This paper introduces a framework for solving combinatorial optimization problems by learning from input-output examples of optimization problems.We introduce a new memory augmented neural model in which the memory is not resettable (i.e the information stored in the memory after processing an input example is kept for the next seen examples).We used deep reinforcement learning to train a memory controller agent to store useful memories.Our model was able to outperform hand-crafted solver on Binary Linear Programming (Binary LP).The proposed model is tested on different Binary LP instances with large number of variables (up to 1000 variables) and constrains (up to 700 constrains).

Wir schlagen ein Gedächtnisnetzwerkmodell vor, um binäre LP-Instanzen zu lösen, bei denen die Gedächtnisinformationen für eine langfristige Verwendung aufbewahrt werden.


Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition.Performance has further been improved by leveraging unlabeled data, often in the form of a language model.In this work, we present the Cold Fusion method, which leverages a pre-trained language model during training, and show its effectiveness on the speech recognition task.We show that Seq2Seq models with Cold Fusion are able to better utilize language information enjoyingi) faster convergence and better generalization, andii) almost complete transfer to a new domain while using less than 10% of the labeled training data.

Wir stellen eine neuartige Methode vor, um Seq2Seq-Modelle mit Sprachmodellen zu trainieren, die schneller konvergieren, besser verallgemeinern und mit weniger als 10 % der gelabelten Daten fast vollständig auf eine neue Domäne übertragen werden können.


A central capability of intelligent systems is the ability to continuously build upon previous experiences to speed up and enhance learning of new tasks.Two distinct research paradigms have studied this question.Meta-learning views this problem as learning a prior over model parameters that is amenable for fast adaptation on a new task, but typically assumes the set of tasks are available together as a batch.In contrast, online (regret based) learning considers a sequential setting in which problems are revealed one after the other, but conventionally train only a single model without any task-specific adaptation.This work introduces an online meta-learning setting, which merges ideas from both the aforementioned paradigms to better capture the spirit and practice of continual lifelong learning.We propose the follow the meta leader (FTML) algorithm which extends the MAML algorithm to this setting.Theoretically, this work provides an O(logT) regret guarantee for the FTML algorithm.Our experimental evaluation on three different large-scale tasks suggest that the proposed algorithm significantly outperforms alternatives based on traditional online learning approaches.

Wir führen die Online-Meta-Learning-Problemstellung ein, um den Geist und die Praxis des kontinuierlichen lebenslangen Lernens besser zu erfassen.


We investigate the extent to which individual attention heads in pretrained transformer language models, such as BERT and RoBERTa, implicitly capture syntactic dependency relations.We employ two methods—taking the maximum attention weight and computing the maximum spanning tree—to extract implicit dependency relations from the attention weights of each layer/head, and compare them to the ground-truth Universal Dependency (UD) trees.We show that, for some UD relation types, there exist heads that can recover the dependency type significantly better than baselines on parsed English text, suggesting that some self-attention heads act as a proxy for syntactic structure.We also analyze BERT fine-tuned on two datasets—the syntax-oriented CoLA and the semantics-oriented MNLI—to investigate whether fine-tuning affects the patterns of their self-attention, but we do not observe substantial differences in the overall dependency relations extracted using our methods.Our results suggest that these models have some specialist attention heads that track individual dependency types, but no generalist head that performs holistic parsing significantly better than a trivial baseline, and that analyzing attention weights directly may not reveal much of the syntactic knowledge that BERT-style models are known to learn.

Die Aufmerksamkeitsgewichte legen nicht vollständig offen, was BERT über die Syntax weiß.


State-of-the-art face super-resolution methods employ deep convolutional neural networks to learn a mapping between low- and high-resolution facial patterns by exploring local appearance knowledge.However, most of these methods do not well exploit facial structures and identity information, and struggle to deal with facial images that exhibit large pose variation and misalignment.In this paper, we propose a novel face super-resolution method that explicitly incorporates 3D facial priors which grasp the sharp facial structures.Firstly, the 3D face rendering branch is set up to obtain 3D priors of salient facial structures and identity knowledge.Secondly, the Spatial Attention Mechanism is used to better exploit this hierarchical information (i.e. intensity similarity, 3D facial structure, identity content) for the super-resolution problem.Extensive experiments demonstrate that the proposed algorithm achieves superior face super-resolution results and outperforms the state-of-the-art.

Wir schlagen eine neuartige Methode zur Gesichtsauflösung vor, die explizit 3D-Gesichtsprioritäten einbezieht, die die scharfen Gesichtsstrukturen erfassen.


We present Cross-View Training (CVT), a simple but effective method for deep semi-supervised learning.On labeled examples, the model is trained with standard cross-entropy loss.On an unlabeled example, the model first performs inference (acting as a "teacher") to produce soft targets.The model then learns from these soft targets (acting as a ``"student").We deviate from prior work by adding multiple auxiliary student prediction layers to the model.The input to each student layer is a sub-network of the full model that has a restricted view of the input  (e.g., only seeing one region of an image).The students can learn from the teacher (the full model) because the teacher sees more of each example.Concurrently, the students improve the quality of the representations used by the teacher as they learn to make predictions with limited data.When combined with Virtual Adversarial Training, CVT improves upon the current state-of-the-art on semi-supervised CIFAR-10 and semi-supervised SVHN.We also apply CVT to train models on five natural language processing tasks using hundreds of millions of sentences of unlabeled data.On all tasks CVT substantially outperforms supervised learning alone, resulting in models that improve upon or are competitive with the current state-of-the-art.

Selbsttraining mit verschiedenen Ansichten der Eingabe liefert hervorragende Ergebnisse für die halbüberwachte Bilderkennung, das Sequenz-Tagging und das Parsing von Abhängigkeiten.


I show how it can be beneficial to express Metropolis accept/reject decisions in terms of comparison with a uniform [0,1] value, and to then update this uniform value non-reversibly, as part of the Markov chain state, rather than sampling it independently each iteration.This provides a small improvement for random walk Metropolis and Langevin updates in high dimensions.  It produces a larger improvement when using Langevin updates with persistent momentum, giving performance comparable to that of Hamiltonian Monte Carlo (HMC) with long trajectories.  This is of significance when some variables are updated by other methods, since if HMC is used, these updates can be done only between trajectories, whereas they can be done more often with Langevin updates.  This is seen for a Bayesian neural network model, in which connection weights are updated by persistent Langevin or HMC, while hyperparameters are updated by Gibbs sampling.

Eine nicht umkehrbare Entscheidung über die Annahme oder Ablehnung kann von Vorteil sein.


We introduce ES-MAML, a new framework for solving the model agnostic meta learning (MAML) problem based on Evolution Strategies (ES).Existing algorithms for MAML are based on policy gradients, and incur significant difficulties when attempting to estimate second derivatives using backpropagation on stochastic policies.We show how ES can be applied to MAML to obtain an algorithm which avoids the problem of estimating second derivatives, and is also conceptually simple and easy to implement.Moreover, ES-MAML can handle new types of nonsmooth adaptation operators, and other techniques for improving performance and estimation of ES methods become applicable.We show empirically that ES-MAML is competitive with existing methods and often yields better adaptation with fewer queries.

Wir bieten einen neuen Rahmen für MAML in der ES/Blackbox-Umgebung und zeigen, dass er deterministische und lineare Strategien, bessere Exploration und nicht-differenzierbare Anpassungsoperatoren ermöglicht.


Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning.Some prominent examples are constrained-to-unconstrained transformations of distributions for use in Hamiltonian Monte-Carlo and constructing flexible and learnable densities such as normalizing flows.We present Bijectors.jl, a software package for transforming distributions implemented in Julia, available at github.com/TuringLang/Bijectors.jl.The package provides a flexible and composable way of implementing transformations of distributions without being tied to a computational framework. We demonstrate the use of Bijectors.jl on improving variational inference by encoding known statistical dependencies into the variational posterior using normalizing flows, providing a general approach to relaxing the mean-field assumption usually made in variational inference.

Wir stellen einen Software-Rahmen für die Transformation von Verteilungen vor und demonstrieren dessen Flexibilität bei der Lockerung von Mean-Field-Annahmen in der Variationsinferenz durch die Verwendung von Kopplungsflüssen zur Replikation von Strukturen aus dem generativen Zielmodell.


Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains.Our empirical motivation is automated microscopy data, where cultured cells are imaged after being exposed to known and unknown chemical perturbations, and each dataset displays significant experimental bias.This paper presents a multi-domain adversarial learning approach, MuLANN, to leverage multiple datasets with overlapping but distinct class sets, in a semi-supervised setting.Our contributions include:i) a bound on the average- and worst-domain risk in MDL, obtained using the H-divergence;ii) a new loss to accommodate semi-supervised multi-domain learning and domain adaptation;iii) the experimental validation of the approach, improving on the state of the art on two standard image benchmarks, and a novel bioimage dataset, Cell.

Adversarial Domain adaptation und Multi-domain learning: Ein neuer Loss um multi- und single-domain Klassen in halbüberwachten Umgebungen zu steuern.


We introduce our Distribution Regression Network (DRN) which performs regression from input probability distributions to output probability distributions.Compared to existing methods, DRN learns with fewer model parameters and easily extends to multiple input and multiple output distributions.On synthetic and real-world datasets, DRN performs similarly or better than the state-of-the-art.Furthermore, DRN generalizes the conventional multilayer perceptron (MLP).In the framework of MLP, each node encodes a real number, whereas in DRN, each node encodes a probability distribution.

Ein lernendes Netz, das den MLP-Rahmen verallgemeinert, um eine Regression von Verteilung zu Verteilung durchzuführen


Existing sequence prediction methods are mostly concerned with time-independent sequences, in which the actual time span between events is irrelevant and the distance between events is simply the difference between their order positions in the sequence.While this time-independent view of sequences is applicable for data such as natural languages, e.g., dealing with words in a sentence, it is inappropriate and inefficient for many real world events that are observed and collected at unequally spaced points of time as they naturally arise, e.g., when a person goes to a grocery store or makes a phone call.The time span between events can carry important information about the sequence dependence of human behaviors.In this work, we propose a set of methods for using time in sequence prediction.Because neural sequence models such as RNN are more amenable for handling token-like input, we propose two methods for time-dependent event representation, based on the intuition on how time is tokenized in everyday life and previous work on embedding contextualization.We also introduce two methods for using next event duration as regularization for training a sequence prediction model.We discuss these methods based on recurrent neural nets.We evaluate these methods as well as baseline models on five datasets that resemble a variety of sequence prediction tasks.The experiments revealed that the proposed methods offer accuracy gain over baseline models in a range of settings.

Vorgeschlagene Methoden für zeitabhängige Ereignisdarstellung und Regularisierung für Sequenzvorhersage; Evaluierung dieser Methoden an fünf Datensätzen, die eine Reihe von Sequenzvorhersageaufgaben beinhalten.


Off-policy reinforcement learning algorithms promise to be applicable in settings where only a fixed data-set (batch) of environment interactions is available and no new experience can be acquired.This property makes these algorithms appealing for real world problems such as robot control.In practice, however, standard off-policy algorithms fail in the batch setting for continuous control.In this paper, we propose a simple solution to this problem.It admits the use of data generated by arbitrary behavior policies and uses a learned prior -- the advantage-weighted behavior model (ABM) -- to bias the RL policy towards actions that have previously been executed and are likely to be successful on the new task.Our method can be seen as an extension of recent work on batch-RL that enables stable learning from conflicting data-sources.We find  improvements on competitive baselines in a variety of RL tasks -- including standard continuous control benchmarks and multi-task learning for simulated and real-world robots.

Wir entwickeln eine Methode für stabiles Offline Reinforcement Learning aus aufgezeichneten Daten. Der Schlüssel dazu ist die Regularisierung der RL-Politik in Richtung eines gelernten "vorteilhaft gewichteten" Modells der Daten.


One of the main challenges in applying graph convolutional neural networks on gene-interaction data is the lack of understanding of the vector space  to which they belong and also the inherent difficulties involved in representing those interactions on a significantly lower dimension, viz Euclidean spaces.The challenge becomes more prevalent when dealing with various types of heterogeneous data.We introduce a systematic, generalized method, called iSOM-GSN, used to transform ``multi-omic'' data with higher dimensions onto a two-dimensional grid.Afterwards, we apply a convolutional neural network to predict disease states of various types.Based on the idea of Kohonen's self-organizing map, we generate a two-dimensional grid for each sample for a given set of genes that represent a gene similarity network.  We have tested the model to predict breast and prostate cancer using gene expression, DNA methylation and copy number alteration, yielding prediction accuracies in the 94-98% range for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 11 input genes for both cases.The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for representation learning, visualization, dimensionality reduction, and interpretation of the results.

In diesem Beitrag wird ein Deep-Learning-Modell vorgestellt, das selbstorganisierende Zuordnungen und neuronale Convolutional Networks für das Repräsentationslernen von Multi-omics-Daten kombiniert.


State-of-the-art performances on language comprehension tasks are achieved by huge language models pre-trained on massive unlabeled text corpora, with very light subsequent fine-tuning in a task-specific supervised manner.It seems the pre-training procedure learns a very good common initialization for further training on various natural language understanding tasks, such that only few steps need to be taken in the parameter space to learn each task.In this work, using Bidirectional Encoder Representations from Transformers (BERT) as an example, we verify this hypothesis by showing that task-specific fine-tuned language models are highly close in parameter space to the pre-trained one.Taking advantage of such observations, we further show that the fine-tuned versions of these huge models, having on the order of $10^8$ floating-point parameters, can be made very computationally efficient.First, fine-tuning only a fraction of critical layers suffices.Second, fine-tuning can be adequately performed by learning a binary multiplicative mask on pre-trained weights, \textit{i.e.} by parameter-sparsification.As a result, with a single effort, we achieve three desired outcomes: (1) learning to perform specific tasks, (2) saving memory by storing only binary masks of certain layers for each task, and (3) saving compute on appropriate hardware by performing sparse operations with model parameters.

Sparsifizierung als Feinabstimmung von Sprachmodellen


We present a simple and effective algorithm designed to address the covariate shift problem in imitation learning.It operates by training an ensemble of policies on the expert demonstration data, and using the variance of their predictions as a cost which is minimized with RL together with a supervised behavioral cloning cost.Unlike adversarial imitation methods, it uses a fixed reward function which is easy to optimize.We prove a regret bound for the algorithm in the tabular setting which is linear in the time horizon multiplied by a coefficient which we show to be low for certain problems in which behavioral cloning fails.We evaluate our algorithm empirically across multiple pixel-based Atari environments and continuous control tasks, and show that it matches or significantly outperforms behavioral cloning and generative adversarial imitation learning.

Verfahren zur Behandlung von Kovariatenverschiebungen beim Imitation Learning unter Verwendung von Ensemble-Unsicherheit


We present and discuss a simple image preprocessing method for learning disentangled latent factors. In particular, we utilize the implicit inductive bias contained in features from networks pretrained on the ImageNet database. We enhance this bias by explicitly fine-tuning such pretrained networks on tasks useful for the NeurIPS2019 disentanglement challenge, such as angle and position estimation or color classification.Furthermore, we train a VAE on regionally aggregate feature maps, and discuss its disentanglement performance using metrics proposed in recent literature.

Wir verwenden eine überwachte Feinabstimmung der Merkmalsvektoren, um die Übertragung von der Simulation auf die reale Welt zu verbessern.


A reinforcement learning agent that needs to pursue different goals across episodes requires a goal-conditional policy.In addition to their potential to generalize desirable behavior to unseen goals, such policies may also enable higher-level planning based on subgoals.In sparse-reward environments, the capacity to exploit information about the degree to which an arbitrary goal has been achieved while another goal was intended appears crucial to enable sample efficient learning.However, reinforcement learning agents have only recently been endowed with such capacity for hindsight.In this paper, we demonstrate how hindsight can be introduced to policy gradient methods, generalizing this idea to a broad class of successful algorithms.Our experiments on a diverse selection of sparse-reward environments show that hindsight leads to a remarkable increase in sample efficiency.

Wir führen die Fähigkeit ein, Informationen darüber zu nutzen, inwieweit ein beliebiges Ziel erreicht wurde, während ein anderes Ziel für die Regeln der Gradientenmethoden vorgesehen war.


Computer vision has undergone a dramatic revolution in performance, driven in large part through deep features trained on large-scale supervised datasets.However, much of these improvements have focused on static image analysis; video understanding has seen rather modest improvements.Even though new datasets and spatiotemporal models have been proposed, simple frame-by-frame classification methods often still remain competitive.We posit that current video datasets are plagued with implicit biases over scene and object structure that can dwarf variations in temporal structure.In this work, we build a video dataset with fully observable and controllable object and scene bias, and which truly requires spatiotemporal understanding in order to be solved.Our dataset, named CATER, is rendered synthetically using a library of standard 3D objects, and tests the ability to recognize compositions of object movements that require long-term reasoning.In addition to being a challenging dataset, CATER also provides a plethora of diagnostic tools to analyze modern spatiotemporal video architectures by being completely observable and controllable.Using CATER, we provide insights into some of the most recent state of the art deep video architectures.

Wir schlagen einen neuen Benchmark für das Verstehen von Videos vor, dessen Aufgaben im Gegensatz zu den meisten bestehenden Videodatensätzen von vornherein zeitliche Schlussfolgerungen erfordern.


We address the efficiency issues caused by the straggler effect in the recently emerged federated learning, which collaboratively trains a model on decentralized non-i.i.d. (non-independent and identically distributed) data across massive worker devices without exchanging training data in the unreliable and heterogeneous networks.We propose a novel two-stage analysis on the error bounds of general federated learning, which provides practical insights into optimization.As a result, we propose a novel easy-to-implement federated learning algorithm that uses asynchronous settings and strategies to control discrepancies between the global model and delayed models and adjust the number of local epochs with the estimation of staleness to accelerate convergence and resist performance deterioration caused by stragglers.Experiment results show that our algorithm converges fast and robust on the existence of massive stragglers.

Wir schlagen einen effizienten und robusten asynchronen Algorithmus für föderiertes Lernen vor, der die Existenz von Nachzüglern berücksichtigt.


Long Short-Term Memory (LSTM) units have the ability to memorise and use long-term dependencies between inputs to generate predictions on time series data.We introduce the concept of modifying the cell state (memory) of LSTMs using rotation matrices parametrised by a new set of trainable weights.This addition shows significant increases of performance on some of the tasks from the bAbI dataset.

Das Hinzufügen eines neuen Satzes von Gewichten zum LSTM, die den Zellspeicher drehen, verbessert die Leistung bei einigen bAbI-Aufgaben.


We address the problem of marginal inference for an exponential family defined over the set of permutation matrices.This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem.We introduce Sinkhorn variational marginal inference as a scalable alternative, a method whose validity is ultimately justified by the so-called Sinkhorn approximation of the permanent.We demonstrate the efectiveness of our method in the problem of probabilistic identification of neurons in the worm C.elegans

Neue Methodik zur variablen marginalen Inferenz von Permutationen auf der Grundlage des Sinkhorn-Algorithmus, angewandt auf die probabilistische Identifizierung von Neuronen


The robustness of neural networks to adversarial examples has received great attention due to security implications.Despite various attack approaches to crafting visually imperceptible adversarial examples, little has been developed towards a comprehensive measure of robustness.In this paper, we provide theoretical justification for converting robustness analysis into a local Lipschitz constant estimation problem, and propose to use the Extreme Value Theory for efficient evaluation.Our analysis yields a novel robustness metric called CLEVER, which is short for Cross Lipschitz Extreme Value for nEtwork Robustness.The proposed CLEVER score is attack-agnostic and is computationally feasible for large neural networks.Experimental results on various networks, including ResNet, Inception-v3 and MobileNet, show that(i) CLEVER is aligned with the robustness indication measured by the $\ell_2$ and $\ell_\infty$ norms of adversarial examples from powerful attacks, and(ii) defended networks using defensive distillation or bounded ReLU indeed give better CLEVER scores.To the best of our knowledge, CLEVER is the first attack-independent robustness metric that can be applied to any neural network classifiers.

Wir schlagen die erste angriffsunabhängige Robustheitsmetrik, a.k.a. CLEVER, vor, die auf jeden Klassifikator eines neuronalen Netzes angewendet werden kann.


Multi-agent collaboration is required by numerous real-world problems.Although distributed setting is usually adopted by practical systems, local range communication and information aggregation still matter in fulfilling complex tasks.For multi-agent reinforcement learning, many previous studies have been dedicated to design an effective communication architecture.However, existing models usually suffer from an ossified communication structure, e.g., most of them predefine a particular communication mode by specifying a fixed time frequency and spatial scope for agents to communicate regardless of necessity.Such design is incapable of dealing with multi-agent scenarios that are capricious and complicated, especially when only partial information is available.Motivated by this, we argue that the solution is to build a spontaneous and self-organizing communication (SSoC) learning scheme.By treating the communication behaviour as an explicit action, SSoC learns to organize communication in an effective and efficient way.Particularly, it enables each agent to spontaneously decide when and who to send messages based on its observed states.In this way, a dynamic inter-agent communication channel is established in an online and self-organizing manner.The agents also learn how to adaptively aggregate the received messages and its own hidden states to execute actions.Various experiments have been conducted to demonstrate that SSoC really learns intelligent message passing among agents located far apart.With such agile communications, we observe that effective collaboration tactics emerge which have not been mastered by the compared baselines.

In diesem Beitrag wird ein Lernverfahren für spontane und selbstorganisierende Kommunikation (SSoC) für Multi-Agenten-RL-Aufgaben vorgeschlagen.


We study the BERT language representation model and the sequence generation model with BERT encoder for multi-label text classification task.We experiment with both models and explore their special qualities for this setting.We also introduce and examine experimentally a mixed model, which is an ensemble of multi-label BERT and sequence generating BERT models.Our experiments demonstrated that BERT-based models and the mixed model, in particular, outperform current baselines in several metrics achieving state-of-the-art results on three well-studied multi-label classification datasets with English texts and two private Yandex Taxi datasets with Russian texts.

Über die Verwendung von BERT als Encoder für die sequentielle Vorhersage von Etiketten bei der Klassifizierung von Texten mit mehreren Etiketten


Click Through Rate (CTR) prediction is a critical task in industrial applications, especially for online social and commerce applications.It is challenging to find a proper way to automatically discover the effective cross features in CTR tasks.We propose a novel model for CTR tasks, called Deep neural networks with Encoder enhanced Factorization Machine (DeepEnFM).Instead of learning the cross features directly, DeepEnFM adopts the Transformer encoder as a backbone to align the feature embeddings with the clues of other fields.The embeddings generated from encoder are beneficial for the further feature interactions.Particularly, DeepEnFM utilizes a bilinear approach to generate different similarity functions with respect to different field pairs.Furthermore, the max-pooling method makes DeepEnFM feasible to capture both the supplementary and suppressing information among different attention heads.Our model is validated on the Criteo and Avazu datasets, and achieves state-of-art performance.

DNN und Encoder verbesserte FM mit bilinearer Aufmerksamkeit und Max-Pooling für CTR


For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence.In real-world scenarios, future states become increasingly uncertain and multi-modal, particularly on long time horizons.Dropout based Bayesian inference provides a computationally tractable, theoretically well grounded approach to learn different hypotheses/models to deal with uncertain futures and make predictions that correspond well to observations -- are well calibrated.However, it turns out that such approaches fall short to capture complex real-world scenes, even falling behind in accuracy when compared to the plain deterministic approaches.This is because the used log-likelihood estimate discourages diversity.In this work, we propose a novel Bayesian formulation for anticipating future scene states which leverages synthetic likelihoods that encourage the learning of diverse models to accurately capture the multi-modal nature of future scene states.We show that our approach achieves accurate state-of-the-art predictions and calibrated probabilities through extensive experiments for scene anticipation on Cityscapes dataset.Moreover, we show that our approach generalizes across diverse tasks such as digit generation and precipitation forecasting.

Dropout-basierte Bayes'sche Inferenz wird erweitert, um mit Multimodalität umgehen zu können, und wird anhand von Aufgaben zur Szenenvorhersage evaluiert.


Conditional generative adversarial networks (cGAN) have led to large improvements in the task of conditional image generation, which lies at the heart of computer vision.The major focus so far has been on performance improvement, while there has been little effort in making cGAN more robust to noise.The regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGAN unreliable for real-world applications.In this work, we introduce a novel conditional GAN model, called RoCGAN, which leverages structure in the target space of the model to address the issue.Our model augments the generator with an unsupervised pathway, which promotes the outputs of the generator to span the target manifold even in the presence of intense noise.We prove that RoCGAN share similar theoretical properties as GAN and experimentally verify that our model outperforms existing state-of-the-art cGAN architectures by a large margin in a variety of domains including images from natural scenes and faces.

Wir stellen eine neue Art von bedingtem GAN vor, das darauf abzielt, die Struktur im Zielraum des Generators zu nutzen. Wir erweitern den Generator mit einem neuen, unbeaufsichtigten Pfad, um die Zielstruktur zu lernen.


Though deep neural networks have achieved the state of the art performance in visual classification, recent studies have shown that they are all vulnerable to the attack of adversarial examples.To solve the problem, some regularization adversarial training methods, constraining the output label or logit, have been studied.In this paper, we propose a novel regularized adversarial training framework ATLPA,namely Adversarial Tolerant Logit Pairing with Attention.Instead of constraining a hard distribution (e.g., one-hot vectors or logit) in adversarial training, ATLPA uses Tolerant Logit which consists of confidence distribution on top-k classes and captures inter-class similarities at the image level.Specifically, in addition to minimizing the empirical loss, ATLPA encourages attention map for pairs of examples to be similar.When applied to clean examples and their adversarial counterparts, ATLPA improves accuracy on adversarial examples over adversarial training.We evaluate ATLPA with the state of the art algorithms, the experiment results show that our method outperforms these baselines with higher accuracy.Compared with previous work, our work is evaluated under highly challenging PGD attack: the maximum perturbation $\epsilon$ is 64 and 128 with 10 to 200 attack iterations.

In diesem Papier schlagen wir ein neuartiges regularisiertes adversariales Trainingssystem ATLPA vor, nämlich Adversarial Tolerant Logit Pairing with Attention.


A fundamental trait of intelligence is the ability to achieve goals in the face of novel circumstances.In this work, we address one such setting which requires solving a task with a novel set of actions.Empowering machines with this ability requires generalization in the way an agent perceives its available actions along with the way it uses these actions to solve tasks.Hence, we propose a framework to enable generalization over both these aspects: understanding an action’s functionality, and using actions to solve tasks through reinforcement learning.Specifically, an agent interprets an action’s behavior using unsupervised representation learning over a collection of data samples reflecting the diverse properties of that action.We employ a reinforcement learning architecture which works over these action representations, and propose regularization metrics essential for enabling generalization in a policy.We illustrate the generalizability of the representation learning method and policy, to enable zero-shot generalization to previously unseen actions on challenging sequential decision-making environments.Our results and videos can be found at sites.google.com/view/action-generalization/

Wir befassen uns mit dem Problem der Generalisierung des Reinforcement Learnings auf ungesehenen Actionräumen.


Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals.The standard way of learning in such models is by estimating the conditional intensity function.  However, parameterizing the intensity function usually incurs several trade-offs.We show how to overcome the limitations of intensity-based approaches by directly modeling the conditional distribution of inter-event times.  We draw on the literature on normalizing flows to design models that are flexible and efficient.We additionally propose a simple mixture model that matches the flexibility of flow-based models, but also permits sampling and computing moments in closed form.  The proposed models achieve state-of-the-art performance in standard prediction tasks and are suitable for novel applications, such as learning sequence embeddings and imputing missing data.

Lernen in zeitlichen Punktprozessen durch Modellierung der bedingten Dichte, nicht der bedingten Intensität.


We propose a novel yet simple neural network architecture for topic modelling.The method is based on training an autoencoder structure where the bottleneck represents the space of the topics distribution and the decoder outputs represent the space of the words distributions over the topics.We exploit an auxiliary decoder to prevent mode collapsing in our model.  A key feature for an effective topic modelling method is having sparse topics and words distributions, where there is a trade-off between the sparsity level of topics and words.This feature is implemented in our model by L-2 regularization and the model hyperparameters take care of the trade-off.  We show in our experiments that our model achieves competitive results compared to the state-of-the-art deep models for topic modelling, despite its simple architecture and training procedure.The “New York Times” and “20 Newsgroups” datasets are used in the experiments.

Ein tiefes Modell für die Themenmodellierung


Voice Conversion (VC) is a task of converting perceived speaker identity from a source speaker to a particular target speaker.Earlier approaches in the literature primarily find a mapping between the given source-target speaker-pairs.Developing mapping techniques for many-to-many VC using non-parallel data, including zero-shot learning remains less explored areas in VC.Most of the many-to-many VC architectures require training data from all the target speakers for whom we want to convert the voices.In this paper, we propose a novel style transfer architecture, which can also be extended to generate voices even for target speakers whose data were not used in the training (i.e., case of zero-shot learning).In particular, propose Adaptive Generative Adversarial Network (AdaGAN), new architectural training procedure help in learning normalized speaker-independent latent representation, which will be used to generate speech with different speaking styles in the context of VC.We compare our results with the state-of-the-art StarGAN-VC architecture.In particular, the AdaGAN achieves 31.73%, and 10.37% relative improvement compared to the StarGAN in MOS tests for speech quality and speaker similarity, respectively.The key strength of the proposed architectures is that it yields these results with less computational complexity.AdaGAN is 88.6% less complex than StarGAN-VC in terms of FLoating Operation Per Second (FLOPS), and 85.46% less complex in terms of trainable parameters.

Neuartiges adaptives, auf Instanznormalisierung basierendes GAN-Framework für nicht parallele Many-to-many- und Zero-Shot-VC.


Self-attention-based Transformer has demonstrated the state-of-the-art performances in a number of natural language processing tasks.Self attention is able to model long-term dependencies, but it may suffer from the extraction of irrelevant information in the context.To tackle the problem, we propose a novel model called Sparse Transformer.Sparse Transformer is able to improve the concentration of attention on the global context through an explicit selection of the most relevant segments.Extensive experimental results on a series of natural language processing tasks, including neural machine translation, image captioning, and language modeling, all demonstrate the advantages of Sparse Transformer in model performance.   Sparse Transformer reaches the state-of-the-art performances in the IWSLT 2015 English-to-Vietnamese translation and IWSLT 2014 German-to-English translation.In addition, we conduct qualitative analysis to account for Sparse Transformer's superior performance.

Diese Arbeit schlägt Sparse Transformer vor, um die Konzentration der Aufmerksamkeit auf den globalen Kontext durch eine explizite Auswahl der relevantesten Segmente für das Lernen von Sequenz zu Sequenz zu verbessern.


Human observers can learn to recognize new categories of objects from a handful of examples, yet doing so with machine perception remains an open challenge.We hypothesize that data-efficient recognition is enabled by representations which make the variability in natural signals more predictable, as suggested by recent perceptual evidence.We therefore revisit and improve Contrastive Predictive Coding, a recently-proposed unsupervised learning framework, and arrive at a representation which enables generalization from small amounts of labeled data.When provided with only 1% of ImageNet labels (i.e. 13 per class), this model retains a strong classification performance, 73% Top-5 accuracy, outperforming supervised networks by 28% (a 65% relative improvement) and state-of-the-art semi-supervised methods by 14%.We also find this representation to serve as a useful substrate for object detection on the PASCAL-VOC 2007 dataset, approaching the performance of representations trained with a fully annotated ImageNet dataset.

Nicht überwachte Repräsentationen, die mit Contrastive Predictive Coding gelernt wurden, ermöglichen eine dateneffiziente Bildklassifizierung.


We demonstrate the possibility of what we call sparse learning: accelerated training of deep neural networks that maintain sparse weights throughout training while achieving dense performance levels.We accomplish this by developing sparse momentum, an algorithm which uses exponentially smoothed gradients (momentum) to identify layers and weights which reduce the error efficiently.Sparse momentum redistributes pruned weights across layers according to the mean momentum magnitude of each layer.Within a layer, sparse momentum grows weights according to the momentum magnitude of zero-valued weights.We demonstrate state-of-the-art sparse performance on MNIST, CIFAR-10, and ImageNet, decreasing the mean error by a relative 8%, 15%, and 6% compared to other sparse algorithms.Furthermore, we show that sparse momentum reliably reproduces dense performance levels while providing up to 5.61x faster training.In our analysis, ablations show that the benefits of momentum redistribution and growth increase with the depth and size of the network.

Die Neuverteilung und Vergrößerung der Gewichte entsprechend der Impulsgröße ermöglicht das Training von spärlichen Netzen aus zufälligen Initialisierungen, die dichte Leistungsniveaus mit 5 % bis 50 % Gewichten erreichen können, während das Training um das bis zu 5,6-fache beschleunigt wird.


To provide principled ways of designing proper Deep Neural Network (DNN) models, it is essential to understand the loss surface of DNNs under realistic assumptions.We introduce interesting aspects for understanding the local minima and overall structure of the loss surface.The parameter domain of the loss surface can be decomposed into regions in which activation values (zero or one for rectified linear units) are consistent.We found that, in each region, the loss surface have properties similar to that of linear neural networks where every local minimum is a global minimum.This means that every differentiable local minimum is the global minimum of the corresponding region.We prove that for a neural network with one hidden layer using rectified linear units under realistic assumptions.There are poor regions that lead to poor local minima, and we explain why such regions exist even in the overparameterized DNNs.

Die Verlustfläche von neuronalen Netzen ist eine disjunkte Vereinigung von Regionen, bei der jedes lokale Minimum ein globales Minimum der entsprechenden Region ist.


Contextualized word representations, such as ELMo and BERT, were shown to perform well on a various of semantic and structural (syntactic) task.In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics, but keeps the structural information.To this end, we automatically generate groups of sentences which are structurally similar but semantically different, and use metric-learning approach to learn a transformation that emphasizes the structural component that is encoded in the vectors.We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics.Finally, we demonstrate the utility of our distilled representations by showing that they outperform the original contextualized representations in few-shot parsing setting.

Wir destillieren aus Sprachmodellen Repräsentationen für die Syntax durch unüberwachtes metrisches Lernen


We introduce a new memory architecture for navigation in previously unseen environments, inspired by landmark-based navigation in animals.The proposed semi-parametric topological memory (SPTM) consists of a (non-parametric) graph with nodes corresponding to locations in the environment and a (parametric) deep network capable of retrieving nodes from the graph based on observations.The graph stores no metric information, only connectivity of locations corresponding to the nodes.We use SPTM as a planning module in a navigation system.Given only 5 minutes of footage of a previously unseen maze, an SPTM-based navigation agent can build a topological map of the environment and use it to confidently navigate towards goals.The average success rate of the SPTM agent in goal-directed navigation across test environments is higher than the best-performing baseline by a factor of three.

Wir stellen eine neue Speicherarchitektur für die Navigation in bisher unbekannten Umgebungen vor, die von der Landmarken-basierten Navigation bei Tieren inspiriert ist.


The available resolution in our visual world is extremely high, if not infinite.Existing CNNs can be applied in a fully convolutional way to images of arbitrary resolution, but as the size of the input increases, they can not capture contextual information.In addition, computational requirements scale linearly to the number of input pixels, and resources are allocated uniformly across the input, no matter how informative different image regions are.We attempt to address these problems by proposing a novel architecture that traverses an image pyramid in a top-down fashion, while it uses a hard attention mechanism to selectively process only the most informative image parts.We conduct experiments on MNIST and ImageNet datasets, and we show that our models can significantly outperform fully convolutional counterparts, when the resolution of the input is that big that the receptive field of the baselines can not adequately cover the objects of interest.Gains in performance come for less FLOPs, because of the selective processing that we follow.Furthermore, our attention mechanism makes our predictions more interpretable, and creates a trade-off between accuracy and complexity that can be tuned both during training and testing time.

Wir schlagen eine neuartige Architektur vor, die eine Bildpyramide von oben nach unten durchläuft und dabei nur die informativsten Regionen auf dem Weg besucht.


Hyperparameter optimization can be formulated as a bilevel optimization problem, where the optimal parameters on the training set depend on the hyperparameters.We aim to adapt regularization hyperparameters for neural networks by fitting compact approximations to the best-response function, which maps hyperparameters to optimal weights and biases.We show how to construct scalable best-response approximations for neural networks by modeling the best-response as a single network whose hidden units are gated conditionally on the regularizer.We justify this approximation by showing the exact best-response for a shallow linear network with L2-regularized Jacobian can be represented by a similar gating mechanism.We fit this model using a gradient-based hyperparameter optimization algorithm which alternates between approximating the best-response around the current hyperparameters and optimizing the hyperparameters using the approximate best-response function.Unlike other gradient-based approaches, we do not require differentiating the training loss with respect to the hyperparameters, allowing us to tune discrete hyperparameters, data augmentation hyperparameters, and dropout probabilities.Because the hyperparameters are adapted online, our approach discovers hyperparameter schedules that can outperform fixed hyperparameter values.Empirically, our approach outperforms competing hyperparameter optimization methods on large-scale deep learning problems.We call our networks, which update their own hyperparameters online during training, Self-Tuning Networks (STNs).

Wir verwenden ein Hypernetzwerk zur Vorhersage optimaler Gewichte bei gegebenen Hyperparametern und trainieren alles gemeinsam.


Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains.Despite outstanding progress, quantitative evaluation of such models often involves multiple distinct metrics to assess different desirable properties, such as image quality, conditional consistency, and intra-conditioning diversity.In this setting, model benchmarking becomes a challenge, as each metric may indicate a different "best" model.In this paper, we propose the Frechet Joint Distance (FJD), which is defined as the Frechet distance between joint distributions of images and conditioning, allowing it to implicitly capture the aforementioned properties in a single metric.We conduct proof-of-concept experiments on a controllable synthetic dataset, which consistently highlight the benefits of FJD when compared to currently established metrics.Moreover, we use the newly introduced metric to compare existing cGAN-based models for a variety of conditioning modalities (e.g. class labels, object masks, bounding boxes, images, and text captions).We show that FJD can be used as a promising single metric for model benchmarking.

Wir schlagen eine neue Metrik zur Bewertung von bedingten GANs vor, die Bildqualität, bedingte Konsistenz und Vielfalt innerhalb der Konditionierung in einem einzigen Maß erfasst.


Combining deep model-free reinforcement learning with on-line planning is a promising approach to building on the successes of deep RL.On-line planning with look-ahead trees has proven successful in environments where transition models are known a priori.However, in complex environments where transition models need to be learned from data, the deficiencies of learned models have limited their utility for planning.To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.TreeQN dynamically constructs a tree by recursively applying a transition model in a learned abstract state space and then aggregating predicted rewards and state-values using a tree backup to estimate Q-values.We also propose ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network.Both approaches are trained end-to-end, such that the learned model is optimised for its actual use in the tree.We show that TreeQN and ATreeC outperform n-step DQN and A2C on a box-pushing task, as well as n-step DQN and value prediction networks (Oh et al., 2017) on multiple Atari games.Furthermore, we present ablation studies that demonstrate the effect of different auxiliary losses on learning transition models.

Wir stellen TreeQN und ATreeC vor, neue Architekturen für tiefes Reinforcement Learning in diskreten Handlungsdomänen, die differenzierbare Online-Baumplanung in die Handlungswertfunktion oder -politik integrieren.


Multi-label classification (MLC) is the task of assigning a set of target labels for a given sample.Modeling the combinatorial label interactions in MLC has been a long-haul challenge.Recurrent neural network (RNN) based encoder-decoder models have shown state-of-the-art performance for solving MLC.However, the sequential nature of modeling label dependencies through an RNN limits its ability in parallel computation, predicting dense labels, and providing interpretable results.In this paper, we propose Message Passing Encoder-Decoder (MPED) Networks,  aiming to provide fast, accurate, and interpretable MLC.MPED networks model the joint prediction of labels by replacing all RNNs in the encoder-decoder architecture with message passing mechanisms and dispense with autoregressive inference entirely.  The proposed models are simple, fast, accurate, interpretable, and structure-agnostic (can be used on known or unknown structured data).Experiments on seven real-world MLC datasets show the proposed models outperform autoregressive RNN models across five different metrics with a significant speedup during training and testing time.

Wir schlagen Message-Passing-Encoder-Decoder-Netzwerke für eine schnelle und genaue Modellierung von Label-Abhängigkeiten für Multi-Label-Klassifikation vor.


Recent few-shot learning algorithms have enabled models to quickly adapt to new tasks based on only a few training samples.Previous few-shot learning works have mainly focused on classification and reinforcement learning.In this paper, we propose a few-shot meta-learning system that focuses exclusively on regression tasks.Our model is based on the idea that the degree of freedom of the unknown function can be significantly reduced if it is represented as a linear combination of a set of sparsifying basis functions.This enables a few labeled samples to approximate the function.We design a Basis Function Learner network to encode basis functions for a task distribution, and a Weights Generator network to generate the weight vector for a novel task.We show that our model outperforms the current state of the art meta-learning methods in various regression tasks.

Wir schlagen eine Methode zur Durchführung von Few-Shot Regression vor, indem wir eine Reihe von Basisfunktionen zur Darstellung der Funktionsverteilung lernen.


Large-scale pre-trained language model, such as BERT, has recently achieved great success in a wide range of language understanding tasks.However, it remains an open question how to utilize BERT for text generation tasks.In this paper, we present a novel approach to addressing this challenge in a generic sequence-to-sequence (Seq2Seq) setting.We first propose a new task, Conditional Masked Language Modeling (C-MLM), to enable fine-tuning of BERT on target text-generation dataset.The fine-tuned BERT (i.e., teacher) is then exploited as extra supervision to improve conventional Seq2Seq models (i.e., student) for text generation.By leveraging BERT's idiosyncratic bidirectional nature, distilling the knowledge learned from BERT can encourage auto-regressive Seq2Seq models to plan ahead, imposing global sequence-level supervision for coherent text generation.Experiments show that the proposed approach significantly outperforms strong baselines of Transformer on multiple text generation tasks, including machine translation (MT) and text summarization.Our proposed model also achieves new state-of-the-art results on the IWSLT German-English and English-Vietnamese MT datasets.

Wir schlagen einen modellunabhängigen Weg vor, BERT für die Texterzeugung zu nutzen und erreichen Verbesserungen gegenüber Transformer bei 2 Aufgaben in 4 Datensätzen.


Humans have the remarkable ability to correctly classify images despite possible degradation.Many studies have suggested that this hallmark of human vision results from the interaction between feedforward signals from bottom-up pathways of the visual cortex and feedback signals provided by top-down pathways.Motivated by such interaction, we propose a new neuro-inspired model, namely Convolutional Neural Networks with Feedback (CNN-F).CNN-F extends CNN with a feedback generative network, combining bottom-up and top-down inference to perform approximate loopy belief propagation.  We show that CNN-F's iterative inference allows for disentanglement of latent variables across layers.We validate the advantages of CNN-F over the baseline CNN.Our experimental results suggest that the CNN-F is more robust to image degradation such as pixel noise, occlusion, and blur.  Furthermore, we show that the CNN-F is capable of restoring original images from the degraded ones with high reconstruction accuracy while introducing negligible artifacts.

CNN-F erweitert CNN um ein generatives Netz mit Rückkopplung für robustes Sehen.


We develop new approximation and statistical learning theories of convolutional neural networks (CNNs) via the ResNet-type structure where the channel size, filter size, and width are fixed.It is shown that a ResNet-type CNN is a universal approximator and its expression ability is no worse than fully-connected neural networks (FNNs) with a \textit{block-sparse} structure even if the size of each layer in the CNN is fixed.Our result is general in the sense that we can automatically translate any approximation rate achieved by block-sparse FNNs into that by CNNs.Thanks to the general theory, it is shown that learning on CNNs satisfies optimality in approximation and estimation of several important function classes.As applications, we consider two types of function classes to be estimated: the Barron class and H\"older class.We prove the clipped empirical risk minimization (ERM) estimator can achieve the same rate as FNNs even the channel size, filter size, and width of CNNs are constant with respect to the sample size.This is minimax optimal (up to logarithmic factors) for the H\"older class.Our proof is based on sophisticated evaluations of the covering number of CNNs and the non-trivial parameter rescaling technique to control the Lipschitz constant of CNNs to be constructed.

Es wird gezeigt, dass CNNs vom Typ ResNet ein universeller Approximator sind und dass ihre Ausdrucksfähigkeit nicht schlechter ist als die von vollständig verbundenen neuronalen Netzen (FNNs) mit einer \textit{block-sparse} Struktur, selbst wenn die Größe jeder Schicht im CNN festgelegt ist.


Few shot image classification aims at learning a classifier from limited labeled data.Generating the classification weights has been applied in many meta-learning approaches for few shot image classification due to its simplicity and effectiveness.However, we argue that it is difficult to generate the exact and universal classification weights for all the diverse query samples from very few training samples.In this work, we introduce Attentive Weights Generation for few shot learning via Information Maximization (AWGIM), which addresses current issues by two novel contributions.i) AWGIM generates different classification weights for different query samples by letting each of query samples attends to the whole support set.ii) To guarantee the generated weights adaptive to different query sample, we re-formulate the problem to maximize the lower bound of mutual information between generated weights and query as well as support data.As far as we can see, this is the first attempt to unify information maximization into few shot learning.Both two contributions are proved to be effective in the extensive experiments and we show that AWGIM is able to achieve state-of-the-art performance on benchmark datasets.

Ein neuartiges "few shot"-Lernverfahren zur Erzeugung abfragespezifischer Klassifikationsgewichte durch Informationsmaximierung.


Conversational question answering (CQA) is a novel QA task that requires the understanding of dialogue context.Different from traditional single-turn machine reading comprehension (MRC), CQA is a comprehensive task comprised of passage reading, coreference resolution, and contextual understanding.In this paper, we propose an innovative contextualized attention-based deep neural network, SDNet, to fuse context into traditional MRC models.Our model leverages both inter-attention and self-attention to comprehend the conversation and passage.Furthermore, we demonstrate a novel method to integrate the BERT contextual model as a sub-module in our network.Empirical results show the effectiveness of SDNet.On the CoQA leaderboard, it outperforms the previous best model's F1 score by 1.6%.Our ensemble model further improves the F1 score by 2.7%.

Eine neuronale Methode zur Beantwortung von Gesprächsfragen mit einem Aufmerksamkeitsmechanismus und einer neuartigen Verwendung von BERT als kontextuellem Einbettungsmittel


Generative adversarial networks (GANs) are one of the most popular approaches when it comes to training generative models, among which variants of Wasserstein GANs are considered superior to the standard GAN formulation in terms of learning stability and sample quality.However, Wasserstein GANs require the critic to be 1-Lipschitz, which is often enforced implicitly by penalizing the norm of its gradient, or by globally restricting its Lipschitz constant via weight normalization techniques.Training with a regularization term penalizing the violation of the Lipschitz constraint explicitly, instead of through the norm of the gradient, was found to be practically infeasible in most situations.Inspired by Virtual Adversarial Training, we propose a method called Adversarial Lipschitz Regularization, and show that using an explicit Lipschitz penalty is indeed viable and leads to competitive performance when applied to Wasserstein GANs, highlighting an important connection between Lipschitz regularization and adversarial training.


Multi-task learning promises to use less data, parameters, and time than training separate single-task models.But realizing these benefits in practice is challenging.In particular, it is difficult to define a suitable architecture that has enough capacity to support many tasks while not requiring excessive compute for each individual task.There are difficult trade-offs when deciding how to allocate parameters and layers across a large set of tasks.To address this, we propose a method for automatically searching over multi-task architectures that accounts for resource constraints.We define a parameterization of feature sharing strategies for effective coverage and sampling of architectures.We also present a method for quick evaluation of such architectures with feature distillation.Together these contributions allow us to quickly optimize for parameter-efficient multi-task models.We benchmark on Visual Decathlon, demonstrating that we can automatically search for and identify architectures that effectively make trade-offs between task resource requirements while maintaining a high level of final performance.

automatische Suche nach Multi-Task-Architekturen, die die Nutzung von Funktionen pro Aufgabe reduzieren


As distributed approaches to natural language semantics have developed and diversified, embedders for linguistic units larger than words (e.g., sentences) have come to play an increasingly important role.  To date, such embedders have been evaluated using benchmark tasks (e.g., GLUE) and linguistic probes.  We propose a comparative approach, nearest neighbor overlap (N2O), that quantifies similarity between embedders in a task-agnostic manner.  N2O requires only a collection of examples and is simple to understand: two embedders are more similar if, for the same set of inputs, there is greater overlap between the inputs' nearest neighbors.  We use N2O to compare 21 sentence embedders and show the effects of different design choices and architectures.

Wir schlagen Nearest Neighbor Overlap vor, ein Verfahren, das die Ähnlichkeit zwischen Einbettungsprogrammen aufgabenunabhängig quantifiziert, und verwenden es, um 21 Satzeinbettungsprogramme zu vergleichen.


Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem.Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poorer sample quality.Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success.This is because the VAE objective forces a trade-off between the data log-likelihood and divergence to the latent prior.The synthetic likelihood ratio term also shows instability during training.We propose a novel objective with a ``"Best-of-Many-Samples" reconstruction cost and a stable direct estimate of the synthetic likelihood.This enables our hybrid VAE-GAN framework to achieve high data log-likelihood and low divergence to the latent prior at the same time and shows significant improvement over both hybrid VAE-GANS and plain GANs in mode coverage and quality.

Wir schlagen ein neues Ziel für das Training hybrider VAE-GANs vor, das zu einer deutlichen Verbesserung der Modusabdeckung und der Qualität führt.


In order to mimic the human ability of continual acquisition and transfer of knowledge across various tasks, a learning system needs the capability for life-long learning, effectively utilizing the previously acquired skills.As such, the key challenge is to transfer and generalize the knowledge learned from one task to other tasks, avoiding interference from previous knowledge and improving the overall performance.In this paper, within the continual learning paradigm, we introduce a method that effectively forgets the less useful data samples continuously across different tasks.The method uses statistical leverage score information to measure the importance of the data samples in every task and adopts frequent directions approach to enable a life-long learning property.This effectively maintains a constant training size across all tasks.We first provide some mathematical intuition for the method and then demonstrate its effectiveness with experiments on variants of MNIST and CIFAR100 datasets.

Eine neue Methode verwendet statistische Leverage-Score-Informationen, um die Wichtigkeit der Datenproben in jeder Aufgabe zu messen, und nimmt häufige Richtungen an, um eine lebenslange Lerneigenschaft zu ermöglichen.


Convolutional neural networks (CNNs) are inherently equivariant to translation.Efforts to embed other forms of equivariance have concentrated solely on rotation.We expand the notion of equivariance in CNNs through the Polar Transformer Network (PTN).PTN combines ideas from the Spatial Transformer Network (STN) and canonical coordinate representations.The result is a network invariant to translation and equivariant to both rotation and scale.PTN is trained end-to-end and composed of three distinct stages: a polar origin predictor, the newly introduced polar transformer module and a classifier.PTN achieves state-of-the-art on rotated MNIST and the newly introduced SIM2MNIST dataset, an MNIST variation obtained by adding clutter and perturbing digits with translation, rotation and scaling.The ideas of PTN are extensible to 3D which we demonstrate through the Cylindrical Transformer Network.

Wir lernen Merkmalskarten, die invariant gegenüber Translation und äquivariant gegenüber Rotation und Skalierung sind.


Meta-learning allows an intelligent agent to leverage prior learning episodes as a basis for quickly improving performance on a novel task.Bayesian hierarchical modeling provides a theoretical framework for formalizing meta-learning as inference for a set of parameters that are shared across tasks.Here, we reformulate the model-agnostic meta-learning algorithm (MAML) of Finn et al. (2017) as a method for probabilistic inference in a hierarchical Bayesian model.In contrast to prior methods for meta-learning via hierarchical Bayes, MAML is naturally applicable to complex function approximators through its use of a scalable gradient descent procedure for posterior inference.Furthermore, the identification of MAML as hierarchical Bayes provides a way to understand the algorithm’s operation as a meta-learning procedure, as well as an opportunity to make use of computational strategies for efficient inference.We use this opportunity to propose an improvement to the MAML algorithm that makes use of techniques from approximate inference and curvature estimation.

Ein spezieller gradientenbasierter Meta-Lernalgorithmus, MAML, ist äquivalent zu einer Inferenzprozedur in einem hierarchischen Bayes'schen Modell. Wir nutzen diese Verbindung, um MAML durch Methoden der approximativen Inferenz und Krümmungsschätzung zu verbessern.


This work provides an automatic machine learning (AutoML) modelling architecture called Autostacker.Autostacker improves the prediction accuracy of machine learning baselines by utilizing an innovative hierarchical stacking architecture and an efficient parameter search algorithm.Neither prior domain knowledge about the data nor feature preprocessing is needed.We significantly reduce the time of AutoML with a naturally inspired algorithm - Parallel Hill Climbing (PHC).By parallelizing PHC, Autostacker can provide candidate pipelines with sufficient prediction accuracy within a short amount of time.These pipelines can be used as is or as a starting point for human experts to build on.By focusing on the modelling process, Autostacker breaks the tradition of following fixed order pipelines by exploring not only single model pipeline but also innovative combinations and structures.As we will show in the experiment section, Autostacker achieves significantly better performance both in terms of test accuracy and time cost comparing with human initial trials and recent popular AutoML system.

Automatisiertes maschinelles Lernsystem mit effizientem Suchalgorithmus und innovativer Struktur zur Bereitstellung besserer Modellgrundlagen.


Surrogate models can be used to accelerate approximate Bayesian computation (ABC).In one such framework the discrepancy between simulated and observed data is modelled with a Gaussian process.So far principled strategies have been proposed only for sequential selection of the simulation locations.To address this limitation, we develop Bayesian optimal design strategies to parallellise the expensive simulations.We also touch the problem of quantifying the uncertainty of the ABC posterior due to the limited budget of simulations.

Wir schlagen prinzipielle Batch-Bayesian-Versuchsplanungsstrategien und eine Methode zur Quantifizierung der Unsicherheit der Posterior-Zusammenfassungen in einem auf Gauß-Process-Surrogate basierenden Rahmen für approximative Bayesianische Berechnungen vor.


Randomly initialized first-order optimization algorithms are the method of choice for solving many high-dimensional nonconvex problems in machine learning, yet general theoretical guarantees cannot rule out convergence to critical points of poor objective value.For some highly structured nonconvex problems however, the success of gradient descent can be understood by studying the geometry of the objective.We study one such problem -- complete orthogonal dictionary learning, and provide converge guarantees for randomly initialized gradient descent to the neighborhood of a global optimum.The resulting rates scale as low order polynomials in the dimension even though the objective possesses an exponential number of saddle points.This efficient convergence can be viewed as a consequence of negative curvature normal to the stable manifolds associated with saddle points, and we provide evidence that this feature is shared by other nonconvex problems of importance as well.

Wir liefern eine effiziente Konvergenzrate für den Gradientenabstieg beim Lernziel des vollständigen orthogonalen Wörterbuchs auf der Grundlage einer geometrischen Analyse.


Coding theory is a central discipline underpinning wireline and wireless modems that are the workhorses of the information age.Progress in coding theory is largely driven by individual human ingenuity with sporadic breakthroughs over the past century.In this paper we study whether it is possible to automate the discovery of decoding algorithms via deep learning.We study a family of sequential codes parametrized by recurrent neural network (RNN) architectures.We show that cre- atively designed and trained RNN architectures can decode well known sequential codes such as the convolutional and turbo codes with close to optimal performance on the additive white Gaussian noise (AWGN) channel, which itself is achieved by breakthrough algorithms of our times (Viterbi and BCJR decoders, representing dynamic programing and forward-backward algorithms).We show strong gen- eralizations, i.e., we train at a specific signal to noise ratio and block length but test at a wide range of these quantities, as well as robustness and adaptivity to deviations from the AWGN setting.

Wir zeigen, dass kreativ gestaltete und trainierte RNN-Architekturen bekannte sequentielle Codes dekodieren können und nahezu optimale Leistungen erzielen.


Adam is shown not being able to converge to the optimal solution in certain cases.Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice.In this paper, we provide a new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods.We argue that there exists an inappropriate correlation between gradient $g_t$ and the second moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size.We demonstrate that such unbalanced step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam.Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$.The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.

Wir analysieren und lösen das Problem der Nicht-Konvergenz von Adam.


Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset.However, in practical applications, we typically have access to multiple sources.In this paper we propose the first approach for Multi-Source Domain Adaptation (MSDA) based on Generative Adversarial Networks.Our method is inspired by the observation that the appearance of a given image depends on three factors: the domain, the style (characterized in terms of low-level features variations) and the content.For this reason we propose to project the image features onto a space where only the dependence from the content is kept, and then re-project this invariant representation onto the pixel space using the target domain and style.In this way, new labeled images can be generated which are used to train a final target classifier.We test our  approach using common MSDA benchmarks, showing that it outperforms state-of-the-art methods.

In diesem Papier schlagen wir eine generative Methode zur Anpassung von Multisource-Domänen vor, die auf der Zerlegung von Inhalts-, Stil- und Domänenfaktoren basiert.


Inferring the most likely configuration for a subset of variables of a joint distribution given the remaining ones – which we refer to as co-generation – is an important challenge that is computationally demanding for all but the simplest settings.This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.In contrast, almost nothing is known about this task when considering recently proposed techniques for modeling high-dimensional distributions, particularly generative adversarial nets (GANs).Therefore, in this paper, we study the occurring challenges for co-generation with GANs.To address those challenges we develop an annealed importance sampling (AIS) based Hamiltonian Monte Carlo (HMC) co-generation algorithm.The presented approach significantly outperforms classical gradient-based methods on synthetic data and on CelebA.

Verwendung von geglättetem Wichtigkeits-Sampling für das Co-Generation Problem.


Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly.We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any derived quantities, but can be shown to be equivalent to maximizing likelihood under some conditions.Our result holds in the non-asymptotic parametric setting, where both the capacity of the model and the number of data examples are finite.We also demonstrate encouraging experimental results.

Wir entwickeln eine neue Methode zur Schätzung von Parametern ohne Likelihood, die unter bestimmten Bedingungen der maximalen Likelihood gleichwertig ist


While most approaches to the problem of Inverse Reinforcement Learning (IRL) focus on estimating a reward function that best explains an expert agent’s policy or demonstrated behavior on a control task, it is often the case that such behavior is more succinctly represented by a simple reward combined with a set of hard constraints.In this setting, the agent is attempting to maximize cumulative rewards subject to these given constraints on their behavior.We reformulate the problem of IRL on Markov Decision Processes (MDPs) such that, given a nominal model of the environment and a nominal reward function, we seek to estimate state, action, and feature constraints in the environment that motivate an agent’s behavior.Our approach is based on the Maximum Entropy IRL framework, which allows us to reason about the likelihood of an expert agent’s demonstrations given our knowledge of an MDP.Using our method, we can infer which constraints can be added to the MDP to most increase the likelihood of observing these demonstrations.We present an algorithm which iteratively infers the Maximum Likelihood Constraint to best explain observed behavior, and we evaluate its efficacy using both simulated behavior and recorded data of humans navigating around an obstacle.

Unsere Methode ermittelt Einschränkungen bei der Aufgabenausführung, indem sie das Prinzip der maximalen Entropie nutzt, um zu quantifizieren, inwieweit sich Demonstrationen von dem erwarteten, nicht eingeschränkten Verhalten unterscheiden.


The success of reinforcement learning in the real world has been limited to instrumented laboratory scenarios, often requiring arduous human supervision to enable continuous learning.In this work, we discuss the required elements of a robotic system that can continually and autonomously improve with data collected in the real world, and propose a particular instantiation of such a system.Subsequently, we investigate a number of challenges of learning without instrumentation -- including the lack of episodic resets, state estimation, and hand-engineered rewards -- and propose simple, scalable solutions to these challenges.We demonstrate the efficacy of our proposed system on dexterous robotic manipulation tasks in simulation and the real world, and also provide an insightful analysis and ablation study of the challenges associated with this learning paradigm.

System zum Erlernen von Roboteraufgaben in der realen Welt mit Reinforcement Learning ohne Instrumentierung


We study the problem of cross-lingual voice conversion in non-parallel speech corpora and one-shot learning setting.Most prior work require either parallel speech corpora or enough amount of training data from a target speaker.However, we convert an arbitrary sentences of an arbitrary source speaker to target speaker's given only one target speaker training utterance.To achieve this, we formulate the problem as learning disentangled speaker-specific and context-specific representations and follow the idea of [1] which uses Factorized Hierarchical Variational Autoencoder (FHVAE).After training FHVAE on multi-speaker training data, given arbitrary source and target speakers' utterance, we estimate those latent representations and then reconstruct the desired utterance of converted voice to that of target speaker.We use multi-language speech corpus to learn a universal model that works for all of the languages.We investigate the use of a one-hot language embedding to condition the model on the language of the utterance being queried and show the effectiveness of the approach.We conduct voice conversion experiments with varying size of training utterances and it was able to achieve reasonable performance with even just one training utterance.We also investigate the effect of using or not using the language conditioning.Furthermore, we visualize the embeddings of the different languages and sexes.Finally, in the subjective tests, for one language and cross-lingual voice conversion, our approach achieved moderately better or comparable results compared to the baseline in speech quality and similarity.

Wir verwenden einen Variational Autoencoder, um Stil und Inhalt zu trennen, und erreichen die Sprachumwandlung durch Modifizierung der Stileinbettung und -dekodierung. Wir verwenden ein mehrsprachiges Sprachkorpus und untersuchen dessen Auswirkungen.


We propose an end-to-end-trainable attention module for convolutional neural network (CNN) architectures built for image classification.The module takes as input the 2D feature vector maps which form the intermediate representations of the input image at different stages in the CNN pipeline, and outputs a 2D matrix of scores for each map.Standard CNN architectures are modified through the incorporation of this module, and trained under the constraint that a convex combination of the intermediate 2D feature vectors, as parametrised by the score matrices, must alone be used for classification.Incentivised to amplify the relevant and suppress the irrelevant or misleading, the scores thus assume the role of attention values.Our experimental observations provide clear evidence to this effect: the learned attention maps neatly highlight the regions of interest while suppressing background clutter.Consequently, the proposed function is able to bootstrap standard CNN architectures for the task of image classification, demonstrating superior generalisation over 6 unseen benchmark datasets.When binarised, our attention maps outperform other CNN-based attention maps, traditional saliency maps, and top object proposals for weakly supervised segmentation as demonstrated on the Object Discovery dataset.We also demonstrate improved robustness against the fast gradient sign method of adversarial attack.

Diese Arbeit schlägt eine Methode vor, die CNNs dazu zwingt, räumliche Aufmerksamkeit zu nutzen, um objektzentriertere Darstellungen zu lernen, die in verschiedener Hinsicht besser sind.


Recurrent neural network(RNN) is an effective neural network in solving very complex supervised and unsupervised tasks.There has been a significant improvement in RNN field such as natural language processing, speech processing, computer vision and other multiple domains.This paper deals with RNN application on different use cases like Incident Detection , Fraud Detection , and Android Malware Classification.The best performing neural network architecture is chosenby conducting different chain of experiments for different network parameters and structures.The network is run up to 1000 epochs with learning rate set in the range of 0.01 to 0.5.Obviously, RNN performed very well when compared to classical machine learning algorithms.This is mainly possible because RNNs implicitly extracts the underlying features and also identifies the characteristics of the data.This lead to better accuracy.

Rekurrente neuronale Netze für Anwendungsfälle im Bereich der Cybersicherheit


Anatomical studies demonstrate that brain reformats input information to generate reliable responses for performing computations.However, it remains unclear how neural circuits encode complex spatio-temporal patterns.We show that neural dynamics are strongly influenced by the phase alignment between the input and the spontaneous chaotic activity.Input alignment along the dominant chaotic projections causes the chaotic trajectories to become stable channels (or attractors), hence, improving the computational capability of a recurrent network.Using mean field analysis, we derive the impact of input alignment on the overall stability of attractors formed.Our results indicate that input alignment determines the extent of intrinsic noise suppression and hence, alters the attractor state stability, thereby controlling the network's inference ability.

Input-Strukturierung entlang des Chaos für Stabilität


Generative adversarial networks are a learning framework that rely on training a discriminator to estimate a measure of difference between a target and generated distributions.GANs, as normally formulated, rely on the generated samples being completely differentiable w.r.t.the generative parameters, and thus do not work for discrete data.We introduce a method for training GANs with discrete data that uses the estimated difference measure from the discriminator to compute importance weights for generated samples, thus providing a policy gradient for training the generator.The importance weights have a strong connection to the decision boundary of the discriminator, and we call our method boundary-seeking GANs (BGANs).We demonstrate the effectiveness of the proposed algorithm with discrete image and character-based natural language generation.  In addition, the boundary-seeking objective extends to continuous data, which can be used to improve stability of training, and we demonstrate this on Celeba, Large-scale Scene Understanding (LSUN) bedrooms, and Imagenet without conditioning.

Wir befassen uns mit dem Training von GANs mit diskreten Daten, indem wir einen Policy-Gradienten formulieren, der sich über f-Divergenzen hinweg verallgemeinern lässt


Policy gradient methods have enjoyed great success in deep reinforcement learning but suffer from high variance of gradient estimates.The high variance problem is particularly exasperated in problems with long horizons or high-dimensional action spaces.To mitigate this issue, we derive a bias-free action-dependent baseline for variance reduction which fully exploits the structural form of the stochastic policy itself and does not make any additional assumptions about the MDP.We demonstrate and quantify the benefit of the action-dependent baseline through both theoretical analysis as well as numerical results, including an analysis of the suboptimality of the optimal state-dependent baseline.The result is a computationally efficient policy gradient algorithm, which scales to high-dimensional control problems, as demonstrated by a synthetic 2000-dimensional target matching task.Our experimental results indicate that action-dependent baselines allow for faster learning on standard reinforcement learning benchmarks and high-dimensional hand manipulation and synthetic tasks.Finally, we show that the general idea of including additional information in baselines for improved variance reduction can be extended to partially observed and multi-agent tasks.

Handlungsabhängige Grundlinien können verzerrungsfrei sein und eine größere Varianzreduzierung bewirken als nur zustandsabhängige Grundlinien für politische Gradientenmethoden.


The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches.The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks.To mitigate this concern, we propose an active multitask learning algorithm that achieves knowledge transfer between tasks.The approach forms a so-called committee for each task that jointly makes decisions and directly shares data across similar tasks.Our approach reduces the number of queries needed during training while maintaining high accuracy on test data.Empirical results on benchmark datasets show significant improvements on both accuracy and number of query requests.

Wir schlagen einen aktiven Multitask-Lernalgorithmus vor, der einen Wissenstransfer zwischen den Aufgaben ermöglicht.


Detection of photo manipulation relies on subtle statistical traces, notoriously removed by aggressive lossy compression employed online.We demonstrate that end-to-end modeling of complex photo dissemination channels allows for codec optimization with explicit provenance objectives.We design a lightweight trainable lossy image codec, that delivers competitive rate-distortion performance, on par with best hand-engineered alternatives, but has lower computational footprint on modern GPU-enabled platforms.Our results show that significant improvements in manipulation detection accuracy are possible at fractional costs in bandwidth/storage.Our codec improved the accuracy from 37% to 86% even at very low bit-rates, well below the practicality of JPEG (QF 20).

Wir lernen einen effizienten verlustbehafteten Bildcodec kennen, der so optimiert werden kann, dass er eine zuverlässige Erkennung von Fotomanipulationen zu einem Bruchteil der Kosten in Bezug auf Nutzlast/Qualität und sogar bei niedrigen Bitraten ermöglicht.


Recurrent Neural Networks have long been the dominating choice for sequence modeling.However, it severely suffers from two issues: impotent in capturing very long-term dependencies and unable to parallelize the sequential computation procedure.Therefore, many non-recurrent sequence models that are built on convolution and attention operations have been proposed recently.Notably, models with multi-head attention such as Transformer have demonstrated extreme effectiveness in capturing long-term dependencies in a variety of sequence modeling tasks.Despite their success, however, these models lack necessary components to model local structures in sequences and heavily rely on position embeddings that have limited effects and require a considerable amount of design efforts.In this paper, we propose the R-Transformer which enjoys the advantages of both RNNs and the multi-head attention mechanism while avoids their respective drawbacks.The proposed model can effectively capture both local structures and global long-term dependencies in sequences without any use of position embeddings.We evaluate R-Transformer through extensive experiments with data from a wide range of domains and the empirical results show that R-Transformer outperforms the state-of-the-art methods by a large margin in most of the tasks.

In diesem Papier wird ein effektives generisches Sequenzmodell vorgeschlagen, das die Stärken von RNNs und Multi-Head Attention nutzt.


Many tasks in natural language processing and related domains require high precision output that obeys dataset-specific constraints.This level of fine-grained control can be difficult to obtain in large-scale neural network models.In this work, we propose a structured latent-variable approach that adds discrete control states within a standard autoregressive neural paradigm.Under this formulation, we can include a range of rich, posterior constraints to enforce task-specific knowledge that is effectively trained into the neural model.This approach allows us to provide arbitrary grounding of internal model decisions, without sacrificing any representational power of neural models.Experiments consider applications of this approach for text generation and part-of-speech induction.For natural language generation, we find that this method improves over standard benchmarks, while also providing fine-grained control.

Ein strukturierter latent-variabler Ansatz, der diskrete Kontrollzustände innerhalb eines standardmäßigen autoregressiven neuronalen Paradigmas hinzufügt, um eine willkürliche Grundlage für interne Modellentscheidungen zu schaffen, ohne die Darstellungsfähigkeit neuronaler Modelle zu beeinträchtigen.


Suppose a deep classification model is trained with samples that need to be kept private for privacy or confidentiality reasons.In this setting, can an adversary obtain the private samples if the classification model is given to the adversary?We call this reverse engineering against the classification model the Classifier-to-Generator (C2G) Attack.This situation arises when the classification model is embedded into mobile devices for offline prediction (e.g., object recognition for the automatic driving car and face recognition for mobile phone authentication).For C2G attack, we introduce a novel GAN, PreImageGAN.In PreImageGAN, the generator is designed to estimate the the sample distribution conditioned by the preimage of classification model $f$, $P(X|f(X)=y)$, where $X$ is the random variable on the sample space and $y$ is the probability vector representing the target label arbitrary specified by the adversary.In experiments, we demonstrate PreImageGAN works successfully with hand-written character recognition and face recognition.In character recognition, we show that, given a recognition model of hand-written digits, PreImageGAN allows the adversary to extract alphabet letter images without knowing that the model is built for alphabet letter images.In face recognition, we show that, when an adversary obtains a face recognition model for a set of individuals, PreImageGAN allows the adversary to extract face images of specific individuals contained in the set, even when the adversary has no knowledge of the face of the individuals.

Schätzung der Verteilung der Trainingsdaten aus dem trainierten Klassifikator unter Verwendung von GAN.


The goal of standard compressive sensing is to estimate an unknown vector from linear measurements under the assumption of sparsity in some basis.Recently, it has been shown that significantly fewer measurements may be required if the sparsity assumption is replaced by the assumption that the unknown vector lies near the range of a suitably-chosen generative model.  In particular, in (Bora {\em et al.}, 2017) it was shown that roughly $O(k\log L)$ random Gaussian measurements suffice for accurate recovery when the $k$-input generative model is bounded and $L$-Lipschitz, and that $O(kd \log w)$ measurements suffice for $k$-input ReLU networks with depth $d$ and width $w$.  In this paper, we establish corresponding algorithm-independent lower bounds on the sample complexity using tools from minimax statistical analysis.  In accordance with the above upper bounds, our results are summarized as follows: (i) We construct an $L$-Lipschitz generative model capable of generating group-sparse signals, and show that the resulting necessary number of measurements is $\Omega(k \log L)$;(ii) Using similar ideas, we construct two-layer ReLU networks of high width requiring $\Omega(k \log w)$ measurements, as well as lower-width deep ReLU networks requiring $\Omega(k d)$ measurements.  As a result, we establish that the scaling laws derived in (Bora {\em et al.}, 2017) are optimal or near-optimal in the absence of further assumptions.

Wir stellen fest, dass die in (Bora et al., 2017) abgeleiteten Skalierungsgesetze optimal oder nahezu optimal sind, wenn keine weiteren Annahmen getroffen werden.


Discovering and exploiting the causal structure in the environment is a crucial challenge for intelligent agents.Here we explore whether modern deep reinforcement learning can be used to train agents to perform causal reasoning.We adopt a meta-learning approach, where the agent learns a policy for conducting experiments via causal interventions, in order to support a subsequent task which rewards making accurate causal inferences.We also found the agent could make sophisticated counterfactual predictions, as well as learn to draw causal inferences from purely observational data.Though powerful formalisms for causal reasoning have been developed, applying them in real-world domains can be difficult because fitting to large amounts of high dimensional data often requires making idealized assumptions.Our results suggest that causal reasoning in complex settings may benefit from powerful learning-based approaches.More generally, this work may offer new strategies for structured exploration in reinforcement learning, by providing agents with the ability to perform—and interpret—experiments.

Meta-Lernen eines Lernalgorithmus, der in der Lage ist, kausale Schlussfolgerungen zu ziehen


Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews.To employ rule based sentiment classification, we require sentiment lexicons.However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages.To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach.The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus.Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb.We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus.Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI).First we build word-context unigram frequency count matrix and transform it to point-wise mutual Information matrix.Using this matrix, we computed the cosine distance of mean vector of seed lists and each word in the corpus vocabulary.Based on the threshold value, the top closest words to the mean vector of seed list are added to the lexicon.Then the mean vector of the new sentiment seed list is updated and process is repeated until we get sufficient terms in the lexicon.Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds.Finally, the lexicon generated in corpus based approach is evaluated.

Entwicklung eines korpusbasierten Algorithmus zur Erstellung eines amharischen Sentiment-Lexikons auf der Grundlage eines Korpus