A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP).We provide a general framework for analyzing this scenario, which we use to design multiple synthetic benchmarks from only modifying the observation space of an MDP.When an agent overfits to different observation spaces even if the underlying MDP dynamics is fixed, we term this observational overfitting.Our experiments expose intriguing properties especially with regards to implicit regularization, and also corroborate results from previous works in RL generalization and supervised learning (SL).

Wir isolieren einen Faktor der RL-Verallgemeinerung, indem wir den Fall analysieren, in dem der Agent nur auf die Beobachtungen überpasst. Wir zeigen, dass architektonische implizite Regularisierungen in diesem Regime auftreten.


We propose a neural language model capable of unsupervised syntactic structure induction.The model leverages the structure information to form better semantic representations and better language modeling.Standard recurrent neural networks are limited by their structure and fail to efficiently use syntactic information.On the other hand, tree-structured recursive networks usually require additional structural supervision at the cost of human expert annotation.In this paper, We propose a novel neural language model, called the Parsing-Reading-Predict Networks (PRPN), that can simultaneously induce the syntactic structure from unannotated sentences and leverage the inferred structure to learn a better language model.In our model, the gradient can be directly back-propagated from the language model loss into the neural parsing network.Experiments show that the proposed model can discover the underlying syntactic structure and achieve state-of-the-art performance on word/character-level language model tasks.

In diesem Papier schlagen wir ein neuartiges neuronales Sprachmodell vor, das so genannte Parsing-Reading-Predict Networks (PRPN), das gleichzeitig die syntaktische Struktur aus unannotierten Sätzen ableiten und die abgeleitete Struktur nutzen kann, um ein besseres Sprachmodell zu lernen.


Unsupervised embedding learning aims to extract good representations from data without the use of human-annotated labels.Such techniques are apparently in the limelight because of the challenges in collecting massive-scale labels required for supervised learning.This paper proposes a comprehensive approach, called Super-AND, which is based on the Anchor Neighbourhood Discovery model.Multiple losses defined in Super-AND make similar samples gather even within a low-density space and keep features invariant against augmentation.As a result, our model outperforms existing approaches in various benchmark datasets and achieves an accuracy of 89.2% in CIFAR-10 with the Resnet18 backbone network, a 2.9% gain over the state-of-the-art.

Wir haben einen umfassenden Ansatz für unüberwachtes Einbettungslernen auf der Grundlage des AND-Algorithmus vorgeschlagen.


Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard.In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution (100,000^2 pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions.The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models.We propose a method for disease available during training.Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge.We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique fromatp the field of semantic segmentation and object detection.

Wir schlagen eine schwach überwachte Lernmethode für die Klassifizierung und Lokalisierung von Krebserkrankungen in extrem hochauflösenden histopathologischen Ganzbildaufnahmen vor, die nur bildweite Markierungen verwendet.


Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions.One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels.We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels.  We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.

Wir schlagen eine neue Methode zur Verwendung von Ontologie-Informationen vor, um die Leistung bei massiven Mehr-Label-Prognosen/Klassifizierungsproblemen zu verbessern.


Generative Adversarial Networks have made data generation possible in various use cases, but in case of complex, high-dimensional distributions it can be difficult to train them, because of convergence problems and the appearance of mode collapse.Sliced Wasserstein GANs and especially the application of the Max-Sliced Wasserstein distance made it possible to approximate Wasserstein distance during training in an efficient and stable way and helped ease convergence problems of these architectures.This method transforms sample assignment and distance calculation into sorting the one-dimensional projection of the samples, which results a sufficient approximation of the high-dimensional Wasserstein distance. In this paper we will demonstrate that the approximation of the Wasserstein distance by sorting the samples is not always the optimal approach and the greedy assignment of the real and fake samples can result faster convergence and better approximation of the original distribution.

Zur Annäherung an die Wasserstein-Distanz wenden wir auf die projizierten Beispiele eine greedy Zuweisung an, anstatt zu sortieren


Lifelong machine learning focuses on adapting to novel tasks without forgetting the old tasks, whereas few-shot learning strives to learn a single task given a small amount of data.These two different research areas are crucial for artificial general intelligence, however, their existing studies have somehow assumed some impractical settings when training the models.For lifelong learning, the nature (or the quantity) of incoming tasks during inference time is assumed to be known at training time.As for few-shot learning, it is commonly assumed that a large number of tasks is available during training.Humans, on the other hand, can perform these learning tasks without regard to the aforementioned assumptions.Inspired by how the human brain works, we propose a novel model, called the Slow Thinking to Learn (STL), that makes sophisticated (and slightly slower) predictions by iteratively considering interactions between current and previously seen tasks at runtime.Having conducted experiments, the results empirically demonstrate the effectiveness of STL for more realistic lifelong and few-shot learning settings.

In diesem Beitrag werden die Wechselwirkungen zwischen den Modellen für schnelles Lernen und langsame Vorhersage untersucht und es wird gezeigt, wie solche Wechselwirkungen die Fähigkeit von Maschinen verbessern können, die Probleme des Joint Lifelong Lernens und des Few-Shot Lernens zu lösen.


Hypernetworks are meta neural networks that generate weights for a main neural network in an end-to-end differentiable manner.Despite extensive applications ranging from multi-task learning to Bayesian deep learning, the problem of optimizing hypernetworks has not been studied to date.We observe that classical weight initialization methods like Glorot & Bengio (2010) and He et al. (2015), when applied directly on a hypernet, fail to produce weights for the mainnet in the correct scale.We develop principled techniques for weight initialization in hypernets, and show that they lead to more stable mainnet weights, lower training loss, and faster convergence.

Die erste prinzipielle Gewichtsinitialisierungsmethode für Hypernetze


For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.VHE randomized GAN (VHE-GAN) encodes an image to decode its associated text, and feeds the variational posterior as the source of randomness into the GAN image generator.We plug three off-the-shelf modules, including a deep topic model, a ladder-structured image encoder, and StackGAN++, into VHE-GAN, which already achieves competitive performance.This further motivates the development of VHE-raster-scan-GAN that generates photo-realistic images in not only a multi-scale low-to-high-resolution manner, but also a hierarchical-semantic coarse-to-fine fashion.By capturing and relating hierarchical semantic and visual concepts with end-to-end training, VHE-raster-scan-GAN achieves state-of-the-art performance in a wide variety of image-text multi-modality learning and generation tasks.

Ein neuartiges Bayes'sches Deep-Learning-Framework, das hierarchische semantische und visuelle Konzepte erfasst und miteinander in Beziehung setzt und bei einer Vielzahl von Bild- und Textmodellierungs- und -generierungsaufgaben gute Ergebnisse erzielt.


Current classical planners are very successful in finding (non-optimal) plans, even for large planning instances.To do so, most planners rely on a preprocessing stage that computes a grounded representation of the task.Whenever the grounded task is too big to be generated (i.e., whenever this preprocess fails) the instance cannot even be tackled by the actual planner.To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible.We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods.Our empirical evaluation attests that the approach is capable of solving planning instances that are too big to be fully grounded.

Diese Arbeit führt partielles Grounding ein, um das Problem zu lösen, das auftritt, wenn der vollständige Grounding-Prozess, d. h. die Übersetzung einer PDDL-Eingabeaufgabe in eine geerdete Darstellung wie STRIPS, aufgrund von Speicher- oder Zeitbeschränkungen nicht durchführbar ist.


In this paper, we introduce a novel method to interpret recurrent neural networks (RNNs), particularly long short-term memory networks (LSTMs) at the cellular level.We propose a systematic pipeline for interpreting individual hidden state dynamics within the network using response characterization methods.The ranked contribution of individual cells to the network's output is computed by analyzing a set of interpretable metrics of their decoupled step and sinusoidal responses.As a result, our method is able to uniquely identify neurons with insightful dynamics, quantify relationships between dynamical properties and test accuracy through ablation analysis, and interpret the impact of network capacity on a network's dynamical distribution.Finally, we demonstrate generalizability and scalability of our method by evaluating a series of different benchmark sequential datasets.

Einführung der Antwort-Charakterisierungsmethode zur Interpretation der Zelldynamik in gelernten Long Short-Term Memory Netzwerken (LSTM).


Decades of research on the neural code underlying spatial navigation have revealed a diverse set of neural response properties.The Entorhinal Cortex (EC) of the mammalian brain contains a rich set of spatial correlates, including grid cells which encode space using tessellating patterns.However, the mechanisms and functional significance of these spatial representations remain largely mysterious.As a new way to understand these neural representations, we trained recurrent neural networks (RNNs) to perform navigation tasks in 2D arenas based on velocity inputs.Surprisingly, we find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells.All these different functional types of neurons have been observed experimentally.The order of the emergence of grid-like and border cells is also consistent with observations from developmental studies.Together, our results suggest that grid cells, border cells and others as observed in EC may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits.

Unseres Wissens ist dies die erste Studie, die zeigt, wie neuronale Repräsentationen des Raums, einschließlich gitterartiger Zellen und Grenzzellen, wie sie im Gehirn beobachtet werden, aus dem Training eines rekurrenten neuronalen Netzes zur Durchführung von Navigationsaufgaben hervorgehen können.


Source separation for music is the task of isolating contributions, or stems, from different instruments recorded individually and arranged together to form a song.Such components include voice, bass, drums and any other accompaniments.While end-to-end models that directly generate the waveform are state-of-the-art in many audio synthesis problems, the best multi-instrument source separation models generate masks on the magnitude spectrum and achieve performances far above current end-to-end, waveform-to-waveform models.We present an in-depth analysis of a new architecture, which we will refer to as Demucs, based on a (transposed) convolutional autoencoder, with a bidirectional LSTM at the bottleneck layer and skip-connections as in U-Networks (Ronneberger et al., 2015).Compared to the state-of-the-art waveform-to-waveform model, Wave-U-Net (Stoller et al., 2018), the main features of our approach in addition of the bi-LSTM are the use of trans-posed convolution layers instead of upsampling-convolution blocks, the use of gated linear units, exponentially growing the number of channels with depth and a new careful initialization of the weights.  Results on the MusDB dataset show that our architecture achieves a signal-to-distortion ratio (SDR) nearly 2.2 points higher than the best waveform-to-waveform competitor (from 3.2 to 5.4 SDR).This makes our model match the state-of-the-art performances on this dataset, bridging the performance gap between models that operate on the spectrogram and end-to-end approaches.

Wir vergleichen die Leistung eines Spektrogramm-basierten Modells mit einem Modell, das durchgängig im Wellenformbereich trainiert wurde.


Although challenging, strategy profile evaluation in large connected learner networks is crucial for enabling the next wave of machine learning applications.Recently, $\alpha$-Rank, an evolutionary algorithm, has been proposed as a solution for ranking joint policy profiles in multi-agent systems.$\alpha$-Rank claimed scalability through a polynomial time implementation with respect to the total number of pure strategy profiles.In this paper, we formally prove that such a claim is not grounded.In fact, we show that $\alpha$-Rank exhibits an exponential complexity in number of agents, hindering its application beyond a small finite number of joint profiles.Realizing such a limitation, we contribute by proposing a scalable evaluation protocol that we title  $\alpha^{\alpha}$-Rank.Our method combines evolutionary dynamics with stochastic optimization and double oracles for \emph{truly} scalable ranking with linear (in number of agents) time and memory complexities.Our contributions allow us, for the first time, to conduct large-scale evaluation experiments of multi-agent systems, where we show successful results on large joint strategy profiles with sizes in the  order of $\mathcal{O}(2^{25})$ (i.e., $\approx \text{$33$ million strategies}$) -- a setting not evaluable using current techniques.

Wir bieten eine skalierbare Lösung für die Multi-Agenten-Evaluierung mit linearer Zeit- und Speicherkomplexität in Bezug auf die Anzahl der Agenten


Deep neural networks are known to be annotation-hungry.Numerous efforts have been devoted to reducing the annotation cost when learning with deep networks.Two prominent directions include learning with noisy labels and semi-supervised learning by exploiting unlabeled data.In this work, we propose DivideMix, a novel framework for learning with noisy labels by leveraging semi-supervised learning techniques.In particular, DivideMix models the per-sample loss distribution with a mixture model to dynamically divide the training data into a labeled set with clean samples and an unlabeled set with noisy samples, and trains the model on both the labeled and unlabeled data in a semi-supervised manner.To avoid confirmation bias, we simultaneously train two diverged networks where each network uses the dataset division from the other network.During the semi-supervised training phase, we improve the MixMatch strategy by performing label co-refinement and label co-guessing on labeled and unlabeled samples, respectively.Experiments on multiple benchmark datasets demonstrate substantial improvements over state-of-the-art methods.Code is available at https://github.com/LiJunnan1992/DivideMix .

Wir schlagen einen neuartigen halb-überwachten Lernansatz mit SOTA-Leistung zur Bekämpfung des Lernens mit verrauschten Etiketten vor.


We present a new algorithm to train a robust neural network against adversarial attacks. Our algorithm is motivated by the following two ideas.First, although recent work has demonstrated that fusing randomness can improve the robustness of neural networks (Liu 2017), we noticed that adding noise blindly to all the layers is not the optimal way to incorporate randomness. Instead, we model randomness under the framework of Bayesian Neural Network (BNN) to formally learn the posterior distribution of models in a scalable way.Second, we formulate the mini-max problem in BNN to learn the best model distribution under adversarial attacks, leading to an adversarial-trained Bayesian neural net.Experiment results demonstrate that the proposed algorithm achieves state-of-the-art performance under strong attacks.On CIFAR-10 with VGG network, our model leads to 14% accuracy improvement compared with adversarial training (Madry 2017) and random self-ensemble (Liu, 2017) under PGD attack with 0.035 distortion, and the gap becomes even larger on a subset of ImageNet.

Wir entwickeln eine Trainingsmethode für Bayes'sche neuronale Netze, die eine viel stärkere Verteidigung gegen White-Box-Angriffe bietet.


Federated learning distributes model training among a multitude of agents, who, guided by privacy concerns, perform training using their local data but share only model parameter updates, for iterative aggregation at the server.In this work, we explore the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence.We explore a number of strategies to carry out this attack, starting with simple boosting of the malicious agent's update to overcome the effects of other agents' updates.To increase attack stealth, we propose an alternating minimization strategy, which alternately optimizes for the training loss and the adversarial objective.We follow up by using parameter estimation for the benign agents' updates to improve on attack success.Finally, we use a suite of interpretability techniques to generate visual explanations of model decisions for both benign and malicious models and show that the explanations are nearly visually indistinguishable.Our results indicate that even a highly constrained adversary can carry out model poisoning attacks while simultaneously maintaining stealth, thus highlighting the vulnerability of the federated learning setting and the need to develop effective defense strategies.

Wirksame Modellvergiftungsangriffe auf das föderierte Lernen, die eine gezielte Fehlklassifizierung der gewünschten Eingaben mit hoher Zuverlässigkeit bewirken können


Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs.Small amounts of noise can destroy the performance of an otherwise state-of-the-art model.To harden models against background noise, practitioners often perform data augmentation, adding artificially-noised examples to the training set, carrying over the original label.In this paper, we hypothesize that a clean example and its superficially perturbed counterparts shouldn't merely map to the same class--- they should map to the same representation.We propose invariant-representation-learning (IRL): At each training iteration, for each training example, we sample a noisy counterpart.We then apply a penalty term to coerce matched representations at each layer (above some chosen layer).Our key results, demonstrated on the LibriSpeech dataset are the following:(i) IRL significantly reduces character error rates (CER)on both `clean' (3.3% vs 6.5%) and `other' (11.0% vs 18.1%) test sets;(ii) on several out-of-domain noise settings (different from those seen during training), IRL's benefits are even more pronounced.Careful ablations confirm that our results are not simply due to shrinking activations at the chosen layers.

In dieser Arbeit stellen wir die Hypothese auf, dass oberflächlich gestörte Datenpunkte nicht nur der gleichen Klasse zugeordnet werden sollten, sondern auch der gleichen Repräsentation zugeordnet werden sollten.


In this paper we design a harmonic acoustic model for pitch detection.This model arranges conventional convolution and sparse convolution in a way such that the global harmonic patterns captured by sparse convolution are composed of the enough number of local patterns captured by layers of conventional convolution.When trained on the MAPS dataset, the harmonic model outperforms all existing pitch detection systems trained on the same dataset.Most impressively, when trained on MAPS with simple data augmentation, the harmonic model with an LSTM layer on top surpasses an up-to-date, more complex pitch detection system trained on the MAESTRO dataset to which complicated data augmentation is applied and whose training split is an order-of-magnitude larger than the training split of MAPS.The harmonic model has demonstrated potential to be used for advanced automatic music transcription (AMT) systems.


Learning domain-invariant representation is a dominant approach for domain generalization.However, previous methods based on domain invariance overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and the invariance.This study proposes a novel method {\em adversarial feature learning under accuracy constraint (AFLAC)}, which maximizes domain invariance within a range that does not interfere with accuracy.Empirical validations show that the performance of AFLAC is superior to that of baseline methods, supporting the importance of considering the dependency and the efficacy of the proposed method to overcome the problem.

Den durch die Abhängigkeit der Klassen von den Domänen verursachten Kompromiss durch die Verbesserung der kontradiktorischen Netze der Domänen anzugehen


Recent advances in deep generative models have lead to remarkable progress in synthesizing high quality images.Following their successful application in image processing and representation learning, an important next step is to consider videos.Learning generative models of video is a much harder task, requiring a model to capture the temporal dynamics of a scene, in addition to the visual presentation of objects.While recent generative models of video have had some success, current progress is hampered by the lack of qualitative metrics that consider visual quality, temporal coherence, and diversity of samples.To this extent we propose Fréchet Video Distance (FVD), a new metric for generative models of video based on FID.We contribute a large-scale human study, which confirms that FVD correlates well with qualitative human judgment of generated videos.

Wir schlagen FVD vor: eine neue Metrik für generative Videomodelle auf der Grundlage von FID. Eine groß angelegte Humanstudie bestätigt, dass FVD gut mit der qualitativen menschlichen Beurteilung von generierten Videos korreliert.


Despite advances in deep learning, artificial neural networks do not learn the same way as humans do.Today, neural networks can learn multiple tasks when trained on them jointly, but cannot maintain performance on learnt tasks when tasks are presented one at a time -- this phenomenon called catastrophic forgetting is a fundamental challenge to overcome before neural networks can learn continually from incoming data.In this work, we derive inspiration from human memory to develop an architecture capable of learning continuously from sequentially incoming tasks, while averting catastrophic forgetting.Specifically, our model consists of a dual memory architecture to emulate the complementary learning systems (hippocampus and the neocortex) in the human brain and maintains a consolidated long-term memory via generative replay of past experiences.We(i) substantiate our claim that replay should be generative,(ii) show the benefits of generative replay and dual memory via experiments, and(iii) demonstrate improved performance retention even for small models with low capacity.Our architecture displays many important characteristics of the human memory and provides insights on the connection between sleep and learning in humans.

Eine dem menschlichen Gehirn nachempfundene duale Gedächtnisarchitektur zum Erlernen von sequentiell eingehenden Aufgaben bei gleichzeitiger Vermeidung von katastrophalem Vergessen.


Most research on lifelong learning applies to images or games, but not language.We present LAMOL, a simple yet effective method for lifelong language learning (LLL) based on language modeling.LAMOL replays pseudo-samples of previous tasks while requiring no extra memory or model capacity.Specifically, LAMOL is a language model that simultaneously learns to solve the tasks and generate training samples.When the model is trained for a new task, it generates pseudo-samples of previous tasks for training alongside data for the new task.The results show that LAMOL prevents catastrophic forgetting without any sign of intransigence and can perform five very different language tasks sequentially with only one model. Overall, LAMOL outperforms previous methods by a considerable margin and is only 2-3% worse than multitasking, which is usually considered the LLL upper bound.The source code is available at https://github.com/jojotenya/LAMOL.

Sprachmodellierung für lebenslanges Sprachenlernen.


Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words.A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a  sequence of continuous vectors.Also, semantic relationships between words, learned from a text corpus, can be encoded in the relative configurations of the embedding vectors.However, storing and accessing embedding vectors for all words in a dictionary requires large amount of space, and may stain systems with limited GPU memory.Here, we used approaches inspired by quantum computing to propose two related methods, word2ket and word2ketXS, for storing word embedding matrix during training and inference in a highly efficient way.Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.

Wir nutzen Ideen aus der Quanteninformatik, um Worteinbettungen vorzuschlagen, die mit viel weniger trainierbaren Parametern auskommen.


One of the distinguishing aspects of human language is its compositionality, which allows us to describe complex environments with limited vocabulary.Previously, it has been shown that neural network agents can learn to communicate in a highly structured, possibly compositional language based on disentangled input (e.g. hand- engineered features).Humans, however, do not learn to communicate based on well-summarized features.In this work, we train neural agents to simultaneously develop visual perception from raw image pixels, and learn to communicate with a sequence of discrete symbols.The agents play an image description game where the image contains factors such as colors and shapes.We train the agents using the obverter technique where an agent introspects to generate messages that maximize its own understanding.Through qualitative analysis, visualization and a zero-shot test, we show that the agents can develop, out of raw image pixels, a language with compositional properties, given a proper pressure from the environment.

Wir trainieren neuronale Netzwerk-Agenten, um eine Sprache mit kompositorischen Eigenschaften aus rohen Pixel-Eingaben zu entwickeln.


The ability to forecast a set of likely yet diverse possible future behaviors of an agent (e.g., future trajectories of a pedestrian) is essential for safety-critical perception systems (e.g., autonomous vehicles).In particular, a set of possible future behaviors generated by the system must be diverse to account for all possible outcomes in order to take necessary safety precautions.It is not sufficient to maintain a set of the most likely future outcomes because the set may only contain perturbations of a dominating single outcome (major mode).While generative models such as variational autoencoders (VAEs) have been shown to be a powerful tool for learning a distribution over future trajectories, randomly drawn samples from the learned implicit likelihood model may not be diverse -- the likelihood model is derived from the training data distribution and the samples will concentrate around the major mode of the data.In this work, we propose to learn a diversity sampling function (DSF) that generates a diverse yet likely set of future trajectories.The DSF maps forecasting context features to a set of latent codes which can be decoded by a generative model (e.g., VAE) into a set of diverse trajectory samples.Concretely, the process of identifying the diverse set of samples is posed as DSF parameter estimation.To learn the parameters of the DSF, the diversity of the trajectory samples is evaluated by a diversity loss based on a determinantal point process (DPP).Gradient descent is performed over the DSF parameters, which in turn moves the latent codes of the sample set to find an optimal set of diverse yet likely trajectories.Our method is a novel application of DPPs to optimize a set of items (forecasted trajectories) in continuous space.We demonstrate the diversity of the trajectories produced by our approach on both low-dimensional 2D trajectory data and high-dimensional human motion data.

Wir lernen eine Diversity-Sampling-Funktion mit DPPs, um einen vielfältigen Satz von Proben aus einem generativen Modell zu erhalten.


There is mounting evidence that pretraining can be valuable for neural network language understanding models, but we do not yet have a clear understanding of how the choice of pretraining objective affects the type of linguistic information that models learn.With this in mind, we compare four objectives---language modeling, translation, skip-thought, and autoencoding---on their ability to induce syntactic and part-of-speech information, holding constant the genre and quantity of training data.We find that representations from language models consistently perform best on our syntactic auxiliary prediction tasks, even when trained on relatively small amounts of data, which suggests that language modeling may be the best data-rich pretraining task for transfer learning applications requiring syntactic information.We also find that a randomly-initialized, frozen model can perform strikingly well on our auxiliary tasks, but that this effect disappears when the amount of training data for the auxiliary tasks is reduced.

Repräsentationen von Sprachmodellen schneiden bei der Vorhersage von syntaktischen Hilfsmitteln durchweg besser ab als Übersetzungsencoder.


We consider the problem of generating configurations that satisfy physical constraints for optimal material nano-pattern design, where multiple (and often conflicting) properties need to be simultaneously satisfied.  Consider, for example, the trade-off between thermal resistance, electrical conductivity, and mechanical stability needed to design a nano-porous template with optimal thermoelectric efficiency.  To that end, we leverage the posterior regularization framework andshow that this constraint satisfaction problem can be formulated as sampling froma Gibbs distribution.  The main challenges come from the black-box nature ofthose physical constraints, since they are obtained via solving highly non-linearPDEs.To overcome those difficulties, we introduce Surrogate-based Constrained Langevin dynamics for black-box sampling.We explore two surrogate approaches.The first approach exploits zero-order approximation of gradients in the Langevin Sampling and we refer to it as Zero-Order Langevin.In practice, this approach can be prohibitive since we still need to often query the expensive PDE solvers.The second approach approximates the gradients in the Langevin dynamics with deep neural networks, allowing us an efficient sampling strategy using the surrogate model.We prove the convergence of those two approaches when the target distribution is log-concave and smooth.We show the effectiveness of both approaches in designing optimal nano-porous material configurations, where the goal is to produce nano-pattern templates with low thermal conductivity and reasonable mechanical stability.

Wir schlagen ein auf Surrogate basierendes beschränktes Langevin-Sampling vor, das im Design von nano-porigen Materialkonfigurationen Anwendung findet.


There is growing interest in geometrically-inspired embeddings for learning hierarchies, partial orders, and lattice structures, with natural applications to transitive relational data such as entailment graphs.Recent work has extended these ideas beyond deterministic hierarchies to probabilistically calibrated models, which enable learning from uncertain supervision and inferring soft-inclusions among concepts, while maintaining the geometric inductive bias of hierarchical embedding models.We build on the Box Lattice model of Vilnis et al. (2018), which showed promising results in modeling soft-inclusions through an overlapping hierarchy of sets, parameterized as high-dimensional hyperrectangles (boxes).However, the hard edges of the boxes present difficulties for standard gradient based optimization; that work employed a special surrogate function for the disjoint case, but we find this method to be fragile.  In this work, we present a novel hierarchical embedding model, inspired by a relaxation of box embeddings into parameterized density functions using Gaussian convolutions over the boxes.Our approach provides an alternative surrogate to the original lattice measure that improves the robustness of optimization in the disjoint case, while also preserving the desirable properties with respect to the original lattice.We demonstrate increased or matching performance on WordNet hypernymy prediction, Flickr caption entailment, and a MovieLens-based market basket dataset.We show especially marked improvements in the case of sparse data, where many conditional probabilities should be low, and thus boxes should be nearly disjoint.

Verbesserung hierarchischer Einbettungsmodelle durch Kernelglättung


We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature.First, we train a neural NER (NNER) model over a small seed of fully-labeled examples.Second, we use a reference set of entity names (e.g., proteins in UniProt) to identify entity mentions with high precision, but low recall, on an unlabeled corpus.Third, we use the NNER model to assign weak labels to the corpus.Finally, we retrain our NNER model iteratively over the augmented training set, including the seed, the reference-set examples, and the weakly-labeled examples, which results in refined labels.We show empirically that this augmented bootstrapping process significantly improves NER performance, and discuss the factors impacting the efficacy of the approach.

Augmented-Bootstrapping-Ansatz, der Informationen aus einer Referenzmenge mit iterativen Verfeinerungen von Soft-Labels kombiniert, um die Erkennung von Namensbestandteilen aus biomedizinischer Literatur zu verbessern.


Quantum machine learning methods have the potential to facilitate learning using extremely large datasets.While the availability of data for training machine learning models is steadily increasing, oftentimes it is much easier to collect feature vectors that to obtain the corresponding labels.One of the approaches for addressing this issue is to use semi-supervised learning, which leverages not only the labeled samples, but also unlabeled feature vectors.Here, we present a quantum machine learning algorithm for training Semi-Supervised Kernel Support Vector Machines.The algorithm uses recent advances in quantum sample-based Hamiltonian simulation to extend the existing Quantum LS-SVM algorithm to handle the semi-supervised term in the loss, while maintaining the same quantum speedup as the Quantum LS-SVM.

Wir erweitern die Quanten-SVMs auf eine halbüberwachte Umgebung, um mit dem wahrscheinlichen Problem vieler fehlender Klassenbezeichnungen in großen Datensätzen umzugehen.


Deep neural networks have become the state-of-the-art models in numerous machine learning tasks.However, general guidance to network architecture design is still missing.In our work, we bridge deep neural network design with numerical differential equations.We show that many effective networks, such as ResNet, PolyNet, FractalNet and RevNet, can be interpreted as different numerical discretizations of differential equations.This finding brings us a brand new perspective on the design of effective deep architectures.We can take advantage of the rich knowledge in numerical analysis to guide us in designing new and potentially more effective deep networks.As an example, we propose a linear multi-step architecture (LM-architecture) which is inspired by the linear multi-step method solving ordinary differential equations.The LM-architecture is an effective structure that can be used on any ResNet-like networks.In particular, we demonstrate that LM-ResNet and LM-ResNeXt (i.e. the networks obtained by applying the LM-architecture on ResNet and ResNeXt respectively) can achieve noticeably higher accuracy than ResNet and ResNeXt on both CIFAR and ImageNet with comparable numbers of trainable parameters.In particular, on both CIFAR and ImageNet, LM-ResNet/LM-ResNeXt can significantly compress (>50%) the original networks while maintaining a similar performance.This can be explained mathematically using the concept of modified equation from numerical analysis.Last but not least, we also establish a connection between stochastic control and noise injection in the training process which helps to improve generalization of the networks.Furthermore, by relating stochastic training strategy with stochastic dynamic system, we can easily apply stochastic training to the networks with the LM-architecture.As an example, we introduced stochastic depth to LM-ResNet and achieve significant improvement over the original LM-ResNet on CIFAR10.

Dieses Werk schlägt eine Brücke zwischen tiefen Netzwerkarchitekturen und numerischen (stochastischen) Differentialgleichungen. Diese neue Perspektive ermöglicht neue Designs von effektiveren tiefen neuronalen Netzen.


Transforming a graphical user interface screenshot created by a designer into computer code is a typical task conducted by a developer in order to build customized software, websites, and mobile applications.In this paper, we show that deep learning methods can be leveraged to train a model end-to-end to automatically generate code from a single input image with over 77% of accuracy for three different platforms (i.e. iOS, Android and web-based technologies).

CNN und LSTM, um markup-ähnlichen Code zu erzeugen, der Bilder von grafischen Benutzeroberflächen beschreibt.


Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity).In this work, we demonstrate that in many practical scenarios hyperbolic embeddings provide a better alternative.

Wir zeigen, dass hyperbolische Einbettungen für anspruchsvolle Computer-Vision-Aufgaben nützlich sind, insbesondere für die Klassifizierung von wenigen Bildern.


High-dimensional time series are common in many domains.Since human cognition is not optimized to work well in high-dimensional spaces, these areas could benefit from interpretable low-dimensional representations.However, most representation learning algorithms for time series data are difficult to interpret.This is due to non-intuitive mappings from data features to salient properties of the representation and non-smoothness over time.To address this problem, we propose a new representation learning framework building on ideas from interpretable discrete dimensionality reduction and deep generative modeling.This framework allows us to learn discrete representations of time series, which give rise to smooth and interpretable embeddings with superior clustering performance.We introduce a new way to overcome the non-differentiability in discrete representation learning and present a gradient-based version of the traditional self-organizing map algorithm that is more performant than the original.Furthermore, to allow for a probabilistic interpretation of our method, we integrate a Markov model in the representation space.This model uncovers the temporal transition structure, improves clustering performance even further and provides additional explanatory insights as well as a natural representation of uncertainty.We evaluate our model in terms of clustering performance and interpretability on static (Fashion-)MNIST data, a time series of linearly interpolated (Fashion-)MNIST images, a chaotic Lorenz attractor system with two macro states, as well as on a challenging real world medical time series application on the eICU data set.Our learned representations compare favorably with competitor methods and facilitate downstream tasks on the real world data.

Wir stellen eine Methode zum Erlernen interpretierbarer Repräsentationen von Zeitreihen vor, die auf Ideen von Variations-Autoencodern, selbstorganisierenden Karten und probabilistischen Modellen beruht.


We propose Significance-Offset Convolutional Neural Network, a deep convolutional network architecture for regression of multivariate asynchronous time series.  The model is inspired by standard autoregressive (AR) models and gating mechanisms used in recurrent neural networks.  It involves an AR-like weighting system, where the final predictor is obtained as a weighted sum of adjusted regressors, while the weights are data-dependent functions learnt through a convolutional network.The architecture was designed for applications on asynchronous time series and is evaluated on such datasets: a hedge fund proprietary dataset of over 2 million quotes for a credit derivative index, an artificially generated noisy autoregressive  series  and  household  electricity  consumption  dataset.   The  pro-posed architecture achieves promising results as compared to convolutional and recurrent neural networks.The code for the numerical experiments and the architecture implementation will be shared online to make the research reproducible.

Convolutional Architecture zum Lernen datenabhängiger Gewichte für die autoregressive Vorhersage von Zeitreihen.


MixUp is a data augmentation scheme in which pairs of training samples and their corresponding labels are mixed using linear coefficients.Without label mixing, MixUp becomes a more conventional scheme: input samples are moved but their original labels are retained.Because samples are preferentially moved in the direction of other classes \iffalse -- which are typically clustered in input space -- \fi we refer to this method as directional adversarial training, or DAT.We show that under two mild conditions, MixUp asymptotically convergences to a subset of DAT.We define untied MixUp (UMixUp), a superset of MixUp wherein training labels are mixed with different linear coefficients to those of their corresponding samples.We show that under the same mild conditions, untied MixUp converges to the entire class of DAT schemes.Motivated by the understanding that UMixUp is both a generalization of MixUp and a form of adversarial training, we experiment with different datasets and loss functions to show that UMixUp provides improved performance over MixUp.In short, we present a novel interpretation of MixUp as belonging to a class highly analogous to adversarial training, and on this basis we introduce a simple generalization which outperforms MixUp.

Wir stellen eine neuartige Interpretation von MixUp vor, die einer Klasse angehört, die in hohem Maße mit dem gegnerischen Training vergleichbar ist, und auf dieser Grundlage führen wir eine einfache Verallgemeinerung ein, die MixUp übertrifft


Plan recognition aims to look for target plans to best explain the observed actions based on plan libraries and/or domain models.Despite the success of previous approaches on plan recognition, they mostly rely on correct action observations. Recent advances in visual activity recognition have the potential of enabling applications such as automated video surveillance.Effective approaches for such problems would require the ability to recognize the plans of agents from video information.Traditional plan recognition algorithms rely on access to detailed planning domain models.One recent promising direction involves learning approximate (or shallow) domain models directly from the observed activity sequences.Such plan recognition approaches expect observed action sequences as inputs.However, visual inference results are often noisy and uncertain, typically represented as a distribution over possible actions.In this work, we develop a visual plan recognition framework that recognizes plans with an approximate domain model learned from uncertain visual data.

Behandlung von Unsicherheiten in der visuellen Wahrnehmung für die Planerkennung


We consider the task of answering complex multi-hop questions using a corpus as a virtual knowledge base (KB).In particular, we describe a neural module, DrKIT, that traverses textual data like a virtual KB, softly following paths of relations between mentions of entities in the corpus.At each step the operation uses a combination of sparse-matrix TFIDF indices and maximum inner product search (MIPS) on a special index of contextual representations.This module is differentiable, so the full system can be trained completely end-to-end using gradient based methods, starting from natural language inputs.We also describe a pretraining scheme for the index mention encoder by generating hard negative examples using existing knowledge bases.We show that DrKIT improves accuracy by 9 points on 3-hop questions in the MetaQA dataset, cutting the gap between text-based and KB-based state-of-the-art by 70%.DrKIT is also very efficient, processing upto 10x more queries per second than existing state-of-the-art QA systems.

Differenzierbarer Multi-Hop-Zugriff auf eine textuelle Wissensbasis mit indizierten kontextuellen Darstellungen


In spite of their great success, traditional factorization algorithms typically do not support features (e.g., Matrix Factorization), or their complexity scales quadratically with the number of features (e.g, Factorization Machine).On the other hand, neural methods allow large feature sets, but are often designed for a specific application.We propose novel deep factorization methods that allow efficient and flexible feature representation.For example, we enable describing items with natural language with complexity linear to the vocabulary size—this enables prediction for unseen items and avoids the cold start problem.We show that our architecture can generalize some previously published single-purpose neural architectures.Our experiments suggest improved training times and accuracy compared to shallow methods.

Skalierbarer Allzweck-Faktorisierungsalgorithmus - hilft auch, das Kaltstartproblem zu umgehen.


Augmented Reality (AR) can assist with physical tasks such as object assembly through the use of “situated instructions”.These instructions can be in the form of videos, pictures, text or guiding animations, where the most helpful media among these is highly dependent on both the user and the nature of the task.Our work supports the authoring of AR tutorials for assembly tasks with little overhead beyond simply performing the task itself.The presented system, AuthAR reduces the time and effort required to build interactive AR tutorials by automatically generating key components of the AR tutorial while the author is assembling the physical pieces.Further, the system guides authors through the process of adding videos, pictures, text and animations to the tutorial.This concurrent assembly and tutorial generation approach allows for authoring of portable tutorials that fit the preferences of different end users.

Wir stellen ein System zur Erstellung von Tutorials mit gemischten Medien vor, das die Erstellung von Videos, Bildern, Text und dynamischen Anweisungen an Ort und Stelle rationalisiert.


Monitoring patients in ICU is a challenging and high-cost task.Hence, predicting the condition of patients during their ICU stay can help provide better acute care and plan the hospital's resources.There has been continuous progress in machine learning research for ICU management, and most of this work has focused on using time series signals recorded by ICU instruments.In our work, we show that adding clinical notes as another modality improves the performance of the model for three benchmark tasks: in-hospital mortality prediction, modeling decompensation, and length of stay forecasting that play an important role in ICU management.While the time-series data is measured at regular intervals, doctor notes are charted at irregular times, making it challenging to model them together.We propose a method to model them jointly, achieving considerable improvement across benchmark tasks over baseline time-series model.

Wir konnten nachweisen, dass die Verwendung von klinischen Notizen in Verbindung mit ICU-Instrumenten die Leistung bei ICU-Management-Benchmark-Aufgaben verbessert.


Existing works in deep Multi-Agent Reinforcement Learning (MARL) mainly focus on coordinating cooperative agents to complete certain tasks jointly.However, in many cases of the real world, agents are self-interested such as employees in a company and clubs in a league.Therefore, the leader, i.e., the manager of the company or the league, needs to provide bonuses to followers for efficient coordination, which we call expensive coordination.The main difficulties of expensive coordination are thati) the leader has to consider the long-term effect and predict the followers' behaviors when assigning bonuses andii) the complex interactions between followers make the training process hard to converge, especially when the leader's policy changes with time.In this work, we address this problem through an event-based deep RL approach.Our main contributions are threefold.(1) We model the leader's decision-making process as a semi-Markov Decision Process and propose a novel multi-agent event-based policy gradient to learn the leader's long-term policy.(2) We exploit the leader-follower consistency scheme to design a follower-aware module and a follower-specific attention module to predict the followers' behaviors and make accurate response to their behaviors.(3) We propose an action abstraction-based policy gradient algorithm to reduce the followers' decision space and thus accelerate the training process of followers.Experiments in resource collections, navigation, and the predator-prey game reveal that our approach outperforms the state-of-the-art methods dramatically.

Wir schlagen einen ereignisbasierten Policy-Gradienten vor, um den Anführer zu trainieren, und einen Action-Abstraction-Policy-Gradienten, um die Follower im Anführer-Follower-Markov-Spiel zu trainieren.


Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task.Of particular interest are the factors that cause language to be compositional---i.e., express meaning by combining words which themselves have meaning.Evolutionary linguists have found that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of  compositionality.In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language.We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization.

Wir nutzen kulturelle Übertragung, um Kompositionalität in Sprachen zu fördern, die aus der Interaktion zwischen neuronalen Agenten entstehen.


Based on our observation that there exists a dramatic drop for the singular values of the fully connected layers or a single feature map of the convolutional layer, and that the dimension of the concatenated feature vector almost equals the summation of the dimension on each feature map, we propose a singular value decomposition (SVD) based approach to estimate the dimension of the deep manifolds for a typical convolutional neural network VGG19.We choose three categories from the ImageNet, namely Persian Cat, Container Ship and Volcano, and determine the local dimension of the deep manifolds of the deep layers through the tangent space of a target image.Through several augmentation methods, we found that the Gaussian noise method is closer to the intrinsic dimension, as by adding random noise to an image we are moving in an arbitrary dimension, and when the rank of the feature matrix of the augmented images does not increase we are very closeto the local dimension of the manifold.We also estimate the dimension of the deep manifold based on the tangent space for each of the maxpooling layers.Our results show that the dimensions of different categories are close to each other and decline quickly along the convolutional layers and fully connected layers.Furthermore, we show that the dimensions decline quickly inside the Conv5 layer.Our work provides new insights for the intrinsic structure of deep neural networks and helps unveiling the inner organization of the black box of deep neural networks.

Wir schlagen eine SVD-basierte Methode vor, um die lokale Dimension der Aktivierungsverteiler in tiefen neuronalen Netzen zu untersuchen.


Large pre-trained Transformers such as BERT have been tremendously effective for many NLP tasks. However, inference in these large-capacity models is prohibitively slow and expensive. Transformers are essentially a stack of self-attention layers which encode each input position using the entire input sequence as its context. However, we find that it may not be necessary to apply this expensive sequence-wide self-attention over at all layers. Based on this observation, we propose a decomposition to a pre-trained Transformer that allows the lower layers to process segments of the input independently enabling parallelism and caching. We show that the information loss due to this decomposition can be recovered in the upper layers with auxiliary supervision during fine-tuning.  We evaluate de-composition with pre-trained BERT models on five different paired-input tasks in question answering, sentence similarity, and natural language inference.  Results show that decomposition enables faster inference (up to 4x), significant memory reduction (up to 70%) while retaining most (up to 99%) of the original performance. We will release the code at<anonymized url>.

Die Inferenz in großen Transformatoren ist aufgrund der Self-Attention in mehreren Schichten teuer. Wir zeigen, dass eine einfache Zerlegungstechnik zu einem schnelleren Modell mit geringem Speicherbedarf führen kann, das genauso genau ist wie die ursprünglichen Modelle.


Exploration while learning representations is one of the main challenges DeepReinforcement Learning (DRL) faces today.As the learned representation is dependant in the observed data, the exploration strategy has a crucial role.The popular DQN algorithm has improved significantly the capabilities of ReinforcementLearning (RL) algorithms to learn state representations from raw data, yet, it usesa naive exploration strategy which is statistically inefficient.The RandomizedLeast Squares Value Iteration (RLSVI) algorithm (Osband et al., 2016), on theother hand, explores and generalizes efficiently via linearly parameterized valuefunctions.However, it is based on hand-designed state representation that requiresprior engineering work for every environment.In this paper, we propose a DeepLearning adaptation for RLSVI.Rather than using hand-design state representation, we use a state representation that is being learned directly from the data by aDQN agent.As the representation is being optimized during the learning process,a key component for the suggested method is a likelihood matching mechanism,which adapts to the changing representations.We demonstrate the importance ofthe various properties of our algorithm on a toy problem and show that our methodoutperforms DQN in five Atari benchmarks, reaching competitive results with theRainbow algorithm.

Eine Deep Learning-Anpassung der Randomized Least Squares Value Iteration


The complexity of large-scale neural networks can lead to poor understanding of their internal  details.We show that this opaqueness provides an opportunity for adversaries to embed unintended functionalities into the network in the form of Trojan horse attacks.Our novel framework hides the existence of a malicious network within a benign transport network.Our attack is flexible, easy to execute, and difficult to detect.We prove theoretically that the malicious network's detection is computationally infeasible and demonstrate empirically that the transport network does not compromise its disguise.Our attack exposes an important, previously unknown loophole that unveils a new direction in machine learning security.

Die Parameter eines trainierten neuronalen Netzwerks können vertauscht werden, um ein völlig separates Modell für eine andere Aufgabe zu erstellen, was die Einbettung von trojanischen Netwerken in ein anderes Netz ermöglicht.


In this paper, we introduce Random Path Generative Adversarial Network (RPGAN) --- an alternative scheme of GANs that can serve as a tool for generative model analysis.While the latent space of a typical GAN consists of input vectors, randomly sampled from the standard Gaussian distribution, the latent space of RPGAN consists of random paths in a generator network.As we show, this design allows to associate different layers of the generator with different regions of the latent space, providing their natural interpretability.With experiments on standard benchmarks, we demonstrate that RPGAN reveals several interesting insights about roles that different layers play in the image generation process.Aside from interpretability, the RPGAN model also provides competitive generation quality and allows efficient incremental learning on new data.

Wir stellen ein alternatives GAN-Design vor, das auf zufälligen Routen im Generator basiert und als Werkzeug für die Interpretierbarkeit generativer Modelle dienen kann.


Deep artificial neural networks can achieve an extremely small difference between training and test accuracies on identically distributed training and test sets, which is a standard measure of generalization.However, the training and test sets may not be sufficiently representative of the empirical sample set, which consists of real-world input samples.When samples are drawn from an underrepresented or unrepresented subset during inference, the gap between the training and inference accuracies can be significant.To address this problem, we first reformulate a classification algorithm as a procedure for searching for a source code that maps input features to classes.We then derive a necessary and sufficient condition for generalization using a universal cognitive similarity metric, namely information distance, based on Kolmogorov complexity.Using this condition, we formulate an optimization problem to learn a more general classification function.To achieve this end, we extend the input features by concatenating encodings of them, and then train the classifier on the extended features.As an illustration of this idea, we focus on image classification, where we use channel codes on the input features as a systematic way to improve the degree to which the training and test sets are representative of the empirical sample set.To showcase our theoretical findings, considering that corrupted or perturbed input features belong to the empirical sample set, but typically not to the training and test sets, we demonstrate through extensive systematic experiments that, as a result of learning a more general classification function, a model trained on encoded input features is significantly more robust to common corruptions, e.g., Gaussian and shot noise, as well as adversarial perturbations, e.g., those found via projected gradient descent, than the model trained on uncoded input features.

Wir präsentieren einen theoretischen und experimentellen Rahmen für die Definition, das Verständnis und das Erreichen von Generalisierung und damit von Robustheit beim Deep Learning, indem wir auf die algorithmische Informationstheorie und die Code-Theorie zurückgreifen.


Many approaches to causal discovery are limited by their inability to discriminate between Markov equivalent graphs given only observational data.We formulate causal discovery as a marginal likelihood based Bayesian model selection problem.We adopt a parameterization based on the notion of the independence of causal mechanisms which renders Markov equivalent graphs distinguishable.We complement this with an empirical Bayesian approach to setting priors so that the actual underlying causal graph is assigned a higher marginal likelihood than its alternatives.Adopting a Bayesian approach also allows for straightforward modeling of unobserved confounding variables, for which we provide a variational algorithm to approximate the marginal likelihood, since this desirable feat renders the computation of the marginal likelihood intractable.We believe that the Bayesian approach to causal discovery both allows the rich methodology of Bayesian inference to be used in various difficult aspects of this problem and provides a unifying framework to causal discovery research.We demonstrate promising results in experiments conducted on real data, supporting our modeling approach and our inference methodology.

Wir stellen die Entdeckung kausaler Strukturen als eine Bayes'sche Modellauswahl dar, die es uns ermöglicht, zwischen Markov-äquivalenten Graphen zu unterscheiden, um den einzigartigen kausalen Graphen zu identifizieren.


The goal of compressed sensing is to learn a structured signal $x$  from a limited number of noisy linear measurements $y\approx Ax$.  In  traditional compressed sensing, ``structure'' is represented by  sparsity in some known basis.  Inspired by the success of deep  learning in modeling images, recent work starting with~\cite{BDJP17}  has instead considered structure to come from a generative model  $G: \R^k \to \R^n$.  We present two results establishing the  difficulty of this latter task, showing that existing bounds are  tight.  First, we provide a lower bound matching the~\cite{BDJP17} upper  bound for compressed sensing from $L$-Lipschitz generative models  $G$.  In particular, there exists such a function that requires  roughly $\Omega(k \log L)$ linear measurements for sparse recovery  to be possible.  This holds even for the more relaxed goal of  \emph{nonuniform} recovery.  Second, we show that generative models generalize sparsity as a  representation of structure.  In particular, we construct a  ReLU-based neural network $G: \R^{2k} \to \R^n$ with $O(1)$ layers  and $O(kn)$ activations per layer, such that the range of $G$  contains all $k$-sparse vectors.

Untere Schranke für Compressed Sensing mit generativen Modellen, die den bekannten oberen Schranken entspricht


We hypothesize that end-to-end neural image captioning systems work seemingly well because they exploit and learn ‘distributional similarity’ in a multimodal feature space, by mapping a test image to similar training images in this space and generating a caption from the same space.To validate our hypothesis, we focus on the ‘image’ side of image captioning, and vary the input image representation but keep the RNN text generation model of a CNN-RNN constant.We propose a sparse bag-of-objects vector as an interpretable representation to investigate our distributional similarity hypothesis.We found that image captioning models(i) are capable of separating structure from noisy input representations;(ii) experience virtually no significant performance loss when a high dimensional representation is compressed to a lower dimensional space;(iii) cluster images with similar visual and linguistic information together;(iv) are heavily reliant on test sets with a similar distribution as the training set;(v) repeatedly generate the same captions by matching images and ‘retrieving’ a caption in the joint visual-textual space.Our experiments all point to one fact: that our distributional similarity hypothesis holds.We conclude that, regardless of the image representation, image captioning systems seem to match images and generate captions in a learned joint image-text semantic subspace.

In diesem Beitrag wird eine empirische Analyse der Rolle verschiedener Arten von Bildrepräsentationen vorgestellt und die Eigenschaften dieser Repräsentationen für die Aufgabe der Bildbeschriftung untersucht.


We present a sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory.To account for the fact that typically there are multiple correct SQL queries with the same or very similar semantics, we draw inspiration from syntactic parsing techniques and propose to train our sequence-to-action models with non-deterministic oracles.We evaluate our models on the WikiSQL dataset and achieve an execution accuracy of 83.7% on the test set, a 2.1% absolute improvement over the models trained with traditional static oracles assuming a single correct target SQL query.When further combined with the execution-guided decoding strategy, our model sets a new state-of-the-art performance at an execution accuracy of 87.1%.

Wir entwerfen inkrementelle Sequenz-zu-Aktion-Parser für Text-zu-SQL-Aufgaben und erzielen SOTA-Ergebnisse. Wir verbessern sie weiter, indem wir nicht-deterministische Orakel verwenden, um mehrere korrekte Aktionssequenzen zu ermöglichen.


We propose Efficient Neural Architecture Search (ENAS), a faster and less expensive approach to automated model design than previous methods.In ENAS, a controller learns to discover neural network architectures by searching for an optimal path within a larger model.The controller is trained with policy gradient to select a path that maximizes the expected reward on the validation set.Meanwhile the model corresponding to the selected path is trained to minimize the cross entropy loss.On the Penn Treebank dataset, ENAS can discover a novel architecture thats achieves a test perplexity of 57.8, which is state-of-the-art among automatic model design methods on Penn Treebank.On the CIFAR-10 dataset, ENAS can design novel architectures that achieve a test error of 2.89%, close to the 2.65% achieved by standard NAS (Zoph et al., 2017).Most importantly, our experiments show that ENAS is more than 10x faster and 100x less resource-demanding than NAS.

Ein Ansatz, der die Suche nach neuronalen Architekturen um das 10-fache beschleunigt und dabei 100-mal weniger Rechenressourcen benötigt.


Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech.Meanwhile, in an important case of heterogenous tabular data, the advantage of DNNs over shallow counterparts remains questionable.In particular, there is no sufficient evidence that deep learning machinery allows constructing methods that outperform gradient boosting decision trees (GBDT), which are often the top choice for tabular problems.In this paper, we introduce Neural Oblivious Decision Ensembles (NODE), a new deep learning architecture, designed to work with any tabular data.In a nutshell, the proposed NODE architecture generalizes ensembles of oblivious decision trees, but benefits from both end-to-end gradient-based optimization and the power of multi-layer hierarchical representation learning.With an extensive experimental comparison to the leading GBDT packages on a large number of tabular datasets, we demonstrate the advantage of the proposed NODE architecture, which outperforms the competitors on most of the tasks.We open-source the PyTorch implementation of NODE and believe that it will become a universal framework for machine learning on tabular data.

Wir schlagen eine neue DNN-Architektur für Deep Learning auf tabellarischen Daten vor


Person re-identification (re-ID) aims at identifying the same persons' images across different cameras.However, domain diversities between different datasets pose an evident challenge for adapting the re-ID model trained on one dataset to another one.State-of-the-art unsupervised domain adaptation methods for person re-ID transferred the learned knowledge from the source domain by optimizing with pseudo labels created by clustering algorithms on the target domain.Although they achieved state-of-the-art performances, the inevitable label noise caused by the clustering procedure was ignored.Such noisy pseudo labels substantially hinders the model's capability on further improving feature representations on the target domain.In order to mitigate the effects of noisy pseudo labels, we propose to softly refine the pseudo labels in the target domain by proposing an unsupervised framework, Mutual Mean-Teaching (MMT), to learn better features from the target domain via off-line refined hard pseudo labels and on-line refined soft pseudo labels in an alternative training manner.  In addition, the common practice is to adopt both the classification loss and the triplet loss jointly for achieving optimal performances in person re-ID models.However, conventional triplet loss cannot work with softly refined labels.To solve this problem, a novel soft softmax-triplet loss is proposed to support learning with soft pseudo triplet labels for achieving the optimal domain adaptation performance.The proposed MMT framework achieves considerable improvements of 14.4%, 18.2%, 13.1% and 16.4% mAP on Market-to-Duke, Duke-to-Market, Market-to-MSMT and Duke-to-MSMT unsupervised domain adaptation tasks.

Ein Rahmenwerk, das die Online-Verfeinerung von Pseudo-Etiketten mit einem neuartigen Softmax-Triplet-Verlust für die unüberwachte Domänenanpassung bei der Re-Identifizierung von Personen durchführt.


We present the first end-to-end verifier of audio classifiers.Compared to existing methods, our approach enables analysis of both, the entire audio processing stage as well as recurrent neural network architectures (e.g., LSTM).The audio processing is verified using novel convex relaxations tailored to feature extraction operations used in audio (e.g., Fast Fourier Transform) while recurrent architectures are certified via a novel binary relaxation for the recurrent unit update.We show the verifier scales to large networks while computing significantly tighter bounds than existing methods for common audio classification benchmarks: on the challenging Google Speech Commands dataset we certify 95% more inputs than the interval approximation (only prior scalable method), for a perturbation of -90dB.

Wir stellen den ersten Ansatz vor, um die Robustheit neuronaler Netze gegen geräuschbasierte Störungen im Audiobereich zu zertifizieren.


Since deep neural networks are over-parameterized, they can memorize noisy examples.We address such memorizing issue in the presence of annotation noise.From the fact that deep neural networks cannot generalize neighborhoods of the features acquired via memorization, we hypothesize that noisy examples do not consistently incur small losses on the network under a certain perturbation.Based on this, we propose a novel training method called Learning with Ensemble Consensus (LEC) that prevents overfitting noisy examples by eliminating them using the consensus of an ensemble of perturbed networks.One of the proposed LECs, LTEC outperforms the current state-of-the-art methods on noisy MNIST, CIFAR-10, and CIFAR-100 in an efficient manner.

In dieser Arbeit wird eine Methode zur Erzeugung und Verwendung von Ensembles vorgestellt, um verrauschte Beispiele in Gegenwart von Annotationsrauschen effektiv zu identifizieren.


Recovering sparse conditional independence graphs from data is a fundamental problem in machine learning with wide applications.A popular formulation of the problem is an $\ell_1$ regularized maximum likelihood estimation.Many convex optimization algorithms have been designed to solve this formulation to recover the graph structure.Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.However, it is a challenging task in this case, since the symmetric positive definiteness (SPD) and sparsity of the matrix are not easy to enforce in learned algorithms, and a direct mapping from data to precision matrix may contain many parameters.We propose a deep learning architecture, GLAD, which uses an Alternating Minimization (AM) algorithm as our model inductive bias, and learns the model parameters via supervised learning.We show that GLAD learns a very compact and effective model for recovering sparse graphs from data.

Ein datengesteuerter Lernalgorithmus, der auf der Abrollung der Alternierenden Minimierungsoptimierung für die Wiederherstellung spärlicher Graphen basiert.


Counterfactual regret minimization (CFR) is a fundamental and effective technique for solving Imperfect Information Games (IIG).However, the original CFR algorithm only works for discrete states and action spaces, and the resulting strategy is maintained as a tabular representation.Such tabular representation limits the method from being directly applied to large games.In this paper, we propose a double neural representation for the IIGs, where one neural network represents the cumulative regret, and the other represents the average strategy.  Such neural representations allow us to avoid manual game abstraction and carry out end-to-end optimization.To make the learning efficient, we also developed several novel techniques including a robust sampling method and a mini-batch Monte Carlo Counterfactual Regret Minimization (MCCFR) method, which may be of independent interests.  Empirically, on games tractable to tabular approaches, neural strategies trained with our algorithm converge comparably to their tabular counterparts, and significantly outperform those based on deep reinforcement learning.  On extremely large games with billions of decision nodes, our approach achieved strong performance while using hundreds of times less memory than the tabular CFR.On head-to-head matches of hands-up no-limit texas hold'em, our neural agent beat the strong agent ABS-CFR by $9.8\pm4.1$ chips per game.It's a successful application of neural CFR in large games.

Wir haben einen doppelten neuronalen Rahmen vorgeschlagen, um ein groß angelegtes unvollkommene Informationen Spiel zu lösen.


We present the first verification that a neural network for perception tasks producesa correct output within a specified tolerance for every input of interest.We definecorrectness relative to a specification which identifies 1) a state space consisting ofall relevant states of the world and 2) an observation process that produces neuralnetwork inputs from the states of the world.Tiling the state and input spaces witha finite number of tiles, obtaining ground truth bounds from the state tiles andnetwork output bounds from the input tiles, then comparing the ground truth andnetwork output bounds delivers an upper bound on the network output error forany input of interest.Results from two case studies highlight the ability of ourtechnique to deliver tight error bounds for all inputs of interest and show how theerror bounds vary over the state and input spaces.

Wir präsentieren den ersten Nachweis, dass ein neuronales Netz für Wahrnehmungsaufgaben für jede Eingabe von Interesse eine korrekte Ausgabe innerhalb einer bestimmten Toleranz erzeugt.


Deep generative models have achieved remarkable progress in recent years.Despite this progress, quantitative evaluation and comparison of generative models remains as one of the important challenges.One of the most popular metrics for evaluating generative models is the log-likelihood.While the direct computation of log-likelihood can be intractable, it has been recently shown that the log-likelihood of some of the most interesting generative models such as variational autoencoders (VAE) or generative adversarial networks (GAN) can be efficiently estimated using annealed importance sampling (AIS).In this work, we argue that the log-likelihood metric by itself cannot represent all the different performance characteristics of generative models, and propose to use rate distortion curves to evaluate and compare deep generative models.We show that we can approximate the entire rate distortion curve using one single run of AIS for roughly the same computational cost as a single log-likelihood estimate.We evaluate lossy compression rates of different deep generative models such as VAEs, GANs (and its variants) and adversarial autoencoders (AAE) on MNIST and CIFAR10, and arrive at a number of insights not obtainable from log-likelihoods alone.

Wir untersuchen Annäherungen an die Ratenverzerrung, um tiefe generative Modelle zu evaluieren, und zeigen, dass Ratenverzerrungskurven mehr Erkenntnisse über das Modell liefern als die Log-Likelihood allein, während sie ungefähr die gleichen Berechnungskosten erfordern.


Although reinforcement learning methods can achieve impressive results in simulation, the real world presents two major challenges: generating samples is exceedingly expensive, and unexpected perturbations or unseen situations cause proficient but specialized policies to fail at test time.Given that it is impractical to train separate policies to accommodate all situations the agent may see in the real world, this work proposes to learn how to quickly and effectively adapt online to new tasks.To enable sample-efficient learning, we consider learning online adaptation in the context of model-based reinforcement learning.Our approach uses meta-learning to train a dynamics model prior such that, when combined with recent data, this prior can be rapidly adapted to the local context.Our experiments demonstrate online adaptation for continuous control tasks on both simulated and real-world agents.We first show simulated agents adapting their behavior online to novel terrains, crippled body parts, and highly-dynamic environments.We also illustrate the importance of incorporating online adaptation into autonomous agents that operate in the real world by applying our method to a real dynamic legged millirobot: We demonstrate the agent's learned ability to quickly adapt online to a missing leg, adjust to novel terrains and slopes, account for miscalibration or errors in pose estimation, and compensate for pulling payloads.

Ein modellbasierter Meta-RL-Algorithmus, der es einem realen Roboter ermöglicht, sich in dynamischen Umgebungen online anzupassen


Model-free deep reinforcement learning approaches have shown superhuman performance in simulated environments (e.g., Atari games, Go, etc).During training, these approaches often implicitly construct a latent space that contains key information for decision making.In this paper, we learn a forward model on this latent space and apply it to model-based planning in miniature Real-time Strategy game with incomplete information (MiniRTS).We first show that the latent space constructed from existing actor-critic models contains relevant information of the game, and design training procedure to learn forward models.We also show that our learned forward model can predict meaningful future state and is usable for latent space Monte-Carlo Tree Search (MCTS), in terms of win rates against rule-based agents.

Das Papier analysiert den latenten Raum, der durch modellfreie Ansätze in einem Miniaturspiel mit unvollständiger Information erlernt wird, trainiert ein Vorwärtsmodell im latenten Raum und wendet es auf die Monte-Carlo-Baumsuche an, was zu einer positiven Leistung führt.


Several state of the art convolutional networks rely on inter-connecting different layers to ease the flow of information and gradient between their input and output layers.These techniques have enabled practitioners to successfully train deep convolutional networks with hundreds of layers.Particularly, a novel way of interconnecting layers was introduced as the Dense Convolutional Network (DenseNet) and has achieved state of the art performance on relevant image recognition tasks.Despite their notable empirical success, their theoretical understanding is still limited.In this work, we address this problem by analyzing the effect of layer interconnection on the overall expressive power of a convolutional network.In particular, the  connections used in DenseNet are compared with other types of inter-layer connectivity.We carry out a tensor analysis on the expressive power inter-connections on convolutional arithmetic circuits (ConvACs) and relate our results to standard convolutional networks.The analysis leads to performance bounds and practical guidelines for design of ConvACs.The generalization of these results are discussed for other kinds of convolutional networks via generalized tensor decompositions.

Wir analysieren die Ausdruckskraft der in DenseNets verwendeten Verbindungen über Tensorzerlegungen.


We consider the following central question in the field of Deep Reinforcement Learning (DRL):How can we use implicit human feedback to accelerate and optimize the training of a DRL algorithm?State-of-the-art methods rely on any human feedback to be provided explicitly, requiring the active participation of humans (e.g., expert labeling, demonstrations, etc.).In this work, we investigate an alternative paradigm, where non-expert humans are silently observing (and assessing) the agent interacting with the environment.The human's intrinsic reactions to the agent's behavior is sensed as implicit feedback by placing electrodes on the human scalp and monitoring what are known as event-related electric potentials.The implicit feedback is then used to augment the agent's learning in the RL tasks.We develop a system to obtain and accurately decode the implicit human feedback (specifically error-related event potentials) for state-action pairs in an Atari-type environment.As a baseline contribution, we demonstrate the feasibility of capturing error-potentials  of a human observer watching an agent learning to play several different Atari-games using an electroencephalogram (EEG) cap, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm with the intent of accelerating its learning of the game.Building atop the baseline, we then make the following novel contributions in our work:(i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.  (ii) We propose two different frameworks to combine recent advances in DRL into the error-potential based feedback system in a sample-efficient manner, allowing humans to provide implicit feedback while training in the loop, or prior to the training of the RL agent.(iii) Finally, we scale the implicit human feedback (via ErrP) based RL to reasonably complex environments (games) and demonstrate the significance of our approach through synthetic and real user experiments.

Wir nutzen implizites menschliches Feedback (über Fehlerpotentiale, EEG), um das Training eines DRL-Algorithmus auf praktische Weise zu beschleunigen und zu optimieren.


Deep learning has demonstrated abilities to learn complex structures, but they can be restricted by available data.Recently, Consensus Networks (CNs) were proposed to alleviate data sparsity by utilizing features from multiple modalities, but they too have been limited by the size of labeled data.In this paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for semi-supervised learning.In TCNs, different modalities of input are compressed into latent representations, which we encourage to become indistinguishable during iterative adversarial training.To understand TCNs two mechanisms, consensus and classification, we put forward its three variants in ablation studies on these mechanisms.To further investigate TCN models, we treat the latent representations as probability distributions and measure their similarities as the negative relative Jensen-Shannon divergences.We show that a consensus state beneficial for classification desires a stable but imperfect similarity between the representations.Overall, TCNs outperform or align with the best benchmark algorithms given 20 to 200 labeled samples on the Bank Marketing and the DementiaBank datasets.

TCN für multimodales halb-überwachtes Lernen + Ablationsstudie seiner Mechanismen + Interpretationen latenter Repräsentationen


Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings.However, some of the most popular of these optimization tools - namely Adam, Adagrad and the more recent Amsgrad - remain to be generalized to Riemannian manifolds.We discuss the difficulty of generalizing such adaptive schemes to the most agnostic Riemannian setting, and then provide algorithms and convergence proofs for geodesically convex objectives in the particular case of a product of Riemannian manifolds, in which adaptivity is implemented across manifolds in the cartesian product.Our generalization is tight in the sense that choosing the Euclidean space as Riemannian manifold yields the same algorithms and regret bounds as those that were already known for the standard algorithms.Experimentally, we show faster convergence and to a lower train loss value for Riemannian adaptive methods over their corresponding baselines on the realistic task of embedding the WordNet taxonomy in the Poincare ball.

Anpassung von Adam, Amsgrad, Adagrad an Riemannsche Mannigfaltigkeiten.


We study the problem of defending deep neural network approaches for image classification from physically realizable attacks.First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiveness against three of the highest profile physical attacks.Next, we propose a new abstract adversarial model, rectangular occlusion attacks, in which an adversary places a small adversarially crafted rectangle in an image, and develop two approaches for efficiently computing the resulting adversarial examples.Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.

Verteidigung gegen physikalisch realisierbare Angriffe auf die Bildklassifizierung


Continual learning is the problem of sequentially learning new tasks or knowledge while protecting previously acquired knowledge.However, catastrophic forgetting poses a grand challenge for neural networks performing such learning process.Thus, neural networks that are deployed in the real world often struggle in scenarios where the data distribution is non-stationary (concept drift), imbalanced, or not always fully available, i.e., rare edge cases.We propose a Differentiable Hebbian Consolidation model which is composed of a Differentiable Hebbian Plasticity (DHP) Softmax layer that adds a rapid learning plastic component (compressed episodic memory) to the fixed (slow changing) parameters of the softmax output layer; enabling learned representations to be retained for a longer timescale.We demonstrate the flexibility of our method by integrating well-known task-specific synaptic consolidation methods to penalize changes in the slow weights that are important for each target task.We evaluate our approach on the Permuted MNIST, Split MNIST and Vision Datasets Mixture benchmarks, and introduce an imbalanced variant of Permuted MNIST --- a dataset that combines the challenges of class imbalance and concept drift.Our proposed model requires no additional hyperparameters and outperforms comparable baselines by reducing forgetting.

Hebbsche plastische Gewichte können sich wie ein komprimierter episodischer Gedächtnisspeicher in neuronalen Netzwerken verhalten und in Kombination mit aufgabenspezifischer synaptischer Konsolidierung die Fähigkeit verbessern, katastrophales Vergessen beim kontinuierlichen Lernen zu lindern.


In order to choose a neural network architecture that will be effective for a particular modeling problem, one must understand the limitations imposed by each of the potential options.These limitations are typically described in terms of information theoretic bounds, or by comparing the relative complexity needed to approximate example functions between different architectures.In this paper, we examine the topological constraints that the architecture of a neural network imposes on the level sets of all the functions that it is able to approximate.This approach is novel for both the nature of the limitations and the fact that they are independent of network depth for a broad family of activation functions.

Diese Arbeit beweist, dass dünne neuronale Netze bestimmte Funktionen nicht annähern können, unabhängig davon, wie tief sie sind.


Program verification offers a framework for ensuring program correctness and therefore systematically eliminating different classes of bugs.Inferring loop invariants is one of the main challenges behind automated verification of real-world programs which often contain many loops.In this paper, we present Continuous Logic Network (CLN), a novel neural architecture for automatically learning loop invariants directly from program execution traces.Unlike existing neural networks, CLNs can learn precise and explicit representations of formulas in Satisfiability Modulo Theories (SMT)  for loop invariants from program execution traces.We develop a new sound and complete semantic mapping for assigning SMT formulas to continuous truth values that allows CLNs to be trained efficiently.We use CLNs to implement a new inference system for loop invariants, CLN2INV, that significantly outperforms existing approaches on the popular Code2Inv dataset.CLN2INV is the first tool to solve all 124 theoretically solvable problems in the Code2Inv dataset.Moreover, CLN2INV takes only 1.1 second on average for each problem, which is 40 times faster than existing approaches.We further demonstrate that CLN2INV can even learn 12 significantly more complex loop invariants than the ones required for the Code2Inv dataset.

Wir stellen das Continuous Logic Network (CLN) vor, eine neuartige neuronale Architektur zum automatischen Lernen von Schleifeninvarianten und allgemeinen SMT-Formeln.


Single cell RNA sequencing (scRNAseq) technology enables quantifying gene expression profiles by individual cells within cancer.Dimension reduction methods have been commonly used for cell clustering analysis and visualization of the data.Current dimension reduction methods tend overly eliminate the expression variations correspond to less dominating characteristics, such we fail to find the homogenious properties of cancer development.In this paper, we proposed a new and clustering analysis method for scRNAseq data, namely BBSC, via implementing a binarization of the gene expression profile into on/off frequency changes with a Boolean matrix factorization.The low rank representation of expression matrix recovered by BBSC increase the resolution in identifying distinct cell types or functions.Application of BBSC on two cancer scRNAseq data successfully discovered both homogeneous and heterogeneous cancer cell clusters.Further finding showed potential in preventing cancer progression.

Unsere Ergebnisse werfen ein Licht auf die Verhinderung des Fortschreitens von Krebs


While normalizing flows have led to significant advances in modeling high-dimensional continuous distributions, their applicability to discrete distributions remains unknown.In this paper, we show that flows can in fact be extended to discrete events---and under a simple change-of-variables formula not requiring log-determinant-Jacobian computations.Discrete flows have numerous applications.We display proofs of concept under 2 flow architectures: discrete autoregressive flows enable bidirectionality, allowing for example tokens in text to depend on both left-to-right and right-to-left contexts in an exact language model; and discrete bipartite flows (i.e., with layer structure from RealNVP) enable parallel generation such as exact nonautoregressive text modeling.

Wir erweitern autoregressive Flüsse und RealNVP auf diskrete Daten.


We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs.The proposed network augments a DNN with unsupervised learning of low-level features using spiking neuron network (SNN) with Spike-Time-Dependent-Plasticity (STDP).The complete network learns to ignore local perturbation while performing global feature detection and classification.The experimental results on CIFAR-10 and ImageNet subset demonstrate improved noise robustness for multiple DNN architectures without sacrificing accuracy on clean images.


Neural embeddings have been used with great success in Natural Language Processing (NLP) where they provide compact representations that encapsulate word similarity and attain state-of-the-art performance in a range of linguistic tasks.The success of neural embeddings has prompted significant amounts of research into applications in domains other than language.One such domain is graph-structured data, where embeddings of vertices can be learned that encapsulate vertex similarity and improve performance on tasks including edge prediction and vertex labelling.For both NLP and graph-based tasks, embeddings in high-dimensional Euclidean spaces have been learned.However, recent work has shown that the appropriate isometric space for embedding complex networks is not the flat Euclidean space, but a negatively curved hyperbolic space.We present a new concept that exploits these recent insights and propose learning neural embeddings of graphs in hyperbolic space.We provide experimental evidence that hyperbolic embeddings significantly outperform Euclidean embeddings on vertex classification tasks for several real-world public datasets.

Wir lernen neuronale Einbettungen von Graphen im hyperbolischen statt im euklidischen Raum


The International Competition on Knowledge Engineering for Planning and Scheduling (ICKEPS) plays a pivotal role in fostering the development of new Knowledge Engineering (KE) tools, and in emphasising the importance of principled approaches for all the different KE aspects that are needed for the successful long-term use of planning in real-world applications.  In this paper, as an exercise in synthesis and for the sake of stimulating thoughts and discussion, we review the format of previous ICKEPS, to suggest alternative formats for future competitions, ideally to motivate someone to step up and organise the next ones.