methods that have heavy computational cost in each iteration, GGN only has minor overhead compared to first-order methods such as SGD.We also give theoretical results to show that for sufficiently wide neural networks, the convergence rate of GGN is quadratic.Furthermore, we provide convergence guarantee for mini-batch GGN algorithm, which is, to our knowledge, the first convergence result for the mini-batch version of a second-order method on overparameterized neural net- works.Preliminary experiments on regression tasks demonstrate that for training standard networks, our GGN algorithm converges much faster and achieves better performance than SGD.

Eine neuartige Gram-Gauß-Newton-Methode zum Trainieren neuronaler Netze, inspiriert durch den neuronalen Tangens-Kernel und die Gauß-Newton-Methode, mit schneller Konvergenzgeschwindigkeit, sowohl theoretisch als auch experimentell.


Recent pretrained sentence encoders achieve state of the art results on language understanding tasks, but does this mean they have implicit knowledge of syntactic structures?We introduce a grammatically annotated development set for the Corpus of Linguistic Acceptability (CoLA; Warstadt et al., 2018), which we use to investigate the grammatical knowledge of three pretrained encoders, including the popular OpenAI Transformer (Radford et al., 2018) and BERT (Devlin et al., 2018).We fine-tune these encoders to do acceptability classification over CoLA and compare the models’ performance on the annotated analysis set.Some phenomena, e.g. modification by adjuncts, are easy to learn for all models, while others, e.g. long-distance movement, are learned effectively only by models with strong overall performance, and others still, e.g. morphological agreement, are hardly learned by any model.

Wir untersuchen das implizite syntaktische Wissen von Satzeinbettungen anhand eines neuen Analysesets von grammatikalisch annotierten Sätzen mit Akzeptanzurteilen.


When considering simultaneously a finite number of tasks, multi-output learning enables one to account for the similarities of the tasks via appropriate regularizers.We propose a generalization of the classical setting to a continuum of tasks by using vector-valued RKHSs.

Wir schlagen eine Erweiterung des Multi-Output-Lernens auf ein Kontinuum von Aufgaben unter Verwendung von Operator-bewerteten Kerneln vor.


We analyze the joint probability distribution on the lengths of thevectors of hidden variables in different layers of a fully connecteddeep network, when the weights and biases are chosen randomly according toGaussian distributions, and the input is binary-valued. We showthat, if the activation function satisfies a minimal set ofassumptions, satisfied by all activation functions that we know thatare used in practice, then, as the width of the network gets large,the ``length process'' converges in probability to a length mapthat is determined as a simple function of the variances of therandom weights and biases, and the activation function.We also show that this convergence may fail for activation functions that violate our assumptions.

Wir beweisen, dass für Aktivierungsfunktionen, die einige Bedingungen erfüllen, die Längen der Vektoren der versteckten Variablen zu einer Längenzuordnung konvergieren, wenn ein tiefes Netzwerk breit wird.


Data augmentation is one of the most effective approaches for improving the accuracy of modern machine learning models, and it is also indispensable to train a deep model for meta-learning.However, most current data augmentation implementations applied in meta-learning are the same as those used in the conventional image classification.In this paper, we introduce a new data augmentation method for meta-learning, which is named as ``Task Level Data Augmentation'' (referred to Task Aug).The basic idea of Task Aug is to increase the number of image classes rather than the number of images in each class.In contrast, with a larger amount of classes, we can sample more diverse task instances during training.This allows us to train a deep network by meta-learning methods with little over-fitting.Experimental results show that our approach achieves state-of-the-art performance on miniImageNet, CIFAR-FS, and FC100 few-shot learning benchmarks.Once paper is accepted, we will provide the link to code.

Wir schlagen einen Ansatz zur Datenerweiterung für das Meta-Lernen vor und beweisen, dass er zulässig ist.


In this paper, we present a general framework for distilling expectations with respect to the Bayesian posterior distribution of a deep neural network, significantly extending prior work on a method known as ``Bayesian Dark Knowledge."  Our generalized framework applies to the case of classification models and takes as input the architecture of a ``teacher" network, a general posterior expectation of interest, and the architecture of a ``student" network.The distillation method performs an online compression of the selected posterior expectation using iteratively generated Monte Carlo samples from the parameter posterior of the teacher model.We further consider the problem of optimizing the student model architecture with respect to an accuracy-speed-storage trade-off.We present experimental results investigating multiple data sets, distillation targets,  teacher model architectures, and approaches to searching for student model architectures.We establish the key result that distilling into a student model with an architecture that matches the teacher, as is done in Bayesian Dark Knowledge, can lead to sub-optimal performance.Lastly, we show that student architecture search methods can identify student models with significantly improved performance.

Ein allgemeiner Rahmen für die Destillation von Bayes'schen posterioren Erwartungen für tiefe neuronale Netze.


Variational Autoencoders (VAEs) have proven to be powerful latent variable models.How- ever, the form of the approximate posterior can limit the expressiveness of the model.Categorical distributions are flexible and useful building blocks for example in neural memory layers.We introduce the Hierarchical Discrete Variational Autoencoder (HD-VAE): a hi- erarchy of variational memory layers.The Concrete/Gumbel-Softmax relaxation allows maximizing a surrogate of the Evidence Lower Bound by stochastic gradient ascent.We show that, when using a limited number of latent variables, HD-VAE outperforms the Gaussian baseline on modelling multiple binary image datasets.Training very deep HD-VAE remains a challenge due to the relaxation bias that is induced by the use of a surrogate objective.We introduce a formal definition and conduct a preliminary theoretical and empirical study of the bias.

In diesem Papier führen wir eine diskrete Hierarchie kategorialer latenter Variablen ein, die wir mit Hilfe der Concrete/Gumbel-Softmax-Relaxation trainieren, und wir leiten eine obere Schranke für die absolute Differenz zwischen dem unbiased und dem biased Ziel ab.


In this paper, we propose a novel technique for improving the stochastic gradient descent (SGD) method to train deep networks, which we term \emph{PowerSGD}.The proposed PowerSGD method simply raises the stochastic gradient to a certain power $\gamma\in[0,1]$ during iterations and introduces only one additional parameter, namely, the power exponent $\gamma$ (when $\gamma=1$, PowerSGD reduces to SGD).We further propose PowerSGD with momentum, which we term \emph{PowerSGDM}, and provide convergence rate analysis on both PowerSGD and PowerSGDM methods.Experiments are conducted on popular deep learning models and benchmark datasets.Empirical results show that the proposed PowerSGD and PowerSGDM obtain faster initial training speed than adaptive gradient methods,  comparable generalization ability with SGD, and improved robustness to hyper-parameter selection and vanishing gradients.PowerSGD is essentially a gradient modifier via a nonlinear transformation.As such, it is orthogonal and complementary to other techniques for accelerating gradient-based optimization.

Wir schlagen eine neue Klasse von Optimierern für die beschleunigte nicht-konvexe Optimierung mittels einer nichtlinearen Gradiententransformation vor.


We aim to build complex humanoid agents that integrate perception, motor control, and memory.In this work, we partly factor this problem into low-level motor control from proprioception and high-level coordination of the low-level skills informed by vision.We develop an architecture capable of surprisingly flexible, task-directed motor control of a relatively high-DoF humanoid body by combining pre-training of low-level motor controllers with a high-level, task-focused controller that switches among low-level sub-policies.The resulting system is able to control a physically-simulated humanoid body to solve tasks that require coupling visual perception from an unstabilized egocentric RGB camera during locomotion in the environment.Supplementary video link:  https://youtu.be/fBoir7PNxPk

Lösen Sie Aufgaben, die die visuell gesteuerte Fortbewegung von Humanoiden beinhalten, indem Sie das Fortbewegungsverhalten aus Bewegungserfassungsdaten wiederverwenden.


The gap between the empirical success of deep learning and the lack of strong theoretical guarantees calls for studying simpler models.By observing that a ReLU neuron is a product of a linear function with a gate (the latter determines whether the neuron is active or not), where both share a jointly trained weight vector, we propose to decouple the two.We introduce GaLU networks — networks in which each neuron is a product of a Linear Unit, defined by a weight vector which is being trained, with a Gate, defined by a different weight vector which is not being trained.Generally speaking, given a base model and a simpler version of it, the two parameters that determine the quality of the simpler version are whether its practical performance is close enough to the base model and whether it is easier to analyze it theoretically.We show that GaLU networks perform similarly to ReLU networks on standard datasets and we initiate a study of their theoretical properties, demonstrating that they are indeed easier to analyze.We believe that further research of GaLU networks may be fruitful for the development of a theory of deep learning.

Wir schlagen Gated Linear Unit-Netzwerke vor - ein Modell, das bei realen Daten ähnliche Leistungen wie ReLU-Netzwerke erbringt, aber theoretisch viel einfacher zu analysieren ist.


Machine learning systems often encounter Out-of-Distribution (OoD) errors when dealing with testing data coming from a different distribution from the one used for training.With their growing use in critical applications, it becomes important to develop systems that are able to accurately quantify its predictive uncertainty and screen out these anomalous inputs.However, unlike standard learning tasks, there is currently no well established guiding principle for designing architectures that can accurately quantify uncertainty.Moreover, commonly used OoD detection approaches are prone to errors and even sometimes assign higher likelihoods to OoD samples.To address these problems, we first seek to identify guiding principles for designing uncertainty-aware architectures, by proposing Neural Architecture Distribution Search (NADS).Unlike standard neural architecture search methods which seek for a single best performing architecture, NADS searches for a distribution of architectures that perform well on a given task, allowing us to identify building blocks common among all uncertainty aware architectures.With this formulation, we are able to optimize a stochastic outlier detection objective and construct an ensemble of models to perform OoD detection.We perform multiple OoD detection experiments and observe that our NADS performs favorably compared to state-of-the-art OoD detection methods.

Wir schlagen eine Architektur-Suchmethode vor, um eine Verteilung von Architekturen zu identifizieren und daraus ein Bayes'sches Ensemble zur Ausreißererkennung zu konstruieren.


Modern applications from Autonomous Vehicles to Video Surveillance generate massive amounts of image data.In this work we propose a novel image outlier detection approach (IOD for short) that leverages the cutting-edge image classifier to discover outliers without using any labeled outlier.We observe that although intuitively the confidence that a convolutional neural network (CNN) has that an image belongs to a particular class could serve as outlierness measure to each image, directly applying this confidence to detect outlier does not work well.This is because CNN often has high confidence on an outlier image that does not belong to any target class due to its generalization ability that ensures the high accuracy in classification.To solve this issue, we propose a Deep Neural Forest-based approach that harmonizes the contradictory requirements of accurately classifying images and correctly detecting the outlier images.Our experiments using several benchmark image datasets including MNIST, CIFAR-10, CIFAR-100, and SVHN demonstrate the effectiveness of our IOD approach for outlier detection, capturing more than 90% of outliers generated by injecting one image dataset into another, while still preserving the classification accuracy of the multi-class classification problem.

Ein neuartiger Ansatz zur Erkennung von Ausreißern in Bilddaten unter Beibehaltung der Klassifizierungsgenauigkeit der Bildklassifizierung


This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources.We design a Dynamic Point-cloud Convolution (D-Conv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extracts local spatial features from sets of neighboring points that surround different elements of the input.This operator maintains the permutation invariance of sequence-to-sequence learning frameworks, while representing neighboring correlations at each time step -- an important aspect in spatiotemporal predictive learning.The D-Conv operator resolves the grid-structural data requirements of existing spatiotemporal forecasting models and can be easily plugged into traditional LSTM architectures with sequence-to-sequence learning and attention mechanisms.    We apply our proposed architecture to two representative, practical use cases that involve point-cloud streams, i.e. mobile service traffic forecasting and air quality indicator forecasting.Our results, obtained with real-world datasets collected in diverse scenarios for each use case, show that CloudLSTM delivers accurate long-term predictions, outperforming a variety of neural network models.

In diesem Beitrag wird CloudLSTM vorgestellt, ein neuer Zweig rekurrenter neuronaler Modelle, der auf die Vorhersage von Datenströmen zugeschnitten ist, die von raumbezogenen Punkthäufungen erzeugt werden.


Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge.For easy access to statistical approaches on relational data, multiple methods to embed a KG as components of R^d have been introduced.We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space.TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications.With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding.We achieve new state-of-the-art performances with signficant margins in Link Prediction and Triple Classification on FB122 dataset, with boosted performance even on test instances that cannot be inferred by logical rules.The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.

Wir schlagen TransINT vor, ein neuartiges und interpretierbares KG-Einbettungsverfahren, das die Implikationsordnung zwischen den Relationen im Einbettungsraum auf erklärbare, robuste und geometrisch kohärente Weise isomorph bewahrt.


Unsupervised domain adaptive object detection aims to learn a robust detector on the domain shift circumstance, where the training (source) domain is label-rich with bounding box annotations, while the testing (target) domain is label-agnostic and the feature distributions between training and testing domains are dissimilar or even totally different.In this paper, we propose a gradient detach based Stacked Complementary Losses (SCL) method that uses detection objective (cross entropy and smooth l1 regression) as the primary objective, and cuts in several auxiliary losses in different network stages to utilize information from the complement data (target images) that can be effective in adapting model parameters to both source and target domains.A gradient detach operation is applied between detection and context sub-networks during training to force networks to learn discriminative representations.We argue that the conventional training with primary objective mainly leverages the information from the source-domain for maximizing likelihood and ignores the complement data in shallow layers of networks, which leads to an insufficient integration within different domains.Thus, our proposed method is a more syncretic adaptation learning process.We conduct comprehensive experiments on seven datasets, the results demonstrate that our method performs favorably better than the state-of-the-art methods by a large margin.For instance, from Cityscapes to FoggyCityscapes, we achieve 37.9% mAP, outperforming the previous art Strong-Weak by 3.6%.

Wir stellen eine neue, auf Gradientenablösung basierende, komplementär-objektive Trainingsstrategie zur domänenadaptiven Objekterkennung vor.


Convolutional neural networks (CNN) have become the most successful and popular approach in many vision-related domains.While CNNs are particularly well-suited for capturing a proper hierarchy of concepts from real-world images, they are limited to domains where data is abundant.Recent attempts have looked into mitigating this data scarcity problem by casting their original single-task problem into a new multi-task learning (MTL) problem.The main goal of this inductive transfer mechanism is to leverage domain-specific information from related tasks, in order to improve generalization on the main task.While recent results in the deep learning (DL) community have shown the promising potential of training task-specific CNNs in a soft parameter sharing framework, integrating the recent DL advances for improving knowledge sharing is still an open problem.In this paper, we propose the Deep Collaboration Network (DCNet), a novel approach for connecting task-specific CNNs in a MTL framework.We define connectivity in terms of two distinct non-linear transformation blocks.One aggregates task-specific features into global features, while the other merges back the global features with each task-specific network.Based on the observation that task relevance depends on depth, our transformation blocks use skip connections as suggested by residual network approaches, to more easily deactivate unrelated task-dependent features.To validate our approach, we employed facial landmark detection (FLD) datasets as they are readily amenable to MTL, given the number of tasks they include.Experimental results show that we can achieve up to 24.31% relative improvement in landmark failure rate over other state-of-the-art MTL approaches.We finally perform an ablation study showing that our approach effectively allows knowledge sharing, by leveraging domain-specific features at particular depths from tasks that we know are related.

Wir schlagen einen neuen Ansatz für die Verbindung von aufgabenspezifischen Netzwerken in einer Multi-Task-Lernumgebung vor, der auf den jüngsten Fortschritten im Bereich der Residual Networks basiert.


Zero-Shot Learning (ZSL) is a classification task where some classes referred as unseen classes have no labeled training images.Instead, we only have side information (or description) about seen and unseen classes, often in the form of semantic or descriptive attributes.Lack of training images from a set of classes restricts the use of standard classification techniques and losses, including the popular cross-entropy loss.The key step in tackling ZSL problem is bridging visual to semantic space via learning a nonlinear embedding.A well established approach is to obtain the semantic representation of the visual information and perform classification in the semantic space.In this paper, we propose a novel architecture of casting ZSL as a fully connected neural-network with cross-entropy loss to embed visual space to semantic space.During training in order to introduce unseen visual information to the network, we utilize soft-labeling based on semantic similarities between seen and unseen classes.To the best of our knowledge, such similarity based soft-labeling is not explored for cross-modal transfer and ZSL.We evaluate the proposed model on five benchmark datasets for zero-shot learning, AwA1, AwA2, aPY, SUN and CUB datasets, and show that, despite the simplicity, our approach achieves the state-of-the-art performance in Generalized-ZSL setting on all of these datasets and outperforms the state-of-the-art for some datasets.

Verwendung des Cross-Entropie-Verlustes für Zero Shot Learning mit Soft Labeling auf ungesehenen Klassen: eine einfache und effektive Lösung, die auf fünf ZSL-Benchmark-Datensätzen die beste Leistung erzielt.


In complex tasks, such as those with large combinatorial action spaces, random exploration may be too inefficient to achieve meaningful learning progress.In this work, we use a curriculum of progressively growing action spaces to accelerate learning.We assume the environment is out of our control, but that the agent may set an internal curriculum by initially restricting its action space.Our approach uses off-policy reinforcement learning to estimate optimal value functions for multiple action spaces simultaneously and efficiently transfers data,  value estimates, and state representations from restricted action spaces to the full task.We show the efficacy of our approach in proof-of-concept control tasks and on challenging large-scale StarCraft micromanagement tasks with large, multi-agent action spaces.

Die schrittweise Vergrößerung des verfügbaren Aktionsraums ist ein großartiger Lehrplan für Lernagenten


Recently, researchers have discovered that the state-of-the-art object classifiers can be fooled easily by small perturbations in the input unnoticeable to human eyes. It is known that an attacker can generate strong adversarial examples if she knows the classifier parameters. Conversely, a defender can robustify the classifier by retraining if she has the adversarial examples. The cat-and-mouse game nature of attacks and defenses raises the question of the presence of equilibria in the dynamics.In this paper, we present a neural-network based attack class to approximate a larger but intractable class of attacks, and formulate the attacker-defender interaction as a zero-sum leader-follower game.We present sensitivity-penalized optimization algorithms to find minimax solutions, which are the best worst-case defenses against whitebox attacks.Advantages of the learning-based attacks and defenses compared to gradient-based attacks and defenses are demonstrated with MNIST and CIFAR-10.

Eine spieltheoretische Lösung für gegnerische Angriffe und Verteidigungsmaßnahmen.


Supervised learning with irregularly sampled time series have been a challenge to Machine Learning methods due to the obstacle of dealing with irregular time intervals.Some papers introduced recently recurrent neural network models that deals with irregularity, but most of them rely on complex mechanisms to achieve a better performance.This work propose a novel method to represent timestamps (hours or dates) as dense vectors using sinusoidal functions, called Time Embeddings.As a data input method it and can be applied to most machine learning models.The method was evaluated with two predictive tasks from MIMIC III, a dataset of irregularly sampled time series of electronic health records.Our tests showed an improvement to LSTM-based and classical machine learning models, specially with very irregular data.

Eine neuartige Methode zur Erstellung dichter Zeitdeskriptoren (Time Embeddings), um einfache Modelle zum Verständnis zeitlicher Strukturen zu erstellen


Community detection in graphs can be solved via spectral methods or posterior inference under certain probabilistic graphical models.Focusing on random graph families such as the stochastic block model, recent research has unified both approaches and identified both statistical and computational detection thresholds in terms of the signal-to-noise ratio.By recasting community detection as a node-wise classification problem on graphs, we can also study it from a learning perspective.We present a novel family of Graph Neural Networks (GNNs) for solving community detection problems in a supervised learning setting.We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multiclass stochastic block models, which is believed to reach the computational threshold in these cases.In particular, we propose to augment GNNs with the non-backtracking operator defined on the line graph of edge adjacencies.The GNNs are achieved good performance on real-world datasets.  In addition, we perform the first analysis of the optimization landscape of using (linear) GNNs to solve community detection problems, demonstrating that under certain simplifications and assumptions, the loss value at any local minimum is close to the loss value at the global minimum/minima.

Wir schlagen eine neuartige Architektur für neuronale Graphen-Netzwerke vor, die auf der Non-Backtracking-Matrix basiert, die über die Kanten-Adjazenzen definiert ist, und demonstrieren ihre Effektivität bei der Erkennung von Gemeinschaften in Graphen.


Residual networks (Resnets) have become a prominent architecture in deep learning.However, a comprehensive understanding of Resnets is still a topic of ongoing research.A recent view argues that Resnets perform iterative refinement of features.We attempt to further expose properties of this aspect.To this end, we study Resnets both analytically and empirically.We formalize the notion of iterative refinement in Resnets by showing that residual architectures naturally encourage features to move along the negative gradient of loss during the feedforward phase.In addition, our empirical analysis suggests that Resnets are able to perform both representation learning and iterative refinement.In general, a Resnet block tends to concentrate representation learning behavior in the first few layers while higher layers perform iterative refinement of features.Finally we observe that sharing residual layers naively leads to representation explosion and hurts generalization performance, and show that simple existing strategies can help alleviating this problem.

Residuale Verbindungen führen wirklich iterative Inferenz durch


We develop end-to-end learned reconstructions for lensless mask-based cameras, including an experimental system for capturing aligned lensless and lensed images for training.  Various reconstruction methods are explored, on a scale from classic iterative approaches (based on the physical imaging model) to deep learned methods with many learned parameters.  In the middle ground, we present several variations of unrolled alternating direction method of multipliers (ADMM) with varying numbers of learned parameters.The network structure combines knowledge of the physical imaging model with learned parameters updated from the data, which compensate for artifacts caused by physical approximations.Our unrolled approach is 20X faster than classic methods and produces better reconstruction quality than both the classic and deep methods on our experimental system.

Wir verbessern die Rekonstruktionszeit und -qualität bei einem experimentellen maskenbasierten linsenlosen Imager mit einem End-to-End-Lernansatz, der das Wissen über das Bildgebungsmodell einbezieht.


Deep learning, a rebranding of deep neural network research works, has achieved a remarkable success in recent years.With multiple hidden layers, deep learning models aim at computing the hierarchical feature representations of the observational data.Meanwhile, due to its severe disadvantages in data consumption, computational resources, parameter tuning costs and the lack of result explainability, deep learning has also suffered from lots of criticism.In this paper, we will introduce a new representation learning model, namely “Sample-Ensemble Genetic Evolutionary Network” (SEGEN), which can serve as an alternative approach to deep learning models.Instead of building one single deep model, based on a set of sampled sub-instances, SEGEN adopts a genetic-evolutionary learning strategy to build a group of unit models generations by generations.The unit models incorporated in SEGEN can be either traditional machine learning models or the recent deep learning models with a much “narrower” and “shallower” architecture.The learning results of each instance at the final generation will be effectively combined from each unit model via diffusive propagation and ensemble learning strategies.From the computational perspective, SEGEN requires far less data, fewer computational resources and parameter tuning efforts, but has sound theoretic interpretability of the learning process and results.Extensive experiments have been done on several different real-world benchmark datasets, and the experimental results obtained by SEGEN have demonstrated its advantages over the state-of-the-art representation learning models.

Wir stellen ein neues Repräsentations-Lernmodell vor, nämlich das "Sample-Ensemble Genetic Evolutionary Network" (SEGEN), das als alternativer Ansatz für Deep-Learning-Modelle dienen kann.


How can we teach artificial agents to use human language flexibly to solve problems in a real-world environment?We have one example in nature of agents being able to solve this problem: human babies eventually learn to use human language to solve problems, and they are taught with an adult human-in-the-loop.Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn a language in this way (3).An outstanding goal is finding an algorithm with a suitable ‘language learning prior’ that allows it to learn human language, while minimizing the number of required human interactions.In this paper, we propose to learn such a prior in simulation, leveraging the increasing amount of available compute for machine learning experiments (1).We call our approach Learning to Learn to Communicate (L2C).Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol.Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations unseen during training, including populations of humans.To show the promise of the L2C framework, we conduct some preliminary experiments in a Lewis signaling game (4), where we show that agentstrained with L2C are able to learn a simple form of human language (represented by a hand-coded compositional language) in fewer iterations than randomly initialized agents.

Wir schlagen vor, Meta-Lernen für effizienteres Sprachenlernen zu nutzen, und zwar durch eine Art "Domänen-Randomisierung".


We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization.  This allows us to explicitly search for schedules that achieve good generalization.We describe the structure of the gradient of a validation error w.r.t.the learning rates, the hypergradient, and based on this we introduce a novel online algorithm.Our method adaptively interpolates between two recently proposed techniques (Franceschi et al., 2017; Baydin et al.,2018), featuring increased stability and faster convergence.We show empirically that the proposed technique compares favorably with baselines and related methodsin terms of final test accuracy.

MARTHE: eine neue Methode zur Anpassung aufgabenspezifischer Lernratenpläne aus der Perspektive der Hyperparameteroptimierung


Recent years have witnessed some exciting developments in the domain of generating images from scene-based text descriptions.These approaches have primarily focused on generating images from a static text description and are limited to generating images in a single pass.They are unable to generate an image interactively based on an incrementally additive text description (something that is more intuitive and similar to the way we describe an image). We propose a method to generate an image incrementally based on a sequence of graphs of scene descriptions (scene-graphs).We propose a recurrent network architecture that preserves the image content generated in previous steps and modifies the cumulative image as per the newly provided scene information.Our model utilizes Graph Convolutional Networks (GCN) to cater to variable-sized scene graphs along with Generative Adversarial image translation networks to generate realistic multi-object images without needing any intermediate supervision during training.We experiment with Coco-Stuff dataset which has multi-object images along with annotations describing the visual scene and show that our model significantly outperforms other approaches on the same dataset in generating visually consistent images for incrementally growing scene graphs.

Interaktive Bilderzeugung aus inkrementell wachsenden Szenegraphen in mehreren Schritten unter Verwendung von GANs unter Beibehaltung des Inhalts der in den vorherigen Schritten erzeugten Bilder


In some important computer vision domains, such as medical or hyperspectral imaging, we care about the classification of tiny objects in large images.However, most Convolutional Neural Networks (CNNs) for image classification were developed using biased datasets that contain large objects, in mostly central image positions.To assess whether classical CNN architectures work well for tiny object classification we build a comprehensive testbed containing two datasets: one derived from MNIST digits and one from histopathology images.This testbed allows controlled experiments to stress-test CNN architectures with a broad spectrum of signal-to-noise ratios.Our observations indicate that: (1) There exists a limit to signal-to-noise below which CNNs fail to generalize and that this limit is affected by dataset size - more data leading to better performances; however, the amount of training data required for the model to generalize scales rapidly with the inverse of the object-to-image ratio (2) in general, higher capacity models exhibit better generalization; (3) when knowing the approximate object sizes, adapting receptive field is beneficial; and (4) for very small signal-to-noise ratio the choice of global pooling operation affects optimization, whereas for relatively large signal-to-noise values, all tested global pooling operations exhibit similar performance.

Wir untersuchen Klassifizierungsszenarien mit geringem und sehr geringem Signal-Rausch-Verhältnis, bei denen Objekte, die mit dem Klassenlabel korrelieren, einen winzigen Teil des gesamten Bildes einnehmen (z. B. medizinische oder hyperspektrale Bildgebung).


Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block.Beyond helping CNNs to handle long-range dependencies, Ramachandran et al. (2019) showed that attention can completely replace convolution and achieve state-of-the-art performance on vision tasks.This raises the question: do learned attention layers operate similarly to convolutional layers?This work provides evidence that attention layers can perform convolution and, indeed, they often learn to do so in practice.Specifically, we prove that a multi-head self-attention layer with sufficient number of heads is at least as expressive as any convolutional layer.Our numerical experiments then show that self-attention layers attend to pixel-grid patterns similarly to CNN layers, corroborating our analysis.Our code is publicly available.

Eine Selbstaufmerksamkeitsschicht kann Convolution durchführen und lernt dies oft in der Praxis.


We introduce a “learning-based” algorithm for the low-rank decomposition problem: given an $n \times d$ matrix $A$, and a parameter $k$, compute a rank-$k$ matrix $A'$ that minimizes the approximation loss $||A- A'||_F$.The algorithm uses a training set of input matrices in order to optimize its performance.Specifically, some of the most efficient approximate algorithms for computing low-rank approximations proceed by computing a projection $SA$, where $S$ is a sparse random $m \times n$ “sketching matrix”, and then performing the singular value decomposition of $SA$.We show how to replace the random matrix $S$ with a “learned” matrix of the same sparsity to reduce the error.Our experiments show that, for multiple types of data sets, a learned sketch matrix can substantially reduce the approximation loss compared to a random matrix $S$, sometimes by one order of magnitude.We also study mixed matrices where only some of the rows are trained and the remaining ones are random, and show that matrices still offer improved performance while retaining worst-case guarantees.

Lernbasierte Algorithmen können die Leistung klassischer Algorithmen für das Low-Rank-Approximationsproblem verbessern und gleichzeitig die Worst-Case-Garantie beibehalten.


Neural conversational models are widely used in applications like personal assistants and chat bots.These models seem to give better performance when operating on word level.However, for fusion languages like French, Russian and Polish vocabulary size sometimes become infeasible since most of the words have lots of word forms.We propose a neural network architecture for transforming normalized text into a grammatically correct one.Our model efficiently employs correspondence between normalized and target words and significantly outperforms character-level models while being 2x faster in training and 20\% faster at evaluation.We also propose a new pipeline for building conversational models: first generate a normalized answer and then transform it into a grammatically correct one using our network.The proposed pipeline gives better performance than character-level conversational models according to assessor testing.

Vorgeschlagene Architektur zur Lösung der Aufgabe der morphologischen Übereinstimmung


This paper proposes the use of spectral element methods \citep{canuto_spectral_1988} for fast and accurate training of Neural Ordinary Differential Equations (ODE-Nets; \citealp{Chen2018NeuralOD}) for system identification.This is achieved by expressing their dynamics as a truncated series of Legendre polynomials.The series coefficients, as well as the network weights, are computed by minimizing the weighted sum of the loss function and the violation of the ODE-Net dynamics.The problem is solved by coordinate descent that alternately minimizes, with respect to the coefficients and the weights, two unconstrained sub-problems using standard backpropagation and gradient methods.The resulting optimization scheme is fully time-parallel and results in a low memory footprint.Experimental comparison to standard methods, such as backpropagation through explicit solvers and the adjoint technique \citep{Chen2018NeuralOD}, on training surrogate models of small and medium-scale dynamical systems shows that it is at least one order of magnitude faster at reaching a comparable value of the loss function.The corresponding testing MSE is one order of magnitude smaller as well, suggesting generalization capabilities increase.

In diesem Beitrag wird die Verwendung von Spektralelementmethoden für ein schnelles und genaues Training von neuronalen gewöhnlichen Differentialgleichungen zur Systemidentifikation vorgeschlagen.


Exploration in sparse reward reinforcement learning remains an open challenge.Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration.Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely.In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning.Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific.It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation.We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite.The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives.A video of our experimental results can be found at https://gofile.io/?c=HpEwTd.

Ein neues intrinsisches Belohnungssignal auf der Grundlage von Nachfolgemerkmalen und eine neuartige Methode zur Kombination von extrinsischer und intrinsischer Belohnung.


Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples.Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings.Existing approaches to finding efficient exploration strategies add auxiliary objectives to promote exploration by the pre-update policy, however, this makes the adaptation using a few gradient steps difficult as the pre-update (exploration) and post-update (exploitation) policies are quite different.Instead, we propose to explicitly model a separate exploration policy for the task distribution.Having two different policies gives more flexibility in training the exploration policy and also makes adaptation to any specific task easier.We show that using self-supervised or supervised learning objectives for adaptation stabilizes the training process and also demonstrate the superior performance of our model compared to prior works in this domain.

Wir schlagen vor, eine separate Explorationsstrategie zu verwenden, um die Trajektorien vor der Adaption in MAML zu sammeln. Wir zeigen auch, dass die Verwendung eines selbstüberwachten Ziels in der inneren Schleife zu einem stabileren Training und einer viel besseren Leistung führt.


The “Supersymmetric Artificial Neural Network” in deep learning (denoted (x; θ, bar{θ})Tw), espouses the importance of considering biological constraints in the aim of further generalizing backward propagation. Looking at the progression of ‘solution geometries’; going from SO(n) representation (such as Perceptron like models) to SU(n) representation (such as UnitaryRNNs) has guaranteed richer and richer representations in weight space of the artificial neural network, and hence better and better hypotheses were generatable.The Supersymmetric Artificial Neural Network explores a natural step forward, namely SU(m|n) representation.These supersymmetric biological brain representations (Perez et al.) can be represented by supercharge compatible special unitary notation SU(m|n), or (x; θ, bar{θ})Tw parameterized by θ, bar{θ}, which are supersymmetric directions, unlike θ seen in the typical non-supersymmetric deep learning model.Notably, Supersymmetric values can encode or represent more information than the typical deep learning model, in terms of “partner potential” signals for example.

Verallgemeinerung der Rückwärtsausbreitung unter Verwendung formaler Methoden der Supersymmetrie.


Regularization-based continual learning approaches generally prevent catastrophic forgetting by augmenting the training loss with an auxiliary objective.However in most practical optimization scenarios with noisy data and/or gradients, it is possible that stochastic gradient descent can inadvertently change critical parameters.In this paper, we argue for the importance of regularizing optimization trajectories directly.We derive a new co-natural gradient update rule for continual learning whereby the new task gradients are preconditioned with the empirical Fisher information of previously learnt tasks.We show that using the co-natural gradient systematically reduces forgetting in continual learning.Moreover, it helps combat overfitting when learning a new task in a low resource scenario.

Die Regulierung der Optimierungskurve mit den Fisher-Informationen der alten Aufgaben reduziert das katastrophale Vergessen erheblich


We study the problem of generating source code in a strongly typed,Java-like programming language, given a label (for example a set ofAPI calls or types) carrying a small amount of information about thecode that is desired.The generated programs are expected to respect a`"realistic" relationship between programs and labels, as exemplifiedby a corpus of labeled programs available during training.Two challenges in such *conditional program generation* are thatthe generated programs must satisfy a rich set of syntactic andsemantic constraints, and that source code contains many low-levelfeatures that impede learning. We address these problems by traininga neural generator not on code but on *program sketches*, ormodels of program syntax that abstract out names and operations thatdo not generalize across programs.During generation, we infer aposterior distribution over sketches, then concretize samples fromthis distribution into type-safe programs using combinatorialtechniques. We implement our ideas in a system for generatingAPI-heavy Java code, and show that it can often predict the entirebody of a method given just a few API calls or data types that appearin the method.

Wir geben eine Methode zur Generierung von typsicheren Programmen in einer Java-ähnlichen Sprache an, die eine kleine Menge an syntaktischen Informationen über den gewünschten Code enthält.


We propose an approach for sequence modeling based on autoregressive normalizing flows.Each autoregressive transform, acting across time, serves as a moving reference frame for modeling higher-level dynamics.This technique provides a simple, general-purpose method for improving sequence modeling, with connections to existing and classical techniques.We demonstrate the proposed approach both with standalone models, as well as a part of larger sequential latent variable models.Results are presented on three benchmark video datasets, where flow-based dynamics improve log-likelihood performance over baseline models.

Wir zeigen, wie autoregressive Flüsse verwendet werden können, um sequentielle latente Variablenmodelle zu verbessern.


It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs.This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks.We discuss a range of copyright detection systems, and why they are particularly vulnerable to attacks.  These vulnerabilities are especially apparent for neural network based systems.  As proof of concept, we describe a well-known music identification method and implement this system in the form of a neural net.We then attack this system using simple gradient methods.Adversarial music created this way successfully fools industrial systems, including the AudioTag copyright detector and YouTube's Content ID system.Our goal is to raise awareness of the threats posed by adversarial examples in this space and to highlight the importance of hardening copyright detection systems to attacks.

Widersprüchliche Beispiele können das Urheberrechtserkennungssystem von YouTube täuschen


Equilibrium Propagation (EP) is a learning algorithm that bridges Machine Learning and Neuroscience, by computing gradients closely matching those of Backpropagation Through Time (BPTT), but with a learning rule local in space.Given an input x and associated target y, EP proceeds in two phases: in the first phase neurons evolve freely towards a first steady state; in the second phase output neurons are nudged towards y until they reach a second steady state.However, in existing implementations of EP, the learning rule is not local in time:the weight update is performed after the dynamics of the second phase have converged and requires information of the first phase that is no longer available physically.This is a major impediment to the biological plausibility of EP and its efficient hardware implementation.In this work, we propose a version of EP named Continual Equilibrium Propagation (C-EP) where neuron and synapse dynamics occur simultaneously throughout the second phase, so that the weight update becomes local in time.We prove theoretically that, provided the learning rates are sufficiently small, at each time step of the second phase the dynamics of neurons and synapses follow the gradients of the loss given by BPTT (Theorem 1).We demonstrate training with C-EP on MNIST and generalize C-EP to neural networks where neurons are connected by asymmetric connections.We show through experiments that the more the network updates follows the gradients of BPTT, the best it performs in terms of training.These results bring EP a step closer to biology while maintaining its intimate link with backpropagation.

Wir schlagen eine kontinuierliche Version der Gleichgewichtsausbreitung vor, bei der Neuronen- und Synapsendynamik während der gesamten zweiten Phase gleichzeitig auftreten, mit theoretischen Garantien und numerischen Simulationen.


There are two main lines of research on visual reasoning: neural module network (NMN) with explicit multi-hop reasoning through handcrafted neural modules, and monolithic network with implicit reasoning in the latent feature space.The former excels in interpretability and compositionality, while the latter usually achieves better performance due to model flexibility and parameter efficiency.  In order to bridge the gap of the two, we present Meta Module Network (MMN), a novel hybrid approach that can efficiently utilize a Meta Module to perform versatile functionalities, while preserving compositionality and interpretability through modularized design.The proposed model first parses an input question into a functional program through a Program Generator.Instead of handcrafting a task-specific network to represent each function like traditional NMN, we use Recipe Encoder to translate the functions into their corresponding recipes (specifications), which are used to dynamically instantiate the Meta Module into Instance Modules.To endow different instance modules with designated functionality, a Teacher-Student framework is proposed, where a symbolic teacher pre-executes against the scene graphs to provide guidelines for the instantiated modules (student) to follow.In a nutshell, MMN adopts the meta module to increase its parameterization efficiency, and uses recipe encoding to improve its generalization ability over NMN.Experiments conducted on the GQA benchmark demonstrates that: (1) MMN achieves significant improvement over both NMN and monolithic network baselines; (2) MMN is able to generalize to unseen but related functions.

Wir schlagen ein neues Meta-Modul-Netzwerk vor, um einige der Einschränkungen früherer neuronaler Modulnetzwerke zu beseitigen und eine starke Leistung auf realistischen visuellen Reasoning-Datensätzen zu erzielen.


We propose a new perspective on adversarial attacks against deep reinforcement learning agents.Our main contribution is CopyCAT, a targeted attack able to consistently lure an agent into following an outsider's policy.It is pre-computed, therefore fast inferred, and could thus be usable in a real-time scenario.We show its effectiveness on Atari 2600 games in the novel read-only setting.In the latter, the adversary cannot directly modify the agent's state -its representation of the environment- but can only attack the agent's observation -its perception of the environment.Directly modifying the agent's state would require a write-access to the agent's inner workings and we argue that this assumption is too strong in realistic settings.

Wir schlagen einen neuen Angriff vor, um die volle Kontrolle über neuronale Richtlinien in realistischen Umgebungen zu übernehmen.


Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems.Previous hybrid recommendation methods are effective to deal with the cold-start issues by extracting real latent factors of cold-start items(users) from side information, but they still suffer low efficiency in online recommendation caused by the expensive similarity search in real latent space.This paper presents a collaborative generated hashing (CGH) to improve the efficiency by denoting users and items as binary codes, which applies to various settings: cold-start users, cold-start items and warm-start ones.Specifically, CGH is designed to learn hash functions of users and items through the Minimum Description Length (MDL) principle; thus, it can deal with various recommendation settings.In addition, CGH initiates a new marketing strategy through mining potential users by a generative step.To reconstruct effective users, the MDL principle is used to learn compact and informative binary codes from the content data.Extensive experiments on two public datasets show the advantages for recommendations in various settings over competing baselines and analyze the feasibility of the application in marketing.

Es kann effektive Hash-Codes für eine effiziente Kaltstart-Empfehlung generieren und gleichzeitig eine praktikable Marketingstrategie bieten.


Recent efforts to combine Representation Learning with Formal Methods, commonly known as the Neuro-Symbolic Methods, have given rise to a new trend of applying rich neural architectures to solve classical combinatorial optimization problems.In this paper, we propose a neural framework that can learn to solve the Circuit Satisfiability problem.Our framework is built upon two fundamental contributions: a rich embedding architecture that encodes the problem structure and an end-to-end differentiable training procedure that mimics Reinforcement Learning and trains the model directly toward solving the SAT problem.The experimental results show the superior out-of-sample generalization performance of our framework compared to the recently developed NeuroSAT method.

Wir schlagen einen neuronalen Rahmen vor, der lernen kann, das Problem der Erfüllbarkeit von Schaltkreisen anhand von (unbeschrifteten) Schaltkreisinstanzen zu lösen.


Sequence generation models such as recurrent networks can be trained with a diverse set of learning algorithms.For example, maximum likelihood learning is simple and efficient, yet suffers from the exposure bias problem.Reinforcement learning like policy gradient addresses the problem but can have prohibitively poor exploration efficiency.A variety of other algorithms such as RAML, SPG, and data noising, have also been developed in different perspectives.This paper establishes a formal connection between these algorithms.We present a generalized entropy regularized policy optimization formulation, and show that the apparently divergent algorithms can all be reformulated as special instances of the framework, with the only difference being the configurations of reward function and a couple of hyperparameters.The unified interpretation offers a systematic view of the varying properties of exploration and learning efficiency.Besides, based on the framework, we present a new algorithm that dynamically interpolates among the existing algorithms for improved learning.Experiments on machine translation and text summarization demonstrate the superiority of the proposed algorithm.

Eine einheitliche Sichtweise verschiedener Lernalgorithmen für die Sequenzerzeugung, wie MLE, RL, RAML, Datenrauschen usw.


We are reporting the SHINRA project, a project for structuring Wikipedia with collaborative construction scheme.The goal of the project is to create a huge and well-structured knowledge base to be used in NLP applications, such as QA, Dialogue systems and explainable NLP systems.It is created based on a scheme of ”Resource by Collaborative Contribution (RbCC)”.We conducted a shared task of structuring Wikipedia, and at the same, submitted results are used to construct a knowledge base.There are machine readable knowledge bases such as CYC, DBpedia, YAGO, Freebase Wikidata and so on, but each of them has problems to be solved.CYC has a coverage problem, and others have a coherence problem due to the fact that these are based on Wikipedia and/or created by many but inherently incoherent crowd workers.In order to solve the later problem, we started a project for structuring Wikipedia using automatic knowledge base construction shared-task.The automatic knowledge base construction shared-tasks have been popular and well studied for decades.However, these tasks are designed only to compare the performances of different systems, and to find which system ranks the best on limited test data.The results of the participated systems are not shared and the systems may be abandoned once the task is over.We believe this situation can be improved by the following changes:1. designing the shared-task to construct knowledge base rather than evaluating only limited test data2. making the outputs of all the systems open to public so that we can run ensemble learning to create the better results than the best systems3. repeating the task so that we can run the task with the larger and better training data from the output of the previous task (bootstrapping and active learning)We conducted “SHINRA2018” with the above mentioned scheme and in this paperwe report the results and the future directions of the project.The task is to extract the values of the pre-defined attributes from Wikipedia pages.We have categorized most of the entities in Japanese Wikipedia (namely 730 thousand entities) into the 200 ENE categories.Based on this data, the shared-task is to extract the values of the attributes from Wikipedia pages.We gave out the 600 training data and the participants are required to submit the attribute-values for all remaining entities of the same category type.Then 100 data out of them for each category are used to evaluate the system output in the shared-task.We conducted a preliminary ensemble learning on the outputs and found 15 F1 score improvement on a category and the average of 8 F1 score improvements on all 5 categories we tested over a strong baseline.Based on this promising results, we decided to conduct three tasks in 2019; multi-lingual categorization task (ML), extraction for the same 5 categories in Japanese with a larger training data (JP-5) and extraction for 34 new categories in Japanese (JP-34).

Wir führen ein "Resource by Collaborative Construction"-Schema ein, um KB, strukturierte Wikipedia zu erstellen


Recent image super-resolution(SR) studies leverage very deep convolutional neural networks and the rich hierarchical features they offered, which leads to better reconstruction performance than conventional methods.However, the small receptive fields in the up-sampling and reconstruction process of those models stop them to take full advantage of global contextual information.This causes problems for further performance improvement.In this paper, inspired by image reconstruction principles of human visual system, we propose an image super-resolution global reasoning network (SRGRN) to effectively learn the correlations between different regions of an image, through global reasoning.Specifically, we propose global reasoning up-sampling module (GRUM) and global reasoning reconstruction block (GRRB).They construct a graph model to perform relation reasoning on regions of low resolution (LR) images.They aim to reason the interactions between different regions in the up-sampling and reconstruction process and thus leverage more contextual information to generate accurate details.Our proposed SRGRN are more robust and can handle low resolution images that are corrupted by multiple types of degradation.Extensive experiments on different benchmark data-sets show that our model outperforms other state-of-the-art methods.Also our model is lightweight and consumes less computing power, which makes it very suitable for real life deployment.

Ein hochmodernes Modell auf der Grundlage globaler Schlussfolgerungen für die Superauflösung von Bildern


Graph Neural Networks (GNNs) have received tremendous attention recently due to their power in handling graph data for different downstream tasks across different application domains.The key of GNN is its graph convolutional filters, and recently various kinds of filters are designed.However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data.In this paper, we focus on addressing the above three questions.We first propose a novel assessment tool to evaluate the effectiveness of graph convolutional filters for a given graph.Using the assessment tool, we find out that there is no single filter as a `silver bullet' that perform the best on all possible graphs.In addition, different graph structure properties will influence the optimal graph convolutional filter's design choice.Based on these findings, we develop Adaptive Filter Graph Neural Network (AFGNN), a simple but powerful model that can adaptively learn task-specific filter.For a given graph, it leverages graph filter assessment as regularization and learns to combine from a set of base filters.Experiments on both synthetic and real-world benchmark datasets demonstrate that our proposed model can indeed learn an appropriate filter and perform well on graph tasks.

Vorschlag für einen Bewertungsrahmen zur Analyse und zum Erlernen von Graph Convolutional Filtern


The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures.In this paper, we propose a pooling operation for GNNs that leverages a differentiable unsupervised loss based on the minCut optimization objective.For each node, our method learns a soft cluster assignment vector that depends on the node features, the target inference task (e.g., a graph classification loss), and, thanks to the minCut objective, also on the connectivity structure of the graph.Graph pooling is obtained by applying the matrix of assignment vectors to the adjacency matrix and the node features.We validate the effectiveness of the proposed pooling method on a variety of supervised and unsupervised tasks.

Eine neue Pooling-Schicht für GNNs, die lernt, wie Knoten entsprechend ihrer Eigenschaften, der Graphenkonnektivität und dem Ziel der nachgelagerten Aufgabe zu poolen sind.


Knowledge graphs are structured representations of real world facts.However, they typically contain only a small subset of all possible facts.Link prediction is the task of inferring missing facts based on existing ones.We propose TuckER, a relatively simple yet powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples.By using this particular decomposition, parameters are shared between relations, enabling multi-task learning.TuckER outperforms previous state-of-the-art models across several standard link prediction datasets.

Wir schlagen TuckER vor, ein relativ einfaches, aber leistungsfähiges lineares Modell zur Linkvorhersage in Wissensgraphen, das auf der Tucker-Dekomposition der binären Tensordarstellung von Wissensgraphen-Tripeln basiert.