Powerful generative models, particularly in Natural Language Modelling, are commonly trained by maximizing a variational lower bound on the data log likelihood. These models often suffer from poor use of their latent variable, with ad-hoc annealing factors used to encourage retention of information in the latent variable. We discuss an alternative and general approach to latent variable modelling, based on an objective that encourages a perfect reconstruction by tying a stochastic autoencoder with a variational autoencoder (VAE). This ensures by design that the latent variable captures information about the observations, whilst retaining the ability to generate well. Interestingly, although our model is fundamentally different to a VAE, the lower bound attained is identical to the standard VAE bound but with the addition of a simple pre-factor; thus, providing a formal interpretation of the commonly used, ad-hoc pre-factors in training VAEs.

In diesem Beitrag wird ein neuartiger generativer Modellierungsrahmen vorgestellt, der den Zusammenbruch latenter Variablen vermeidet und die Verwendung bestimmter Ad-hoc-Faktoren beim Training von variationalen Autoencodern verdeutlicht.

Die Arbeit schlägt vor, das Problem eines variationalen Autoencoders zu lösen, der die latenten Variablen ignoriert.

Diese Arbeit schlägt vor, einen stochastischen Autoencoder zum ursprünglichen VAE-Modell hinzuzufügen, um das Problem zu lösen, dass der LSTM Decoder eines Sprachmodells zu stark sein könnte, um die Informationen der latenten Variablen zu ignorieren.

In diesem Beitrag wird AutoGen vorgestellt, das einen generativen variationalen Autoencoder mit einem auf Autoencoder basierenden High-Fidelity-Rekonstruktionsmodell kombiniert, um die latente Repräsentation besser zu nutzen.


This paper studies the problem of domain division which aims to segment instances drawn from different probabilistic distributions. This problem exists in many previous recognition tasks, such as Open Set Learning (OSL) and Generalized Zero-Shot Learning (G-ZSL), where the testing instances come from either seen or unseen/novel classes with different probabilistic distributions. Previous works only calibrate the conﬁdent prediction of classiﬁers of seen classes (WSVM Scheirer et al. (2014)) or taking unseen classes as outliers Socher et al. (2013). In contrast, this paper proposes a probabilistic way of directly estimating and ﬁne-tuning the decision boundary between seen and unseen classes. In particular, we propose a domain division algorithm to split the testing instances into known, unknown and uncertain domains, and then conduct recognition tasks in each domain. Two statistical tools, namely, bootstrapping and KolmogorovSmirnov (K-S) Test, for the ﬁrst time, are introduced to uncover and ﬁne-tune the decision boundary of each domain. Critically, the uncertain domain is newly introduced in our framework to adopt those instances whose domain labels cannot be predicted conﬁdently. Extensive experiments demonstrate that our approach achieved the state-of-the-art performance on OSL and G-ZSL benchmarks.

Diese Arbeit untersucht das Problem der Domänenaufteilung durch Segmentierung von Instanzen, die aus verschiedenen  probabilistischen Verteilungen stammen.

Dieser Beitrag befasst sich mit dem Problem der Neuheitserkennung beim Lernen mit offenen Mengen und beim verallgemeinerten Zero-Shot Lernen und schlägt eine mögliche Lösung vor.

Ein Ansatz zur Trennung von Bereichen, der auf Bootstrapping basiert, um Ähnlichkeitsschwellenwerte für bekannte Klassen zu ermitteln, gefolgt von einem Kolmogorov-Smirnoff-Test zur Verfeinerung der Bootstrapping-In-Distributionszonen.

Schlägt vor, einen neuen Bereich, den unsicheren Bereich, einzuführen, um die Unterscheidung zwischen gesehenen und ungesehenen Bereichen beim Open-Set und verallgemeinerten Zero-Shot Lernen besser zu handhaben.


Stochastic gradient descent (SGD) is widely believed to perform implicit regularization when used to train deep neural networks, but the precise manner in which this occurs has thus far been elusive. We prove that SGD minimizes an average potential over the posterior distribution of weights along with an entropic regularization term. This potential is however not the original loss function in general. So SGD does perform variational inference, but for a different loss than the one used to compute the gradients. Even more surprisingly, SGD does not even converge in the classical sense: we show that the most likely trajectories of SGD for deep networks do not behave like Brownian motion around critical points. Instead, they resemble closed loops with deterministic components. We prove that such out-of-equilibrium behavior is a consequence of highly non-isotropic gradient noise in SGD; the covariance matrix of mini-batch gradients for deep networks has a rank as small as 1% of its dimension. We provide extensive empirical validation of these claims, proven in the appendix.

SGD führt implizit eine Variationsinferenz durch; das Gradientenrauschen ist in hohem Maße nicht isotrop, so dass SGD nicht einmal zu kritischen Punkten des ursprünglichen Verlusts konvergiert.

Diese Arbeit bietet eine Variationsanalyse von SGD als Nicht-Gleichgewichtsprozess.

Dieser Beitrag diskutiert die regulierte Zielfunktion, die durch Standard-SGD im Kontext neuronaler Netze minimiert wird, und bietet eine Perspektive der Variationsinferenz unter Verwendung der Fokker-Planck-Gleichung.

Entwicklung einer Theorie zur Untersuchung der Auswirkungen von stochastischem Gradientenrauschen für SGD, insbesondere für tiefe neuronale Netzmodelle.


The current dominant paradigm for imitation learning relies on strong supervision of expert actions to learn both 'what' and 'how' to imitate. We pursue an alternative paradigm wherein an agent first explores the world without any expert supervision and then distills its experience into a goal-conditioned skill policy with a novel forward consistency loss. In our framework, the role of the expert is only to communicate the goals (i.e., what to imitate) during inference. The learned policy is then employed to mimic the expert (i.e., how to imitate) after seeing just a sequence of images demonstrating the desired task. Our method is 'zero-shot' in the sense that the agent never has access to expert actions during training or for the task demonstration at inference. We evaluate our zero-shot imitator in two real-world settings: complex rope manipulation with a Baxter robot and navigation in previously unseen office environments with a TurtleBot. Through further experiments in VizDoom simulation, we provide evidence that better mechanisms for exploration lead to learning a more capable policy which in turn improves end task performance. Videos, models, and more details are available at https://pathak22.github.io/zeroshot-imitation/.

Agenten können lernen, ausschließlich visuelle Demonstrationen (ohne Handlungen) zur Testzeit zu imitieren, nachdem sie zur Trainingszeit aus eigener Erfahrung und ohne jegliche Form der Überwachung gelernt haben.

In diesem Beitrag wird ein Ansatz für Zero-Shot visuelles Lernen durch das Erlernen parametrischer Fähigkeitsfunktionen vorgeschlagen.

Eine Arbeit über die Imitation einer Aufgabe, die nur während der Inferenz präsentiert wird, wobei das Lernen auf selbstüberwachte Weise erfolgt und der Agent während des Trainings verwandte, aber unterschiedliche Aufgaben erforscht.

Schlägt eine Methode zur Umgehung des Problems der kostspieligen Expertendemonstration vor, indem die zufällige Erkundung eines Agenten genutzt wird, um verallgemeinerbare Fähigkeiten zu erlernen, die ohne spezielles Vortraining angewendet werden können.


Distributional Semantics Models(DSM) derive word space from linguistic items
 in context. Meaning is obtained by defining a distance measure between vectors
 corresponding to lexical entities. Such vectors present several problems. This
 work concentrates on quality of word embeddings, improvement of word embedding
 vectors, applicability of a novel similarity metric used ‘on top’ of the
 word embeddings. In this paper we provide comparison between two methods
 for post process improvements to the baseline DSM vectors. The counter-fitting
 method which enforces antonymy and synonymy constraints into the Paragram
 vector space representations recently showed improvement in the vectors’ capability
 for judging semantic similarity. The second method is our novel RESM
 method applied to GloVe baseline vectors. By applying the hubness reduction
 method, implementing relational knowledge into the model by retrofitting synonyms
 and providing a new ranking similarity definition RESM that gives maximum
 weight to the top vector component values we equal the results for the ESL
 and TOEFL sets in comparison with our calculations using the Paragram and Paragram
 + Counter-fitting methods. For SIMLEX-999 gold standard since we cannot
 use the RESM the results using GloVe and PPDB are significantly worse compared
 to Paragram. Apparently, counter-fitting corrects hubness. The Paragram
 or our cosine retrofitting method are state-of-the-art results for the SIMLEX-999
 gold standard. They are 0.2 better for SIMLEX-999 than word2vec with sense
 de-conflation (that was announced to be state-of the-art method for less reliable
 gold standards). Apparently relational knowledge and counter-fitting is more important
 for judging semantic similarity than sense determination for words. It is to
 be mentioned, though that Paragram hyperparameters are fitted to SIMLEX-999
 results. The lesson is that many corrections to word embeddings are necessary
 and methods with more parameters and hyperparameters perform better.

Die Arbeit beschreibt ein Verfahren zur Verbesserung von Wortvektorraummodellen mit einer Bewertung von Paragram- und GloVe-Modellen für Ähnlichkeitsbenchmarks.

In diesem Beitrag wird ein neuer Algorithmus vorgeschlagen, der GloVe-Wortvektoren anpasst und dann eine nicht-euklidische Ähnlichkeitsfunktion zwischen ihnen verwendet.

Die Autoren stellen Beobachtungen zu den Schwächen der bestehenden Vektorraummodelle vor und nennen einen 6-stufigen Ansatz zur Verfeinerung bestehender Wortvektoren.


Recurrent neural networks have achieved excellent performance in many applications. However, on portable devices with limited resources, the models are often too large to deploy. For applications on the server with large scale concurrent requests, the latency during inference can also be very critical for costly computing resources. In this work, we address these problems by quantizing the network, both weights and activations, into multiple binary codes {-1,+1}. We formulate the quantization as an optimization problem. Under the key observation that once the quantization coefficients are fixed the binary codes can be derived efficiently by binary search tree, alternating minimization is then applied.   We test the quantization for two well-known RNNs, i.e., long short term memory (LSTM) and gated recurrent unit (GRU), on the language models. Compared with the full-precision counter part, by 2-bit quantization we can achieve ~16x memory saving and  ~6x real inference acceleration on CPUs, with only a reasonable loss in the accuracy. By 3-bit quantization, we can achieve almost no loss in the accuracy or even surpass the original model, with ~10.5x memory saving and ~3x real inference acceleration. Both results beat the exiting quantization works with large margins.   We extend our alternating quantization to image classification tasks. In both RNNs and feedforward neural networks, the method also achieves  excellent performance.

Wir schlagen eine neue Quantisierungsmethode vor und wenden sie zur Quantisierung von RNNs sowohl für die Kompression als auch für die Beschleunigung an.

In dieser Arbeit wird eine Multibit Quantisierungsmethode für rekurrente neuronale Netze vorgeschlagen.

Eine Technik zur Quantisierung von Gewichtsmatrizen neuronaler Netze und ein alternierendes Optimierungsverfahren zur Schätzung der Menge von k binären Vektoren und Koeffizienten, die den ursprünglichen Vektor am besten repräsentieren.


The goal of this paper is to demonstrate a method for tensorizing neural networks based upon an efficient way of approximating scale invariant quantum states, the Multi-scale Entanglement Renormalization Ansatz (MERA). We employ MERA as a replacement for linear layers in a neural network and test this implementation on the CIFAR-10 dataset. The proposed method outperforms factorization using tensor trains, providing greater compression for the same level of accuracy and greater accuracy for the same level of compression. We demonstrate MERA-layers with 3900 times fewer parameters and a reduction in accuracy of less than 1% compared to the equivalent fully connected layers.

Wir ersetzen die vollständig verknüpften Schichten eines neuronalen Netzes durch den Multiskalen-Verschränkungs-Renormalisierungssatz, eine Art Quantenoperation, die Korrelationen über große Entfernungen beschreibt.

In dem Beitrag schlagen die Autoren vor, die MERA-Tensorisierungstechnik zur Komprimierung neuronaler Netze zu verwenden.

Eine neue Parametrisierung linearer Zuordnungen für den Einsatz in neuronalen Netzen, die eine hierarchische Faktorisierung der linearen Zuordnung verwendet, die die Anzahl der Parameter reduziert und gleichzeitig die Modellierung relativ komplexer Wechselwirkungen ermöglicht.

Studien zur Komprimierung von Feed-Forward-Schichten unter Verwendung von Tensor-Zerlegungen niedrigen Ranges und Erforschung einer baumartigen Zerlegung.


Deep learning models have outperformed traditional methods in many fields such
 as natural language processing and computer vision. However, despite their
 tremendous success, the methods of designing optimal Convolutional Neural Networks
 (CNNs) are still based on heuristics or grid search. The resulting networks
 obtained using these techniques are often overparametrized with huge computational
 and memory requirements. This paper focuses on a structured, explainable
 approach towards optimal model design that maximizes accuracy while keeping
 computational costs tractable. We propose a single-shot analysis of a trained CNN
 that uses Principal Component Analysis (PCA) to determine the number of filters
 that are doing significant transformations per layer, without the need for retraining.
 It can be interpreted as identifying the dimensionality of the hypothesis space
 under consideration. The proposed technique also helps estimate an optimal number
 of layers by looking at the expansion of dimensions as the model gets deeper.
 This analysis can be used to design an optimal structure of a given network on
 a dataset, or help to adapt a predesigned network on a new dataset. We demonstrate
 these techniques by optimizing VGG and AlexNet networks on CIFAR-10,
 CIFAR-100 and ImageNet datasets.

Wir präsentieren eine Single-Shot Analyse eines trainierten neuronalen Netzes, um Redundanzen zu entfernen und die optimale Netzstruktur zu ermitteln.

In dieser Arbeit wird eine Reihe von Heuristiken zur Identifizierung einer guten Architektur für neuronale Netze vorgeschlagen, die auf der PCA der Aktivierungen der Einheiten über den Datensatz basieren.

In diesem Beitrag wird ein Rahmen für die Optimierung von Architekturen neuronaler Netze durch die Identifizierung redundanter Filter in verschiedenen Schichten vorgestellt.


Recent work has introduced attacks that extract the architecture information of deep neural networks (DNN), as this knowledge enhances an adversary’s capability to conduct attacks on black-box networks. This paper presents the first in-depth security analysis of DNN fingerprinting attacks that exploit cache side-channels.   First, we define the threat model for these attacks:  our adversary does not need the ability to query the victim model; instead, she runs a co-located process on the host machine victim ’s deep learning  (DL) system is running and passively monitors the accesses of the target functions in the shared framework.   Second, we introduce DeepRecon, an attack that reconstructs the architecture of the victim network by using the internal information extracted via Flush+Reload, a cache side-channel technique. Once the attacker observes function invocations that map directly to architecture attributes of the victim network, the attacker can reconstruct the victim’s entire network architecture.   In our evaluation, we demonstrate that an attacker can accurately reconstruct two complex networks (VGG19 and ResNet50) having only observed one forward propagation. Based on the extracted architecture attributes, we also demonstrate that an attacker can build a meta-model that accurately fingerprints the architecture and family of the pre-trained model in a transfer learning setting. From this meta-model,  we evaluate the importance of the observed attributes in the fingerprinting process. Third, we propose and evaluate new framework-level defense techniques that obfuscate our attacker’s observations. Our empirical security analysis represents a step toward understanding the DNNs’ vulnerability to cache side-channel attacks.

Wir führen die erste eingehende Sicherheitsanalyse von DNN-Fingerprinting Angriffen durch, die Cache Seitenkanäle ausnutzen, was einen Schritt zum Verständnis der Anfälligkeit von DNNs für Seitenkanalangriffe darstellt.

In diesem Beitrag wird das Problem des Fingerprints von neuronalen Netzwerkarchitekturen unter Verwendung von Cache-Seitenkanälen betrachtet und es werden Verteidigungsmaßnahmen gegen Sicherheit durch Unklarheit diskutiert.

In diesem Beitrag werden Cache-Seitenkanalangriffe durchgeführt, um Attribute eines Opfermodells zu extrahieren und auf dessen Architektur zu schließen. Außerdem wird gezeigt, dass sie eine nahezu perfekte Klassifizierungsgenauigkeit erreichen können.


Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although being a widely-adopted approach, using cross entropy as the primary objective exploits mostly the information from the ground-truth class for maximizing data likelihood, and largely ignores information from the complement (incorrect) classes. We argue that, in addition to the primary objective, training also using a complement objective that leverages information from the complement classes can be effective in improving model performance. This motivates us to study a new training paradigm that maximizes the likelihood of the ground-truth class while neutralizing the probabilities of the complement classes. We conduct extensive experiments on multiple tasks ranging from computer vision to natural language understanding. The experimental results confirm that, compared to the conventional training with just one primary objective, training also with the complement objective further improves the performance of the state-of-the-art models across all tasks. In addition to the accuracy improvement, we also show that models trained with both primary and complement objectives are more robust to single-step adversarial attacks.

Wir schlagen Complement Objective Training (COT) vor, ein neues Trainingsparadigma, das sowohl die primären als auch die komplementären Ziele optimiert, um die Parameter neuronaler Netze effektiv zu lernen.

Es wird erwogen, das Ziel der Cross-Entropy durch eine Maximierung des "Komplement"-Ziels zu ergänzen, das darauf abzielt, die vorhergesagten Wahrscheinlichkeiten von Klassen, die nicht der Grundwahrheit entsprechen, zu neutralisieren.

Die Autoren schlagen ein sekundäres Ziel für die Softmax-Minimierung vor, das auf der Auswertung der von den falschen Klassen gesammelten Informationen beruht und zu einem neuen Trainingsansatz führt.

Befasst sich mit dem Training neuronaler Netze für Klassifizierungs- oder Sequenzerstellungsaufgaben unter Verwendung von Cross-Entropie-Verlusten.


We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional parameters nor multiple forward passes nor input preprocessing nor out-of-distribution datasets. We show that our method performs comparably to more computationally expensive methods and outperforms baselines on our experiments from image recognition and sentiment analysis domains.

Unsicherheitsabschätzung in einem einzigen Vorwärtsdurchlauf ohne zusätzliche lernbare Parameter.

Eine neue Methode zur Berechnung von Output-Unsicherheitsschätzungen in DNNs für Klassifizierungsprobleme, die mit den modernsten Methoden zur Unsicherheitsschätzung übereinstimmt und diese bei Aufgaben zur Erkennung von Abweichungen von der Verteilung übertrifft.

Die Autoren stellen den gehemmte nSoftmax vor, eine Modifikation des Softmax durch Hinzufügen einer konstanten Aktivierung, die ein Maß für die Unsicherheit darstellt.


When deep learning is applied to sensitive data sets, many privacy-related implementation issues arise. These issues are especially evident in the healthcare, finance, law and government industries. Homomorphic encryption could allow a server to make inferences on inputs encrypted by a client, but to our best knowledge, there has been no complete implementation of common deep learning operations, for arbitrary model depths, using homomorphic encryption. This paper demonstrates a novel approach, efficiently implementing many deep learning functions with bootstrapped homomorphic encryption. As part of our implementation, we demonstrate Single and Multi-Layer Neural Networks, for the Wisconsin Breast Cancer dataset, as well as a Convolutional Neural Network for MNIST. Our results give promising directions for privacy-preserving representation learning, and the return of data control to users.

Wir haben ein funktionsreiches System für Deep Learning mit verschlüsselten Eingaben entwickelt, das verschlüsselte Ausgaben erzeugt und die Privatsphäre wahrt.

Ein Framework für private Deep Learning Modellinferenz unter Verwendung von FHE-Schemata, die schnelles Bootstrapping unterstützen und somit die Rechenzeit reduzieren können.

In dem Beitrag wird eine Möglichkeit vorgestellt, ein neuronales Netz mit Hilfe homomorpher Verschlüsselung sicher zu bewerten.


In this paper, we introduce a system called GamePad that can be used to explore the application of machine learning methods to theorem proving in the Coq proof assistant. Interactive theorem provers such as Coq enable users to construct machine-checkable proofs in a step-by-step manner. Hence, they provide an opportunity to explore theorem proving with human supervision. We use GamePad to synthesize proofs for a simple algebraic rewrite problem and train baseline models for a formalization of the Feit-Thompson theorem. We address position evaluation (i.e., predict the number of proof steps left) and tactic prediction (i.e., predict the next proof step) tasks, which arise naturally in tactic-based theorem proving.

Wir stellen ein System namens GamePad vor, um die Anwendung von Methoden des maschinellen Lernens auf das Theorembeweisen im Coq-Beweisassistenten zu untersuchen.

Dieser Artikel beschreibt ein System zur Anwendung von maschinellem Lernen auf interaktive Theorembeweise, konzentriert sich auf die Aufgaben der Taktikvorhersage und der Positionsbewertung und zeigt, dass ein neuronales Modell ein SVM bei beiden Aufgaben übertrifft.

Schlägt vor, dass Techniken des maschinellen Lernens bei der Erstellung von Beweisen im Theorembeweiser Coq eingesetzt werden.


Deep neural networks are usually huge, which significantly limits the deployment on low-end devices. In recent years, many
 weight-quantized models have  been proposed. They have small storage and fast inference, but training can still be time-consuming. This can be improved with distributed learning. To reduce the high communication cost due to worker-server synchronization, recently gradient quantization has also been proposed to train deep networks with full-precision weights.
 In this paper, we theoretically study how the combination of both weight and gradient quantization affects convergence.
 We show  that (i) weight-quantized models converge to an error related to the weight quantization resolution and weight dimension; (ii) quantizing gradients slows convergence by a factor related to the gradient quantization resolution and dimension; and (iii) clipping the gradient before quantization renders this factor dimension-free, thus allowing the use of fewer bits for gradient quantization. Empirical experiments confirm the theoretical convergence results, and demonstrate that quantized networks can speed up training and have comparable performance as full-precision networks.

In dieser Arbeit haben wir das effiziente Training von verlustbewussten gewichtsquantisierten Netzen mit quantisiertem Gradienten in einer verteilten Umgebung sowohl theoretisch als auch empirisch untersucht.

Diese Arbeit untersucht die Konvergenzeigenschaften der verlustbewussten Gewichtsquantisierung mit verschiedenen Gradientenpräzisionen in einer verteilten Umgebung und bietet eine Konvergenzanalyse für die Gewichtsquantisierung mit voll-präzisen, quantisierten, und quantisierten abgeschnittenen Gradienten.

Die Autoren schlagen eine Analyse der Auswirkungen der gleichzeitigen Quantisierung der Gewichte und Gradienten beim Training eines parametrisierten Modells in einer vollständig synchronisierten verteilten Umgebung vor.


Sequential learning, also called lifelong learning, studies the problem of learning tasks in a sequence with access restricted to only the data of the current task. In this paper we look at a scenario with fixed model capacity, and postulate that the learning process should not be selfish, i.e. it should account for future tasks to be added and thus leave enough capacity for them. To achieve Selfless Sequential Learning we study different regularization strategies and activation functions. We find that
 imposing sparsity at the level of the representation (i.e. neuron activations) is more beneficial for sequential learning than encouraging parameter sparsity. In particular, we propose a novel regularizer, that encourages representation sparsity by means of neural inhibition. It results in few active neurons which in turn leaves more free neurons to be utilized by upcoming tasks. As neural inhibition over an entire layer can be too drastic, especially for complex tasks requiring strong representations,
 our regularizer only inhibits other neurons in a local neighbourhood, inspired by lateral inhibition processes in the brain. We combine our novel regularizer with state-of-the-art lifelong learning methods that penalize changes to important previously learned parts of the network. We show that our new regularizer leads to increased sparsity which translates in consistent performance improvement on diverse datasets.

Eine Regularisierungsstrategie zur Verbesserung der Leistung des sequentiellen Lernens.

Ein neuartiger, auf Regularisierung basierender Ansatz für das sequentielle Lernproblem unter Verwendung eines Modells fester Größe, das zusätzliche Terme zum Verlust hinzufügt, das die Seltenheit der Darstellung fördert und katastrophales Vergessen bekämpft.

Diese Arbeit befasst sich mit dem Problem des katastrophalen Vergessens beim lebenslangen Lernen, indem es regulierte Lernstrategien vorschlägt.


A Synaptic Neural Network (SynaNN) consists of synapses and neurons. Inspired by the synapse research of neuroscience, we built a synapse model with a nonlinear synapse function of excitatory and inhibitory channel probabilities. Introduced the concept of surprisal space and constructed a commutative diagram, we proved that the inhibitory probability function -log(1-exp(-x)) in surprisal space is the topologically conjugate function of the inhibitory complementary probability 1-x in probability space. Furthermore, we found that the derivative of the synapse over the parameter in the surprisal space is equal to the negative Bose-Einstein distribution. In addition, we constructed a fully connected synapse graph (tensor) as a synapse block of a synaptic neural network. Moreover, we proved the gradient formula of a cross-entropy loss function over parameters, so synapse learning can work with the gradient descent and backpropagation algorithms. In the proof-of-concept experiment, we performed an MNIST training and testing on the MLP model with synapse network as hidden layers.

Ein synaptisches neuronales Netzwerk mit Synapsengraphen und Lernen, das die Eigenschaft der topologischen Konjugation und der Bose-Einstein-Verteilung im Surprisal-Raum aufweist.

Die Autoren schlagen ein hybrides neuronales Netzwerk vor, das aus einem Synapsengraphen besteht, der in ein standardmäßiges neuronales Netzwerk eingebettet werden kann.

stellt ein biologisch inspiriertes neuronales Netzmodell vor, das auf den erregenden und hemmenden Ionenkanälen in den Membranen echter Zellen basiert.


Many types of relations in physical, biological, social and information systems can be modeled as homogeneous or heterogeneous concept graphs. Hence, learning from and with graph embeddings has drawn a great deal of research interest recently, but only ad hoc solutions have been obtained this far. In this paper, we conjecture that the one-shot supervised learning mechanism is a bottleneck in improving the performance of the graph embedding learning algorithms, and propose to extend this by introducing a multi-shot unsupervised learning framework. Empirical results on several real-world data set show that the proposed model consistently and significantly outperforms existing state-of-the-art approaches on knowledge base completion and graph based multi-label classification tasks.

Verallgemeinerte Modelle zur Grapheneinbettung.

Ein verallgemeinerter Ansatz zur Einbettung von Wissensgraphen, der die Einbettungen auf der Grundlage von drei verschiedenen gleichzeitigen Zielen erlernt und gleich gut oder sogar besser abschneidet als die bestehenden State-of-the-Art-Ansätze.

Bewältigt die Aufgabe, Einbettungen von multirelationalen Graphen mit Hilfe eines neuronalen Netzes zu lernen.

Schlägt eine neue Methode, GEN, zur Berechnung von Einbettungen von Multibeziehungsgraphen vor, insbesondere, dass so genannte E-Zellen und R-Zellen Anfragen der Form (h,r,?),(?r,t) und (h,?,t) beantworten können.


We introduce and study minimax curriculum learning (MCL), a new method for adaptively selecting a sequence of training subsets for a succession of stages in machine learning. The subsets are encouraged to be small and diverse early on, and then larger, harder, and allowably more homogeneous in later stages. At each stage, model weights and training sets are chosen by solving a joint continuous-discrete minimax optimization, whose objective is composed of a continuous loss (reflecting training set hardness) and a discrete submodular promoter of diversity for the chosen subset. MCL repeatedly solves a sequence of such optimizations with a schedule of increasing training set size and decreasing pressure on diversity encouragement. We reduce MCL to the minimization of a surrogate function handled by submodular maximization and continuous gradient methods. We show that MCL achieves better performance and, with a clustering trick, uses fewer labeled samples for both shallow and deep models while achieving the same performance. Our method involves repeatedly solving constrained submodular maximization of an only slowly varying function on the same ground set. Therefore, we develop a heuristic method that utilizes the previous submodular maximization solution as a warm start for the current submodular maximization process to reduce computation while still yielding a guarantee.

Minimax Curriculum Learning ist eine maschinelle Lernmethode, bei der die wünschenswerte Härte erhöht und die Vielfalt planmäßig reduziert wird.

Ein Curriculum Lernansatz, der eine submodulare Mengenfunktion verwendet, die die Vielfalt der während des Trainings ausgewählten Beispiele erfasst.

In dem Beitrag wird das MiniMax Curriculum Lernen als ein Ansatz für das adaptive Training von Modellen durch die Bereitstellung verschiedener Teilmengen von Daten vorgestellt.


Progress in probabilistic generative models has accelerated, developing richer models with neural architectures, implicit densities, and with scalable algorithms for their Bayesian inference. However, there has been limited progress in models that capture causal relationships, for example, how individual genetic factors cause major human diseases. In this work, we focus on two challenges in particular: How do we build richer causal models, which can capture highly nonlinear relationships and interactions between multiple causes? How do we adjust for latent confounders, which are variables influencing both cause and effect and which prevent learning of causal relationships? To address these challenges, we synthesize ideas from causality and modern probabilistic modeling. For the first, we describe implicit causal models, a class of causal models that leverages neural architectures with an implicit density. For the second, we describe an implicit causal model that adjusts for confounders by sharing strength across examples. In experiments, we scale Bayesian inference on up to a billion genetic measurements. We achieve state of the art accuracy for identifying causal factors: we significantly outperform the second best result by an absolute difference of 15-45.3%.

Implizite Modelle, angewandt auf Kausalität und Genetik.

Die Autoren schlagen vor, das implizite Modell zu verwenden, um das Genom-weite Assoziationsproblem anzugehen.

In diesem Beitrag werden Lösungen für die Probleme bei genomweiten Assoziationsstudien vorgeschlagen, die durch die Populationsstruktur und das potenzielle Vorhandensein nichtlinearer Wechselwirkungen zwischen verschiedenen Teilen des Genoms entstehen, und es werden Brücken zwischen statistischer Genetik und ML geschlagen.

Vorstellung eines nichtlinearen generativen Modells für GWAS, das die Populationsstruktur modelliert, wobei Nichtlinearitäten mit Hilfe von neuronalen Netzen als nichtlineare Funktionsapproximatoren modelliert werden und die Inferenz mit Hilfe von likelihood-freier Variationsinferenz durchgeführt wird.


Few-shot learning trains image classifiers over datasets with few examples per category.
 It poses challenges for the optimization algorithms, which typically require many examples to fine-tune the model parameters for new categories.
 Distance-learning-based approaches avoid the optimization issue by embedding the images into a metric space and applying the nearest neighbor classifier for new categories. In this paper, we propose to exploit the object-level relation to learn the image relation feature, which is converted into a distance directly.
 For a new category, even though its images are not seen by the model, some objects may appear in the training images. Hence, object-level relation is useful for inferring the relation of images from unseen categories. Consequently, our model generalizes well for new categories without fine-tuning.
 Experimental results on benchmark datasets show that our approach outperforms state-of-the-art methods.

Few-Shot Lernen durch Ausnutzung der Beziehung auf Objektebene, um die Beziehung auf Bildebene zu lernen (Ähnlichkeit).

Diese Arbeit befasst sich mit dem Problem des Few-Shot Lernens, indem es einen auf Einbettung basierenden Ansatz vorschlägt, der lernt, Merkmale auf Objektebene zwischen Support- und Query-Set-Beispielen zu vergleichen.

Schlägt eine Few-Shot Lernmethode vor, die die Beziehung zwischen verschiedenen Bildern auf Objektebene auf der Grundlage der Suche nach nahen Nachbarn ausnutzt und Merkmalskarten von zwei Eingabebildern zu einer Merkmalszuordnung zusammenfügt.


Word embeddings are widely used in machine learning based natural language processing systems. It is common to use pre-trained word embeddings which provide benefits such as reduced training time and improved overall performance. There has been a recent interest in applying natural language processing techniques to programming languages. However, none of this recent work uses pre-trained embeddings on code tokens. Using extreme summarization as the downstream task, we show that using pre-trained embeddings on code tokens provides the same benefits as it does to natural languages, achieving: over 1.9x speedup, 5\% improvement in test loss, 4\% improvement in F1 scores, and resistance to over-fitting. We also show that the choice of language used for the embeddings does not have to match that of the task to achieve these benefits and that even embeddings pre-trained on human languages provide these benefits to programming languages.

Forscher, die Techniken zur Verarbeitung natürlicher Sprache auf Quellcode anwenden, verwenden keine Form von vortrainierten Einbettungen, wir zeigen, dass sie dies tun sollten.

In diesem Beitrag wird untersucht, ob das Pretraining von Worteinbettungen für Programmiersprachencode mit Hilfe von NLP-ähnlichen Sprachmodellen einen Einfluss auf die Aufgabe der Zusammenfassung von extremem Code hat.

Diese Arbeit zeigt, wie das Pretraining von Wortvektoren anhand von Code-Korpussen zu Repräsentationen führt, die besser geeignet sind als zufällig initialisierte und trainierte Repräsentationen für die Vorhersage von Funktions-/Methodennamen.


Recently, Approximate Policy Iteration (API) algorithms have achieved super-human proficiency in two-player zero-sum games such as Go, Chess, and Shogi without human data. These API algorithms iterate between two policies: a slow policy (tree search), and a fast policy (a neural network). In these two-player games, a reward is always received at the end of the game. However, the Rubik’s Cube has only a single solved state, and episodes are not guaranteed to terminate. This poses a major problem for these API algorithms since they rely on the reward received at the end of the game. We introduce Autodidactic Iteration: an API algorithm that overcomes the problem of sparse rewards by training on a distribution of states that allows the reward to propagate from the goal state to states farther away. Autodidactic Iteration is able to learn how to solve the Rubik’s Cube and the 15-puzzle without relying on human data. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves — less than or equal to solvers that employ human domain knowledge.

Wir lösen den Rubik's Cube mit reinem Reinforcement Learning.

Lösung zum Lösen des Rubik Cube durch Reinforcement Learning (RL) mit Monte Carlo Baumsuche (MCTS) durch autodidaktische Iteration.

In dieser Arbeit wird der Rubik's Cube mit Hilfe einer annähernden Iterationsmethode, der so genannten autodidaktischen Iteration, gelöst, wobei das Problem der spärlichen Belohnungen durch die Schaffung eines eigenen Belohnungssystems überwunden wird.

Einführung eines tiefen RL-Algorithmus zur Lösung des Rubik-Würfels, der den riesigen Zustandsraum und die sehr spärliche Belohnung des Rubik-Würfels bewältigt.


Answering compositional questions requiring multi-step reasoning is challenging for current models. We introduce an end-to-end differentiable model for interpreting questions, which is inspired by formal approaches to semantics. Each span of text is represented by a denotation in a knowledge graph, together with a vector that captures ungrounded aspects of meaning. Learned composition modules recursively combine constituents, culminating in a grounding for the complete sentence which is an answer to the question. For example, to interpret ‘not green’, the model will represent ‘green’ as a set of entities, ‘not’ as a trainable ungrounded vector, and then use this vector to parametrize a composition function to perform a complement operation. For each sentence, we build a parse chart subsuming all possible parses, allowing the model to jointly learn both the composition operators and output structure by gradient descent. We show the model can learn to represent a variety of challenging semantic operators, such as quantifiers, negation, disjunctions and composed relations on a synthetic question answering task. The model also generalizes well to longer sentences than seen in its training data, in contrast to LSTM and RelNet baselines. We will release our code.

Wir beschreiben ein differenzierbares Ende-zu-Ende Modell für QA, das lernt, Textabschnitte in der Frage als Denotationen im Wissensgraphen darzustellen, indem es sowohl neuronale Module für die Komposition als auch die syntaktische Struktur des Satzes lernt.

In diesem Beitrag wird ein Modell für die Beantwortung visueller Fragen vorgestellt, das sowohl Parameter als auch Strukturvorherseher für ein modulares neuronales Netz erlernen kann, ohne überwachte Strukturen oder Unterstützung durch einen syntaktischen Parser.

Schlägt vor, ein Modell zur Beantwortung von Fragen nur aus Antworten und einer KB zu trainieren, indem latente Bäume gelernt werden, die die Syntax erfassen und die Semantik von Wörtern lernen.


Deep learning software demands reliability and performance. However, many of the existing deep learning frameworks are software libraries that act as an unsafe DSL in Python and a computation graph interpreter. We present DLVM, a design and implementation of a compiler infrastructure with a linear algebra intermediate representation, algorithmic differentiation by adjoint code generation, domain- specific optimizations and a code generator targeting GPU via LLVM. Designed as a modern compiler infrastructure inspired by LLVM, DLVM is more modular and more generic than existing deep learning compiler frameworks, and supports tensor DSLs with high expressivity. With our prototypical staged DSL embedded in Swift, we argue that the DLVM system enables a form of modular, safe and performant frameworks for deep learning.

Wir stellen eine neuartige Compiler-Infrastruktur vor, die die Unzulänglichkeiten bestehender Deep-Learning-Frameworks behebt.

Vorschlag zur Umstellung von Ad-hoc Code Generierung in Deep Learning Engines auf bewährte Verfahren für Compiler und Sprachen.

In diesem Beitrag wird ein Compiler-Framework vorgestellt, das die Definition von domänenspezifischen Sprachen für Deep Learning Systeme ermöglicht und Kompilierungsphasen definiert, die Standardoptimierungen und spezielle Optimierungen für neuronale Netze nutzen können.

In diesem Beitrag wird ein DLVM vorgestellt, das die Vorteile eines Tensor-Compilers nutzt.


In this work, we focus on the problem of grounding language by training an agent
 to follow a set of natural language instructions and navigate to a target object
 in a 2D grid environment. The agent receives visual information through raw
 pixels and a natural language instruction telling what task needs to be achieved.
 Other than these two sources of information, our model does not have any prior
 information of both the visual and textual modalities and is end-to-end trainable.
 We develop an attention mechanism for multi-modal fusion of visual and textual
 modalities that allows the agent to learn to complete the navigation tasks and also
 achieve language grounding. Our experimental results show that our attention
 mechanism outperforms the existing multi-modal fusion mechanisms proposed in
 order to solve the above mentioned navigation task. We demonstrate through the
 visualization of attention weights that our model learns to correlate attributes of
 the object referred in the instruction with visual representations and also show
 that the learnt textual representations are semantically meaningful as they follow
 vector arithmetic and are also consistent enough to induce translation between instructions
 in different natural languages. We also show that our model generalizes
 effectively to unseen scenarios and exhibit zero-shot generalization capabilities.
 In order to simulate the above described challenges, we introduce a new 2D environment
 for an agent to jointly learn visual and textual modalities

Aufmerksamkeitsbasierte Architektur für das Erlernen von Sprache durch Reinforcement Learning in einer neuen anpassbaren 2D-Gitterumgebung.

Der Beitrag befasst sich mit dem Problem der Navigation anhand einer Anweisung und schlägt einen Ansatz vor, der textliche und visuelle Informationen über einen Aufmerksamkeitsmechanismus kombiniert.

In diesem Beitrag wird das Problem der Befolgung von Anweisungen in natürlicher Sprache bei einer Ich-Perspektive einer a priori unbekannten Umgebung betrachtet und eine Methode der neuronalen Architektur vorgeschlagen.

Untersucht das Problem der Navigation zu einem Zielobjekt in einer 2D-Gitterumgebung, indem man einer gegebenen natürlichsprachlichen Beschreibung folgt und visuelle Informationen als rohe Pixel erhält.


 Current end-to-end machine reading and question answering (Q\&A) models are primarily based on recurrent neural networks (RNNs) with attention. Despite their success, these models are often slow for both training and inference due to the sequential nature of RNNs. We propose a new Q\&A architecture called QANet, which does not require recurrent networks:  Its encoder consists exclusively of convolution and self-attention, where convolution models local interactions and self-attention models global interactions. On the SQuAD dataset, our model is 3x to 13x faster in training and 4x to 9x faster in inference, while achieving equivalent accuracy to recurrent models. The speed-up gain allows us to train the model with much more data. We hence combine our model with data generated by backtranslation from a neural machine translation model.
 On the SQuAD dataset, our single model, trained with augmented data, achieves 84.6 F1 score on the test set, which is significantly better than the best published F1 score of 81.8.

Eine einfache Architektur, die aus Convolutions und Aufmerksamkeit besteht, erzielt Ergebnisse, die mit den am besten dokumentierten rekurrenten Modellen vergleichbar sind.

Ein schnelles, leistungsstarkes Paraphrasierungsverfahren auf der Grundlage von Datenerweiterung und ein nicht-rekurrentes Leseverständnismodell, das nur Convolutions und Aufmerksamkeit verwendet.

In dieser Arbeit wird vorgeschlagen, CNNs und Selbstaufmerksamkeitsmodule anstelle von LSTMs einzusetzen und das RC-Modelltraining mit Passagenparaphrasen zu erweitern, die von einem neuronalen Paraphrasierungsmodell generiert werden, um die RC-Leistung zu verbessern.

In diesem Beitrag wird ein Modell für das Leseverständnis vorgestellt, das Convolutions und Aufmerksamkeit verwendet, und es wird vorgeschlagen, zusätzliche Trainingsdaten durch Paraphrasierung auf der Grundlage von standardmäßiger neuronaler Maschinenübersetzung zu ergänzen.


Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective.

 In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.

Wir stellen Spherical CNNs vor, ein Convolutional Network für sphärische Signale, und wenden es auf die Erkennung von 3D-Modellen und die Regression molekularer Energie an.

Die Arbeit schlägt einen Rahmen für die Konstruktion von sphärischen Convolutional Networks vor, der auf einer neuartigen Synthese mehrerer bestehender Konzepte beruht.

In diesem Beitrag geht es darum, wie Convolutional Neural Networks so erweitert werden können, dass sie über eine eingebaute sphärische Invarianz verfügen, und es werden Werkzeuge aus der nicht-abelschen harmonischen Analyse verwendet, um dieses Ziel zu erreichen.

Die Autoren entwickeln ein neuartiges Schema zur Darstellung sphärischer Daten von Grund auf.


We propose a novel method that makes use of deep neural networks and gradient decent to perform automated design on complex real world engineering tasks. Our approach works by training a neural network to mimic the fitness function of a design optimization task and then, using the differential nature of the neural network, perform gradient decent to maximize the fitness. We demonstrate this methods effectiveness by designing an optimized heat sink and both 2D and 3D airfoils that maximize the lift drag ratio under steady state flow conditions. We highlight that our method has two distinct benefits over other automated design approaches. First, evaluating the neural networks prediction of fitness can be orders of magnitude faster then simulating the system of interest. Second, using gradient decent allows the design space to be searched much more efficiently then other gradient free methods. These two strengths work together to overcome some of the current shortcomings of automated design.

Ein Verfahren für die automatisierte Planung von realen Objekten wie Kühlkörpern und Tragflächenprofilen, das neuronale Netze und Gradientenabstieg einsetzt.

Neuronales Netz (Parametrisierung und Vorhersage) und Gradientenabstieg (Backpropogation) für die automatische Planung von technischen Aufgaben.

In diesem Beitrag wird die Verwendung eines tiefen Netzwerks vorgestellt, um das Verhalten eines komplexen physikalischen Systems zu approximieren und dann optimale Geräte zu entwerfen, indem dieses Netzwerk im Hinblick auf seine Eingaben optimiert wird.


Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whether turning the adversarial min-max problem into an optimization problem by replacing the maximization part with its dual improves the quality of the resulting alignment and explore its connections to Maximum Mean Discrepancy. Our empirical results suggest that using the dual formulation for the restricted family of linear discriminators results in a more stable convergence to a desirable solution when compared with the performance of a primal min-max GAN-like objective and an MMD objective under the same restrictions. We test our hypothesis on the problem of aligning two synthetic point clouds on a plane and on a real-image domain adaptation problem on digits. In both cases, the dual formulation yields an iterative procedure that gives more stable and monotonic improvement over time.

Wir schlagen eine duale Version der logistischen adversen Distanz für den Merkmalsabgleich vor und zeigen, dass sie stabilere Gradientenschritt-Iterationen liefert als das Min-Max-Ziel.

Die Arbeit befasst sich mit der Fixierung von GANs auf der Berechnungsebene.

Diese Arbeit untersucht eine duale Formulierung eines adversarial Verlusts, die auf einer Obergrenze des logistischen Verlusts basiert, und verwandelt das Standard-Min-Max-Problem des adversarial Trainings in ein einziges Minimierungsproblem.

Schlägt vor, das GAN-Sattelpunktziel (für einen logistischen Regressionsdiskriminator) als Minimierungsproblem durch Dualisierung des Maximum-Likelihood-Ziels für die regularisierte logistische Regression neu zu formulieren.


  There are many applications scenarios for which the computational
  performance and memory footprint of the prediction phase of Deep
  Neural Networks (DNNs) need to be optimized. Binary Deep Neural
  Networks (BDNNs) have been shown to be an effective way of achieving
  this objective. In this paper, we show how Convolutional Neural
  Networks (CNNs) can be implemented using binary
  representations. Espresso is a compact, yet powerful
  library written in C/CUDA that features all the functionalities
  required for the forward propagation of CNNs, in a binary file less
  than 400KB, without any external dependencies. Although it is mainly
  designed to take advantage of massive GPU parallelism, Espresso also
  provides an equivalent CPU implementation for CNNs. Espresso
  provides special convolutional and dense layers for BCNNs,
  leveraging bit-packing and bit-wise computations
  for efficient execution. These techniques provide a speed-up of
  matrix-multiplication routines, and at the same time, reduce memory
  usage when storing parameters and activations. We experimentally
  show that Espresso is significantly faster than existing
  implementations of optimized binary neural networks (~ 2
  orders of magnitude). Espresso is released under the Apache 2.0
  license and is available at http://github.com/organization/project.

Implementierung von binären neuronalen Netzen auf dem neuesten Stand der Rechenleistung.

Die Arbeit stellt eine in C/CUDA geschriebene Bibliothek vor, die alle für die Forward Propagation von BCNNs erforderlichen Funktionen enthält.

Dieser Beitrag baut auf Binary-NET auf und erweitert es auf CNN-Architekturen, bietet Optimierungen, die die Geschwindigkeit des Vorwärtspasses verbessern, und stellt optimierten Code für Binary CNN bereit.


Optimal selection of a subset of items from a given set is a hard problem that requires combinatorial optimization. In this paper, we propose a subset selection algorithm that is trainable with gradient based methods yet achieves near optimal performance via submodular optimization. We focus on the task of identifying a relevant set of sentences for claim verification in the context of the FEVER task. Conventional methods for this task look at sentences on their individual merit and thus do not optimize the informativeness of sentences as a set. We show that our proposed method which builds on the idea of unfolding a greedy algorithm into a computational graph allows both interpretability and gradient based training. The proposed differentiable greedy network (DGN) outperforms discrete optimization algorithms as well as other baseline methods in terms of precision and recall.

Wir schlagen einen Algorithmus zur Auswahl von Teilmengen vor, der mit gradientenbasierten Methoden trainiert werden kann und durch submodulare Optimierung eine nahezu optimale Leistung erzielt.

Vorschlagen eines auf einem neuronalen Netz basierenden Modells, das eine submodulare Funktion integriert, indem es eine gradientenbasierte Optimierungstechnik mit einem submodularen Rahmen namens 'Differentiable Greedy Network' (DGN) kombiniert.

Vorschlagen eines neuronalen Netz, das eine Teilmenge von Elementen auswählt (z. B. die Auswahl von k Sätzen, die sich am meisten auf eine Behauptung beziehen, aus einer Menge von abgerufenen Dokumenten).


The joint optimization of representation learning and clustering in the embedding space has experienced a breakthrough in recent years. In spite of the advance, clustering with representation learning has been limited to flat-level categories, which oftentimes involves cohesive clustering with a focus on instance relations. To overcome the limitations of flat clustering, we introduce hierarchically clustered representation learning (HCRL), which simultaneously optimizes representation learning and hierarchical clustering in the embedding space. Specifically, we place a nonparametric Bayesian prior on embeddings to handle dynamic mixture hierarchies under the variational autoencoder framework, and to adopt the generative process of a hierarchical-versioned Gaussian mixture model. Compared with a few prior works focusing on unifying representation learning and hierarchical clustering, HCRL is the first model to consider a generation of deep embeddings from every component of the hierarchy, not just leaf components. This generation process enables more meaningful separations and mergers of clusters via branches in a hierarchy. In addition to obtaining hierarchically clustered embeddings, we can reconstruct data by the various abstraction levels, infer the intrinsic hierarchical structure, and learn the level-proportion features. We conducted evaluations with image and text domains, and our quantitative analyses showed competent likelihoods and the best accuracies compared with the baselines.

Wir führen das hierarchisch geclusterte Repräsentationslernen (HCRL) ein, das gleichzeitig das Repräsentationslernen und das hierarchische Clustering im Einbettungsraum optimiert.

In dem Beitrag wird vorgeschlagen, das verschachtelte CRP als Clustermodell und nicht als Themenmodell zu verwenden.

stellt eine neuartige hierarchische Clustering-Methode über einem Einbettungsraum vor, bei der sowohl der Einbettungsraum als auch das hierarchische Clustering gleichzeitig erlernt werden.


We introduce a novel geometric perspective and unsupervised model augmentation framework for transforming traditional deep (convolutional) neural networks into adversarially robust classifiers. Class-conditional probability densities based on Bayesian nonparametric mixtures of factor analyzers (BNP-MFA) over the input space are used to design soft decision labels for feature to label isometry. Classconditional distributions over features are also learned using BNP-MFA to develop plug-in maximum a posterior (MAP) classifiers to replace the traditional multinomial logistic softmax classification layers. This novel unsupervised augmented framework, which we call geometrically robust networks (GRN), is applied to CIFAR-10, CIFAR-100, and to Radio-ML (a time series dataset for radio modulation recognition). We demonstrate the robustness of GRN models to adversarial attacks from fast gradient sign method, Carlini-Wagner, and projected gradient descent.

Wir entwickeln ein statistisch-geometrisches, unüberwachtes Lernverfahren für tiefe neuronale Netze, um sie robust gegen adversarial Angriffe zu machen.

Umwandlung traditioneller tiefer neuronaler Netze in robuste adversarial Kalssifizierer unter Verwendung von GRNs.

Vorschlagen einer Verteidigung, die auf klassenbedingten Merkmalsverteilungen basiert, um tiefe neuronale Netze in robuste Klassifizierer zu verwandeln.


Reinforcement learning in environments with large state-action spaces is challenging, as exploration can be highly inefficient. Even if the dynamics are simple, the optimal policy can be combinatorially hard to discover. In this work, we propose a hierarchical approach to structured exploration to improve the sample efficiency of on-policy exploration in large state-action spaces. The key idea is to model a stochastic policy as a hierarchical latent variable model, which can learn low-dimensional structure in the state-action space, and to define exploration by sampling from the low-dimensional latent space. This approach enables lower sample complexity, while preserving policy expressivity. In order to make learning tractable, we derive a joint learning and exploration strategy by combining hierarchical variational inference with actor-critic learning. The benefits of our learning approach are that 1) it is principled, 2) simple to implement, 3) easily scalable to settings with many actions and 4) easily composable with existing deep learning approaches. We demonstrate the effectiveness of our approach on learning a deep centralized multi-agent policy, as multi-agent environments naturally have an exponentially large state-action space. In this setting, the latent hierarchy implements a form of multi-agent coordination during exploration and execution (MACE). We demonstrate empirically that MACE can more efficiently learn optimal policies in challenging multi-agent games with a large number (~20) of agents, compared to conventional baselines. Moreover, we show that our hierarchical structure leads to meaningful agent coordination.

Effizienteres tiefes Reinforcement Learning in großen Zustands-Aktionsräumen durch strukturierte Exploration mit tiefen hierarchischen Richtlinien.

Eine Methode zur Koordinierung des Agentenverhaltens unter Verwendung von Strategien, die eine gemeinsame latente Struktur haben, eine Methode zur variablen Optimierung der Strategien, um die koordinierten Strategien zu optimieren, und eine Ableitung der variablen, hierarchischen Aktualisierung der Autoren.

Diese Arbeit schlägt eine algorithmische Innovation vor, die aus hierarchischen latenten Variablen für die koordinierte Exploration in Multi-Agenten Umgebungen besteht.


Much attention has been devoted recently to the generalization puzzle in deep learning: large, deep networks can generalize well, but existing theories bounding generalization error are exceedingly loose, and thus cannot explain this striking performance. Furthermore, a major hope is that knowledge may transfer across tasks, so that multi-task learning can improve generalization on individual tasks. However we lack analytic theories that can quantitatively predict how the degree of knowledge transfer depends on the relationship between the tasks. We develop an analytic theory of the nonlinear dynamics of generalization in deep linear networks, both within and across tasks. In particular, our theory provides analytic solutions to the training and testing error of deep networks as a function of training time, number of examples, network size and initialization, and the task structure and SNR. Our theory reveals that deep networks progressively learn the most important task structure first, so that generalization error at the early stopping time primarily depends on task structure and is independent of network size. This suggests any tight bound on generalization error must take into account task structure, and explains observations about real data being learned faster than random data. Intriguingly our theory also reveals the existence of a learning algorithm that proveably out-performs neural network training through gradient descent. Finally, for transfer learning, our theory reveals that knowledge transfer depends sensitively, but computably, on the SNRs and input feature alignments of pairs of tasks.

Wir bieten viele Einblicke in die Generalisierung neuronaler Netze aus dem theoretisch nachvollziehbaren linearen Fall.

Die Autoren untersuchen ein einfaches Modell linearer Netzwerke zum Verständnis von Generalisierung und Transferlernen.


We conduct a mathematical analysis on the Batch normalization (BN) effect on gradient backpropagation in residual network training in this work, which is believed to play a critical role in addressing the gradient vanishing/explosion problem. Specifically, by analyzing the mean and variance behavior of the input and the gradient in the forward and backward passes through the BN and residual branches, respectively, we show that they work together to confine the gradient variance to a certain range across residual blocks in backpropagation. As a result, the gradient vanishing/explosion problem is avoided. Furthermore, we use the same analysis to discuss the tradeoff between depth and width of a residual network and demonstrate that shallower yet wider resnets have stronger learning performance than deeper yet thinner resnets.

Durch die Batch-Normalisierung bleibt die Gradientenvarianz während des gesamten Trainings erhalten, wodurch die Optimierung stabilisiert wird.

In dieser Arbeit wurde die Auswirkung der Batch-Normalisierung auf die Gradienten Backpropagation in residualen Netzen analysiert.


To study how mental object representations are related to behavior, we estimated sparse, non-negative representations of objects using human behavioral judgments on images representative of 1,854 object categories. These representations predicted a latent similarity structure between objects, which captured most of the explainable variance in human behavioral judgments. Individual dimensions in the low-dimensional embedding were found to be highly reproducible and interpretable as conveying degrees of taxonomic membership, functionality, and perceptual attributes. We further demonstrated the predictive power of the embeddings for explaining other forms of human behavior, including categorization, typicality judgments, and feature ratings, suggesting that the dimensions reflect human conceptual representations of objects beyond the specific task.

Menschliche Verhaltensbeurteilungen werden verwendet, um spärliche und interpretierbare Darstellungen von Objekten zu erhalten, die sich auf andere Aufgaben übertragen lassen.

Dieser Beitrag beschreibt ein groß angelegtes Experiment zu menschlichen Objekt-/Semantikrepräsentationen und ein Modell solcher Repräsentationen.

In diesem Beitrag wird ein neues Repräsentationssystem für Objektrepräsentationen entwickelt, das auf der Grundlage von Daten trainiert wird, die aus menschlichen Beurteilungen von Bildern gewonnen wurden.

Ein neuer Ansatz zum Erlernen eines spärlichen, positiven, interpretierbaren semantischen Raums, der die menschlichen Ähnlichkeitsurteile maximiert, indem er so trainiert wird, dass die Vorhersage menschlicher Ähnlichkeitsurteile speziell maximiert wird.


We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering.

 We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, natural language reformulations of an initial question and aggregates the returned evidence to yield the best answer.

 The reformulation system is trained end-to-end to maximize answer quality using policy gradient. We evaluate on SearchQA, a dataset of complex questions extracted from Jeopardy!. The agent outperforms a state-of-the-art base model, playing the role of the environment, and other benchmarks.

We also analyze the language that the agent has learned while interacting with the question answering system. We find that successful question reformulations look quite different from natural language paraphrases. The agent is able to discover non-trivial reformulation strategies that resemble classic information retrieval techniques such as term re-weighting (tf-idf) and stemming.

Wir schlagen einen Agenten vor, der zwischen dem Benutzer und einem Black-Box-System zur Beantwortung von Fragen sitzt und lernt, Fragen so umzuformulieren, dass die bestmöglichen Antworten herauskommen.

In diesem Beitrag wird eine aktive Beantwortung von Fragen durch einen Ansatz des verstärkten Lernens vorgeschlagen, der lernt, Fragen so umzuformulieren, dass sie die bestmöglichen Antworten liefern.

Beschreibt anschaulich, wie die Forscher zwei Modelle für die Umformulierung von Fragen und die Auswahl von Antworten während der Beantwortung von Fragen entwickelt und aktiv trainiert haben.


Most deep latent factor models choose simple priors for simplicity, tractability
 or not knowing what prior to use. Recent studies show that the choice of
 the prior may have a profound effect on the expressiveness of the model,
 especially when its generative network has limited capacity. In this paper, we propose to learn a proper prior from data for adversarial autoencoders
 (AAEs). We introduce the notion of code generators to transform manually selected
 simple priors into ones that can better characterize the data distribution. Experimental results show that the proposed model can generate better image quality and learn better disentangled representations than
 AAEs in both supervised and unsupervised settings. Lastly, we present its
 ability to do cross-domain translation in a  text-to-image synthesis task.

Lernen von Prioritäten für adversarial Autoencoder.

Schlägt eine einfache Erweiterung der adversarial Autoencoder für die bedingte Bilderzeugung vor.

Konzentriert sich auf adversarial Autoencoder und führt ein Codegenerator-Netzwerk ein, um einen einfachen Prior in einen umzuwandeln, der zusammen mit dem Generator die Datenverteilung besser abbilden kann.


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

Generierung von Text unter Verwendung von Satzeinbettungen aus Skip-Thought Vektoren mit Hilfe von generativen adversarial Netzen.

Beschreibt die Anwendung von generativen adversarial Netzen zur Modellierung von Textdaten mit Hilfe von Skigedankenvektoren und Experimenten mit verschiedenen GANs für zwei unterschiedliche Datensätze.


The novel \emph{Unbiased Online Recurrent Optimization} (UORO) algorithm allows for online learning of general recurrent computational graphs such as recurrent network models. It works in a streaming fashion and avoids backtracking through past activations and inputs. UORO is computationally as costly as \emph{Truncated Backpropagation Through Time} (truncated BPTT), a widespread algorithm for online learning of recurrent networks \cite{jaeger2002tutorial}.  UORO is a modification of \emph{NoBackTrack} \cite{DBLP:journals/corr/OllivierC15} that bypasses the need for model sparsity and makes implementation easy in current deep learning frameworks, even for complex models.   Like NoBackTrack, UORO provides unbiased gradient estimates; unbiasedness is the core hypothesis in stochastic gradient descent theory, without which convergence to a local optimum is not guaranteed. On the contrary, truncated BPTT does not provide this property, leading to possible divergence.   On synthetic tasks where truncated BPTT is shown to diverge, UORO converges. For instance, when a parameter has a positive short-term but negative long-term influence, truncated BPTT diverges unless the truncation span is very significantly longer than the intrinsic temporal range of the interactions, while UORO performs well thanks to the unbiasedness of its gradients.

Stellt eine unvoreingenommene und leicht zu implementierende Online-Gradientenschätzung für rekurrente Modelle vor.

Die Autoren stellen einen neuen Ansatz für das Online-Lernen der Parameter rekurrenter neuronaler Netze aus langen Sequenzen vor, der die Imitation der abgeschnittenen Backpropagation durch die Zeit überwindet.

In diesem Beitrag wird das Online-Training von RNNs auf prinzipielle Weise angegangen, und es wird eine Modifikation von RTRL und die Verwendung eines Forward Ansatzes für die Gradientenberechnung vorgeschlagen.


We present a deep learning-based method for super-resolving coarse (low-resolution) labels assigned to groups of image pixels into pixel-level (high-resolution) labels, given the joint distribution between those low- and high-resolution labels. This method involves a novel loss function that minimizes the distance between a distribution determined by a set of model outputs and the corresponding distribution given by low-resolution labels over the same set of outputs. This setup does not require that the high-resolution classes match the low-resolution classes and can be used in high-resolution semantic segmentation tasks where high-resolution labeled data is not available. Furthermore, our proposed method is able to utilize both data with low-resolution labels and any available high-resolution labels, which we show improves performance compared to a network trained only with the same amount of high-resolution data.
 We test our proposed algorithm in a challenging land cover mapping task to super-resolve labels at a 30m resolution to a separate set of labels at a 1m resolution. We compare our algorithm with models that are trained on high-resolution data and show that 1) we can achieve similar performance using only low-resolution data; and 2) we can achieve better performance when we incorporate a small amount of high-resolution data in our training. We also test our approach on a medical imaging problem, resolving low-resolution probability maps into high-resolution segmentation of lymphocytes with accuracy equal to that of fully supervised models.

Superauflösung grober Labels in Beschriftungen auf Pixelebene, angewandt auf Luftbilder und medizinische Scans.

Eine Methode zur Superauflösung von groben, niedrig aufgelösten Segmentierungslabels, wenn die gemeinsame Verteilung von niedrig und hoch aufgelösten Labels bekannt ist.


We propose a novel framework for combining datasets via alignment of their associated intrinsic dimensions. Our approach assumes that the two datasets are sampled from a common latent space, i.e., they measure equivalent systems. Thus, we expect there to exist a natural (albeit unknown) alignment of the data manifolds associated with the intrinsic geometry of these datasets, which are perturbed by measurement artifacts in the sampling process. Importantly, we do not assume any individual correspondence (partial or complete) between data points. Instead, we rely on our assumption that a subset of data features have correspondence across datasets. We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets. We compute a correlation matrix between diffusion coordinates of the datasets by considering graph (or manifold) Fourier coefficients of corresponding data features. We then orthogonalize this correlation matrix to form an isometric transformation between the diffusion maps of the datasets. Finally, we apply this transformation to the diffusion coordinates and construct a unified diffusion geometry of the datasets together. We show that this approach successfully corrects misalignment artifacts, and allows for integrated data.

Wir schlagen eine Methode vor, um die latenten Merkmale, die aus verschiedenen Datensätzen gelernt wurden, mithilfe harmonischer Korrelationen abzugleichen.

Es wird vorgeschlagen, Merkmalskorrespondenzen zu verwenden, um einen vielfältigen Abgleich zwischen Datenstapeln aus denselben Proben vorzunehmen, um die Sammlung gesstörter Messungen zu vermeiden.


Reinforcement learning (RL) has proven to be a powerful paradigm for deriving complex behaviors from simple reward signals in a wide range of environments. When applying RL to continuous control agents in simulated physics environments, the body is usually considered to be part of the environment. However, during evolution the physical body of biological organisms and their controlling brains are co-evolved, thus exploring a much larger space of actuator/controller configurations. Put differently, the intelligence does not reside only in the agent's mind, but also in the design of their body.
 We propose a method for uncovering strong agents, consisting of a good combination of a body and policy, based on combining RL with an evolutionary procedure. Given the resulting agent, we also propose an approach for identifying the body changes that contributed the most to the agent performance. We use the Shapley value from cooperative game theory to find the fair contribution of individual components, taking into account synergies between components.
 We evaluate our methods in an environment similar to the the recently proposed Robo-Sumo task, where agents in a 3D environment with simulated physics compete in tipping over their opponent or pushing them out of the arena. Our results show that the proposed methods are indeed capable of generating strong agents, significantly outperforming baselines that focus on optimizing the agent policy alone.

 A video is available at: www.youtube.com/watch?v=eei6Rgom3YY

Die Entwicklung der Körperform bei RL-gesteuerten Agenten verbessert deren Leistung (und hilft beim Lernen).

PEOM-Algorithmus, der den Shapley-Wert einbezieht, um die Entwicklung zu beschleunigen, indem der Beitrag jedes Körperteils ermittelt wird.


Many practical reinforcement learning problems contain catastrophic states that the optimal policy visits infrequently or never. Even on toy problems, deep reinforcement learners periodically revisit these states, once they are forgotten under a new policy. In this paper, we introduce intrinsic fear, a learned reward shaping that accelerates deep reinforcement learning and guards oscillating policies against periodic catastrophes. Our approach incorporates a second model trained via supervised learning to predict the probability of imminent catastrophe. This score acts as a penalty on the Q-learning objective. Our theoretical analysis demonstrates that the perturbed objective yields the same average return under strong assumptions and an $\epsilon$-close average return under weaker assumptions. Our analysis also shows robustness to classification errors. Equipped with intrinsic fear, our DQNs solve the toy environments and improve on the Atari games Seaquest, Asteroids, and Freeway.

Gestalten Sie die Belohnung mit intrinsischer Motivation, um katastrophale Zustände zu vermeiden und das katastrophale Vergessen abzuschwächen.

Ein RL-Algorithmus, der den DQN-Algorithmus mit einem parallel trainierten Angstmodell kombiniert, um katastrophale Zustände vorherzusagen.

Die Arbeit untersucht katastrophales Vergessen in RL, indem es Aufgaben hervorhebt, bei denen ein DQN in der Lage ist, zu lernen, katastrophale Ereignisse zu vermeiden, solange es das Vergessen vermeidet.


Convolution is an efficient technique to obtain abstract feature representations using hierarchical layers in deep networks. Although performing convolution in Euclidean geometries is fairly straightforward, its extension to other topological spaces---such as a sphere S^2 or a unit ball B^3---entails unique challenges. In this work, we propose a novel `"volumetric convolution" operation that can effectively convolve arbitrary functions in B^3. We develop a theoretical framework for "volumetric convolution" based on Zernike polynomials and efficiently implement it as a differentiable and an easily pluggable layer for deep networks. Furthermore, our formulation leads to derivation of a  novel formula to measure the symmetry of a function in B^3 around an arbitrary axis, that is useful in 3D shape analysis tasks. We demonstrate the efficacy of proposed volumetric convolution operation on a possible use-case i.e., 3D object recognition task.

Ein neuartiger Convolution Operator für automatisches Repräsentationslernen in der Einheitskugel.

Diese Arbeit steht im Zusammenhang mit den jüngsten Arbeiten über sphärische CNN und äquivariante SE(n)-Netzwerke und erweitert frühere Ideen auf volumetrische Daten in der Einheitskugel.

Er schlägt vor, volumetrische Convolutions auf Convolution Networks zu verwenden, um die Einheitskugel zu lernen, und erörtert die Methodik und die Ergebnisse des Prozesses.


Learning in environments with large state and action spaces, and sparse rewards, can hinder a Reinforcement Learning (RL) agent’s learning through trial-and-error. For instance, following natural language instructions on the Web (such as booking a flight ticket) leads to RL settings where input vocabulary and number of actionable elements on a page can grow very large. Even though recent approaches improve the success rate on relatively simple environments with the help of human demonstrations to guide the exploration, they still fail in environments where the set of possible instructions can reach millions. We approach the aforementioned problems from a different perspective and propose guided RL approaches that can generate unbounded amount of experience for an agent to learn from. Instead of learning from a complicated instruction with a large vocabulary, we decompose it into multiple sub-instructions and schedule a curriculum in which an agent is tasked with a gradually increasing subset of these relatively easier sub-instructions. In addition, when the expert demonstrations are not available, we propose a novel meta-learning framework that generates new instruction following tasks and trains the agent more effectively. We train DQN, deep reinforcement learning agent, with Q-value function approximated with a novel QWeb neural network architecture on these smaller, synthetic instructions. We evaluate the ability of our agent to generalize to new instructions onWorld of Bits benchmark, on forms with up to 100 elements, supporting 14 million possible instructions. The QWeb agent outperforms the baseline without using any human demonstration achieving 100% success rate on several difficult environments.

Wir trainieren Strategien des Reinforcement Learning mit Hilfe von Belohnungserweiterung, Curriculum-Lernen und Meta-Lernen, um erfolgreich durch Webseiten zu navigieren.

Entwickelt eine Lehrplan-Lernmethode für das Training eines RL-Agenten zur Navigation in einem Web, basierend auf der Idee, eine Anweisung in mehrere Unteranweisungen zu zerlegen.


Labeled text classification datasets are typically only available in a few select languages. In order to train a model for e.g news categorization in a language $L_t$ without a suitable text classification dataset there are two options. The first option is to create a new labeled dataset by hand, and the second option is to transfer label information from an existing labeled dataset in a source language $L_s$ to the target language $L_t$. In this paper we propose a method for sharing label information across languages by means of a language independent text encoder. The encoder will give almost identical representations to multilingual versions of the same text. This means that labeled data in one language can be used to train a classifier that works for the rest of the languages. The encoder is trained independently of any concrete classification task and can therefore subsequently be used for any classification task.   We show that it is possible to obtain good performance even in the case where only a comparable corpus of texts is available.

Sprachübergreifende Textklassifizierung durch universelle Kodierung.

In diesem Beitrag wird ein Ansatz zur sprachübergreifenden Textklassifikation durch die Verwendung vergleichbarer Korpora vorgeschlagen.

Lernen von sprachenübergreifenden Einbettungen und Trainieren eines Klassifizierers unter Verwendung etikettierter Daten in der Ausgangssprache, um das Lernen eines sprachenübergreifenden Textkategorisierers ohne etikettierte Informationen in der Zielsprache anzugehen.


Syntax is a powerful abstraction for language understanding. Many downstream tasks require segmenting input text into meaningful constituent chunks (e.g., noun phrases or entities); more generally, models for learning semantic representations of text benefit from integrating syntax in the form of parse trees (e.g., tree-LSTMs). Supervised parsers have traditionally been used to obtain these trees, but lately interest has increased in unsupervised methods that induce syntactic representations directly from unlabeled text. To this end, we propose the deep inside-outside recursive autoencoder (DIORA), a fully-unsupervised method for discovering syntax that simultaneously learns representations for constituents within the induced tree. Unlike many prior approaches, DIORA does not rely on supervision from auxiliary downstream tasks and is thus not constrained to particular domains. Furthermore, competing approaches do not learn explicit phrase representations along with tree structures, which limits their applicability to phrase-based tasks. Extensive experiments on unsupervised parsing, segmentation, and phrase clustering demonstrate the efficacy of our method. DIORA achieves the state of the art in unsupervised parsing (46.9 F1) on the benchmark WSJ dataset.

In dieser Arbeit schlagen wir Deep Inside-Outside Recursive Auto-Encoder (DIORA) vor, eine vollständig unbeaufsichtigte Methode zur Entdeckung der Syntax bei gleichzeitigem Lernen von Repräsentationen für die entdeckten Konstituenten.

Ein neuronales latentes Baummodell, das mit einem Auto-Encoding Ziel trainiert wird, das den Stand der Technik beim unüberwachten Konstituenten Parsing erreicht und die syntaktische Struktur besser erfasst als andere latente Baummodelle.

Die Arbeit schlägt ein Modell für unüberwachtes Dependency Parsing (latente Bauminduktion) vor, das auf einer Kombination des Inside-Outside-Algorithmus mit neuronaler Modellierung (rekursive Auto-Encoder) basiert.


Careful tuning of the learning rate, or even schedules thereof, can be crucial to effective neural net training. There has been much recent interest in gradient-based meta-optimization, where one tunes hyperparameters, or even learns an optimizer, in order to minimize the expected loss when the training procedure is unrolled. But because the training procedure must be unrolled thousands of times, the meta-objective must be defined with an orders-of-magnitude shorter time horizon than is typical for neural net training. We show that such short-horizon meta-objectives cause a serious bias towards small step sizes, an effect we term short-horizon bias. We introduce a toy problem, a noisy quadratic cost function, on which we analyze short-horizon bias by deriving and comparing the optimal schedules for short and long time horizons. We then run meta-optimization experiments (both offline and online) on standard benchmark datasets, showing that meta-optimization chooses too small a learning rate by multiple orders of magnitude, even when run with a moderately long time horizon (100 steps) typical of work in the area. We believe short-horizon bias is a fundamental problem that needs to be addressed if meta-optimization is to scale to practical neural net training regimes.

Wir untersuchen die Verzerrung des kurzsichtigen Meta-Optimierungsziels.

In diesem Beitrag werden ein vereinfachtes Modell und ein Problem vorgeschlagen, um die kurzfristige Verzerrung der Meta-Optimierung der Lernrate zu demonstrieren.

In diesem Beitrag wird die Frage der verkürzten Backpropagation für die Meta-Optimierung anhand einer Reihe von Experimenten an einem Spielzeugproblem untersucht.


Mainstream captioning models often follow a sequential structure to generate cap-
 tions, leading to issues such as introduction of irrelevant semantics, lack of diversity
 in the generated captions, and inadequate generalization performance. In this paper,
 we present an alternative paradigm for image captioning, which factorizes the
 captioning procedure into two stages: (1) extracting an explicit semantic represen-
 tation from the given image; and (2) constructing the caption based on a recursive
 compositional procedure in a bottom-up manner. Compared to conventional ones,
 our paradigm better preserves the semantic content through an explicit factorization
 of semantics and syntax. By using the compositional generation procedure, caption
 construction follows a recursive structure, which naturally fits the properties of
 human language. Moreover, the proposed compositional procedure requires less
 data to train, generalizes better, and yields more diverse captions.

Eine hierarchische und kompositorische Methode zur Erstellung von Beschriftungen.

In dieser Arbeit wird eine besser interpretierbare Methode für Bildunterschriften vorgestellt.


While many approaches to make neural networks more fathomable have been proposed, they are restricted to interrogating the network with input data. Measures for characterizing and monitoring structural properties, however, have not been developed. In this work, we propose neural persistence, a complexity measure for neural network architectures based on topological data analysis on weighted stratified graphs. To demonstrate the usefulness of our approach, we show that neural persistence reflects best practices developed in the deep learning community such as dropout and batch normalization. Moreover, we derive a neural persistence-based stopping criterion that shortens the training process while achieving comparable accuracies as early stopping based on validation loss.

Wir entwickeln ein neues topologisches Komplexitätsmaß für tiefe neuronale Netze und zeigen, dass es die wichtigsten Eigenschaften dieser Netze erfasst.

In diesem Beitrag wird der Begriff der neuronalen Persistenz vorgeschlagen, ein topologisches Maß für die Zuordnung von Punktwerten zu vollständig verbundenen Schichten in einem neuronalen Netz.

In der Arbeit wird vorgeschlagen, die Komplexität eines neuronalen Netzes anhand seiner nullten persistenten Homologie zu analysieren.


Deep neural networks (DNNs) are vulnerable to adversarial examples, which are carefully crafted instances aiming to cause prediction errors for DNNs. Recent research on adversarial examples has examined local neighborhoods in the input space of DNN models. However, previous work has limited what regions to consider, focusing either on low-dimensional subspaces or small balls. In this paper, we argue that information from larger neighborhoods, such as from more directions and from greater distances, will better characterize the relationship between adversarial examples and the DNN models. First, we introduce an attack, OPTMARGIN, which generates adversarial examples robust to small perturbations. These examples successfully evade a defense that only considers a small ball around an input instance. Second, we analyze a larger neighborhood around input instances by looking at properties of surrounding decision boundaries, namely the distances to the boundaries and the adjacent classes. We find that the boundaries around these adversarial examples do not resemble the boundaries around benign examples. Finally, we show that, under scrutiny of the surrounding decision boundaries, our OPTMARGIN examples do not convincingly mimic benign examples. Although our experiments are limited to a few specific attacks, we hope these findings will motivate new, more evasive attacks and ultimately, effective defenses.

Die Betrachtung der Entscheidungsgrenzen um eine Eingabe herum gibt Ihnen mehr Informationen als eine feste kleine Nachbarschaft.

Die Autoren stellen einen neuartigen Angriff zur Generierung von adversarial Beispielen vor, bei dem sie Klassifikatoren angreifen, die durch zufällige Klassifizierung von kleinen L2-Störungen erstellt wurden.

Ein neuer Ansatz zur Erzeugung von adversarial Angriffen auf ein neuronales Netz und eine Methode zur Verteidigung eines neuronalen Netzes gegen diese Angriffe.


Machine learning models are usually tuned by nesting optimization of model weights inside the optimization of hyperparameters.   We give a method to collapse this nested optimization into joint stochastic optimization of both weights and hyperparameters.   Our method trains a neural network to output approximately optimal weights as a function of hyperparameters.   We show that our method converges to locally optimal weights and hyperparameters for sufficiently large hypernets.   We compare this method to standard hyperparameter optimization strategies and demonstrate its effectiveness for tuning thousands of hyperparameters.

Wir trainieren ein neuronales Netz, um annähernd optimale Gewichte in Abhängigkeit von den Hyperparametern auszugeben.

Hyper-Netzwerke für die Optimierung von Hyper-Parametern in neuronalen Netzwerken.


Estimating covariances between financial assets plays an important role in risk management. In practice, when the sample size is small compared to the number of variables, the empirical estimate is known to be very unstable. Here, we propose a novel covariance estimator based on the Gaussian Process Latent Variable Model (GP-LVM). Our estimator can be considered as a non-linear extension of standard factor models with readily interpretable parameters reminiscent of market betas. Furthermore, our Bayesian treatment naturally shrinks the sample covariance matrix towards a more structured matrix given by the prior and thereby systematically reduces estimation errors. Finally, we discuss some financial applications of the GP-LVM model.

Schätzung der Kovarianzmatrix von Finanzanlagen mit Gauß-Prozess-Modellen mit latente Variablen.

Veranschaulicht, wie das Gaussian Process Latent Variable Model (GP-LVM) klassische lineare Faktormodelle für die Schätzung von Kovarianzmatrizen in Portfolio-Optimierungsproblemen ersetzen kann.

In dieser Arbeit werden Standard-GPLVMs verwendet, um die Kovarianzstruktur und eine latente Raumdarstellung von S&P500-Finanzzeitreihen zu modellieren, um Portfolios zu optimieren und fehlende Werte vorherzusagen.

In diesem Papier wird vorgeschlagen, ein GPLVM zur Modellierung von Finanzerträgen zu verwenden.


We study how, in generative adversarial networks, variance in the discriminator's output affects the generator's ability to learn the data distribution. In particular, we contrast the results from various well-known techniques for training GANs when the discriminator is near-optimal and updated multiple times per update to the generator. As an alternative, we propose an additional method to train GANs by explicitly modeling the discriminator's output as a bi-modal Gaussian distribution over the real/fake indicator variables. In order to do this, we train the Gaussian classifier to match the target bi-modal distribution implicitly through meta-adversarial training. We observe that our new method, when trained together with a strong discriminator, provides meaningful, non-vanishing gradients.

Wir führen meta-adversariales Lernen ein, eine neue Technik zur Regularisierung von GANs, und schlagen eine Trainingsmethode vor, die explizit die Ausgangsverteilung des Diskriminators kontrolliert.

Die Arbeit schlägt Varianz regularisierendes adversarial Lernen für das Training von GANs vor, um sicherzustellen, dass der Gradient für den Generator nicht verschwindet.


We introduce NoisyNet, a deep reinforcement learning agent with parametric noise added to its weights, and show that the induced stochasticity of the agent’s policy can be used to aid efficient exploration. The parameters of the noise are learned with gradient descent along with the remaining network weights.   NoisyNet is straightforward to implement and adds little computational overhead. We find that replacing the conventional exploration heuristics for A3C, DQN and Dueling agents (entropy reward and epsilon-greedy respectively) with NoisyNet yields substantially higher scores for a wide range of Atari games, in some cases advancing the agent from sub to super-human performance.

Ein Deep Reinforcement Learning Agent, dessen Gewichte mit parametrischen Störungen versehen sind, kann zur Unterstützung einer effizienten Erkundung eingesetzt werden.

In diesem Beitrag werden NoisyNets vorgestellt, neuronale Netze, deren Parameter durch eine parametrische Störfunktion gestört werden, und die eine erhebliche Leistungsverbesserung gegenüber grundlegenden Algorithmen für tiefes Reinforcement Learning erzielen.

Neue Explorationsmethode für tiefe RL durch Einspeisung von Störungen in die Gewichte der tiefen Netze, wobei die Störungen verschiedene Formen annehmen können.


Localization is the problem of estimating the location of an autonomous agent from an observation and a map of the environment. Traditional methods of localization, which filter the belief based on the observations, are sub-optimal in the number of steps required, as they do not decide the actions taken by the agent. We propose "Active Neural Localizer", a fully differentiable neural network that learns to localize efficiently. The proposed model incorporates ideas of traditional filtering-based localization methods, by using a structured belief of the state with multiplicative interactions to propagate belief, and combines it with a policy model to minimize the number of steps required for localization. Active Neural Localizer is trained end-to-end with reinforcement learning. We use a variety of simulation environments for our experiments which include random 2D mazes, random mazes in the Doom game engine and a photo-realistic environment in the Unreal game engine. The results on the 2D environments show the effectiveness of the learned policy in an idealistic setting while results on the 3D environments demonstrate the model's capability of learning the policy and perceptual model jointly from raw-pixel based RGB observations. We also show that a model trained on random textures in the Doom environment generalizes well to a photo-realistic office space environment in the Unreal engine.

Active Neural Localizer, ein vollständig differenzierbares neuronales Netz, das mit Hilfe von Deep Reinforcement Learning eine effiziente Lokalisierung lernt.

In diesem Beitrag wird das Problem der Lokalisierung auf einer bekannten Karte unter Verwendung eines Glaubensnetzes als RL-Problem formuliert, bei dem das Ziel des Agenten darin besteht, die Anzahl der Schritte zu minimieren, um sich selbst zu lokalisieren.

Dies ist eine klare und interessante Arbeit, die ein parametrisiertes Netzwerk zur Auswahl von Aktionen für einen Roboter in einer simulierten Umgebung aufbaut.