An obstacle that prevents the wide adoption of (deep) reinforcement learning (RL) in control systems is its need for a large number of interactions with the environment in order to master a skill. The learned skill usually generalizes poorly across domains and re-training is often necessary when presented with a new task. We present a framework that combines techniques in \textit{formal methods} with \textit{hierarchical reinforcement learning} (HRL). The set of techniques we provide allows for the convenient specification of tasks with logical expressions, learns hierarchical policies (meta-controller and low-level controllers) with well-defined intrinsic rewards using any RL methods and is able to construct new skills from existing ones without additional learning. We evaluate the proposed methods in a simple grid world simulation as well as simulation on a Baxter robot.

Kombinieren Sie zeitliche Logik mit hierarchischem Reinforcement Learning für die Komposition von Fähigkeiten.

Die Arbeit bietet eine Strategie zur Konstruktion eines Produkt-MDPs aus einem ursprünglichen MDP und dem mit einer LTL-Formel verbundenen Automaten.

Es wird vorgeschlagen, zeitliche Logik mit hierarchischem Reinforcement Learning zu verbinden, um die Zusammensetzung von Fähigkeiten zu vereinfachen.


The tremendous memory and computational complexity of Convolutional Neural Networks (CNNs) prevents the inference deployment on resource-constrained systems. As a result, recent research focused on CNN optimization techniques, in particular quantization, which allows weights and activations of layers to be represented with just a few bits while achieving impressive prediction performance. However, aggressive quantization techniques still fail to achieve full-precision prediction performance on state-of-the-art CNN architectures on large-scale classification tasks. In this work we propose a method for weight and activation quantization that is scalable in terms of quantization levels (n-ary representations) and easy to compute while maintaining the performance close to full-precision CNNs. Our weight quantization scheme is based on trainable scaling factors and a nested-means clustering strategy which is robust to weight updates and therefore exhibits good convergence properties. The flexibility of nested-means clustering enables exploration of various n-ary weight representations with the potential of high parameter compression. For activations, we propose a linear quantization strategy that takes the statistical properties of batch normalization into account. We demonstrate the effectiveness of our approach using state-of-the-art models on ImageNet.

Wir schlagen ein Quantisierungsschema für Gewichte und Aktivierungen von tiefen neuronalen Netzen vor. Dadurch wird der Speicherbedarf erheblich reduziert und die Inferenz beschleunigt.

CNN-Modellkomprimierung und Beschleunigung der Inferenz durch Quantisierung.


Reinforcement learning (RL) agents optimize only the features specified in a reward function and are indifferent to anything left out inadvertently. This means that we must not only specify what to do, but also the much larger space of what not to do. It is easy to forget these preferences, since these preferences are already satisfied in our environment. This motivates our key insight: when a robot is deployed in an environment that humans act in, the state of the environment is already optimized for what humans want. We can therefore use this implicit preference information from the state to fill in the blanks. We develop an algorithm based on Maximum Causal Entropy IRL and use it to evaluate the idea in a suite of proof-of-concept environments designed to show its properties. We find that information from the initial state can be used to infer both side effects that should be avoided as well as preferences for how the environment should be organized. Our code can be found at https://github.com/HumanCompatibleAI/rlsp.

Wenn ein Roboter in einer Umgebung eingesetzt wird, in der Menschen gehandelt haben, ist der Zustand der Umgebung bereits für die Wünsche der Menschen optimiert, und wir können dies nutzen, um auf menschliche Präferenzen zu schließen.

Die Autoren schlagen vor, die explizit angegebene Belohnungsfunktion eines RL-Agenten durch zusätzliche Belohnungen/Kosten zu ergänzen, die aus dem Anfangszustand und einem Modell der Zustandsdynamik abgeleitet werden.

In dieser Arbeit wird ein Weg vorgeschlagen, die implizite Information im Ausgangszustand mit Hilfe von IRL abzuleiten und die abgeleitete Belohnung mit einer vorgegebenen Belohnung zu kombinieren.


Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We identify the atomic building blocks of existing methods, and decouple the assumptions they enforce from the mathematical tools they rely on. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.

Systematische Kategorisierung von Regularisierungsmethoden für Deep Learning, die ihre Gemeinsamkeiten aufzeigt.

Versuch, eine Taxonomie für Regularisierungstechniken zu erstellen, die beim Deep Learning eingesetzt werden.


Deep neural networks are surprisingly efficient at solving practical tasks,
 but the theory behind this phenomenon is only starting to catch up with
 the practice. Numerous works show that depth is the key to this efficiency.
 A certain class of deep convolutional networks – namely those that correspond
 to the Hierarchical Tucker (HT) tensor decomposition – has been
 proven to have exponentially higher expressive power than shallow networks.
 I.e. a shallow network of exponential width is required to realize
 the same score function as computed by the deep architecture. In this paper,
 we prove the expressive power theorem (an exponential lower bound on
 the width of the equivalent shallow network) for a class of recurrent neural
 networks – ones that correspond to the Tensor Train (TT) decomposition.
 This means that even processing an image patch by patch with an RNN
 can be exponentially more efficient than a (shallow) convolutional network
 with one hidden layer. Using theoretical results on the relation between
 the tensor decompositions we compare expressive powers of the HT- and
 TT-Networks. We also implement the recurrent TT-Networks and provide
 numerical evidence of their expressivity.

Wir beweisen die exponentielle Effizienz von rekurrenten neuronalen Netzen gegenüber flachen Netzen.

Die Autoren vergleichen die Komplexität von Tensor-Train Netzen mit Netzen, die durch CP-Zerlegung strukturiert sind.


Probabilistic modelling is a principled framework to perform model aggregation, which has been a primary mechanism to combat mode collapse in the context of Generative Adversarial Networks (GAN). In this paper, we propose a novel probabilistic framework for GANs, ProbGAN, which iteratively learns a distribution over generators with a carefully crafted prior. Learning is efficiently triggered by a tailored stochastic gradient Hamiltonian Monte Carlo with a novel gradient approximation to perform Bayesian inference. Our theoretical analysis further reveals that our treatment is the first probabilistic framework that yields an equilibrium where generator distributions are faithful to the data distribution. Empirical evidence on synthetic high-dimensional multi-modal data and image databases (CIFAR-10, STL-10, and ImageNet) demonstrates the superiority of our method over both start-of-the-art multi-generator GANs and other probabilistic treatment for GANs.

Eine neue probabilistische Behandlung für GAN mit theoretischer Garantie.

In diesem Beitrag wird ein Bayes'sches GAN vorgeschlagen, das theoretische Garantien für die Konvergenz zur realen Verteilung hat und Likelihoods über den Generator und den Diskriminator mit Logarithmen proportional zu den traditionellen GAN-Zielfunktionen setzt.


In the adversarial-perturbation problem of neural networks, an adversary starts with a neural network model $F$ and a point $\bfx$ that $F$ classifies correctly, and applies a \emph{small perturbation} to  $\bfx$ to produce another point $\bfx'$ that $F$ classifies \emph{incorrectly}.  In this paper, we propose taking into account \emph{the inherent confidence information} produced by models when studying adversarial perturbations, where a natural measure of ``confidence'' is \|F(\bfx)\|_\infty$ (i.e. how confident $F$ is about its prediction?) . Motivated by a thought experiment based on the manifold assumption, we propose a ``goodness property'' of models which states that \emph{confident regions of a good model should be well separated}. We give formalizations of this property and examine existing robust training objectives in view of them. Interestingly, we find that a recent objective by Madry et al. encourages training a model that satisfies well our formal version of the goodness property, but has a weak control of points that are wrong but with low confidence. However, if Madry et al.'s model is indeed a good solution to their objective, then good and bad points are now distinguishable and we can try to embed uncertain points back to the closest confident region to get (hopefully) correct predictions. We thus propose embedding objectives and algorithms, and perform an empirical study using this method. Our experimental results are encouraging: Madry et al.'s model wrapped with our embedding procedure achieves almost perfect success rate in defending against attacks that the base model fails on, while retaining good generalization behavior.

Verteidigung gegen nachteilige Störungen neuronaler Netze aufgrund vielfältiger Annahmen.

In dem Manuskript werden zwei Zielfunktionen vorgeschlagen, die auf der Annahme der Vielfältigkeit basieren und als Abwehrmechanismen gegen adversarial Beispiele dienen.

Verteidigung gegen adversarial Angriffe auf der Grundlage der Annahme, dass natürliche Daten vielfältig sind.


Recently Neural Architecture Search (NAS) has aroused great interest in both academia and industry, however it remains challenging because of its huge and non-continuous search space. Instead of applying evolutionary algorithm or reinforcement learning as previous works, this paper proposes a Direct Sparse Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning view to NAS problem. In specific, we start from a completely connected block, and then introduce scaling factors to scale the information flow between operations. Next, we impose sparse regularizations to prune useless connections in the architecture. Lastly, we derive an efficient and theoretically sound optimization method to solve it. Our method enjoys both advantages of differentiability and efficiency, therefore can be directly applied to large datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an average test error 2.84%, while on the ImageNet dataset DSO-NAS achieves 25.4% test error under 600M FLOPs with 8 GPUs in 18 hours.

Suche nach einer neuronalen Architektur in einem einzigen Schritt durch direkte spärliche Optimierung.

Es wird eine Methode zur Architektursuche vorgestellt, bei der Verbindungen mit spärlicher Regularisierung entfernt werden.

In diesem Beitrag wird die direkte Sparse Optimierung vorgeschlagen, eine Methode, die es ermöglicht, neuronale Architekturen für spezifische Probleme zu einem vernünftigen Rechenaufwand zu erhalten.

In dieser Arebit wird eine Suchmethode für neuronale Architekturen vorgeschlagen, die auf einer direkten Sparse Optimierung basiert.


Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger teacher networks into smaller student networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher, into the training of a student network whose weights are quantized to a limited set of levels. The second method,  differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model.   We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to full-precision teacher models, while providing order of magnitude compression, and inference speedup that is linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.

Erzielt modernste Genauigkeit für quantisierte, flache Netze durch Nutzung der Destillation.

Vorschläge für kleine und kostengünstige Modelle durch die Kombination von Destillation und Quantisierung für Experimente zur Bildverarbeitung und neuronaler maschineller Übersetzung.

In diesem Beitrag wird ein Rahmen für die Verwendung des Lehrermodells zur Unterstützung der Kompression für das Deep Learning Modell im Rahmen der Modellkompression vorgestellt.


Previous work has demonstrated the benefits of incorporating additional linguistic annotations such as syntactic trees into neural machine translation. However the cost of obtaining those syntactic annotations is expensive for many languages and the quality of unsupervised learning linguistic structures is too poor to be helpful. In this work, we aim to improve neural machine translation via source side dependency syntax but without explicit annotation. We propose a set of models that learn to induce dependency trees on the source side and learn to use that information on the target side. Importantly, we also show that our dependency trees capture important syntactic features of language and improve translation quality on two language pairs En-De and En-Ru.

Diese Arbeit beschreibt eine Methode zur Induktion von quellenseitigen Abhängigkeitsstrukturen im Dienste der neuronalen maschinellen Übersetzung.


Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.

Lernen indem von einer einzigen Demonstration aus rückwärts gearbeitet wird, selbst wenn diese ineffizient ist, und dem Agenten progressiv erlauben nach und nach mehr Aufgaben selbst zu lösen.

In diesem Beitrag wird eine Methode zur Steigerung der Effizienz von spärlichen Belohnungs-RL-Methoden durch ein Rückwärts Curriculum auf Expertendemonstrationen vorgestellt.

Die Arbeit stellt eine Strategie zur Lösung von spärlichen Belohnungsaufgaben mit RL vor, indem Anfangszustände aus Demonstrationen gesampelt werden.


Episodic memory is a psychology term which refers to the ability to recall specific events from the past. We suggest one advantage of this particular type of memory is the ability to easily assign credit to a specific state when remembered information is found to be useful. Inspired by this idea, and the increasing popularity of external memory mechanisms to handle long-term dependencies in deep learning systems, we propose a novel algorithm which uses a reservoir sampling procedure to maintain an external memory consisting of a fixed number of past states. The algorithm allows a deep reinforcement learning agent to learn online to preferentially remember those states which are found to be useful to recall later on. Critically this method allows for efficient online computation of gradient estimates with respect to the write process of the external memory. Thus unlike most prior mechanisms for external memory it is feasible to use in an online reinforcement learning setting.

Externer Speicher für Online-Reinforcement Learning basierend auf der Schätzung von Gradienten über eine neuartige Reservoir-Sampling-Technik.

Die Arbeit schlägt einen modifizierten Ansatz für RL vor, bei dem ein zusätzliches "episodisches Gedächtnis" vom Agenten gehalten wird und ein "Abfragenetzwerk" verwendet wird, das auf dem aktuellen Zustand basiert.


We achieve bias-variance decomposition for Boltzmann machines using an information geometric formulation. Our decomposition leads to an interesting phenomenon that the variance does not necessarily increase when more parameters are included in Boltzmann machines, while the bias always decreases. Our result gives a theoretical evidence of the generalization ability of deep learning architectures because it provides the possibility of increasing the representation power with avoiding the variance inflation.

Wir erreichen eine Bias-Varianz-Zerlegung für Boltzmann-Maschinen mit Hilfe einer informationsgeometrischen Formulierung.

Das Ziel dieses Artikels ist es, die Effektivität und Verallgemeinerbarkeit von Deep Learning zu analysieren, indem eine theoretische Analyse der Bias-Varianz-Zerlegung für hierarchische Modelle, insbesondere Boltzmann-Maschinen, vorgestellt wird.

Die Arbeit kommt zu dem Schluss, dass es möglich ist, sowohl die Verzerrung als auch die Varianz in einem hierarchischen Modell zu reduzieren.


Recurrent Neural Networks (RNNs) are powerful tools for solving sequence-based problems, but their efficacy and execution time are dependent on the size of the network.   Following recent work in simplifying these networks with model pruning and a novel mapping of work onto GPUs, we design an efficient implementation for sparse RNNs.   We investigate several optimizations and tradeoffs: Lamport timestamps, wide memory loads, and a bank-aware weight layout.   With these optimizations, we achieve speedups of over 6x over the next best algorithm for a hidden layer of size 2304, batch size of 4, and a density of 30%.   Further, our technique allows for models of over 5x the size to fit on a GPU for a speedup of 2x, enabling larger networks to help advance the state-of-the-art.   We perform case studies on NMT and speech recognition tasks in the appendix, accelerating their recurrent layers by up to 3x.

Kombination von Netzwerk Pruning und persistenten Kernels zu einer praktischen, schnellen und genauen Netzimplementierung.

In diesem Beitrag werden spärliche persistente RNNs vorgestellt, ein Mechanismus, der die bestehende Arbeit des Speicherns von RNN-Gewichten auf einem Chip durch Pruning ergänzt.


Weight pruning has proven to be an effective method in reducing the model size and computation cost while not sacrificing the model accuracy. Conventional sparse matrix formats, however, involve irregular index structures with large storage requirement and sequential reconstruction process, resulting in inefficient use of highly parallel computing resources. Hence, pruning is usually restricted to inference with a batch size of one, for which an efficient parallel matrix-vector multiplication method exists. In this paper, a new class of sparse matrix representation utilizing Viterbi algorithm that has a high, and more importantly, fixed index compression ratio regardless of the pruning rate, is proposed. In this approach, numerous sparse matrix candidates are first generated by the Viterbi encoder, and then the one that aims to minimize the model accuracy degradation is selected by the Viterbi algorithm. The model pruning process based on the proposed Viterbi encoder and Viterbi algorithm is highly parallelizable, and can be implemented efficiently in hardware to achieve low-energy, high-performance index decoding process. Compared with the existing magnitude-based pruning methods, index data storage requirement can be further compressed by 85.2% in MNIST and 83.9% in AlexNet while achieving similar pruning rate. Even compared with the relative index compression technique, our method can still reduce the index storage requirement by 52.7% in MNIST and 35.5% in AlexNet.

Wir stellen eine neue Pruning-Methode und ein spärliches Matrixformat vor, um eine hohe Indexkomprimierung und eine parallele Indexdekodierung zu ermöglichen.

Die Autoren verwenden die Viterbi-Codierung, um den Index der Sparse-Matrix eines Pruned Netzwerks drastisch zu komprimieren, wodurch einer der wichtigsten Speicher-Overheads reduziert und die Inferenz in der parallelen Umgebung beschleunigt wird.


Learning policies for complex tasks that require multiple different skills is a major challenge in reinforcement learning (RL). It is also a requirement for its deployment in real-world scenarios. This paper proposes a novel framework for efficient multi-task reinforcement learning. Our framework trains agents to employ hierarchical policies that decide when to use a previously learned policy and when to learn a new skill. This enables agents to continually acquire new skills during different stages of training. Each learned task corresponds to a human language description. Because agents can only access previously learned skills through these descriptions, the agent can always provide a human-interpretable description of its choices. In order to help the agent learn the complex temporal dependencies necessary for the hierarchical policy, we provide it with a stochastic temporal grammar that modulates when to rely on previously learned skills and when to execute new skills. We validate our approach on Minecraft games designed to explicitly test the ability to reuse previously learned skills while simultaneously learning new skills.

Ein neuartiges hierarchisches Regelnetzwerk, das bereits erlernte Fähigkeiten neben und als Teilkomponenten von neuen Fähigkeiten wiederverwenden kann, indem es die zugrunde liegenden Beziehungen zwischen den Fähigkeiten entdeckt.

Diese Arbeit zielt darauf ab, hierarchische Regeln zu lernen, indem eine rekursive Regelstruktur verwendet wird, die durch eine stochastische zeitliche Grammatik geregelt wird.

In diesem Beitrag wird ein Ansatz zum Erlernen hierarchischer Strategien in einem Kontext des lebenslangen Lernens vorgeschlagen, indem Strategien gestapelt und dann eine explizite "Switch"-Strategie verwendet wird.


Embeddings are a fundamental component of many modern machine learning and natural language processing models.
 Understanding them and visualizing them is essential for gathering insights about the information they capture and the behavior of the models.
 State of the art in analyzing embeddings consists in projecting them in two-dimensional planes without any interpretable semantics associated to the axes of the projection, which makes detailed analyses and comparison among multiple sets of embeddings challenging.
 In this work, we propose to use explicit axes defined as algebraic formulae over embeddings to project them into a lower dimensional, but semantically meaningful subspace, as a simple yet effective analysis and visualization methodology.
 This methodology assigns an interpretable semantics to the measures of variability and the axes of visualizations, allowing for both comparisons among different sets of embeddings and fine-grained inspection of the embedding spaces.
 We demonstrate the power of the proposed methodology through a series of case studies that make use of visualizations constructed around the underlying methodology and through a user study. The results show how the methodology is effective at providing more profound insights than classical projection methods and how it is widely applicable to many other use cases.

Wir schlagen vor, explizite vektoralgebraische Formelprojektionen als alternative Methode zur Visualisierung von Einbettungsräumen zu verwenden, die speziell auf zielgerichtete Analyseaufgaben zugeschnitten ist und in unserer Benutzerstudie t-SNE übertrifft.

Analyse der Einbettung von Psaces auf nicht-parametrische Weise (anhand von Beispielen).


Learning deep networks which can resist large variations between training andtesting data is essential to build accurate and robust image classifiers.   Towardsthis end, a typical strategy is to apply data augmentation to enlarge the trainingset.    However,  standard  data  augmentation  is  essentially  a  brute-force  strategywhich is inefficient,  as it performs all the pre-defined transformations  to everytraining sample. In this paper, we propose a principled approach to train networkswith  significantly  improved  resistance  to  large  variations  between  training  andtesting data.   This is achieved by embedding a learnable transformation moduleinto the introspective networks (Jin et al., 2017; Lazarow et al., 2017; Lee et al.,2018), which is a convolutional neural network (CNN) classifier empowered withgenerative capabilities.   Our approach alternatively synthesizes pseudo-negativesamples with learned transformations and enhances the classifier by retraining itwith synthesized samples.   Experimental results verify that our approach signif-icantly improves the ability of deep networks to resist large variations betweentraining and testing data and achieves classification accuracy improvements onseveral benchmark datasets, including MNIST, affNIST, SVHN and CIFAR-10.

Wir schlagen einen prinzipiellen Ansatz vor, der Klassifizierer mit der Fähigkeit ausstattet, größeren Abweichungen zwischen Trainings- und Testdaten auf intelligente und effiziente Weise zu widerstehen.

Introspektives Lernen zum Umgang mit Datenvariationen zur Testzeit.

Diese Arbeit schlägt die Verwendung von gelernten Transformationsnetzwerken vor, die in introspektive Netzwerken eingebettet sind, um die Klassifizierungsleistung mit synthetisierten Beispielen zu verbessern.


It is well known that it is possible to construct "adversarial examples"
 for neural networks: inputs which are misclassified by the network
 yet indistinguishable from true data. We propose a simple
 modification to standard neural network architectures, thermometer
 encoding, which significantly increases the robustness of the network to
 adversarial examples. We demonstrate this robustness with experiments
 on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show that
 models with thermometer-encoded inputs consistently have higher accuracy
 on adversarial examples, without decreasing generalization.
 State-of-the-art accuracy under the strongest known white-box attack was
 increased from 93.20% to 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10.
 We explore the properties of these networks, providing evidence
 that thermometer encodings help neural networks to
 find more-non-linear decision boundaries.

Diskretisierung der Eingaben führt zu Robustheit gegenüber adversarial Beispielen.

Die Autoren präsentieren eine eingehende Studie über die Diskretisierung / Quantisierung der Eingabe als Schutz gegen adversarial Beispiele.


Low-precision training is a promising way of decreasing the time and energy cost of training machine learning models.
 Previous work has analyzed low-precision training algorithms, such as low-precision stochastic gradient descent, and derived theoretical bounds on their convergence rates.
 These bounds tend to depend on the dimension of the model $d$ in that the number of bits needed to achieve a particular error bound increases as $d$ increases.
 This is undesirable because a motivating application for low-precision training is large-scale models, such as deep learning, where $d$ can be huge.
 In this paper, we prove dimension-independent bounds for low-precision training algorithms that use fixed-point arithmetic, which lets us better understand what affects the convergence of these algorithms as parameters scale.
 Our methods also generalize naturally to let us prove new convergence bounds on low-precision training with other quantization schemes, such as low-precision floating-point computation and logarithmic quantization.

Wir haben dimensionsunabhängige Schranken für Trainingsalgorithmen mit geringer Genauigkeit bewiesen.

In diesem Beitrag werden Bedingungen diskutiert, unter denen die Konvergenz von Trainingsmodellen mit niedrigpräzisen Gewichten nicht von der Modelldimension abhängt.


We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and ERL2. Results are presented on a novel environment we call 'Krazy World'  and a set of maze environments. We show E-MAML and ERL2 deliver better performance on tasks where exploration is important.

Änderungen an MAML und RL2, die eine bessere Erkundung ermöglichen sollen.

Die Arbeit schlägt einen Trick vor, um die Zielfunktionen zu erweitern, um die Exploration in Meta-RL auf der Grundlage von zwei neuen Meta RL Algorithmen voranzutreiben.


We propose a new class of probabilistic neural-symbolic models for visual question answering (VQA) that provide interpretable explanations of their decision making in the form of programs, given a small annotated set of human programs. The key idea of our approach is to learn a rich latent space which effectively propagates program annotations from known questions to novel questions. We do this by formalizing prior work on VQA, called module networks (Andreas, 2016) as discrete, structured, latent variable models on the joint distribution over questions and answers given images, and devise a procedure to train the model effectively. Our results on a dataset of compositional questions about SHAPES (Andreas, 2016) show that our model generates more interpretable programs and obtains better accuracy on VQA in the low-data regime than prior work.

Ein probabilistisches neuronales symbolisches Modell mit einem latenten Programmraum für besser interpretierbare Fragebeantwortung.

In diesem Beitrag wird ein diskretes, strukturiertes latentes Variablenmodell für die Beantwortung visueller Fragen vorgeschlagen, das eine kompositionelle Generalisierung und Schlussfolgerung mit erheblichem Leistungs- und Fähigkeitsgewinn beinhaltet.


The ability to deploy neural networks in real-world, safety-critical systems is severely limited by the presence of adversarial examples: slightly perturbed inputs that are misclassified by the network. In recent years, several techniques have been proposed for training networks that are robust to such examples; and each time stronger attacks have been devised, demonstrating the shortcomings of existing defenses. This highlights a key difficulty in designing an effective defense: the inability to assess a network's robustness against future attacks. We propose to address this difficulty through formal verification techniques. We construct ground truths: adversarial examples with a provably-minimal distance from a given input point. We demonstrate how ground truths can serve to assess the effectiveness of attack techniques, by comparing the adversarial examples produced by those attacks to the ground truths; and also of defense techniques, by computing the distance to the ground truths before and after the defense is applied, and measuring the improvement. We use this technique to assess recently suggested attack and defense techniques.

Wir nutzen die formale Verifikation, um die Effektivität von Techniken zum Auffinden von Gegenbeispielen oder zur Abwehr von Gegenbeispielen zu bewerten.

In diesem Beitrag wird eine Methode zur Berechnung von adversarial Beispielen mit minimalem Abstand zu den ursprünglichen Eingaben vorgeschlagen.

Die Autoren schlagen vor, Beispiele mit nachweislich minimalem Abstand als Instrument zur Bewertung der Robustheit eines trainierten Netzes zu verwenden.

Die Arbeit beschreibt eine Methode zur Generierung von adversarial Beispielen, die einen minimalen Abstand zu dem Trainingsbeispiel haben, das zu ihrer Generierung verwendet wurde.


This paper introduces a new framework for open-domain question answering in which the retriever and the reader \emph{iteratively interact} with each other. The framework is agnostic to the architecture of the machine reading model provided it has \emph{access} to the token-level hidden representations of the reader. The retriever uses fast nearest neighbor search that allows it to scale to corpora containing millions of paragraphs. A gated recurrent unit updates the query at each step conditioned on the \emph{state} of the reader and the \emph{reformulated} query is used to re-rank the paragraphs by the retriever. We conduct analysis and show that iterative interaction helps in retrieving informative paragraphs from the corpus. Finally, we show that our multi-step-reasoning framework brings consistent improvement when applied to two widely used reader architectures (\drqa and \bidaf) on various large open-domain datasets ---\tqau, \quasart, \searchqa, and \squado\footnote{Code and pretrained models are available at \url{https://github.com/rajarshd/Multi-Step-Reasoning}}.

Die Interaktion zwischen Paragraphen Retriever und maschinellem Lesegerät erfolgt über Reinforcement Learning, um große Verbesserungen bei offenen Datensätzen zu erzielen.

Die Arbeit stellt einen neuen Rahmen für die bidirektionale Interaktion zwischen Dokumentensuchmaschine und Leser für die Beantwortung von Fragen in offenen Bereichen mit der Idee des 'Leserzustands' von Leser zu Suchmaschine vor.

In dem Beitrag wird ein Modell für das maschinelle Lesen von mehreren Dokumenten vorgeschlagen, das aus drei verschiedenen Teilen und einem Algorithmus besteht.


Many imaging tasks require global information about all pixels in an image. Conventional bottom-up classification networks globalize information by decreasing resolution; features are pooled and down-sampled into a single output. But for semantic segmentation and object detection tasks, a network must provide higher-resolution pixel-level outputs. To globalize information while preserving resolution, many researchers propose the inclusion of sophisticated auxiliary blocks, but these come at the cost of a considerable increase in network size and computational cost. This paper proposes stacked u-nets (SUNets), which iteratively combine features from different resolution scales while maintaining resolution. SUNets leverage the information globalization power of u-nets in a deeper net- work architectures that is capable of handling the complexity of natural images. SUNets perform extremely well on semantic segmentation tasks using a small number of parameters.

Es wird eine neue Architektur vorgestellt, die die Informationsglobalisierungskraft von U-Netzen in einem tieferen Netz nutzt und ohne Schnickschnack aufgabenübergreifend gute Leistungen erbringt.

Eine Netzarchitektur für die semantische Bildsegmentierung, die auf der Zusammenstellung eines Stapels grundlegender U-Netz-Architekturen basiert, die die Anzahl der Parameter reduziert und die Ergebnisse verbessert.

Hier wird eine gestapelte U-Netz-Architektur für die Bildsegmentierung vorgeschlagen.


Asking questions is an important ability for a chatbot. This paper focuses on question generation. Although there are existing works on question generation based on a piece of descriptive text, it remains to be a very challenging problem. In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text. The key reason for proposing the new problem is that in practical applications, we found that useful questions need to be targeted toward some relevant topics. One almost never asks a random question in a conversation. Due to the fact that given a descriptive text, it is often possible to ask many types of questions, generating a question without knowing what it is about is of limited use. To solve the problem, we propose a novel neural network that is able to generate topic-specific questions. One major advantage of this model is that it can be trained directly using a question-answering corpus without requiring any additional annotations like annotating topics in the questions or answers. Experimental results show that our model outperforms the state-of-the-art baseline.

Wir schlagen ein neuronales Netz vor, das in der Lage ist, themenspezifische Fragen zu generieren.

Präsentiert einen auf neuronalen Netzen basierenden Ansatz zur Generierung themenspezifischer Fragen mit der Begründung, dass themenspezifische Fragen in praktischen Anwendungen sinnvoller sind.

Schlägt eine themenbasierte Generierungsmethode vor, die ein LSTM zur Extraktion von Themen mittels einer zweistufigen Kodierungstechnik verwendet.


Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option
 to restore voluntary movements after paralysis. These devices are based on the
 ability to extract information about movement intent from neural signals recorded
 using multi-electrode arrays chronically implanted in the motor cortices of the
 brain. However, the inherent loss and turnover of recorded neurons requires repeated
 recalibrations of the interface, which can potentially alter the day-to-day
 user experience. The resulting need for continued user adaptation interferes with
 the natural, subconscious use of the BMI. Here, we introduce a new computational
 approach that decodes movement intent from a low-dimensional latent representation
 of the neural data. We implement various domain adaptation methods
 to stabilize the interface over significantly long times. This includes Canonical
 Correlation Analysis used to align the latent variables across days; this method
 requires prior point-to-point correspondence of the time series across domains.
 Alternatively, we match the empirical probability distributions of the latent variables
 across days through the minimization of their Kullback-Leibler divergence.
 These two methods provide a significant and comparable improvement in the performance
 of the interface. However, implementation of an Adversarial Domain
 Adaptation Network trained to match the empirical probability distribution of the
 residuals of the reconstructed neural signals outperforms the two methods based
 on latent variables, while requiring remarkably few data points to solve the domain
 adaptation problem.

Wir implementieren ein adversariales Domänenanpassungsnetzwerk, um eine feste Gehirn-Maschine-Schnittstelle gegen allmähliche Veränderungen der aufgezeichneten neuronalen Signale zu stabilisieren.

Beschreibt einen neuen Ansatz für implantierte Gehirn-Maschine Schnittstellen, um Kalibrierungsprobleme und Kovariatenverschiebungen zu lösen.

Die Autoren definieren ein BMI, das einen Autoencoder verwendet, und gehen dann auf das Problem der Datendrift im BMI ein.


Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and even execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome certain well-studied learning challenges that are also fundamental to infants learning their first words. While it is notable that models with no meaningful prior knowledge overcome these learning obstacles, AI researchers and practitioners currently lack a clear understanding of exactly how they do so. Here we address this question as a way of achieving a clearer general understanding of grounded language learning, both to inform future research and to improve confidence in model predictions. For maximum control and generality, we focus on a simple neural network-based language learning agent trained via policy-gradient methods to interpret synthetic linguistic instructions in a simulated 3D world. We apply experimental paradigms from developmental psychology to this agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel way to visualise and analyse semantic representation in grounded language learning agents that yields a plausible computational account of the observed effects.

Analyse und Verständnis der Art und Weise, wie Agenten in neuronalen Netzen lernen, einfache, grundierte Sprache zu verstehen.

Die Autoren verbinden psychologische Versuchsmethoden mit dem Verständnis, wie die Blackbox der Deep Learning Methoden Probleme löst.

In diesem Beitrag wird eine Analyse der Agenten vorgestellt, die durch Reinforcement Learning in einer einfachen Umgebung, die verbale Anweisungen mit visuellen Informationen kombiniert, eine grundierte Sprache lernen.


Humans easily recognize object parts and their hierarchical structure by watching how they move; they can then predict how each part moves in the future. In this paper, we propose a novel formulation that simultaneously learns a hierarchical, disentangled object representation and a dynamics model for object parts from unlabeled videos. Our Parts, Structure, and Dynamics (PSD) model learns to, first, recognize the object parts via a layered image representation; second, predict hierarchy via a structural descriptor that composes low-level concepts into a hierarchical structure; and third, model the system dynamics by predicting the future. Experiments on multiple real and synthetic datasets demonstrate that our PSD model works well on all three tasks: segmenting object parts, building their hierarchical structure, and capturing their motion distributions.

Erlernen von Objektteilen, hierarchischer Struktur und Dynamik durch Beobachten ihrer Bewegung.

Schlägt ein unbeaufsichtigtes Lernmodell vor, das lernt, Objekte in Teile zu zerlegen, eine hierarchische Struktur für die Teile vorherzusagen und auf der Grundlage der zerlegten Teile und der Hierarchie Bewegungen vorherzusagen.


A successful application of convolutional architectures is to increase the resolution of single low-resolution images -- a image restoration task called super-resolution (SR). Naturally, SR is of value to resource constrained devices like mobile phones, electronic photograph frames and televisions to enhance image quality. However, SR demands perhaps the most extreme amounts of memory and compute operations of any mainstream vision task known today, preventing SR from being deployed to devices that require them. In this paper, we perform a early systematic study of system resource efficiency for SR, within the context of a variety of architectural and low-precision approaches originally developed for discriminative neural networks. We present a rich set of insights, representative SR architectures, and efficiency trade-offs; for example, the prioritization of ways to compress models to reach a specific memory and computation target and techniques to compact SR models so that they are suitable for DSPs and FPGAs. As a result of doing so, we manage to achieve better and comparable performance with previous models in the existing literature, highlighting the practicality of using existing efficiency techniques in SR tasks. Collectively, we believe these results provides the foundation for further research into the little explored area of resource efficiency for SR.

Wir entwickeln ein Verständnis für ressourceneffiziente Techniken zur Super-Resolution.

Die Arbeit schlägt eine detaillierte empirische Bewertung der Kompromisse vor, die von verschiedenen Convolutional Neural Networks bei dem Problem der Superauflösung erzielt werden.

In diesem Beitrag wird vorgeschlagen, die Effizienz der Systemressourcen für Netze mit hoher Auflösung zu verbessern.


Neural networks are known to be a class of highly expressive functions able to fit even random input-output mappings with 100% accuracy. In this work we present properties of neural networks that complement this aspect of expressivity. By using tools from Fourier analysis, we show that deep ReLU networks are biased towards low frequency functions, meaning that they cannot have local fluctuations without affecting their global behavior. Intuitively, this property is in line with the observation that over-parameterized networks find simple patterns that generalize across data samples. We also investigate how the shape of the data manifold affects expressivity by showing evidence that learning high frequencies gets easier with increasing manifold complexity, and present a theoretical understanding of this behavior. Finally, we study the robustness of the frequency components with respect to parameter perturbation, to develop the intuition that the parameters must be finely tuned to express high frequency functions.

Wir untersuchen ReLU-Netzwerke in der Fourier-Domäne und zeigen ein merkwürdiges Verhalten.

Fourier-Analyse von ReLU-Netzwerken, bei der festgestellt wurde, dass sie auf das Lernen niedriger Frequenzen ausgerichtet sind.

Diese Arbeit enthält theoretische und empirische Beiträge zum Thema Fourier-Koeffizienten von neuronalen Netzen.


Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confidence. However, this can fail to represent uncertainty which can arise when the input is ambiguous, e.g., due to occlusion or blurriness. This work addresses this issue and explicitly models the uncertainty by “hedging” the location of each input in the embedding space. We introduce the hedged instance embedding (HIB) in which embeddings are modeled as random variables and the model is trained under the variational information bottleneck principle (Alemi et al., 2016; Achille & Soatto, 2018). Empirical results on our new N-digit MNIST dataset show that our method leads to the desired behavior of “hedging its bets” across the embedding space upon encountering ambiguous inputs. This results in improved performance for image matching and classification tasks, more structure in the learned embedding space, and an ability to compute a per-exemplar uncertainty measure which is correlated with downstream performance.

In dem Beitrag wird vorgeschlagen, Wahrscheinlichkeitsverteilungen anstelle von Punkten für Einbettungsaufgaben wie Erkennung und Überprüfung zu verwenden.

In der Arbeit wird eine Alternative zur derzeitigen Punkteinbettung und eine Technik zu deren Schulung vorgeschlagen.

Die Arbeit schlägt ein Modell vor, das unsichere Einbettungen verwendet, um Deep Learning auf Bayes'sche Anwendungen zu erweitern.


Convolution neural networks typically consist of many convolutional layers followed by several fully-connected layers.   While convolutional layers map between high-order activation tensors, the fully-connected layers operate on flattened activation vectors.   Despite its success, this approach has notable drawbacks. Flattening discards the multi-dimensional structure of the activations, and the fully-connected layers require a large number of parameters.
 We present two new techniques to address these problems.   First, we introduce tensor contraction layers which can replace the ordinary fully-connected layers in a neural network. Second, we introduce tensor regression layers, which express the output of a neural network as a low-rank multi-linear mapping from a high-order activation tensor to the softmax layer.   Both the contraction and regression weights are learned end-to-end by backpropagation. By imposing low rank on both, we use significantly fewer parameters.   Experiments on the ImageNet dataset show that applied to the popular VGG and ResNet architectures, our methods significantly reduce the number of parameters in the fully connected layers (about 65% space savings) while negligibly impacting accuracy.

Wir schlagen Tensorkontraktion und Tensorregressionsschichten mit niedrigem Rang vor, um die multilineare Struktur im gesamten Netzwerk zu erhalten und zu nutzen, was zu einer enormen Platzersparnis mit geringen bis keinen Auswirkungen auf die Leistung führt.

In diesem Beitrag werden neue Schichtenarchitekturen für neuronale Netze vorgeschlagen, die eine Low-Rank-Darstellung von Tensoren verwenden.

In dieser Arbeit werden Tensorzerlegung und Tensorregression in CNN integriert, indem eine neue Tensorregressionsschicht verwendet wird.


We explore ways of incorporating bilingual dictionaries to enable semi-supervised
 neural machine translation. Conventional back-translation methods have shown
 success in leveraging target side monolingual data. However, since the quality of
 back-translation models is tied to the size of the available parallel corpora, this
 could adversely impact the synthetically generated sentences in a low resource
 setting. We propose a simple data augmentation technique to address both this
 shortcoming. We incorporate widely available bilingual dictionaries that yield
 word-by-word translations to generate synthetic sentences. This automatically
 expands the vocabulary of the model while maintaining high quality content. Our
 method shows an appreciable improvement in performance over strong baselines.

Wir verwenden zweisprachige Wörterbücher zur Datenerweiterung für die neuronale maschinelle Übersetzung.

In diesem Beitrag wird die Verwendung zweisprachiger Wörterbücher zur Erstellung synthetischer Quellen für einsprachige Zieldaten untersucht, um NMT-Modelle zu verbessern, die mit kleinen Mengen paralleler Daten trainiert wurden.


Rewards are sparse in the real world and most of today's reinforcement learning algorithms struggle with such sparsity. One solution to this problem is to allow the agent to create rewards for itself - thus making rewards dense and more suitable for learning. In particular, inspired by curious behaviour in animals, observing something novel could be rewarded with a bonus. Such bonus is summed up with the real task reward - making it possible for RL algorithms to learn from the combined reward. We propose a new curiosity method which uses episodic memory to form the novelty bonus. To determine the bonus, the current observation is compared with the observations in memory. Crucially, the comparison is done based on how many environment steps it takes to reach the current observation from those in memory - which incorporates rich information about environment dynamics. This allows us to overcome the known "couch-potato" issues of prior work - when the agent finds a way to instantly gratify itself by exploiting actions which lead to hardly predictable consequences. We test our approach in visually rich 3D environments in ViZDoom, DMLab and MuJoCo. In navigational tasks from ViZDoom and DMLab, our agent outperforms the state-of-the-art curiosity method ICM. In MuJoCo, an ant equipped with our curiosity module learns locomotion out of the first-person-view curiosity only. The code is available at https://github.com/google-research/episodic-curiosity/.

Wir schlagen ein neuartiges Modell der Neugier vor, das auf dem episodischen Gedächtnis und der Idee der Erreichbarkeit basiert und uns erlaubt, die bekannten "Couch-Potato" Probleme früherer Arbeiten zu überwinden.

Schlägt vor, Explorationsboni in RL-Algorithmen zu vergeben, indem größere Boni für Beobachtungen vergeben werden, die in Umgebungsschritten weiter entfernt sind.

Die Autoren schlagen einen Explorationsbonus vor, der bei spärlichen Belohnungs-RL-Problemen helfen soll, und betrachten viele Experimente in komplexen 3D-Umgebungen.


We introduce a new dataset of logical entailments for the purpose of measuring models' ability to capture and exploit the structure of logical expressions against an entailment prediction task. We use this task to compare a series of architectures which are ubiquitous in the sequence-processing literature, in addition to a new model class---PossibleWorldNets---which computes entailment as a ``convolution over possible worlds''. Results show that convolutional networks present the wrong inductive bias for this class of problems relative to LSTM RNNs, tree-structured neural networks outperform LSTM RNNs due to their enhanced ability to exploit the syntax of logic, and PossibleWorldNets outperform all benchmarks.

Wir führen einen neuen Datensatz mit logischen Folgerungen ein, um die Fähigkeit von Modellen zu messen, die Struktur von logischen Ausdrücken zu erfassen und zu nutzen, und zwar anhand einer Aufgabe zur Vorhersage von Folgerungen.

Die Arbeit schlägt ein neues Modell vor, um tiefe Modelle für die Erkennung von logischen Folgerungen als Produkt von kontinuierlichen Funktionen über mögliche Welten zu verwenden.

Schlägt ein neues Modell für maschinelles Lernen mit Vorhersage der logischen Folgerung vor.


Deep convolutional neural network (DCNN) based supervised learning is a widely practiced approach for large-scale image classification.   However, retraining these large networks to accommodate new, previously unseen data demands high computational time and energy requirements. Also, previously seen training samples may not be available at the time of retraining. We propose an efficient training methodology and incrementally growing a DCNN to allow new classes to be learned while sharing part of the base network. Our proposed methodology is inspired by transfer learning techniques, although it does not forget previously learned classes. An updated network for learning new set of classes is formed using previously learned convolutional layers (shared from initial part of base network) with addition of few newly added convolutional kernels included in the later layers of the network. We evaluated the proposed scheme on several recognition applications. The classification accuracy achieved by our approach is comparable to the regular incremental learning approach (where networks are updated with new training samples only, without any network sharing).

Die Arbeit handelt von einer neuen energieeffizienten Methode für inkrementelles Lernen.

Er schlägt ein Verfahren für inkrementelles Lernen als Transferlernen vor.

In diesem Beitrag wird eine Methode vorgestellt, mit der tiefe Convolutional Neural Networks inkrementell trainiert werden können, wobei die Daten in kleinen Batches über einen bestimmten Zeitraum hinweg verfügbar sind.

Stellt einen Ansatz für klasseninkrementelles Lernen mit tiefen Netzen vor, indem er drei verschiedene Lernstrategien für den endgültigen/besten Ansatz vorschlägt.


Recurrent neural networks (RNNs) are widely used to model sequential data but
 their non-linear dependencies between sequence elements prevent parallelizing
 training over sequence length. We show the training of RNNs with only linear
 sequential dependencies can be parallelized over the sequence length using the
 parallel scan algorithm, leading to rapid training on long sequences even with
 small minibatch size. We develop a parallel linear recurrence CUDA kernel and
 show that it can be applied to immediately speed up training and inference of
 several state of the art RNN architectures by up to 9x.  We abstract recent work
 on linear RNNs into a new framework of linear surrogate RNNs and develop a
 linear surrogate model for the long short-term memory unit, the GILR-LSTM, that
 utilizes parallel linear recurrence.  We extend sequence learning to new
 extremely long sequence regimes that were previously out of reach by
 successfully training a GILR-LSTM on a synthetic sequence classification task
 with a one million timestep dependency.

Paralleles Scannen zur Parallelisierung linearer rekurrenter neuronaler Netze verwenden. Modell mit einer Länge von 1 Million Abhängigkeiten trainieren.

Schlägt vor, RNN durch Anwendung der Methode von Blelloch zu beschleunigen.

Die Autoren schlagen einen parallelen Algorithmus für lineare Surrogate-RNNs vor, der Geschwindigkeitssteigerungen gegenüber den bestehenden Implementierungen von Quasi-RNN, SRU und LSTM ermöglicht.


Neural text generation models are often autoregressive language models or seq2seq models. Neural autoregressive and seq2seq models that generate text by sampling words sequentially, with each word conditioned on the previous model, are state-of-the-art for several machine translation and summarization benchmarks. These benchmarks are often defined by validation perplexity even though this is not a direct measure of sample quality. Language models are typically trained via maximum likelihood and most often with teacher forcing. Teacher forcing is well-suited to optimizing perplexity but can result in poor sample quality because generating text requires conditioning on sequences of words that were never observed at training time. We propose to improve sample quality using Generative Adversarial Network (GANs), which explicitly train the generator to produce high quality samples and have shown a lot of success in image generation. GANs were originally to designed to output differentiable values, so discrete language generation is challenging for them. We introduce an actor-critic conditional GAN that fills in missing text conditioned on the surrounding context. We show qualitatively and quantitatively, evidence that this produces more realistic text samples compared to a maximum likelihood trained model.

GAN in natürlicher Sprache zum Ausfüllen von Lücken.

In diesem Beitrag wird vorgeschlagen, Text mithilfe von GANs zu generieren.

Generierung von Textproben mit Hilfe von GAN und einem Mechanismus zum Auffüllen fehlender Wörter in Abhängigkeit vom umgebenden Text.


Parametric texture models have been applied successfully to synthesize artificial images. Psychophysical studies show that under defined conditions observers are unable to differentiate between model-generated and original natural textures. In industrial applications the reverse case is of interest: a texture analysis system should decide if human observers are able to discriminate between a reference and a novel texture. For example, in case of inspecting decorative surfaces the de- tection of visible texture anomalies without any prior knowledge is required. Here, we implemented a human-vision-inspired novelty detection approach. Assuming that the features used for texture synthesis are important for human texture percep- tion, we compare psychophysical as well as learnt texture representations based on activations of a pretrained CNN in a novelty detection scenario. Additionally, we introduce a novel objective function to train one-class neural networks for novelty detection and compare the results to standard one-class SVM approaches. Our experiments clearly show the differences between human-vision-inspired texture representations and learnt features in detecting visual anomalies. Based on a dig- ital print inspection scenario we show that psychophysical texture representations are able to outperform CNN-encoded features.

Vergleich von psychophysischen und CNN-kodierten Texturrepräsentationen in einer Anwendung zur Erkennung von Neuheiten in einem neuronalen Einklassen-Netzwerk.

Dieser Artikel konzentriert sich auf die Erkennung von Neuheiten und zeigt, dass psychophysikalische Darstellungen die VGG-Encoder-Merkmale in einigen Bereichen dieser Aufgabe übertreffen können.

In diesem Beitrag wird die Erkennung von Anomalien in Texturen untersucht und eine originelle Verlustfunktion vorgeschlagen.

Schlägt vor, zwei Anomalie-Detektoren aus drei verschiedenen Modellen zu trainieren, um Wahrnehmungsanomalien in visuellen Texturen zu erkennen.


In representation learning (RL), how to make the learned representations easy to interpret and less overfitted to training data are two important but challenging issues. To address these problems, we study a new type of regularization approach that encourages the supports of weight vectors in RL models to have small overlap, by simultaneously promoting near-orthogonality among vectors and sparsity of each vector. We apply the proposed regularizer to two models: neural networks (NNs) and sparse coding (SC), and develop an efficient ADMM-based algorithm for regularized SC. Experiments on various datasets demonstrate that weight vectors learned under our regularizer are more interpretable and have better generalization performance.

Wir schlagen einen neuartigen Regularisierungsansatz vor, der die Nicht-Überlappung beim Repräsentationslernen fördert, um die Interpretierbarkeit zu verbessern und die Überanpassung zu reduzieren.

Die Arbeit führt einen Matrix-Regulierer ein, um gleichzeitig sowohl Seltenheit als auch annähernde Orthogonalität zu induzieren.

Der Artikel untersucht eine Regularisierungsmethode, um die Seltenheit zu fördern und die Überlappung zwischen den Trägern der Gewichtsvektoren in den gelernten Darstellungen zu reduzieren, um die Interpretierbarkeit zu verbessern und eine Überanpassung zu vermeiden.

In dem Beitrag wird ein neuer Regularisierungsansatz vorgeschlagen, der gleichzeitig dazu führt, dass die Gewichtsvektoren (W) spärlich und orthogonal zueinander sind.


Successful recurrent models such as long short-term memories (LSTMs) and gated recurrent units (GRUs) use \emph{ad hoc} gating mechanisms.   Empirically these models have been found to improve the learning of medium to long term temporal dependencies and to help with vanishing gradient issues.

 We prove that learnable gates in a recurrent model formally provide \emph{quasi-invariance to general time transformations} in the input data. We recover part of the LSTM architecture from a simple axiomatic approach.

 This result leads to a new way of initializing gate biases in LSTMs and GRUs. Experimentally, this new \emph{chrono initialization} is shown to greatly improve learning of long term dependencies, with minimal implementation effort.

Beweist, dass Gating-Mechanismen gegenüber Zeittransformationen invariant sind. Einführung und Test einer neuen Initialisierung für LSTMs auf der Grundlage dieser Erkenntnis.

Die Arbeit verbindet die Entwicklung von rekurrenten Netzwerken und deren Auswirkungen auf die Reaktion des Netzwerks auf Zeittransformationen und nutzt dies, um ein einfaches Initialisierungsschema für die Verzerrung zu entwickeln.


Teaching plays a very important role in our society, by spreading human knowledge and educating our next generations. A good teacher will select appropriate teaching materials, impact suitable methodologies, and set up targeted examinations, according to the learning behaviors of the students. In the field of artificial intelligence, however, one has not fully explored the role of teaching, and pays most attention to machine \emph{learning}. In this paper, we argue that equal attention, if not more, should be paid to teaching, and furthermore, an optimization framework (instead of heuristics) should be used to obtain good teaching strategies. We call this approach ``learning to teach''. In the approach, two intelligent agents interact with each other: a student model (which corresponds to the learner in traditional machine learning algorithms), and a teacher model (which determines the appropriate data, loss function, and hypothesis space to facilitate the training of the student model). The teacher model leverages the feedback from the student model to optimize its own teaching strategies by means of reinforcement learning, so as to achieve teacher-student co-evolution. To demonstrate the practical value of our proposed approach, we take the training of deep neural networks (DNN) as an example, and show that by using the learning to teach techniques, we are able to use much less training data and fewer iterations to achieve almost the same accuracy for different kinds of DNN models (e.g., multi-layer perceptron, convolutional neural networks and recurrent neural networks) under various machine learning tasks (e.g., image classification and text understanding).

Wir schlagen ein neues Framework für die automatische Steuerung des maschinellen Lernprozesses vor und überprüfen die Wirksamkeit des "Learning to teach".

Diese Arbeit konzentriert sich auf das "maschinelle Lernen" und schlägt vor, das Reinforcement Learning zu nutzen, indem die Belohnung als die Lerngeschwindigkeit des Lernenden definiert wird und die Parameter des Lehrers mit Hilfe des Policy-Gradienten aktualisiert werden.

Die Autoren definieren ein Deep Learning Modell, das aus vier Komponenten besteht: einem Studentenmodell, einem Lehrermodell, einer Verlustfunktion und einem Datensatz.

Vorschlagen eines Frameworks für das "Lernen zu lehren", das der Auswahl der Daten entspricht, die dem Lernenden präsentiert werden.


We present DL2, a system for training and querying neural networks with logical constraints. The key idea is to translate these constraints into a differentiable loss with desirable mathematical properties and to then either train with this loss in an iterative manner or to use the loss for querying the network for inputs subject to the constraints. We empirically demonstrate that DL2 is effective in both training and querying scenarios, across a range of constraints and data sets.

Ein differenzierbarer Verlust für logische Beschränkungen beim Training und bei der Abfrage neuronaler Netze.

Ein Rahmenwerk für die Umwandlung von Abfragen über Parameter und Eingabe-/Ausgabepaare für neuronale Netze in differenzierbare Verlustfunktionen und eine zugehörige deklarative Sprache für die Spezifikation dieser Abfragen.

Diese Arbeit befasst sich mit dem Problem der Kombination logischer Ansätze mit neuronalen Netzen, indem eine logische Formel in eine nicht-negative Verlustfunktion für ein neuronales Netz übersetzt wird.


Genetic algorithms have been widely used in many practical optimization problems.
 Inspired by natural selection, operators, including mutation, crossover
 and selection, provide effective heuristics for search and black-box optimization.
 However, they have not been shown useful for deep reinforcement learning, possibly
 due to the catastrophic consequence of parameter crossovers of neural networks.
 Here, we present Genetic Policy Optimization (GPO), a new genetic algorithm
 for sample-efficient deep policy optimization. GPO uses imitation learning
 for policy crossover in the state space and applies policy gradient methods for mutation.
 Our experiments on MuJoCo tasks show that GPO as a genetic algorithm
 is able to provide superior performance over the state-of-the-art policy gradient
 methods and achieves comparable or higher sample efficiency.

Auf genetischen Algorithmen basierender Ansatz zur Optimierung von Strategien für tiefe neuronale Netze.

Die Autoren stellen einen Algorithmus für das Training von Ensembles von Strategienetzwerken vor, der regelmäßig verschiedene Strategien im Ensemble miteinander mischt.

In diesem Beitrag wird eine vom genetischen Algorithmus inspirierte Methode zur Optimierung von Richtlinien vorgeschlagen, die die Mutations- und Crossover-Operatoren in Richtliniennetzwerken nachahmt.


To make deep neural networks feasible in resource-constrained environments (such as mobile devices), it is beneficial to quantize models by using low-precision weights. One common technique for quantizing neural networks is the straight-through gradient method, which enables back-propagation through the quantization mapping. Despite its empirical success, little is understood about why the straight-through gradient method works.
 Building upon a novel observation that the straight-through gradient method is in fact identical to the well-known Nesterov’s dual-averaging algorithm on a quantization constrained optimization problem, we propose a more principled alternative approach, called ProxQuant , that formulates quantized network training as a regularized learning problem instead and optimizes it via the prox-gradient method. ProxQuant does back-propagation on the underlying full-precision vector and applies an efficient prox-operator in between stochastic gradient steps to encourage quantizedness. For quantizing ResNets and LSTMs, ProxQuant outperforms state-of-the-art results on binary quantization and is on par with state-of-the-art on multi-bit quantization. We further perform theoretical analyses showing that ProxQuant converges to stationary points under mild smoothness assumptions, whereas variants such as lazy prox-gradient method can fail to converge in the same setting.

Ein prinzipielles Framework für die Modellquantisierung unter Verwendung der proximalen Gradientenmethode, mit empirischer Bewertung und theoretischen Konvergenzanalysen.

Schlägt die ProxQuant-Methode zum Trainieren neuronaler Netze mit quantisierten Gewichten vor.

Schlägt vor, binäre Netze und ihre Varianten mit Hilfe des proximalen Gradientenabstiegs zu lösen.


Background: Statistical mechanics results (Dauphin et al. (2014); Choromanska et al. (2015)) suggest that local minima with high error are exponentially rare in high dimensions. However, to prove low error guarantees for Multilayer Neural Networks (MNNs), previous works so far required either a heavily modified MNN model or training method, strong assumptions on the labels (e.g., “near” linear separability), or an unrealistically wide hidden layer with \Omega\(N) units.

 Results: We examine a MNN with one hidden layer of piecewise linear units, a single output, and a quadratic loss. We prove that, with high probability in the limit of N\rightarrow\infty datapoints, the volume of differentiable regions of the empiric loss containing sub-optimal differentiable local minima is exponentially vanishing in comparison with the same volume of global minima, given standard normal input of dimension d_0=\tilde{\Omega}(\sqrt{N}), and a more realistic number of d_1=\tilde{\Omega}(N/d_0) hidden units. We demonstrate our results numerically: for example, 0% binary classification training error on CIFAR with only N/d_0 = 16 hidden neurons.

Schlechte lokale Minima verschwinden in einem mehrschichtigen neuronalen Netz: Ein Beweis mit vernünftigeren Annahmen als bisher.

In Netzen mit einer einzigen verborgenen Schicht nimmt das Volumen der suboptimalen lokalen Minima im Vergleich zu den globalen Minima exponentiell ab.

In diesem Beitrag wird untersucht, warum Standard-SGD-Algorithmen auf der Grundlage neuronaler Netze zu "guten" Lösungen konvergieren.


Deep neural networks are vulnerable to adversarial examples, which can mislead classifiers by adding imperceptible perturbations. An intriguing property of adversarial examples is their good transferability, making black-box attacks feasible in real-world applications. Due to the threat of adversarial attacks, many methods have been proposed to improve the robustness, and several state-of-the-art defenses are shown to be robust against transferable adversarial examples. In this paper, we identify the attention shift phenomenon, which may hinder the transferability of adversarial examples to the defense models. It indicates that the defenses rely on different discriminative regions to make predictions compared with normally trained models. Therefore, we propose an attention-invariant attack method to generate more transferable adversarial examples. Extensive experiments on the ImageNet dataset validate the effectiveness of the proposed method. Our best attack fools eight state-of-the-art defenses at an 82% success rate on average based only on the transferability, demonstrating the insecurity of the defense techniques.

Wir schlagen eine aufmerksamkeitsinvariante Angriffsmethode vor, um mehr übertragbare adversarial Beispiele für Blackbox-Angriffe zu generieren, die modernste Verteidigungsmaßnahmen mit einer hohen Erfolgsquote überlisten können.

Die Arbeit schlägt einen neuen Weg vor, um den Stand der Technik bei der Abwehr von gegnerischen Angriffen auf CNN zu überwinden.

In diesem Beitrag wird die Vermutung geäußert, dass die "Aufmerksamkeitsverschiebung" eine Schlüsseleigenschaft ist, die dazu führt, dass adversarial Angriffe nicht übertragen werden können, und es wird eine aufmerksamkeitsinvariante Angriffsmethode vorgeschlagen.


We present Merged-Averaged Classifiers via Hashing (MACH) for $K$-classification with large $K$. Compared to traditional one-vs-all classifiers that require $O(Kd)$ memory and inference cost, MACH only need $O(d\log{K})$ memory while only requiring $O(K\log{K} + d\log{K})$ operation for inference. MACH is the first generic $K$-classification algorithm, with provably theoretical guarantees, which requires $O(\log{K})$ memory without any assumption on the relationship between classes. MACH uses universal hashing to reduce classification with a large number of classes to few independent classification task with very small (constant) number of classes. We provide theoretical quantification of accuracy-memory tradeoff by showing the first connection between extreme classification and heavy hitters. With MACH we can train ODP dataset with 100,000 classes and 400,000 features on a single Titan X GPU (12GB), with the classification accuracy of 19.28\%, which is the best-reported accuracy on this dataset. Before this work, the best performing baseline is a one-vs-all classifier that requires 40 billion parameters (320 GB model size) and achieves 9\% accuracy.   In contrast, MACH can achieve 9\% accuracy with 480x reduction in the model size (of mere 0.6GB). With MACH, we also demonstrate complete training of fine-grained imagenet dataset (compressed size 104GB), with 21,000 classes, on a single GPU.

Wie man 100.000 Klassen auf einem einzigen Grafikprozessor trainiert.

Vorschlagen einer effizienten Hashing-Methode MACH für die Softmax-Approximation im Kontext eines großen Ausgaberaums, die sowohl Speicher als auch Rechenzeit spart.

Eine Methode zur Klassifizierung von Problemen mit einer großen Anzahl von Klassen in einem Mehrklassenumfeld, demonstriert an ODP- und Imagenet-21K Datensätzen.

Die Arbeit stellt ein auf Hashing basierendes Verfahren zur Verringerung der Speicher- und Berechnungszeit für die K-Wege-Klassifizierung vor, wenn K groß ist.


Gradient-based optimization is the foundation of deep learning and reinforcement learning.
 Even when the mechanism being optimized is unknown or not differentiable, optimization using high-variance or biased gradient estimates is still often the best strategy. We introduce a general framework for learning low-variance, unbiased gradient estimators for black-box functions of random variables, based on gradients of a learned function.
 These estimators can be jointly trained with model parameters or policies, and are applicable in both discrete and continuous settings. We give unbiased, adaptive analogs of state-of-the-art reinforcement learning methods such as advantage actor-critic. We also demonstrate this framework for training discrete latent-variable models.

Wir stellen eine allgemeine Methode zur unverzerrten Schätzung von Gradienten von Black-Box-Funktionen von Zufallsvariablen vor. Wir wenden diese Methode auf diskrete Variationsinferenz und Reinforcement Learning an.

Schlägt einen neuen Ansatz zur Durchführung des Gradientenabstiegs für die Blackbox-Optimierung oder das Training diskreter latenter Variablenmodelle vor.


Do GANS (Generative Adversarial Nets) actually learn the target distribution? The foundational paper of Goodfellow et al. (2014) suggested they do, if they were given sufficiently large deep nets, sample size, and computation time. A recent theoretical analysis in Arora et al. (2017) raised doubts whether the same holds when discriminator has bounded size. It showed that the training objective can approach its optimum value even if the generated distribution has very low support. In other words, the training objective is unable to prevent mode collapse. The current paper makes two contributions. (1) It proposes a novel test for estimating support size using the birthday paradox of discrete probability. Using this  evidence is presented that well-known GANs approaches do learn distributions of fairly low support.   (2) It theoretically studies encoder-decoder GANs architectures (e.g., BiGAN/ALI), which were proposed to learn more meaningful features via GANs, and consequently to also solve the mode-collapse issue. Our result shows that such encoder-decoder training objectives also cannot guarantee learning of the full distribution because they cannot prevent serious mode collapse. More seriously, they cannot prevent learning meaningless codes for data, contrary to usual intuition.

Wir schlagen einen Schätzer für die Unterstützungsgröße der gelernten Verteilung von GANs vor, um zu zeigen, dass sie in der Tat unter einem Mode-Kollaps leiden, und wir beweisen, dass Encoder-Decoder GANs dieses Problem ebenfalls nicht vermeiden.

In dem Beitrag wird versucht, die Größe der Unterstützung für Lösungen, die von typischen GANs erzeugt werden, experimentell zu schätzen.

Diese Arbeit schlägt einen cleveren neuen Test vor, der auf dem Geburtstags-Paradoxon basiert, um die Vielfalt in generativen Beispielen zu messen. Die Ergebnisse des Experiments werden dahingehend interpretiert, dass der Modus-Kollaps in einer Reihe von modernen generativen Modellen stark ist.

Die Arbeit verwendet das Geburtstagsparadoxon, um zu zeigen, dass einige GAN-Architekturen Verteilungen mit relativ geringer Unterstützung erzeugen.


Due to their complex nature, it is hard to characterize the ways in which machine learning models can misbehave or be exploited when deployed. Recent work on adversarial examples, i.e. inputs with minor perturbations that result in substantially different model predictions, is helpful in evaluating the robustness of these models by exposing the adversarial scenarios where they fail. However, these malicious perturbations are often unnatural, not semantically meaningful, and not applicable to complicated domains such as language. In this paper, we propose a framework to generate natural and legible adversarial examples that lie on the data manifold, by searching in semantic space of dense and continuous data representation, utilizing the recent advances in generative adversarial networks. We present generated adversaries to demonstrate the potential of the proposed approach for black-box classifiers for a wide range of applications such as image classification, textual entailment, and machine translation. We include experiments to show that the generated adversaries are natural, legible to humans, and useful in evaluating and analyzing black-box classifiers.

Wir schlagen ein Framework vor, um natürliche Gegner gegen Black-Box-Klassifikatoren sowohl für visuelle als auch für textuelle Domänen zu generieren, indem wir die Suche nach Gegnern im latenten semantischen Raum vornehmen.

Schlägt eine Methode zur Erstellung von semantischen Gegenbeispielen vor.

Schlägt einen Rahmen zur Erzeugung natürlicher Gegenbeispiele durch die Suche nach Gegenspielern in einem latenten Raum mit dichter und kontinuierlicher Datendarstellung vor.


Kronecker-factor Approximate Curvature (Martens & Grosse, 2015) (K-FAC) is a 2nd-order optimization method which has been shown to give state-of-the-art performance on large-scale neural network optimization tasks (Ba et al., 2017).   It is based on an approximation to the Fisher information matrix (FIM) that makes assumptions about the particular structure of the network and the way it is parameterized. The original K-FAC method was applicable only to fully-connected networks, although it has been recently extended by Grosse & Martens (2016) to handle convolutional networks as well. In this work we extend the method to handle RNNs by introducing a novel approximation to the FIM for RNNs. This approximation works by modelling the covariance structure between the gradient contributions at different time-steps using a chain-structured linear Gaussian graphical model, summing the various cross-covariances, and computing the inverse in closed form. We demonstrate in experiments that our method significantly outperforms general purpose state-of-the-art optimizers like SGD with momentum and Adam on several challenging RNN training tasks.

Wir erweitern die K-FAC Methode auf RNNs, indem wir eine neue Familie von Fisher Approximationen entwickeln.

Die Autoren erweitern die K-FAC-Methode auf RNNs und stellen drei Möglichkeiten der Annäherung von F vor. Sie zeigen Optimierungsergebnisse für drei Datensätze, die ADAM sowohl in der Anzahl der Aktualisierungen als auch in der Rechenzeit übertreffen.

Schlägt vor, die Optimierungsmethode mit dem Kronecker-Faktor Appropriate Curvature auf rekurrente neuronale Netze zu erweitern.

Die Autoren stellen eine Methode zweiter Ordnung vor, die speziell für RNNs konzipiert ist.


Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of trained convolutional filters e.g., spatial correlations of weights. We define DWP in the form of an implicit distribution and propose a method for variational inference with such type of implicit priors. In experiments, we show that DWP improves the performance of Bayesian neural networks when training data are limited, and initialization of weights with samples from DWP accelerates training of conventional convolutional neural networks.

Das generative Modell für Kernel von Convolutional Neural Networks, das beim Training auf neuen Datensätzen als Prior-Verteilung fungiert.

Eine Methode zur Modellierung von Convolutional Neural Networks unter Verwendung einer Bayes-Methode.

Die Idee ist, einen Prior für einen Hilfsdatensatz zu ermitteln und diesen Prior dann über die CNN-Filter zu verwenden, um die Inferenz für einen Datensatz von Interesse zu starten.

Diese Arbeit untersucht das Erlernen informativer Prioritäten für Convolutional Neural Network Modelle mit ähnlichen Problemdomänen, indem es Autoencoder verwendet, um eine aussagekräftige Priorität für die gefilterten Gewichte der trainierten Netze zu erhalten.


The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements.   Motivated by the potential for an in-the-field cell sorting detector, we examine a Synechocystis sp. PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or deplete cultures.   We use deep learning techniques to both successfully classify cells and generate a mask segmenting the cells/condition from the background. Further, we use the classification accuracy to guide a data-driven, iterative feature selection method, allowing the design neural networks requiring 90% fewer input features with little accuracy degradation.

Wir haben Deep Learning Techniken auf die Segmentierung hyperspektraler Bilder und die iterative Auswahl von Merkmalen angewandt.

Schlägt ein gieriges Verfahren zur Auswahl einer Teilmenge hochkorrelierter Spektralmerkmale bei einer Klassifizierungsaufgabe vor.

Die Arbeit untersucht die Verwendung neuronaler Netze für die Klassifizierung und Segmentierung der hyperspektralen Bildgebung (HSI) von Zellen.

Klassifizierung von Zellen und Implementierung von Zellsegmentierung auf der Grundlage von Deep-Learning-Techniken mit Reduzierung der Eingangsmerkmale.


Data Interpretation is an important part of Quantitative Aptitude exams and requires an individual to answer questions grounded in plots such as bar charts, line graphs, scatter plots, \textit{etc}. Recently, there has been an increasing interest in building models which can perform this task by learning from datasets containing triplets of the form \{plot, question, answer\}. Two such datasets have been proposed in the recent past which contain plots generated from synthetic data with limited (i) $x-y$ axes variables (ii) question templates and (iii) answer vocabulary and hence do not adequately capture the challenges posed by this task. To overcome these limitations of existing datasets, we introduce a new dataset containing $9.7$ million question-answer pairs grounded over $270,000$ plots with three main differentiators. First, the plots in our dataset contain a wide variety of realistic $x$-$y$ variables such as CO2 emission, fertility rate, \textit{etc. } extracted from  real word data sources such as World Bank, government sites, \textit{etc}. Second, the questions in our dataset are more complex as they are based on templates extracted from interesting questions asked by a crowd of workers using a fraction of these plots. Lastly, the answers in our dataset are not restricted to a small vocabulary and a large fraction of the answers seen at test time are not present in the training vocabulary. As a result, existing models for Visual Question Answering which largely use end-to-end models in a multi-class classification framework cannot be used for this task. We establish initial results on this dataset and emphasize the complexity of the task using a multi-staged modular pipeline with various sub-components to (i) extract relevant data from the plot and convert it to a semi-structured table (ii) combine the question with this table and use compositional semantic parsing to arrive at a logical form from which the answer can be derived. We believe that such a modular framework is the best way to go forward as it would enable the research community to independently make progress on all the sub-tasks involved in plot question answering.

Wir haben einen neuen Datensatz für die Dateninterpretation über Parzellen erstellt und schlagen auch eine Basislinie für dieselben vor.

Die Autoren schlagen eine Pipeline zur Lösung des DIP-Problems vor, die das Lernen aus Datensätzen mit Tripletts der Form {Plot, Frage, Antwort} beinhaltet.

Schlägt einen Algorithmus vor, der die in wissenschaftlichen Diagrammen dargestellten Daten interpretieren kann.


Learning to predict complex time-series data is a fundamental challenge in a range of disciplines including Machine Learning, Robotics, and Natural Language Processing. Predictive State Recurrent Neural Networks (PSRNNs) (Downey et al.) are a state-of-the-art approach for modeling time-series data which combine the benefits of probabilistic filters and Recurrent Neural Networks into a single model. PSRNNs leverage the concept of Hilbert Space Embeddings of distributions (Smola et al.) to embed predictive states into a Reproducing Kernel Hilbert Space, then estimate, predict, and update these embedded states using Kernel Bayes Rule. Practical implementations of PSRNNs are made possible by the machinery of Random Features, where input features are mapped into a new space where dot products approximate the kernel well. Unfortunately PSRNNs often require a large number of RFs to obtain good results, resulting in large models which are slow to execute and slow to train. Orthogonal Random Features (ORFs) (Choromanski et al.) is an improvement on RFs which has been shown to decrease the number of RFs required for pointwise kernel approximation. Unfortunately, it is not clear that ORFs can be applied to PSRNNs, as PSRNNs rely on Kernel Ridge Regression as a core component of their learning algorithm, and the theoretical guarantees of ORF do not apply in this setting. In this paper, we extend the theory of ORFs to Kernel Ridge Regression and show that ORFs can be used to obtain Orthogonal PSRNNs (OPSRNNs), which are smaller and faster than PSRNNs. In particular, we show that OPSRNN models clearly outperform LSTMs and furthermore, can achieve accuracy similar to PSRNNs with an order of magnitude smaller number of features needed.

Verbesserung prädiktiver rekurrenter neuronaler Netze durch orthogonale Zufallsmerkmale.

Schlägt vor, die Leistung von rekurrenten neuronalen Netzen mit Vorhersagecharakter durch Berücksichtigung orthogonaler Zufallsmerkmale zu verbessern.

Die Arbeit befasst sich mit dem Problem der Ausbildung prädiktiver rekurrenter neuronaler Netze und liefert zwei Beiträge.


Training deep neural networks requires many training samples, but in practice training labels are expensive to obtain and may be of varying quality, as some may be from trusted expert labelers while others might be from heuristics or other sources of weak supervision such as crowd-sourcing. This creates a fundamental quality- versus-quantity trade-off in the learning process. Do we learn from the small amount of high-quality data or the potentially large amount of weakly-labeled data? We argue that if the learner could somehow know and take the label-quality into account when learning the data representation, we could get the best of both worlds. To this end, we propose “fidelity-weighted learning” (FWL), a semi-supervised student- teacher approach for training deep neural networks using weakly-labeled data. FWL modulates the parameter updates to a student network (trained on the task we care about) on a per-sample basis according to the posterior confidence of its label-quality estimated by a teacher (who has access to the high-quality labels). Both student and teacher are learned from the data. We evaluate FWL on two tasks in information retrieval and natural language processing where we outperform state-of-the-art alternative semi-supervised methods, indicating that our approach makes better use of strong and weak labels, and leads to better task-dependent data representations.

Wir schlagen Fidelity-weighted Learning vor, einen halb-überwachten Lehrer-Schüler-Ansatz für das Training neuronaler Netze mit schwach beschrifteten Daten.

Diese Arbeit schlägt einen Ansatz für das Lernen mit schwacher Überwachung vor, indem es einen sauberen und einen gestörten Datensatz verwendet und von einem Lehrer- und einem Schülernetzwerk ausgeht.

In dem Beitrag wird versucht, tiefe neuronale Netzmodelle mit wenigen gelabelten Trainingsbeispielen zu trainieren.

Die Autoren schlagen einen Ansatz für das Training von Deep-Learning-Modellen für Situationen vor, in denen es nicht genügend zuverlässige kommentierte Daten gibt.


Online learning has attracted great attention due to the increasing demand for systems that have the ability of learning and evolving. When the data to be processed is also high dimensional and dimension reduction is necessary for visualization or prediction enhancement, online dimension reduction will play an essential role. The purpose of this paper is to propose new online learning approaches for supervised dimension reduction. Our first algorithm is motivated by adapting the sliced inverse regression (SIR), a pioneer and effective algorithm for supervised dimension reduction, and making it implementable in an incremental manner. The new algorithm, called incremental sliced inverse regression (ISIR), is able to update the subspace of significant factors with intrinsic lower dimensionality fast and efficiently when new observations come in. We also refine the algorithm by using an overlapping technique  and develop an incremental overlapping sliced inverse regression (IOSIR) algorithm. We verify the effectiveness and efficiency of both algorithms by simulations and real data applications.

Wir haben zwei neue Ansätze vorgeschlagen, die inkrementelle geschnittene inverse Regression und die inkrementelle überlappende geschnittene inverse Regression, um eine überwachte Dimensionsreduktion in einer Online-Lernmethode zu implementieren.

Untersucht das Problem der ausreichenden Dimensionsreduzierung und schlägt einen inkrementellen Algorithmus zur invertierten Regression vor.

In diesem Beitrag wird ein Online-Lernalgorithmus für die überwachte Dimensionsreduzierung vorgeschlagen, die so genannte inkrementelle geschnittene inverse Regression.


This paper presents a storage-efficient learning model titled Recursive Binary Neural Networks for embedded and mobile devices having a limited amount of on-chip data storage such as hundreds of kilo-Bytes. The main idea of the proposed model is to recursively recycle data storage of weights (parameters) during training. This enables a device with a given storage constraint to train and instantiate a neural network classifier with a larger number of weights on a chip, achieving better classification accuracy. Such efficient use of on-chip storage reduces off-chip storage accesses, improving energy-efficiency and speed of training. We verified the proposed training model with deep and convolutional neural network classifiers on the MNIST and voice activity detection benchmarks. For the deep neural network, our model achieves data storage requirement of as low as 2 bits/weight, whereas the conventional binary neural network learning models require data storage of 8 to 32 bits/weight. With the same amount of data storage, our model can train a bigger network having more weights, achieving 1% less test error than the conventional binary neural network learning model. To achieve the similar classification error, the conventional binary neural network model requires 4× more data storage for weights than our proposed model. For the convolution neural network classifier, the proposed model achieves 2.4% less test error for the same on-chip storage or 6× storage savings to achieve the similar accuracy.

Wir schlagen ein Lernmodell vor, das es DNN ermöglicht, mit nur 2 Bit/Gewicht zu lernen, was besonders nützlich für das Lernen auf dem Gerät ist.

Schlägt eine Methode zur schrittweisen Diskretisierung eines NN vor, um Speicher und Leistung zu verbessern.


Within-class variation in a high-dimensional dataset can be modeled as being on a low-dimensional manifold due to the constraints of the physical processes producing that variation (e.g., translation, illumination, etc.). We desire a method for learning a representation of the manifolds induced by identity-preserving transformations that can be used to increase robustness, reduce the training burden, and encourage interpretability in machine learning tasks. In particular, what is needed is a representation of the transformation manifold that can robustly capture the shape of the manifold from the input data, generate new points on the manifold, and extend transformations outside of the training domain without significantly increasing the error. Previous work has proposed algorithms to efficiently learn analytic operators (called transport operators) that define the process of transporting one data point on a manifold to another.   The main contribution of this paper is to define two transfer learning methods that use this generative manifold representation to learn natural transformations and incorporate them into new data. The first method uses this representation in a novel randomized approach to transfer learning that employs the learned generative model to map out unseen regions of the data space. These results are shown through demonstrations of transfer learning in a data augmentation task for few-shot image classification. The second method use of transport operators for injecting specific transformations into new data examples which allows for realistic image animation and informed data augmentation.   These results are shown on stylized constructions using the classic swiss roll data structure and in demonstrations of transfer learning in a data augmentation task for few-shot image classification. We also propose the use of transport operators for injecting transformations into new data examples which allows for realistic image animation.

Das Lernen von Transportoperatoren auf Mannigfaltigkeiten bildet eine wertvolle Darstellung für Aufgaben wie Transferlernen.

Verwendet ein Wörterbuch-Lernsystem, um vielfältige Transportoperatoren auf erweiterten USPS-Ziffern zu lernen.

Die Arbeit berücksichtigt den Rahmen des vielfältigen Transportoperator-Lernens von Culpepper und Olshausen (2009), und interpretiert es als Erhalt einer MAP-Schätzung unter einem probabilistischen generativen Modell.


The seemingly infinite diversity of the natural world arises from a relatively small set of coherent rules, such as the laws of physics or chemistry. We conjecture that these rules give rise to regularities that can be discovered through primarily unsupervised experiences and represented as abstract concepts. If such representations are compositional and hierarchical, they can be recombined into an exponentially large set of new concepts. This paper describes SCAN (Symbol-Concept Association Network), a new framework for learning such abstractions in the visual domain. SCAN learns concepts through fast symbol association, grounding them in disentangled visual primitives that are discovered in an unsupervised manner. Unlike state of the art multimodal generative model baselines, our approach requires very few pairings between symbols and images and makes no assumptions about the form of symbol representations. Once trained, SCAN is capable of multimodal bi-directional inference, generating a diverse set of image samples from symbolic descriptions and vice versa. It also allows for traversal and manipulation of the implicit hierarchy of visual concepts through symbolic instructions and learnt logical recombination operations. Such manipulations enable SCAN to break away from its training data distribution and imagine novel visual concepts through symbolically instructed recombination of previously learnt concepts.

Wir stellen ein neuronales Variationsmodell zum Erlernen sprachgeleiteter kompositorischer visueller Konzepte vor.

Schlägt eine neuartige neuronale Netzarchitektur vor, die Objektkonzepte durch die Kombination von Beta-VAE und SCAN erlernt.

In diesem Beitrag wird ein VAE-basiertes Modell für die Übersetzung zwischen Bildern und Text vorgestellt, dessen latente Repräsentation sich gut für die Anwendung symbolischer Operationen eignet, was ihnen eine aussagekräftigere Sprache für die Auswahl von Bildern aus Texten verleiht.

Diese Arbeit schlägt ein neues Modell namens SCAN (Symbol-Konzept-Assoziationsnetzwerk) für hierarchisches Konzeptlernen vor und ermöglicht die Verallgemeinerung auf neue Konzepte, die mit Hilfe logischer Operatoren aus bestehenden Konzepten zusammengesetzt werden.