In many real-world learning scenarios, features are only acquirable at a cost constrained under a budget.In this paper, we propose a novel approach for cost-sensitive feature acquisition at the prediction-time.The suggested method acquires features incrementally based on a context-aware feature-value function.We formulate the problem in the reinforcement learning paradigm, and introduce a reward function based on the utility of each feature.Specifically, MC dropout sampling is used to measure expected variations of the model uncertainty which is used as a feature-value function.Furthermore, we suggest sharing representations between the class predictor and value function estimator networks.The suggested approach is completely online and is readily applicable to stream learning setups.The solution is evaluated on three different datasets including the well-known MNIST dataset as a benchmark as well as two cost-sensitive datasets: Yahoo Learning to Rank and a dataset in the medical domain for diabetes classification.According to the results, the proposed method is able to efficiently acquire features and make accurate predictions.

Ein Online-Algorithmus für die kostenbewusste Erfassung und Vorhersage von Merkmalen


This paper revisits the problem of sequence modeling using convolutional architectures. Although both convolutional and recurrent architectures havealong history in sequence prediction, the current "default" mindset in much ofthe deep learning community is that generic sequence modeling is best handledusing recurrent networks. The goal of this paper is to question this assumption. Specifically, we consider a simple generic temporal convolution network (TCN),which adopts features from modern ConvNet architectures such as a dilations and residual connections. We show that on a variety of sequence modeling tasks,including many frequently used as benchmarks for evaluating recurrent networks,the TCN outperforms baseline RNN methods (LSTMs, GRUs, and vanilla RNNs) andsometimes even highly specialized approaches. We further show that thepotential "infinite memory" advantage that RNNs have over TCNs is largelyabsent in practice: TCNs indeed exhibit longer effective history sizes than their recurrent counterparts.  As a whole, we argue that it may be time to (re)consider ConvNets as the default "go to" architecture for sequence modeling.

Wir argumentieren, dass Covolutional Networks als Standardausgangspunkt für Sequenzmodellierungsaufgaben betrachtet werden sollten.


Deep neural networks work well at approximating complicated functions when provided with data and trained by gradient descent methods.At the same time, there is a vast amount of existing functions that programmatically solve different tasks in a precise manner eliminating the need for training.In many cases, it is possible to decompose a task to a series of functions, of which for some we may prefer to use a neural network to learn the functionality, while for others the preferred method would be to use existing black-box functions.We propose a method for end-to-end training of a base neural network that integrates calls to existing black-box functions.We do so by approximating the black-box functionality with a differentiable neural network in a way that drives the base network to comply with the black-box function interface during the end-to-end optimization process.At inference time, we replace the differentiable estimator with its external black-box non-differentiable counterpart such that the base network output matches the input arguments of the black-box function.Using this ``Estimate and Replace'' paradigm, we train a neural network, end to end, to compute the input to black-box functionality while eliminating the need for intermediate labels.We show that by leveraging the existing precise black-box function during inference, the integrated model generalizes better than a fully differentiable model, and learns more efficiently compared to RL-based methods.

Training von DNNs, um Black-Box-Funktionen mit Zwischenetiketten zu verbinden, unter Verwendung eines Schätzer-Teilnetzes, das nach dem Training durch die Black-Box ersetzt werden kann


This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC.  It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic.  Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor.We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm.We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.

Wir schlagen einen dualen akteurskritischen Algorithmus vor, der auf prinzipielle Weise aus der dualen Lagrangeschen Form der Bellman-Optimalitätsgleichung abgeleitet ist. Der Algorithmus erreicht in mehreren Benchmarks die beste Leistung.


Training a model to perform a task typically requires a large amount of data from the domains in which the task will be applied.However, it is often the case that data are abundant in some domains but scarce in others.Domain adaptation deals with the challenge of adapting a model trained from a data-rich source domain to perform well in a data-poor target domain.In general, this requires learning plausible mappings between domains.CycleGAN is a powerful framework that efficiently learns to map inputs from one domain to another using adversarial training and a cycle-consistency constraint.However, the conventional approach of enforcing cycle-consistency via reconstruction may be overly restrictive in cases where one or more domains have limited training data.In this paper, we propose an augmented cyclic adversarial learning model that enforces the cycle-consistency constraint via an external task specific model, which encourages the preservation of task-relevant content as opposed to exact reconstruction.This task specific model both relaxes the cycle-consistency constraint and complements the role of the discriminator during training, serving as an augmented information source for learning the mapping.We explore adaptation in speech and visual domains in low resource in supervised setting.In speech domains, we adopt a speech recognition model from each domain as the task specific model.Our approach improves absolute performance of speech recognition by 2% for female speakers in the TIMIT dataset, where the majority of training samples are from male voices.In low-resource visual domain adaptation, the results show that our approach improves absolute performance by 14% and 4% when adapting SVHN to MNIST and vice versa, respectively, which outperforms unsupervised domain adaptation methods that require high-resource unlabeled target domain.

Eine robuste Domänenanpassung durch den Einsatz eines aufgabenspezifischen Verlustes beim zyklischen adversen Lernen


The success of popular algorithms for deep reinforcement learning, such as policy-gradients and Q-learning, relies heavily on the availability of an informative reward signal at each timestep of the sequential decision-making process.When rewards are only sparsely available during an episode, or a rewarding feedback is provided only after episode termination, these algorithms perform sub-optimally due to the difficultly in credit assignment.Alternatively, trajectory-based policy optimization methods, such as cross-entropy method and evolution strategies, do not require per-timestep rewards, but have been found to suffer from high sample complexity by completing forgoing the temporal nature of the problem.Improving the efficiency of RL algorithms in real-world problems with sparse or episodic rewards is therefore a pressing need.In this work, we introduce a self-imitation learning algorithm that exploits and explores well in the sparse and episodic reward settings.We view each policy as a state-action visitation distribution and formulate policy optimization as a divergence minimization problem.We show that with Jensen-Shannon divergence, this divergence minimization problem can be reduced into a policy-gradient algorithm with shaped rewards learned from experience replays.Experimental results indicate that our algorithm works comparable to existing algorithms in environments with dense rewards, and significantly better in environments with sparse and episodic rewards.We then discuss limitations of self-imitation learning, and propose to solve them by using Stein variational policy gradient descent with the Jensen-Shannon kernel to learn multiple diverse policies.We demonstrate its effectiveness on a challenging variant of continuous-control MuJoCo locomotion tasks.

Optimierung der Politik durch Verwendung vergangener guter Rollouts des Agenten; Lernen von geformten Belohnungen über Divergenzminimierung; SVPG mit JS-Kernel für populationsbasierte Exploration.


We study the precise mechanisms which allow autoencoders to encode and decode a simple geometric shape, the disk.In this carefully controlled setting, we are able to describe the specific form of the optimal solution to the minimisation problem of the training step.We show that the autoencoder indeed approximates this solution during training.Secondly, we identify a clear failure in the generalisation capacity of the autoencoder, namely its inability to interpolate data.Finally, we explore several regularisation schemes to resolve the generalisation problem.Given the great attention that has been recently given to the generative capacity of neural networks, we believe that studying in depth simple geometric cases sheds some light on the generation process and can provide a minimal requirement experimental setup for more complex architectures.

Wir untersuchen die Funktionsweise von Autoencodern in einer einfachen Umgebung und empfehlen neue Strategien für deren Regularisierung, um eine bessere Generalisierung mit latenter Interpolation für die Bildsynthese zu erreichen.


We present a simple idea that allows to record a speaker in a given language and synthesize their voice in other languages that they may not even know.These techniques open a wide range of potential applications such as cross-language communication, language learning or automatic video dubbing.We call this general problem multi-language speaker-conditioned speech synthesis and we present a simple but strong baseline for it.Our model architecture is similar to the encoder-decoder Char2Wav model or Tacotron.The main difference is that, instead of conditioning on characters or phonemes that are specific to a given language, we condition on a shared phonetic representation that is universal to all languages.This cross-language phonetic representation of text allows to synthesize speech in any language while preserving the vocal characteristics of the original speaker.Furthermore, we show that fine-tuning the weights of our model allows us to extend our results to speakers outside of the training dataset.

Wir stellen eine einfache Idee vor, die es ermöglicht, einen Sprecher in einer bestimmten Sprache aufzunehmen und seine Stimme in anderen Sprachen zu synthetisieren, die er vielleicht nicht einmal kennt.


The goal of imitation learning (IL) is to enable a learner to imitate expert behavior given expert demonstrations.Recently, generative adversarial imitation learning (GAIL) has shown significant progress on IL for complex continuous tasks.However, GAIL and its extensions require a large number of environment interactions during training.In real-world environments, the more an IL method requires the learner to interact with the environment for better imitation, the more training time it requires, and the more damage it causes to the environments and the learner itself.We believe that IL algorithms could be more applicable to real-world problems if the number of interactions could be reduced. In this paper, we propose a model-free IL algorithm for continuous control.Our algorithm is made up mainly three changes to the existing adversarial imitation learning (AIL) methods –(a) adopting off-policy actor-critic (Off-PAC) algorithm to optimize the learner policy,(b) estimating the state-action value using off-policy samples without learning reward functions, and(c) representing the stochastic policy function so that its outputs are bounded.Experimental results show that our algorithm achieves competitive results with GAIL while significantly reducing the environment interactions.

In dieser Arbeit haben wir einen modellfreien, richtlinienunabhängigen IL-Algorithmus für kontinuierliche Kontrolle vorgeschlagen. Experimentelle Ergebnisse zeigten, dass unser Algorithmus wettbewerbsfähige Ergebnisse mit GAIL erzielt und gleichzeitig die Interaktionen mit der Umgebung deutlich reduziert.


The dominant approach to unsupervised "style transfer'' in text is based on the idea of learning a latent representation, which is independent of the attributes specifying its "style''.In this paper, we show that this condition is not necessary and is not always met in practice, even with domain adversarial training that explicitly aims at learning such disentangled representations.We thus propose a new model that controls several factors of variation in textual data where this condition on disentanglement is replaced with a simpler mechanism based on back-translation.Our method allows control over multiple attributes, like gender, sentiment, product type, etc., and a more fine-grained control on the trade-off between content preservation and change of style with a pooling operator in the latent space.Our experiments demonstrate that the fully entangled model produces better generations, even when tested on new and more challenging benchmarks comprising reviews with multiple sentences and multiple attributes.

Ein System zum Umschreiben von Text, der von mehreren kontrollierbaren Attributen abhängt


Vanilla RNN with ReLU activation have a simple structure that is amenable to systematic dynamical systems analysis and interpretation, but they suffer from the exploding vs. vanishing gradients problem.Recent attempts to retain this simplicity while alleviating the gradient problem are based on proper initialization schemes or orthogonality/unitary constraints on the RNN’s recurrency matrix, which, however, comes with limitations to its expressive power with regards to dynamical systems phenomena like chaos or multi-stability.Here, we instead suggest a regularization scheme that pushes part of the RNN’s latent subspace toward a line attractor configuration that enables long short-term memory and arbitrarily slow time scales.We show that our approach excels on a number of benchmarks like the sequential MNIST or multiplication problems, and enables reconstruction of dynamical systems which harbor widely different time scales.

Wir entwickeln einen neuen Optimierungsansatz für einfache ReLU-basierte RNN, der ein langes Kurzzeitgedächtnis und die Identifikation beliebiger nichtlinearer dynamischer Systeme mit sehr unterschiedlichen Zeitskalen ermöglicht.


In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem.Wasserstein GANs have largely promoted the stability over the original GANs by introducing Wasserstein distance, but still remain unstable and are prone to a variety of failure modes.In this paper, we present a general framework named Wasserstein-Bounded GAN (WBGAN), which improves a large family of WGAN-based approaches by simply adding an upper-bound constraint to the Wasserstein term.Furthermore, we show that WBGAN can reasonably measure the difference of distributions which almost have no intersection.Experiments demonstrate that WBGAN can stabilize as well as accelerate convergence in the training processes of a series of WGAN-based variants.

Vorschlag eines verbesserten Rahmens für WGANs und Nachweis seiner besseren Leistung in Theorie und Praxis.


Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sample or exploring differences between a pair of samples applied to a third sample.In this paper, we show that the latent space operations used in the literature so far induce a distribution mismatch between the resulting outputs and the prior distribution the model was trained on.To address this, we propose to use distribution matching transport maps to ensure that such  latent space operations preserve the prior distribution, while minimally modifying the original operation. Our experimental results validate that the proposed operations give higher quality samples compared to the original operations.

Operationen im latenten Raum des GAN können zu einer Ungleichverteilung im Vergleich zur Trainingsverteilung führen.


Neural program embeddings have shown much promise recently for a variety of program analysis tasks, including program synthesis, program repair, code completion, and fault localization.However, most existing program embeddings are based on syntactic features of programs, such as token sequences or abstract syntax trees.Unlike images and text, a program has well-deﬁned semantics that can be difﬁcult to capture by only considering its syntax (i.e. syntactically similar programs can exhibit vastly different run-time behavior), which makes syntax-based program embeddings fundamentally limited.We propose a novel semantic program embedding that is learned from program execution traces.Our key insight is that program states expressed as sequential tuples of live variable values not only capture program semantics more precisely, but also offer a more natural ﬁt for Recurrent Neural Networks to model.We evaluate different syntactic and semantic program embeddings on the task of classifying the types of errors that students make in their submissions to an introductory programming class and on the CodeHunt education platform.Our evaluation results show that the semantic program embeddings signiﬁcantly outperform the syntactic program embeddings based on token sequences and abstract syntax trees.In addition, we augment a search-based program repair system with predictions made from our semantic embedding and demonstrate signiﬁcantly improved search efﬁciency.

Ein neuer Weg zum Erlernen der semantischen Programmeinbettung


In this paper, we propose a generalization of the BN algorithm, diminishing batch normalization (DBN), where we update the BN parameters in a diminishing moving average way.Batch normalization (BN) is very effective in accelerating the convergence of a neural network training phase that it has become a common practice. Our proposed DBN algorithm remains the overall structure of the original BN algorithm while introduces a weighted averaging update to some trainable parameters. We provide an analysis of the convergence of the DBN algorithm that converges to a stationary point with respect to trainable parameters.Our analysis can be easily generalized for original BN algorithm by setting some parameters to constant.To the best knowledge of authors, this analysis is the first of its kind for convergence with Batch Normalization introduced.We analyze a two-layer model with arbitrary activation function. The primary challenge of the analysis is the fact that some parameters are updated by gradient while others are not. The convergence analysis applies to any activation function that satisfies our common assumptions.For the analysis, we also show the sufficient and necessary conditions for the stepsizes and diminishing weights to ensure the convergence. In the numerical experiments, we use more complex models with more layers and ReLU activation.We observe that DBN outperforms the original BN algorithm on Imagenet, MNIST, NI and CIFAR-10 datasets with reasonable complex FNN and CNN models.

Wir schlagen eine Erweiterung der Batch-Normalisierung vor, zeigen eine erstmalige Konvergenzanalyse für diese Erweiterung und zeigen in numerischen Experimenten, dass sie eine bessere Leistung hat als die ursprüngliche Batch-Normalisierung.


Generative models such as Variational Auto Encoders (VAEs) and Generative Adversarial Networks (GANs) are typically trained for a fixed prior distribution in the latent space, such as uniform or Gaussian.After a trained model is obtained, one can sample the Generator in various forms for exploration and understanding, such as interpolating between two samples, sampling in the vicinity of a sample or exploring differences between a pair of samples applied to a third sample.However, the latent space operations commonly used in the literature so far induce a distribution mismatch between the resulting outputs and the prior distribution the model was trained on.Previous works have attempted to reduce this mismatch with heuristic modification to the operations or by changing the latent distribution and re-training models.In this paper, we propose a framework for modifying the latent space operations such that the distribution mismatch is fully eliminated.Our approach is based on optimal transport maps, which adapt the latent space operations such that they fully match the prior distribution, while minimally modifying the original operation.Our matched operations are readily obtained for the commonly used operations and distributions and require no adjustment to the training procedure.

Wir schlagen einen Rahmen für die Modifizierung der latenten Raumoperationen vor, so dass die Verteilungsfehlanpassung zwischen den resultierenden Ausgaben und der vorherigen Verteilung, auf der das generative Modell trainiert wurde, vollständig beseitigt wird.


The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research.Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are crucial for generating responses in a chit-chat system.To overcome this problem, we propose an end to end multi-stream deep learning architecture which learns unified embeddings for query-response pairs by leveraging contextual information from memory networks and syntactic information by incorporating Graph Convolution Networks (GCN) over their dependency parse.A stream of this network also utilizes transfer learning by pre-training a bidirectional transformer to extract semantic representation for each input sentence and incorporates external knowledge through the neighbourhood of the entities from a Knowledge Base (KB).We benchmark these embeddings on next sentence prediction task and significantly improve upon the existing techniques.Furthermore, we use AMUSED to represent query and responses along with its context to develop a retrieval based conversational agent which has been validated by expert linguists to have comprehensive engagement with humans.

Dieses Papier bietet einen Multi-Stream-End-to-End-Ansatz, um einheitliche Einbettungen für Frage-Antwort-Paare in Dialogsystemen zu lernen, indem kontextuelle, syntaktische, semantische und externe Informationen zusammen genutzt werden.


We examine techniques for combining generalized policies with search algorithms to exploit the strengths and overcome the weaknesses of each when solving probabilistic planning problems.The Action Schema Network (ASNet) is a recent contribution to planning that uses deep learning and neural networks to learn generalized policies for probabilistic planning problems.ASNets are well suited to problems where local knowledge of the environment can be exploited to improve performance, but may fail to generalize to problems they were not trained on.Monte-Carlo Tree Search (MCTS) is a forward-chaining state space search algorithm for optimal decision making which performs simulations to incrementally build a search tree and estimate the values of each state.Although MCTS can achieve state-of-the-art results when paired with domain-specific knowledge, without this knowledge, MCTS requires a large number of simulations in order to obtain reliable estimates in the search tree.By combining ASNets with MCTS, we are able to improve the capability of an ASNet to generalize beyond the distribution of problems it was trained on, as well as enhance the navigation of the search space by MCTS.

Techniken zur Kombination verallgemeinerter Strategien mit Suchalgorithmen, um die Stärken und Schwächen beider Verfahren bei der Lösung probabilistischer Planungsprobleme zu nutzen


Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice.When the train and test distributions are mismatched, accuracy can plummet.Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment.In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers.We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions.AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.

Wir erreichen den neuesten Stand der Technik in Bezug auf die Robustheit gegenüber Datenverschiebungen und behalten die Kalibrierung auch dann bei, wenn die Genauigkeit abnimmt.


Automatic Piano Fingering is a hard task which computers can learn using data.As data collection is hard and expensive, we propose to automate this process by automatically extracting fingerings from public videos and MIDI files, using computer-vision techniques.Running this process on 90 videos results in the largest dataset for piano fingering with more than 150K notes.We show that when running a previously proposed model for automatic piano fingering on our dataset and then fine-tuning it on manually labeled piano fingering data, we achieve state-of-the-art results.In addition to the fingering extraction method, we also introduce a novel method for transferring deep-learning computer-vision models to work on out-of-domain data, by fine-tuning it on out-of-domain augmentation proposed by a Generative Adversarial Network (GAN).For demonstration, we anonymously release a visualization of the output of our process for a single video on https://youtu.be/Gfs1UWQhr5Q

Wir extrahieren automatisch Informationen über den Fingersatz aus Videos von Klavierspielen, um sie in automatischen Modellen zur Vorhersage des Fingersatzes zu verwenden.


Domain adaptation refers to the problem of leveraging labeled data in a source domain to learn an accurate model in a target domain where labels are scarce or unavailable.A recent approach for finding a common representation of the two domains is via domain adversarial training (Ganin & Lempitsky, 2015), which attempts to induce a feature extractor that matches the source and target feature distributions in some feature space.However, domain adversarial training faces two critical limitations:1) if the feature extraction function has high-capacity, then feature distribution matching is a weak constraint,2) in non-conservative domain adaptation (where no single classifier can perform well in both the source and target domains), training the model to do well on the source domain hurts performance on the target domain.In this paper, we address these issues through the lens of the cluster assumption, i.e., decision boundaries should not cross high-density data regions.We propose two novel and related models:1) the Virtual Adversarial Domain Adaptation (VADA) model, which combines domain adversarial training with a penalty term that punishes the violation the cluster assumption;2) the Decision-boundary Iterative Refinement Training with a Teacher (DIRT-T) model, which takes the VADA model as initialization and employs natural gradient steps to further minimize the cluster assumption violation.Extensive empirical results demonstrate that the combination of these two models significantly improve the state-of-the-art performance on the digit, traffic sign, and Wi-Fi recognition domain adaptation benchmarks.

SOTA auf unüberwachter Domänenanpassung durch Ausnutzung der Cluster-Annahme.


In this paper, we propose Continuous Graph Flow, a generative continuous flow based method that aims to model complex distributions of graph-structured data.  Once learned, the model can be applied to an arbitrary graph, defining a probability density over the random variables represented by the graph.It is formulated as an ordinary differential equation system with shared and reusable functions that operate over the graphs.  This leads to a new type of neural graph message passing scheme that performs continuous message passing over time.This class of models offers several advantages: a flexible representation that can generalize to variable data dimensions; ability to model dependencies in complex data distributions; reversible and memory-efficient; and exact and efficient computation of the likelihood of the data.We demonstrate the effectiveness of our model on a diverse set of generation tasks across different domains: graph generation, image puzzle generation, and layout generation from scene graphs.Our proposed model achieves significantly better performance compared to  state-of-the-art models.

Generative Graphenmodelle auf der Grundlage der Verallgemeinerung der Nachrichtenübermittlung auf kontinuierliche Zeit unter Verwendung gewöhnlicher Differentialgleichungen


The practical successes of deep neural networks have not been matched by theoretical progress that satisfyingly explains their behavior.In this work, we study the information bottleneck (IB) theory of deep learning, which makes three specific claims: first, that deep networks undergo two distinct phases consisting of an initial fitting phase and a subsequent compression phase; second, that the compression phase is causally related to the excellent generalization performance of deep networks; and third, that the compression phase occurs due to the diffusion-like behavior of stochastic gradient descent.Here we show that none of these claims hold true in the general case.Through a combination of analytical results and simulation, we demonstrate that the information plane trajectory is predominantly a function of the neural nonlinearity employed: double-sided saturating nonlinearities like tanh yield a compression phase as neural activations enter the saturation regime, but linear activation functions and single-sided saturating nonlinearities like the widely used ReLU in fact do not.Moreover, we find that there is no evident causal connection between compression and generalization: networks that do not compress are still capable of generalization, and vice versa.Next, we show that the compression phase, when it exists, does not arise from stochasticity in training by demonstrating that we can replicate the IB findings using full batch gradient descent rather than stochastic gradient descent.Finally, we show that when an input domain consists of a subset of task-relevant and task-irrelevant information, hidden representations do compress the task-irrelevant information, although the overall information about the input may monotonically increase with training time, and that this compression happens concurrently with the fitting process rather than during a subsequent compression period.

Wir zeigen, dass mehrere Behauptungen der Theorie des Informationsengpasses beim Deep Learning im allgemeinen Fall nicht zutreffen.


Over the past four years, neural networks have been proven vulnerable to adversarial images: targeted but imperceptible image perturbations lead to drastically different predictions.We show that adversarial vulnerability increases with the gradients of the training objective when viewed as a function of the inputs.For most current network architectures, we prove that the L1-norm of these gradients grows as the square root of the input size.These nets therefore become increasingly vulnerable with growing image size.Our proofs rely on the network’s weight distribution at initialization, but extensive experiments confirm that our conclusions still hold after usual training.

Neuronale Netze haben von Haus aus große Gradienten; das macht sie anfällig für negative Einflüsse.


Effective performance of neural networks depends critically on effective tuning of optimization hyperparameters, especially learning rates (and schedules thereof).We present Amortized Proximal Optimization (APO), which takes the perspective that each optimization step should approximately minimize a proximal objective (similar to the ones used to motivate natural gradient and trust region policy optimization).Optimization hyperparameters are adapted to best minimize the proximal objective after one weight update.We show that an idealized version of APO (where an oracle minimizes the proximal objective exactly) achieves global convergence to stationary point and locally second-order convergence to global optimum for neural networks.APO incurs minimal computational overhead.We experiment with using APO to adapt a variety of optimization hyperparameters online during training, including (possibly layer-specific) learning rates, damping coefficients, and gradient variance exponents.For a variety of network architectures and optimization algorithms (including SGD, RMSprop, and K-FAC), we show that with minimal tuning, APO performs competitively with carefully tuned optimizers.

Wir stellen die Amortisierte Proximale Optimierung (APO) vor, eine Methode zur Online-Anpassung einer Vielzahl von Optimierungshyperparametern während des Trainings, einschließlich Lernraten, Dämpfungskoeffizienten und Varianzexponenten des Gradienten.


Dense word vectors have proven their values in many downstream NLP tasks over the past few years.However, the dimensions of such embeddings are not easily interpretable.Out of the d-dimensions in a word vector, we would not be able to understand what high or low values mean.Previous approaches addressing this issue have mainly focused on either training sparse/non-negative constrained word embeddings, or post-processing standard pre-trained word embeddings.On the other hand, we analyze conventional word embeddings trained with Singular Value Decomposition, and reveal similar interpretability.We use a novel eigenvector analysis method inspired from Random Matrix Theory and show that semantically coherent groups not only form in the row space, but also the column space.This allows us to view individual word vector dimensions as human-interpretable semantic features.

Wir zeigen, dass die hervorstechenden Dimensionen von Wortvektoren als semantische Merkmale interpretiert werden können, ohne dass irgendwelche Einschränkungen oder Nachbearbeitungen erforderlich sind.


Neural networks in the brain and in neuromorphic chips confer systems with the ability to perform multiple cognitive tasks.However, both kinds of networks experience a wide range of physical perturbations, ranging from damage to edges of the network to complete node deletions, that ultimately could lead to network failure.A critical question is to understand how the computational properties of neural networks change in response to node-damage and whether there exist strategies to repair these networks in order to compensate for performance degradation.Here, we study the damage-response characteristics of two classes of neural networks, namely multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) trained to classify images from MNIST and CIFAR-10 datasets respectively.We also propose a new framework to discover efficient repair strategies to rescue damaged neural networks.The framework involves defining damage and repair operators for dynamically traversing the neural networks loss landscape, with the goal of mapping its salient geometric features.Using this strategy, we discover features that resemble path-connected attractor sets in the loss landscape.We also identify that a dynamic recovery scheme, where networks are constantly damaged and repaired, produces a group of networks resilient to damage as it can be quickly rescued.Broadly, our work shows that we can design fault-tolerant networks by applying on-line retraining consistently during damage for real-time applications in biology and machine learning.

Strategie zur Reparatur beschädigter neuronaler Netze


Automatic question generation from paragraphs is an important and challenging problem, particularly due to the long context from paragraphs.In this paper, we propose and study two hierarchical models for the task of question generation from paragraphs.Specifically, we propose(a) a novel hierarchical BiLSTM model with selective attention and(b) a novel hierarchical Transformer architecture, both of which learn hierarchical representations of paragraphs. We model a paragraph in terms of its constituent sentences, and a sentence in terms of its constituent words.While the introduction of the attention mechanism benefits the hierarchical BiLSTM model, the hierarchical Transformer, with its inherent attention and positional encoding mechanisms also performs better than flat transformer model.We conducted empirical evaluation on the widely used SQuAD and MS MARCO datasets using standard metrics. The results demonstrate the overall effectiveness of the hierarchical models over their flat counterparts. Qualitatively, our hierarchical models are able to generate fluent and relevant questions.

Automatische Fragengenerierung aus Absätzen unter Verwendung hierarchischer Modelle


Many deployed learned models are black boxes: given input, returns output.Internal information about the model, such as the architecture, optimisation procedure, or training data, is not disclosed explicitly as it might contain proprietary information or make the system more vulnerable.This work shows that such attributes of neural networks can be exposed from a sequence of queries.This has multiple implications.On the one hand, our work exposes the vulnerability of black-box neural networks to different types of attacks -- we show that the revealed internal information helps generate more effective adversarial examples against the black box model.On the other hand, this technique can be used for better protection of private content from automatic recognition models using adversarial examples.Our paper suggests that it is actually hard to draw a line between white box and black box models.

Die Abfrage eines neuronalen Black-Box-Netzes offenbart viele Informationen über dieses Netz; wir schlagen neuartige "Metamodelle" vor, um effektiv Informationen aus einer Black-Box zu extrahieren.


Inverse reinforcement learning (IRL) is used to infer the reward function from the actions of an expert running a Markov Decision Process (MDP).A novel approach using variational inference for learning the reward function is proposed in this research.Using this technique, the intractable posterior distribution of the continuous latent variable (the reward function in this case) is analytically approximated to appear to be as close to the prior belief while trying to reconstruct the future state conditioned on the current state and action.The reward function is derived using a well-known deep generative model known as Conditional Variational Auto-encoder (CVAE) with Wasserstein loss function, thus referred to as Conditional Wasserstein Auto-encoder-IRL (CWAE-IRL), which can be analyzed as a combination of the backward and forward inference.This can then form an efficient alternative to the previous approaches to IRL while having no knowledge of the system dynamics of the agent.Experimental results on standard benchmarks such as objectworld and pendulum show that the proposed algorithm can effectively learn the latent reward function in complex, high-dimensional environments.

Verwendung eines überwachten latenten Variablenmodellierungsrahmens zur Bestimmung der Belohnung bei einer Aufgabe des inversen Reinforcement Learnings


The travelling salesman problem (TSP) is a well-known combinatorial optimization problem with a variety of real-life applications.We tackle TSP by incorporating machine learning methodology and leveraging the variable neighborhood search strategy.More precisely, the search process is considered as a Markov decision process (MDP), where a 2-opt local search is used to search within a small neighborhood, while a Monte Carlo tree search (MCTS) method (which iterates through simulation, selection and back-propagation steps), is used to sample a number of targeted actions within an enlarged neighborhood.This new paradigm clearly distinguishes itself from the existing machine learning (ML) based paradigms for solving the TSP, which either uses an end-to-end ML model, or simply applies traditional techniques after ML for post optimization.Experiments based on two public data sets show that, our approach clearly dominates all the existing learning based TSP algorithms in terms of performance, demonstrating its high potential on the TSP.More importantly, as a general framework without complicated hand-crafted rules, it can be readily extended to many other combinatorial optimization problems.

Diese Arbeit kombiniert die Monte-Carlo-Baumsuche mit der lokalen 2-Opt-Suche in einem variablen Nachbarschaftsmodus, um das TSP effektiv zu lösen.


Significant strides have been made toward designing better generative models in recent years.Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data.For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can’t easily reproduce these structures.We propose to address this problem by incorporating programs representing global structure into the generative model—e.g., a 2D for-loop may represent a configuration of windows.Furthermore, we propose a framework for learning these models by leveraging program synthesis to generate training data.On both synthetic and real-world data, we demonstrate that our approach is substantially better than the state-of-the-art at both generating and completing images that contain global structure.

Anwendung der Programmsynthese auf die Aufgaben der Bildvervollständigung und -erzeugung innerhalb eines Deep Learning Frameworks


Learning control policies in robotic tasks requires a large number of interactions due to small learning rates, bounds on the updates or unknown constraints.In contrast humans can infer protective and safe solutions after a single failure or unexpected observation. In order to reach similar performance, we developed a hierarchical Bayesian optimization algorithm that replicates the cognitive inference and memorization process for avoiding failures in motor control tasks.A Gaussian Process implements the modeling and the sampling of the acquisition function.This enables rapid learning with large learning rates while a mental replay phase ensures that policy regions that led to failures are inhibited during the sampling process.    The features of the hierarchical Bayesian optimization method are evaluated in a simulated and physiological humanoid postural balancing task.We quantitatively compare the human learning performance to our learning approach by evaluating the deviations of the center of mass during training.Our results show that we can reproduce the efficient learning of human subjects in postural control tasks which provides a testable model for future physiological motor control tasks.In these postural control tasks, our method outperforms standard Bayesian Optimization in the number of interactions to solve the task, in the computational demands and in the frequency of observed failures.

In diesem Beitrag wird ein Berechnungsmodell für die effiziente Anpassung der menschlichen Haltungskontrolle vorgestellt, das auf hierarchischen Erfassungsfunktionen mit bekannten Merkmalen basiert.


Deep reinforcement learning has achieved great success in many previously difficult reinforcement learning tasks, yet recent studies show that deep RL agents are also unavoidably susceptible to adversarial perturbations, similar to deep neural networks in classification tasks.Prior works mostly focus on model-free adversarial attacks and agents with discrete actions.In this work, we study the problem of continuous control agents in deep RL with adversarial attacks and propose the first two-step algorithm based on learned model dynamics.Extensive experiments on various MuJoCo domains (Cartpole, Fish, Walker, Humanoid) demonstrate that our proposed framework is much more effective and efficient than model-free based attacks baselines in degrading agent performance as well as driving agents to unsafe states.

Wir untersuchen das Problem der kontinuierlichen Kontrolle von Agenten in tiefen RL mit gegnerischen Angriffen und schlagen einen zweistufigen Algorithmus vor, der auf gelernter Modelldynamik basiert.


A leading hypothesis for the surprising generalization of neural networks is that the dynamics of gradient descent bias the model towards simple solutions, by searching through the solution space in an incremental order of complexity.We formally define the notion of incremental learning dynamics and derive the conditions on depth and initialization for which this phenomenon arises in deep linear models.Our main theoretical contribution is a dynamical depth separation result, proving that while shallow models can exhibit incremental learning dynamics, they require the initialization to be exponentially small for these dynamics to present themselves.However, once the model becomes deeper, the dependence becomes polynomial and incremental learning can arise in more natural settings.We complement our theoretical findings by experimenting with deep matrix sensing, quadratic neural networks and with binary classification using diagonal and convolutional linear networks, showing all of these models exhibit incremental learning.

Wir untersuchen die Spärlichkeit verursachende Verzerrung von tiefen Modellen, die durch ihre Lerndynamik verursacht wird.


Generative modeling of high dimensional data like images is a notoriously difficult and ill-defined problem.In particular, how to evaluate a learned generative model is unclear.In this paper, we argue that *adversarial learning*, pioneered with generative adversarial networks (GANs), provides an interesting framework to implicitly define more meaningful task losses for unsupervised tasks, such as for generating "visually realistic" images.By relating GANs and structured prediction under the framework of statistical decision theory, we put into light links between recent advances in structured prediction theory and the choice of the divergence in GANs.We argue that the insights about the notions of "hard" and "easy" to learn losses can be analogously extended to adversarial divergences.We also discuss the attractive properties of parametric adversarial divergences for generative modeling, and perform experiments to show the importance of choosing a divergence that reflects the final task.

Parametrische adversarische Divergenzen definieren implizit aussagekräftigere Aufgabenverluste für die generative Modellierung. Wir ziehen Parallelen zur strukturierten Vorhersage, um die Eigenschaften dieser Divergenzen und ihre Fähigkeit zur Codierung der interessierenden Aufgabe zu untersuchen.


Experimental reproducibility and replicability are critical topics in machine learning.Authors have often raised concerns about their lack in scientific publications to improve the quality of the field.Recently, the graph representation learning field has attracted the attention of a wide research community, which resulted in a large stream of works.As such, several Graph Neural Network models have been developed to effectively tackle graph classification.However, experimental procedures often lack rigorousness and are hardly reproducible.Motivated by this, we provide an overview of common practices that should be avoided to fairly compare with the state of the art.To counter this troubling trend, we ran more than 47000 experiments in a controlled and uniform framework to re-evaluate five popular models across nine common benchmarks.Moreover, by comparing GNNs with structure-agnostic baselines we provide convincing evidence that, on some datasets, structural information has not been exploited yet.We believe that this work can contribute to the development of the graph learning field, by providing a much needed grounding for rigorous evaluations of graph classification models.

Wir bieten einen rigorosen Vergleich verschiedener Graph Neural Networks für die Graphklassifizierung.


Data augmentation (DA) is fundamental against overfitting in large convolutional neural networks, especially with a limited training dataset.In images, DA is usually based on heuristic transformations, like geometric or color transformations.Instead of using predefined transformations, our work learns data augmentation directly from the training data by learning to transform images with an encoder-decoder architecture combined with a spatial transformer network.The transformed images still belong to the same class, but are new, more complex samples for the classifier.Our experiments show that our approach is better than previous generative data augmentation methods, and comparable to predefined transformation methods when training an image classifier.

Automatisches Lernen von Datenerweiterungen unter Verwendung einer GAN-basierten Architektur zur Verbesserung eines Bildklassifizierers


We consider the problem of information compression from high dimensional data.Where many studies consider the problem of compression by non-invertible trans- formations, we emphasize the importance of invertible compression.We introduce new class of likelihood-based auto encoders with pseudo bijective architecture, which we call Pseudo Invertible Encoders.We provide the theoretical explanation of their principles.We evaluate Gaussian Pseudo Invertible Encoder on MNIST, where our model outperform WAE and VAE in sharpness of the generated images.

Neue Klasse von Autoencodern mit pseudo-invertierbarer Architektur


We exploit a recently derived inversion scheme for arbitrary deep neural networks to develop a new semi-supervised learning framework that applies to a wide range of systems and problems.  The approach reaches current state-of-the-art methods on MNIST and provides reasonable performances on SVHN and CIFAR10.Through the introduced method, residual networks are for the first time applied to semi-supervised tasks.Experiments with one-dimensional signals highlight the generality of the method.Importantly, our approach is simple, efficient, and requires no change in the deep network architecture.

Wir nutzen ein Inversionsschema für beliebige tiefe neuronale Netze, um einen neuen halbüberwachten Lernrahmen zu entwickeln, der auf viele Topologien anwendbar ist.


Deep learning has become a widely used tool in many computational and classification problems. Nevertheless obtaining and labeling data, which is needed for strong results, is often expensive or even not possible. In this paper three different algorithmic approaches to deal with limited access to data are evaluated and compared to each other. We show the drawbacks and benefits of each method. One successful approach, especially in one- or few-shot learning tasks, is the use of external data during the classification task. Another  successful approach, which achieves state of the art results in semi-supervised learning (SSL) benchmarks, is consistency regularization.Especially virtual adversarial training (VAT) has shown strong results and will be investigated in this paper. The aim of consistency regularization is to force the network not to change the output, when the input or the network itself is perturbed.Generative adversarial networks (GANs) have also shown strong empirical results. In many approaches the GAN architecture is used in order to create additional data and therefor to increase the generalization capability of the classification network.Furthermore we consider the use of unlabeled data for further performance improvement. The use of unlabeled data is investigated both for GANs and VAT.

Vergleich von Siamesischen Neuronalen Netzen, GANs und VAT für das Few-Shot Learning.


This paper introduces a new neural structure called FusionNet, which extends existing attention approaches from three perspectives.First, it puts forward a novel concept of "History of Word" to characterize attention information from the lowest word-level embedding up to the highest semantic-level representation.Second, it identifies an attention scoring function that better utilizes the "history of word" concept.Third, it proposes a fully-aware multi-level attention mechanism to capture the complete information in one text (such as a question) and exploit it in its counterpart (such as context or passage) layer by layer.We apply FusionNet to the Stanford Question Answering Dataset (SQuAD) and it achieves the first position for both single and ensemble model on the official SQuAD leaderboard at the time of writing (Oct. 4th, 2017).Meanwhile, we verify the generalization of FusionNet with two adversarial SQuAD datasets and it sets up the new state-of-the-art on both datasets: on AddSent, FusionNet increases the best F1 metric from 46.6% to 51.4%; on AddOneSent, FusionNet boosts the best F1 metric from 56.0% to 60.7%.

Wir schlagen eine leichtgewichtige Verbesserung der Aufmerksamkeit und eine neuronale Architektur, FusionNet, vor, um SotA auf SQuAD und adversarial SQuAD zu erreichen.


We propose a novel hierarchical generative model with a simple Markovian structure and a corresponding inference model.Both the generative and inference model are trained using the adversarial learning paradigm.We demonstrate that the hierarchical structure supports the learning of progressively more abstract representations as well as providing semantically meaningful reconstructions with different levels of fidelity.Furthermore, we show that minimizing the Jensen-Shanon divergence between the generative and inference network is enough to minimize the reconstruction error. The resulting semantically meaningful hierarchical latent structure discovery is exemplified on the CelebA dataset.  There, we show that the features learned by our model in an unsupervised way outperform the best handcrafted features. Furthermore, the extracted features remain competitive when compared to several recent deep supervised approaches on an attribute prediction task on CelebA.Finally, we leverage the model's inference network to achieve state-of-the-art performance on a semi-supervised variant of the MNIST digit classification task.

Adversarial trainiertes hierarchisches generatives Modell mit robuster und semantisch gelernter latenter Repräsentation.


Conservation laws are considered to be fundamental laws of nature.It has broad application in many fields including physics, chemistry, biology, geology, and engineering.Solving the differential equations associated with conservation laws is a major branch in computational mathematics.Recent success of machine learning, especially deep learning, in areas such as computer vision and natural language processing, has attracted a lot of attention from the community of computational mathematics and inspired many intriguing works in combining machine learning with traditional methods.In this paper, we are the first to explore the possibility and benefit of solving nonlinear conservation laws using deep reinforcement learning.As a proof of concept, we focus on 1-dimensional scalar conservation laws.We deploy the machinery of deep reinforcement learning to train a policy network that can decide on how the numerical solutions should be approximated in a sequential and spatial-temporal adaptive manner.We will show that the problem of solving conservation laws can be naturally viewed as a sequential decision making process and the numerical schemes learned in such a way can easily enforce long-term accuracy. Furthermore, the learned policy network is carefully designed to determine a good local discrete approximation based on the current state of the solution, which essentially makes the proposed method a meta-learning approach.In other words, the proposed method is capable of learning how to discretize for a given situation mimicking human experts.Finally, we will provide details on how the policy network is trained, how well it performs compared with some state-of-the-art numerical solvers such as WENO schemes, and how well it generalizes.Our code is released anomynously at \url{https://github.com/qwerlanksdf/L2D}.

Wir stellen fest, dass numerische PDE-Löser als Markov-Desicion-Prozesse betrachtet werden können, und schlagen vor, Reinforcement Learning zur Lösung skalarer 1D-Erhaltungsgesetze einzusetzen


We present a neural rendering architecture that helps variational autoencoders (VAEs) learn disentangled representations.Instead of the deconvolutional network typically used in the decoder of VAEs, we tile (broadcast) the latent vector across space, concatenate fixed X- and Y-“coordinate” channels, and apply a fully convolutional network with 1x1 stride.This provides an architectural prior for dissociating positional from non-positional features in the latent space, yet without providing any explicit supervision to this effect.We show that this architecture, which we term the Spatial Broadcast decoder, improves disentangling, reconstruction accuracy, and generalization to held-out regions in data space.  We show the Spatial Broadcast Decoder is complementary to state-of-the-art (SOTA) disentangling techniques and when incorporated improves their performance.

Wir stellen eine neuronale Rendering-Architektur vor, die VAEs dabei hilft, entkoppelte latente Repräsentationen zu lernen.


Similar to humans and animals, deep artificial neural networks exhibit critical periods during which a temporary stimulus deficit can impair the development of a skill.The extent of the impairment depends on the onset and length of the deficit window, as in animal models, and on the size of the neural network.Deficits that do not affect low-level statistics, such as vertical flipping of the images, have no lasting effect on performance and can be overcome with further training.  To better understand this phenomenon, we use the Fisher Information of the weights to measure the effective connectivity between layers of a network during training.  Counterintuitively, information rises rapidly in the early phases of training, and then decreases, preventing redistribution of information resources in a phenomenon we refer to as a loss of "Information Plasticity".  Our analysis suggests that the first few epochs are critical for the creation of strong connections that are optimal relative to the input data distribution.Once such strong connections are created, they do not appear to change during additional training.These findings suggest that the initial learning transient, under-scrutinized compared to asymptotic behavior, plays a key role in determining the outcome of the training process.Our findings, combined with recent theoretical results in the literature, also suggest that forgetting (decrease of information in the weights) is critical to achieving invariance and disentanglement in representation learning.Finally, critical periods are not restricted to biological systems, but can emerge naturally in learning systems, whether biological or artificial, due to fundamental constrains arising from learning dynamics and information processing.

Sensorische Defizite in frühen Trainingsphasen können sowohl in künstlichen als auch in neuronalen Netzen zu irreversiblen Leistungseinbußen führen, was auf Informationsphänomene als gemeinsame Ursache hindeutet und auf die Bedeutung des anfänglichen Einschwingens und Vergessens hinweist.


We propose a method to incrementally learn an embedding space over the domain of network architectures, to enable the careful selection of architectures for evaluation during compressed architecture search.Given a teacher network, we search for a compressed network architecture by using Bayesian Optimization (BO) with a kernel function defined over our proposed embedding space to select architectures for evaluation.We demonstrate that our search algorithm can significantly outperform various baseline methods, such as random search and reinforcement learning (Ashok et al., 2018).The compressed architectures found by our method are also better than the state-of-the-art manually-designed compact architecture ShuffleNet (Zhang et al., 2018).We also demonstrate that the learned embedding space can be transferred to new settings for architecture search, such as a larger teacher network or a teacher network in a different architecture family, without any training.

Wir schlagen eine Methode zum inkrementellen Lernen eines Einbettungsraums über die Domäne der Netzwerkarchitekturen vor, um eine sorgfältige Auswahl von Architekturen für die Evaluierung während der komprimierten Architektursuche zu ermöglichen.


Reinforcement  Learning  (RL) problem can be solved in two different ways - the Value function-based approach and the policy optimization-based approach - to eventually arrive at an optimal policy for the given environment.One of the recent breakthroughs in reinforcement learning is the use of deep neural networks as function approximators to approximate the value function or q-function in a reinforcement learning scheme.This has led to results with agents automatically learning how to play games like alpha-go showing better-than-human performance.Deep Q-learning networks (DQN) and  Deep Deterministic Policy Gradient (DDPG) are two such methods that have shown state-of-the-art results in recent times.Among the many variants of RL, an important class of problems is where the state and action spaces are continuous --- autonomous robots, autonomous vehicles, optimal control are all examples of such problems that can lend themselves naturally to reinforcement based algorithms, and have continuous state and action spaces.In this paper, we adapt and combine approaches such as DQN and DDPG in novel ways to outperform the earlier results for continuous state and action space problems.  We believe these results are a valuable addition to the fast-growing body of results on Reinforcement Learning, more so for continuous state and action space problems.

Verbesserung der Leistung eines RL-Agenten in einem kontinuierlichen Handlungs- und Zustandsraum durch priorisierte Erfahrungswiedergabe und Parameterrauschen.


Natural Language Inference (NLI) task requires an agent to determine the logical relationship between a natural language premise and a natural language hypothesis.We introduce Interactive Inference Network (IIN), a novel class of neural network architectures that is able to achieve high-level understanding of the sentence pair by hierarchically extracting semantic features from interaction space.We show that an interaction tensor (attention weight) contains semantic information to solve natural language inference, and a denser interaction tensor contains richer semantic information.One instance of such architecture, Densely Interactive Inference Network (DIIN), demonstrates the state-of-the-art performance on large scale NLI copora and large-scale NLI alike corpus.It's noteworthy that DIIN achieve a greater than 20% error reduction on the challenging Multi-Genre NLI (MultiNLI) dataset with respect to the strongest published system.

zeigen, dass mehrkanalige Aufmerksamkeitsgewichte semantische Merkmale enthalten, um die Aufgabe der Inferenz natürlicher Sprache zu lösen.


Determinantal Point Processes (DPPs) provide an elegant and versatile way to sample sets of items that balance the point-wise quality with the set-wise diversity of selected items.For this reason, they have gained prominence in many machine learning applications that rely on subset selection.However, sampling from a DPP over a ground set of size N is a costly operation, requiring in general an O(N^3) preprocessing cost and an O(Nk^3) sampling cost for subsets of size k. We approach this problem by introducing DppNets: generative deep models that produce DPP-like samples for arbitrary ground sets.  We develop an inhibitive attention mechanism based on transformer networks that captures a notion of dissimilarity between feature vectors.  We show theoretically that such an approximation is sensible as it maintains the guarantees of inhibition or dissimilarity that makes DPP so powerful and unique.  Empirically, we demonstrate that samples from our model receive high likelihood under the more expensive DPP alternative.

Wir approximieren Determinantal Point Processes mit neuronalen Netzen und begründen unser Modell theoretisch und empirisch.


This paper introduces a network architecture to solve the structure-from-motion (SfM) problem via feature-metric bundle adjustment (BA), which explicitly enforces multi-view geometry constraints in the form of feature-metric error.The whole pipeline is differentiable, so that the network can learn suitable features that make the BA problem more tractable.Furthermore, this work introduces a novel depth parameterization to recover dense per-pixel depth.The network first generates several basis depth maps according to the input image, and optimizes the final depth as a linear combination of these basis depth maps via feature-metric BA.The basis depth maps generator is also learned via end-to-end training.The whole system nicely combines domain knowledge (i.e. hard-coded multi-view geometry constraints) and deep learning (i.e. feature learning and basis depth maps learning) to address the challenging dense SfM problem.Experiments on large scale real data prove the success of the proposed method.

In diesem Beitrag wird eine Netzwerkarchitektur zur Lösung des Structure-from-Motion (SfM)-Problems mittels Feature-Bundle-Anpassung (BA) vorgestellt.


Temporal Difference Learning with function approximation is known to be unstable.Previous work like \citet{sutton2009fast} and \citet{sutton2009convergent} has presented alternative objectives that are stable to minimize.However, in practice, TD-learning with neural networks requires various tricks like using a target network that updates slowly \citep{mnih2015human}.In this work we propose a constraint on the TD update that minimizes change to the target values.This constraint can be applied to the gradients of any TD objective, and can be easily applied to nonlinear function approximation.We validate this update by applying our technique to deep Q-learning, and training without a target network.We also show that adding this constraint on Baird's counterexample keeps Q-learning from diverging.

Wir zeigen, dass das Hinzufügen einer Beschränkung zu TD-Updates das Lernen stabilisiert und Deep Q-learning ohne ein Zielnetzwerk ermöglicht


We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets.DuoRC contains 186,089 unique question-answer pairs created from a collection of 7680 pairs of movie plots where each pair in the collection reflects two versions of the same movie - one from Wikipedia and the other from IMDb - written by two different authors.We asked crowdsourced workers to create questions from one version of the plot and a different set of workers to extract or synthesize corresponding answers from the other version.This unique characteristic of DuoRC where questions and answers are created from different versions of a document narrating the same underlying story, ensures by design, that there is very little lexical overlap between the questions created from one version and the segments containing the answer in the other version.Further, since the two versions have different level of plot detail, narration style, vocabulary, etc., answering questions from the second version requires deeper language understanding and incorporating background knowledge not available in the given text.Additionally, the narrative style of passages arising from movie plots (as opposed to typical descriptive passages in existing datasets) exhibits the need to perform complex reasoning over events across multiple sentences.Indeed, we observe that state-of-the-art neural RC models which have achieved near human performance on the SQuAD dataset, even when coupled with traditional NLP techniques to address the challenges presented in DuoRC exhibit very poor performance (F1 score of 37.42% on DuoRC v/s 86% on SQuAD dataset).This opens up several interesting research avenues wherein DuoRC could complement other Reading Comprehension style datasets to explore novel neural approaches for studying language understanding.

Wir schlagen DuoRC vor, einen neuartigen Datensatz für das Leseverständnis (RC), der 186.089 von Menschen erzeugte QA-Paare enthält, die aus einer Sammlung von 7680 Paaren paralleler Filmhandlungen erstellt wurden, und führen eine RC-Aufgabe ein, bei der eine Version der Handlung gelesen und Fragen beantwortet werden müssen, die aus der anderen Version erstellt wurden; dies erfordert komplexes logisches Denken und ein tieferes Sprachverständnis, um die geringe lexikalische Überschneidung zwischen der Handlung und der Frage zu überwinden.


We consider the problem of weakly supervised structured prediction (SP) with reinforcement learning (RL) – for example, given a database table and a question, perform a sequence of computation actions on the table, which generates a response and  receives  a  binary  success-failure  reward.   This  line  of  research  has  been successful by leveraging RL to directly optimizes the desired metrics of the SP tasks – for example, the accuracy in question answering or BLEU score in machine translation.  However, different from the common RL settings, the environment dynamics is deterministic in SP, which hasn’t been fully utilized by the model-freeRL methods that are usually applied.Since SP models usually have full access to the environment dynamics, we propose to apply model-based RL methods, which rely on planning as a primary model component.We demonstrate the effectiveness of planning-based SP with a Neural Program Planner (NPP), which, given a set of candidate programs from a pretrained search policy, decides which program is the most promising considering all the information generated from executing these programs.We evaluate NPP on weakly supervised program synthesis from natural language(semantic parsing) by stacked learning a planning module based on pretrained search policies.On the WIKITABLEQUESTIONS benchmark, NPP achieves a new state-of-the-art of 47.2% accuracy.

Eine modellbasierte Planungskomponente verbessert RL-basiertes semantisches Parsing auf WikiTableQuestions.


Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost.To address this cost, a number of quantization schemeshave been proposed - but most of these techniques focused on quantizing weights, which are relatively smaller in size compared to activations.This paper proposes a novel quantization scheme for activations during training - that enables neural networks to work well with ultra low precision weights and activations without any significant accuracy degradation.  This technique, PArameterized Clipping acTi-vation (PACT), uses an activation clipping parameter α that is optimized duringtraining to find the right quantization scale.PACT allows quantizing activations toarbitrary bit precisions, while achieving much better accuracy relative to publishedstate-of-the-art quantization schemes.We show, for the first time, that both weights and activations can be quantized to 4-bits of precision while still achieving accuracy comparable to full precision networks across a range of popular models and datasets.We also show that exploiting these reduced-precision computational units in hardware can enable a super-linear improvement in inferencing performance dueto a significant reduction in the area of accelerator compute engines coupled with the ability to retain the quantized model and activation data in on-chip memories.

Eine neue Art der Quantisierung der Aktivierung eines tiefen neuronalen Netzes durch parametrisiertes Clipping, das die Quantisierungsskala durch stochastischen Gradientenabstieg optimiert.


Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting.This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts.To better understand this phenomenon, we analyze the behavior of pruning over the course of training, finding that pruning's effect on generalization relies more on the instability it generates (defined as the drops in test accuracy immediately following pruning) than on the final size of the pruned model.We demonstrate that even the pruning of unimportant parameters can lead to such instability, and show similarities between pruning and regularizing by injecting noise, suggesting a mechanism for pruning-based generalization improvements that is compatible with the strong generalization recently observed in over-parameterized networks.

Wir zeigen, dass Pruning-Methoden, die eine größere Instabilität in den Verlust einbringen, auch zu einer verbesserten Generalisierung führen, und untersuchen die Mechanismen, die diesem Effekt zugrunde liegen.


Neural networks trained with backpropagation, the standard algorithm of deep learning which uses weight transport, are easily fooled by existing gradient-based adversarial attacks.This class of attacks are based on certain small perturbations of the inputs to make networks misclassify them.We show that less biologically implausible deep neural networks trained with feedback alignment, which do not use weight transport, can be harder to fool, providing actual robustness.Tested on MNIST, deep neural networks trained without weight transport (1) have an adversarial accuracy of 98% compared to 0.03% for neural networks trained with backpropagation and (2) generate non-transferable adversarial examples.However, this gap decreases on CIFAR-10 but is still significant particularly for small perturbation magnitude less than 1 ⁄ 2.

Weniger biologisch unplausible tiefe neuronale Netze, die ohne Gewichtstransport trainiert werden, können schwerer zu täuschen sein.


Chemical reactions can be described as the stepwise redistribution of electrons in molecules.As such, reactions are often depicted using "arrow-pushing" diagrams which show this movement as a sequence of arrows.We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data.Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of(a) being easy for chemists to interpret,(b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and(c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants.We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings.Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines.Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.

Ein generatives Modell zur Reaktionsvorhersage, das die mechanistischen Elektronenschritte einer Reaktion direkt aus den Rohdaten der Reaktion lernt.


When machine learning models are used for high-stakes decisions, they should predict accurately, fairly, and responsibly.To fulfill these three requirements, a model must be able to output a reject option (i.e. say "``I Don't Know") when it is not qualified to make a prediction.In this work, we propose learning to defer, a method by which a model can defer judgment to a downstream decision-maker such as a human user.We show that learning to defer generalizes the rejection learning framework in two ways: by considering the effect of other agents in the decision-making process, and by allowing for optimization of complex objectives.We propose a learning algorithm which accounts for potential biases held by decision-makerslater in a pipeline.Experiments on real-world datasets demonstrate that learningto defer can make a model not only more accurate but also less biased.Even whenoperated by highly biased users, we show thatdeferring models can still greatly improve the fairness of the entire pipeline.

Die Möglichkeit, "Ich weiß es nicht" zu sagen, kann die Fairness eines Klassifizierers verbessern, ohne dass die Genauigkeit zu sehr darunter leidet, und diese Verbesserung wird noch effektiver, wenn der Klassifizierer Einblick in die nachgelagerte Entscheidungsfindung hat.


Hierarchical Task Networks (HTN) generate plans using a decomposition process guided by extra domain knowledge to guide search towards a planning task.While many HTN planners can make calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls.Conversely, the few classical planners that are capable of using external calls (often called semantic attachments) during planning do so in much more limited ways by generating a fixed number of ground operators at problem grounding time.In this paper we develop the notion of semantic attachments for HTN planning using semi co-routines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner.The resulting planner can then use such co-routines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables).We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.

Ein Ansatz zur Durchführung der HTN-Planung unter Verwendung externer Prozeduren zur Auswertung von Prädikaten zur Laufzeit (semantische Anhänge).


Recent literature suggests that averaged word vectors followed by simple post-processing outperform many deep learning methods on semantic textual similarity tasks.Furthermore, when averaged word vectors are trained supervised on large corpora of paraphrases, they achieve state-of-the-art results on standard STS benchmarks.Inspired by these insights, we push the limits of word embeddings even further.We propose a novel fuzzy bag-of-words (FBoW) representation for text that contains all the words in the vocabulary simultaneously but with different degrees of membership, which are derived from similarities between word vectors.We show that max-pooled word vectors are only a special case of fuzzy BoW and should be compared via fuzzy Jaccard index rather than cosine similarity.Finally, we propose DynaMax, a completely unsupervised and non-parametric similarity measure that dynamically extracts and max-pools good features depending on the sentence pair.This method is both efficient and easy to implement, yet outperforms current baselines on STS tasks by a large margin and is even competitive with supervised word vectors trained to directly optimise cosine similarity.

Max-gepoolte Wortvektoren mit Fuzzy-Jacard-Set-Ähnlichkeit sind eine äußerst wettbewerbsfähige Grundlage für semantische Ähnlichkeit; wir schlagen eine einfache dynamische Variante vor, die noch besser abschneidet.


State-of-the-art results in imitation learning are currently held by adversarial methods that iteratively estimate the divergence between student and expert policies and then minimize this divergence to bring the imitation policy closer to expert behavior.Analogous techniques for imitation learning from observations alone (without expert action labels), however, have not enjoyed the same ubiquitous successes. Recent work in adversarial methods for generative models has shown that the measure used to judge the discrepancy between real and synthetic samples is an algorithmic design choice, and that different choices can result in significant differences in model performance.Choices including Wasserstein distance and various $f$-divergences have already been explored in the adversarial networks literature, while more recently the latter class has been investigated for imitation learning.Unfortunately, we find that in practice this existing imitation-learning framework for using $f$-divergences suffers from numerical instabilities stemming from the combination of function approximation and policy-gradient reinforcement learning.In this work, we alleviate these challenges and offer a reparameterization of adversarial imitation learning as $f$-divergence minimization before further extending the framework to handle the problem of imitation from observations only.Empirically, we demonstrate that our design choices for coupling imitation learning and $f$-divergences are critical to recovering successful imitation policies.Moreover, we find that with the appropriate choice of $f$-divergence, we can obtain imitation-from-observation algorithms that outperform baseline approaches and more closely match expert performance in continous-control tasks with low-dimensional observation spaces.With high-dimensional observations, we still observe a significant gap with and without action labels, offering an interesting avenue for future work.

Das übergeordnete Ziel dieser Arbeit ist es, durch die Verwendung von f-Divergenzen eine stichprobeneffiziente Nachahmung von Expertendemonstrationen zu ermöglichen, sowohl mit als auch ohne die Bereitstellung von Experten-Handlungsbezeichnungen.


Momentary fluctuations in attention (perceptual accuracy) correlate with neural activity fluctuations in primate visual areas.Yet, the link between such momentary neural fluctuations and attention state remains to be shown in the human brain.We investigate this link using a real-time cognitive brain machine interface (cBMI) based on steady state visually evoked potentials (SSVEPs): occipital EEG potentials evoked by rhythmically flashing stimuli.Tracking momentary fluctuations in SSVEP power, in real-time, we presented stimuli time-locked to when this power reached (predetermined) high or low thresholds.We observed a significant increase in discrimination accuracy (d') when stimuli were triggered during high (versus low) SSVEP power epochs, at the location cued for attention.Our results indicate a direct link between attention’s effects on perceptual accuracy and and neural gain in EEG-SSVEP power, in the human brain.

Mit einem kognitiven Brain-Machine-Interface zeigen wir einen direkten Zusammenhang zwischen Aufmerksamkeitseffekten auf die Wahrnehmungsgenauigkeit und der neuronalen Verstärkung der EEG-SSVEP-Leistung im menschlichen Gehirn.


Recent studies have demonstrated the vulnerability of deep convolutional neural networks against adversarial examples.Inspired by the observation that the intrinsic dimension of image data is much smaller than its pixel space dimension and the vulnerability of neural networks grows with the input dimension, we propose to embed high-dimensional input images into a low-dimensional space to perform classification.However, arbitrarily projecting the input images to a low-dimensional space without regularization will not improve the robustness of deep neural networks.We propose a new framework, Embedding Regularized Classifier (ER-Classifier), which improves the adversarial robustness of the classifier through embedding regularization.Experimental results on several benchmark datasets show that, our proposed framework achieves state-of-the-art performance against strong adversarial attack methods.

Ein allgemeiner und einfach zu verwendender Rahmen, der die Widerstandsfähigkeit von tiefen Klassifikationsmodellen durch Einbettungsregularisierung verbessert.


We investigate task clustering for deep learning-based multi-task and few-shot learning in the settings with large numbers of diverse tasks.Our method measures task similarities using cross-task transfer performance matrix.Although this matrix provides us critical information regarding similarities between tasks, the uncertain task-pairs, i.e., the ones with extremely asymmetric transfer scores, may collectively mislead clustering algorithms to output an inaccurate task-partition.Moreover, when the number of tasks is large, generating the full transfer performance matrix can be very time consuming.To overcome these limitations, we propose a novel task clustering algorithm to estimate the similarity matrix based on the theory of matrix completion.The proposed algorithm can work on partially-observed similarity matrices based on only sampled task-pairs with reliable scores, ensuring its efficiency and robustness.Our theoretical analysis shows that under mild assumptions, the reconstructed matrix perfectly matches the underlying “true” similarity matrix with an overwhelming probability.The final task partition is computed by applying an efficient spectral clustering algorithm to the recovered matrix.Our results show that the new task clustering method can discover task clusters that benefit both multi-task learning and few-shot learning setups for sentiment classification and dialog intent classification tasks.

Wir schlagen einen auf Matrix-Vervollständigung basierenden Aufgaben-Clustering-Algorithmus für tiefes Multitasking und "few-shot learning" in Umgebungen mit einer großen Anzahl unterschiedlicher Aufgaben vor.


The resemblance between the methods used in studying quantum-many body physics and in machine learning has drawn considerable attention.In particular, tensor networks (TNs) and deep learning architectures bear striking similarities to the extent that TNs can be used for machine learning.Previous results used one-dimensional TNs in image recognition, showing limited scalability and a request of high bond dimension.In this work, we train two-dimensional hierarchical TNs to solve image recognition problems, using a training algorithm derived from the multipartite entanglement renormalization ansatz (MERA).This approach overcomes scalability issues and implies novel mathematical connections among quantum many-body physics, quantum information theory, and machine learning.While keeping the TN unitary in the training phase, TN states can be defined, which optimally encodes each class of the images into a quantum many-body state.We study the quantum features of the TN states, including quantum entanglement and fidelity.We suggest these quantities could be novel properties that characterize the image classes, as well as the machine learning tasks.Our work could be further applied to identifying possible quantum properties of certain artificial intelligence methods.

Dieser Ansatz überwindet Skalierbarkeitsprobleme und impliziert neuartige mathematische Verbindungen zwischen Quantenvielteilchenphysik, Quanteninformationstheorie und maschinellem Lernen.


Predicting outcomes and planning interactions with the physical world are long-standing goals for machine learning.A variety of such tasks involves continuous physical systems, which can be described by partial differential equations (PDEs) with many degrees of freedom.Existing methods that aim to control the dynamics of such systems are typically limited to relatively short time frames or a small number of interaction parameters.We present a novel hierarchical predictor-corrector scheme which enables neural networks to learn to understand and control complex nonlinear physical systems over long time frames.We propose to split the problem into two distinct tasks: planning and control.To this end, we introduce a predictor network that plans optimal trajectories and a control network that infers the corresponding control parameters.Both stages are trained end-to-end using a differentiable PDE solver.We demonstrate that our method successfully develops an understanding of complex physical systems and learns to control them for tasks involving PDEs such as the incompressible Navier-Stokes equations.

Wir trainieren eine Kombination von neuronalen Netzen, um optimale Flugbahnen für komplexe physikalische Systeme vorherzusagen.


The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -- minima where the output of the network is resilient to small random noise added to its parameters. So far this observation has been used to provide generalization guarantees only for neural networks whose parameters are either \textit{stochastic} or \textit{compressed}.In this work, we present a general PAC-Bayesian framework that leverages this observation to provide a bound on the original network learned -- a network that is deterministic and uncompressed.  What enables us to do this is a key novelty in our approach: our framework allows us to show that if on training data, the interactions between the weight matrices satisfy certain conditions that imply a wide training loss minimum, these conditions themselves {\em generalize} to the interactions between the matrices on test data, thereby implying a wide test loss minimum.We then apply our general framework in a setup where we assume that the pre-activation values of the network are not too small (although we assume this only on the training data).In this setup, we provide a generalization guarantee for the original (deterministic, uncompressed) network, that does not scale with product of the spectral norms of the weight matrices -- a guarantee that would not have been possible with prior approaches.

Wir bieten eine PAC-Bayes-basierte Generalisierungsgarantie für unkomprimierte, deterministische tiefe Netzwerke, indem wir die Rauschresistenz des Netzwerks auf den Trainingsdaten auf die Testdaten verallgemeinern.


Training neural networks to be certifiably robust is critical to ensure their safety against adversarial attacks.However, it is currently very difficult to train a neural network that is both accurate and certifiably robust.In this work we take a step towards addressing this challenge.We prove that for every continuous function $f$, there exists a network $n$ such that:(i) $n$ approximates $f$ arbitrarily close, and(ii) simple interval bound propagation of a region $B$ through $n$ yields a result that is arbitrarily close to the optimal output of $f$ on $B$.Our result can be seen as a Universal Approximation Theorem for interval-certified ReLU networks.To the best of our knowledge, this is the first work to prove the existence of accurate, interval-certified networks.

Wir beweisen, dass es für eine große Klasse von Funktionen f ein intervallzertifiziertes robustes Netzwerk gibt, das f bis zu einer beliebigen Genauigkeit approximiert.


In this paper, we propose an efficient framework to accelerate convolutional neural networks.We utilize two types of acceleration methods: pruning and hints.Pruning can reduce model size by removing channels of layers.Hints can improve the performance of student model by transferring knowledge from teacher model.We demonstrate that pruning and hints are complementary to each other.On one hand, hints can benefit pruning by maintaining similar feature representations.On the other hand, the model pruned from teacher networks is a good initialization for student model, which increases the transferability between two networks.Our approach performs pruning stage and hints stage iteratively to further improve theperformance.Furthermore, we propose an algorithm to reconstruct the parameters of hints layer and make the pruned model more suitable for hints.Experiments were conducted on various tasks including classification and pose estimation.Results on CIFAR-10, ImageNet and COCO demonstrate the generalization and superiority of our framework.

Dies ist eine Arbeit, die darauf abzielt, alle bestehenden Pruning- und Mimicmethoden zu verbessern.


For typical sequence prediction problems such as language generation, maximum likelihood estimation (MLE) has commonly been adopted as it encourages the predicted sequence most consistent with the ground-truth sequence to have the highest probability of occurring.However, MLE focuses on once-to-all matching between the predicted sequence and gold-standard, consequently treating all incorrect predictions as being equally incorrect.We refer to this drawback as {\it negative diversity ignorance} in this paper.Treating all incorrect predictions as equal unfairly downplays the nuance of these sequences' detailed token-wise structure.To counteract this, we augment the MLE loss by introducing an extra Kullback--Leibler divergence term derived by comparing a data-dependent Gaussian prior and the detailed training prediction.The proposed data-dependent Gaussian prior objective (D2GPo) is defined over a prior topological order of tokens and is poles apart from the data-independent Gaussian prior (L2 regularization) commonly adopted in smoothing the training of MLE.Experimental results show that the proposed method makes effective use of a more detailed prior in the data and has improved performance in typical language generation tasks, including supervised and unsupervised machine translation, text summarization, storytelling, and image captioning.

Wir führen ein zusätzliches datenabhängiges Gauß'sches Prioritätsziel ein, um das derzeitige MLE-Training zu ergänzen, das dazu dient, das Vorwissen in den "ground-truth"-Daten zu erfassen.


We  propose an  interactive  classification approach  for natural  language  queries.Instead of classifying given the natural language query only, we ask the user for additional information using a sequence of binary and multiple-choice questions.At each turn, we use a policy controller to decide if to present a question or pro-vide the user the final answer, and select the best question to ask by maximizing the system information gain.Our formulation enables bootstrapping the system without any interaction data, instead relying on non-interactive crowdsourcing an-notation tasks.Our evaluation shows the interaction helps the system increase its accuracy and handle ambiguous queries, while our approach effectively balances the number of questions and the final accuracy.

Wir schlagen einen interaktiven Ansatz für die Klassifizierung von natürlichsprachlichen Anfragen vor, indem wir den Benutzer um zusätzliche Informationen bitten,  unter der Nutzung des Informationgains und eines Reinforcement Learning Policy Controllers.


Convolutional neural networks (CNNs) have achieved state of the art performance on recognizing and representing audio, images, videos and 3D volumes; that is, domains where the input can be characterized by a regular graph structure. However, generalizing CNNs to irregular domains like 3D meshes is challenging.Additionally, training data for 3D meshes is often limited.In this work, we generalize convolutional autoencoders to mesh surfaces.We perform spectral decomposition of meshes and apply convolutions directly in frequency space.In addition, we use max pooling and introduce upsampling within the network to represent meshes in a low dimensional space.We construct a complex dataset of 20,466 high resolution meshes with extreme facial expressions and encode it using our Convolutional Mesh Autoencoder.Despite limited training data, our method outperforms state-of-the-art PCA models of faces with 50% lower error,  while using 75% fewer parameters.

Convolutional Autoencoders verallgemeinert auf Mesh-Oberflächen für die Kodierung und Rekonstruktion von extremen 3D-Gesichtsausdrücken.


Computing distances between examples is at the core of many learning algorithms for time series.Consequently, a great deal of work has gone into designing effective time series distance measures.We present Jiffy, a simple and scalable distance metric for multivariate time series.Our approach is to reframe the task as a representation learning problem---rather than design an elaborate distance function, we use a CNN to learn an embedding such that the Euclidean distance is effective.By aggressively max-pooling and downsampling, we are able to construct this embedding using a highly compact neural network.Experiments on a diverse set of multivariate time series datasets show that our approach consistently outperforms existing methods.

Jiffy ist ein Convolutional Ansatz zum Erlernen einer Abstandsmetrik für multivariate Zeitreihen, der bestehende Methoden in Bezug auf die Klassifizierungsgenauigkeit der nächsten Nachbarn übertrifft.


Prefrontal cortex (PFC) is a part of the brain which is responsible for behavior repertoire.Inspired by PFC functionality and connectivity,  as well as human behavior formation process, we propose a novel modular architecture of neural networks with a Behavioral Module (BM) and corresponding end-to-end training strategy.  This approach allows the efficient learning of behaviors and preferences representation.This property is particularly useful for user modeling (as for dialog agents) and recommendation tasks, as allows learning personalized representations of different user states.  In the experiment with video games playing, the resultsshow that the proposed method allows separation of main task’s objectives andbehaviors between different BMs.The experiments also show network extendability through independent learning of new behavior patterns.Moreover, we demonstrate a strategy for an efficient transfer of newly learned BMs to unseen tasks.

Eine erweiterbare modulare Architektur wird für die Entwicklung einer Vielzahl von Agentenverhaltensweisen im DQN vorgeschlagen.