High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases. Experiments on a large-scale dataset collected from Reddit show that our model constructs arguments with more topic-relevant content than a popular sequence-to-sequence generation model according to both automatic evaluation and human assessments.

与えられたstatementをもとに、議論の文章を生成する。 初めにstatementに関連したWikipediaのパラグラフを抽出し、キーフレーズ生成と議論文章生成を行う。 少しずつ学習タスクを難しくするカリキュラム学習を採用している。


Distant supervision has become the standard method for relation extraction. However, even though it is an efficient method, it does not come at no cost—The resulted distantly-supervised training samples are often very noisy. To combat the noise, most of the recent state-of-the-art approaches focus on selecting one-best sentence or calculating soft attention weights over the set of the sentences of one specific entity pair. However, these methods are suboptimal, and the false positive problem is still a key stumbling bottleneck for the performance. We argue that those incorrectly-labeled candidate sentences must be treated with a hard decision, rather than being dealt with soft attention weights. To do this, our paper describes a radical solution—We explore a deep reinforcement learning strategy to generate the false-positive indicator, where we automatically recognize false positives for each relation type without any supervised information. Unlike the removal operation in the previous studies, we redistribute them into the negative examples. The experimental results show that the proposed strategy significantly improves the performance of distant supervision comparing to state-of-the-art systems.

関係抽出タスクに強化学習を用いる。 distant supervisionで問題になるFalse-Positiveデータのフィルタリングに強化学習を利用する。 データのフィルタリングのフレームワークの提案であるため、実際に関係抽出を行うモデルには自由なモデルを設定できるのが強み。


We introduce a novel framework for image captioning that can produce natural language explicitly grounded in entities that object detectors find in the image. Our approach reconciles classical slot filling approaches (that are generally better grounded in images) with modern neural captioning approaches (that are generally more natural sounding and accurate). Our approach first generates a sentence `template' with slot locations explicitly tied to specific image regions. These slots are then filled in by visual concepts identified in the regions by object detectors. The entire architecture (sentence template generation and slot filling with object detectors) is end-to-end differentiable. We verify the effectiveness of our proposed model on different image captioning tasks. On standard image captioning and novel object captioning, our model reaches state-of-the-art on both COCO and Flickr30k datasets. We also demonstrate that our model has unique advantages when the train and test distributions of scene compositions -- and hence language priors of associated captions -- are different. Code has been made available at: this https URL

キャプション中のSlotと物体検出情報を紐付けた(Slotの種類は、単語のEntity等から事前に定義しておく)物体検出(Faster-RCNN)情報から紐づけた自然言語により、キャプションを生成できる新しいフレームワワークを提案。


Attention mechanisms in biological perception are thought to select subsets of perceptual information for more sophisticated processing which would be prohibitive to perform on all sensory inputs. In computer vision, however, there has been relatively little exploration of hard attention, where some information is selectively ignored, in spite of the success of soft attention, where information is re-weighted and aggregated, but never filtered out. Here, we introduce a new approach for hard attention and find it achieves very competitive performance on a recently-released visual question answering datasets, equalling and in some cases surpassing similar soft attention architectures while entirely ignoring some features. Even though the hard attention mechanism is thought to be non-differentiable, we found that the feature magnitudes correlate with semantic relevance, and provide a useful signal for our mechanism's attentional selection criterion. Because hard attention selects important features of the input information, it can also be more efficient than analogous soft attention mechanisms. This is especially important for recent approaches that use non-local pairwise operations, whereby computational and memory costs are quadratic in the size of the set of features.

重要な箇所に重みを掛けるSoft-Attentionでなく、生体における本来の"Attention"に近いHard-Attention(重要な特徴のみサンプルする)を改良した研究。VQAのタスクで画像/質問それぞれをEncodeした結果を合算し、そのベクトルのL2距離から関連度を計測、閾値以上のパート以外にマスクをかける。


Machine learning models are notoriously difficult to interpret and debug. This is particularly true of neural networks. In this work, we introduce automated software testing techniques for neural networks that are well-suited to discovering errors which occur only for rare inputs. Specifically, we develop coverage-guided fuzzing (CGF) methods for neural networks. In CGF, random mutations of inputs to a neural network are guided by a coverage metric toward the goal of satisfying user-specified constraints. We describe how fast approximate nearest neighbor algorithms can provide this coverage metric. We then discuss the application of CGF to the following goals: finding numerical errors in trained neural networks, generating disagreements between neural networks and quantized versions of those networks, and surfacing undesirable behavior in character level language models. Finally, we release an open source library called TensorFuzz that implements the described techniques.

ニューラルネットに対しカバレッジテストを行う手法の提案。ツール(TensorFuzz)も公開されるとのこと。数件の実サンプルを元にノイズを加えたデータを生成して入力、アクティベーションのパターンをクラスタリングし既存のクラスタから遠い場合検証データに加えるという形。


Over the last several years, the field of natural language processing has been propelled forward by an explosion in the use of deep learning models. This survey provides a brief introduction to the field and a quick overview of deep learning architectures and methods. It then sifts through the plethora of recent studies and summarizes a large assortment of relevant contributions. Analyzed research areas include several core linguistic processing issues in addition to a number of applications of computational linguistics. A discussion of the current state of the art is then provided along with recommendations for future research in the field.

自然言語処理におけるDNNの適用事例についてまとめたサーベイ。DNNだけでなく、SVMや決定木も含めた昔ながらのモデルについてもきちんと言及されている。


We create a new NLI test set that shows the deficiency of state-of-the-art models in inferences that require lexical and world knowledge. The new examples are simpler than the SNLI test set, containing sentences that differ by at most one word from sentences in the training set. Yet, the performance on the new test set is substantially worse across systems trained on SNLI, demonstrating that these systems are limited in their generalization ability, failing to capture many simple inferences.

文関係の推論を行うタスクで、語彙と文法の識別能力を個別に検証した論文。文構造はそのままに同じ意味の単語に置き換えた(sadとunhappyなど)データセットを作成し検証。置き換え後の単語が未知語にならないようにしても、既存のSOTAモデルの精度がガタ落ちしたという結果。


Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks. In this work, we conduct a thorough investigation of stable recurrent models. Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent. Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks. Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime. Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.

重みに制約を与えたRNNは、長さを切った全結合のネットワークでほぼ近似できるとした研究。理論の前提となる、重みについての制約と勾配についての制約がネットワークのパフォーマンスに影響を与えないことを実験で確認し、理論が成立するとしている。


We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs that learned to implement handwritten digit classification and the function of an imaging lens at terahertz spectrum. With the existing plethora of 3D-printing and other lithographic fabrication methods as well as spatial-light-modulators, this all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that can learn to perform unique tasks using D2NNs.

ニューラルネットワークの伝搬処理を波動(光)の伝搬とみなし、光を利用した演算を試みた研究。実際にネットワークの重みを表現する伝播/反射を行う板を3Dプリントで作成し(5層の全結合NN)、MNISTの手書き文字の分類に成功。なお実際の入力は光なので、学習時も入力する数字を光の振幅に変換する。


Early prognosis of Alzheimer's dementia is hard. Mild cognitive impairment (MCI) typically precedes Alzheimer's dementia, yet only a fraction of MCI individuals will progress to dementia, even when screened using biomarkers. We propose here to identify a subset of individuals who share a common brain signature highly predictive of oncoming dementia. This signature was composed of brain atrophy and functional dysconnectivity and discovered using a machine learning model in patients suffering from dementia. The model recognized the same brain signature in MCI individuals, 90% of which progressed to dementia within three years. This result is a marked improvement on the state-of-the-art in prognostic precision, while the brain signature still identified 47% of all MCI progressors. We thus discovered a sizable MCI subpopulation which represents an excellent recruitment target for clinical trials at the prodromal stage of Alzheimer's disease.

軽度認知障害（MCI）からアルツハイマー型痴呆（AD）への進行を高精度（CN：認知機能正常者と、AD：アルツハイマー型痴呆者を９０％の精度で分けられるときに、１００％の精度で）で予測できるようにした機械学習の手法。


Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteristics, generating both predictions and their confidence intervals. Our unsupervised model predicts changes in total ADAS-Cog scores with the same accuracy as specifically trained supervised models and identifies sub-components associated with word recall as predictive of progression. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer's Disease.

病気の進行は人によって違うことがあるが、機械学習手法で、個人別に予測してくれるものはなかった。アルツハイマー病等のデータ（18ヶ月間の1908人の患者、42の臨床的特徴量）を用いて、個人別疾患進行予測モデルを作った。認知試験、臨床検査、臨床特性との関連性をシミュレートし、予測と信頼区間の両方を生成している。


We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and approaches based on visualization of activations are constrained by the resolution of the convolutional layer. The complementarity of these methods improves the understanding of 3D-CNNs in Alzheimer's disease classification from different perspectives.

アルツハイマー病早期診断のための、ディープラーニング手法（3次元畳み込みニューラルネットワーク）の透明性を高められた手法の紹介。特に視覚的説明を生成するための手法（階層的な3D画像セグメンテーションの感度分析、空間マップ上のネットワークアクティベーションを視覚化）。


Local surrogate models, to approximate the local decision boundary of a black-box classifier, constitute one approach to generate explanations for the rationale behind an individual prediction made by the back-box. This paper highlights the importance of defining the right locality, the neighborhood on which a local surrogate is trained, in order to approximate accurately the local black-box decision boundary. Unfortunately, as shown in this paper, this issue is not only a parameter or sampling distribution challenge and has a major impact on the relevance and quality of the approximation of the local black-box decision boundary and thus on the meaning and accuracy of the generated explanation. To overcome the identified problems, quantified with an adapted measure and procedure, we propose to generate surrogate-based explanations for individual predictions based on a sampling centered on particular place of the decision boundary, relevant for the prediction to be explained, rather than on the prediction itself as it is classically done. We evaluate the novel approach compared to state-of-the-art methods and a straightforward improvement thereof on four UCI datasets.

局所性の定義，即ち，説明器を学習する際に利用する標本の生成方法が，予測の説明の精度に大きく影響することを示した研究．説明対象の予測の標本から最も近く，かつ，ブラックボックス分類器の決定境界付近に位置する標本を見つけ出し，その近傍で標本を生成，説明器の学習に利用する方法を提案した．


In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of nested, dense skip pathways. The re-designed skip pathways aim at reducing the semantic gap between the feature maps of the encoder and decoder sub-networks. We argue that the optimizer would deal with an easier learning task when the feature maps from the decoder and encoder networks are semantically similar. We have evaluated UNet++ in comparison with U-Net and wide U-Net architectures across multiple medical image segmentation tasks: nodule segmentation in the low-dose CT scans of chest, nuclei segmentation in the microscopy images, liver segmentation in abdominal CT scans, and polyp segmentation in colonoscopy videos. Our experiments demonstrate that UNet++ with deep supervision achieves an average IoU gain of 3.9 and 3.4 points over U-Net and wide U-Net, respectively.

画像の特徴抽出によく利用されるUNetを改良したUNet++の提案。UNetでは同階層のEncoder/Decoderを直接接続するが、UNet++ではEncoderの各階層から復元した結果を使いDecoderまでDense CNNで接続する。これによりEncoder/Decoderの表現差異が小さくなるようにしている。また、各階層から復元した結果にそれぞれLossを適用する。


Clustering is a class of unsupervised learning methods that has been extensively applied and studied in computer vision. Little work has been done to adapt it to the end-to-end training of visual features on large scale datasets. In this work, we present DeepCluster, a clustering method that jointly learns the parameters of a neural network and the cluster assignments of the resulting features. DeepCluster iteratively groups the features with a standard clustering algorithm, k-means, and uses the subsequent assignments as supervision to update the weights of the network. We apply DeepCluster to the unsupervised training of convolutional neural networks on large datasets like ImageNet and YFCC100M. The resulting model outperforms the current state of the art by a significant margin on all the standard benchmarks.

End-to-Endで、CNNを利用したクラスタリングを行う研究。CNNで特徴抽出=>k-meansでクラスタリング=>そのラベルで学習、というステップを繰り返すことでクラスタリングを行う。ただ、無策で行うと巨大な1クラスタに全画像を収めて精度100%になる可能性があるため、空クラスタを防止したりラベルの偏りがないように工夫したりしている。


Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.

各タスクに共通する良い初期値を学習するMAMLは、パラメーター数が多い場合推定が困難という問題があった。そこで、パラメーターを低次元の潜在表現から復元することで対応したという研究(学習データから潜在表現の分布を推定し、サンプルする過程はVAEと同様)。


We provide a detailed overview of the various approaches that were proposed to date to solve the task of Open Information Extraction. We present the major challenges that such systems face, show the evolution of the suggested approaches over time and depict the specific issues they address. In addition, we provide a critique of the commonly applied evaluation procedures for assessing the performance of Open IE systems and highlight some directions for future work.

大量の文書から物事の関係性を抽出するOpen Information Extractionのサーベイ論文。学習データを(あまり)必要としない/マルチドメインのデータに適用可能/効率的に処理できる、という3要件が必要としている。過去に発表されたシステムについて、どのような評価が行われたかについてもまとめられている


Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.

ロボットのコトンロールを、少ないサンプルから学習する手法のサーベイ。シミュレーションやデモ、環境情報から何らか(モデル/戦略/報酬)の事前分布を改定するのが基本で、多くの手法はこれらの組み合わせを行っている。


Several researchers have argued that a machine learning system's interpretability should be defined in relation to a specific agent or task: we should not ask if the system is interpretable, but to whom is it interpretable. We describe a model intended to help answer this question, by identifying different roles that agents can fulfill in relation to the machine learning system. We illustrate the use of our model in a variety of scenarios, exploring how an agent's role influences its goals, and the implications for defining interpretability. Finally, we make suggestions for how our model could be useful to interpretability researchers, system developers, and regulatory bodies auditing machine learning systems.

既存の解釈可能性に関する研究は，解釈可能性の定義や研究動機に一貫性がなかった．本草案は，解釈可能性の形式化に貢献するため，機械学習システムは「誰にとって」解釈可能であるべきかを探った．そして，機械学習システムと何らかの目的を持つ 6 つの役割からなるエコシステムモデルを提案した．


Recent advances in interpretable Machine Learning (iML) and eXplainable AI (XAI) construct explanations based on the importance of features in classification tasks. However, in a high-dimensional feature space this approach may become unfeasible without restraining the set of important features. We propose to utilize the human tendency to ask questions like "Why this output (the fact) instead of that output (the foil)?" to reduce the number of features to those that play a main role in the asked contrast. Our proposed method utilizes locally trained one-versus-all decision trees to identify the disjoint set of rules that causes the tree to classify data points as the foil and not as the fact. In this study we illustrate this approach on three benchmark classification tasks.

既存の説明手法の多くは，説明時，予測に寄与する特徴量を提示するが，次元が大きくなるにつれ，その数は多くなってゆき，解釈が難しくなる．本研究は，「なぜ B でなく A と予測したのか？」という問いに対して一対他方式の決定木を学習することで，提示する特徴量の数を抑えることに成功した．


The study of algorithms to automatically answer visual questions currently is motivated by visual question answering (VQA) datasets constructed in artificial VQA settings. We propose VizWiz, the first goal-oriented VQA dataset arising from a natural VQA setting. VizWiz consists of over 31,000 visual questions originating from blind people who each took a picture using a mobile phone and recorded a spoken question about it, together with 10 crowdsourced answers per visual question. VizWiz differs from the many existing VQA datasets because (1) images are captured by blind photographers and so are often poor quality, (2) questions are spoken and so are more conversational, and (3) often visual questions cannot be answered. Evaluation of modern algorithms for answering visual questions and deciding if a visual question is answerable reveals that VizWiz is a challenging dataset. We introduce this dataset to encourage a larger community to develop more generalized algorithms that can assist blind people.

世界中のエンジニアに、視覚障害者支援をするためのVizWizと呼ばれるデータセットを公開する。VizWizは、視覚障害者によって撮影された31,000以上の写真と「この写真に写っているものはなに？」等の質問で構成されている。障害者視点で撮影されたデータはこれまでなかったので直接視覚障害者のニーズに答えられる。１）視覚的な質問に対する答えを予測し、２）視覚的な質問に答えることができないかどうかを予測したい。


Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid a fraud detection team in their work. To this end, we designed two novel dashboards combining various state-of-the-art explanation techniques. These enable the domain expert to analyze and understand predictions, dramatically speeding up the process of filtering potential fraud cases. Finally, we discuss the lessons learned and outline open research issues.

Achmea 社の保険金詐欺検出に関する事例研究．ドメインエキスパートらの予測結果に対する理解を促すため，3 種類のモデル説明技術を用いて，2 つのダッシュボードを設計，実務に適用した．その際に得た学びと，新たに生じた課題について議論している．


We introduce new, fine-grained action and emotion recognition tasks defined on non-staged videos, recorded during robot-assisted therapy sessions of children with autism. The tasks present several challenges: a large dataset with long videos, a large number of highly variable actions, children that are only partially visible, have different ages and may show unpredictable behaviour, as well as non-standard camera viewpoints. We investigate how state-of-the-art 3d human pose reconstruction methods perform on the newly introduced tasks and propose extensions to adapt them to deal with these challenges. We also analyze multiple approaches in action and emotion recognition from 3d human pose data, establish several baselines, and discuss results and their implications in the context of child-robot interaction.

ヒューマノイドロボット用自閉症児の感情認識タスクの紹介。自閉症児は、他人の表情を読み取り行動することが苦手と言われる。一方ルールベースで動くコンピュータなどにはうまく対処しているため、ロボットを自閉症児の他人とのインタラクションを高めるために使用。感情認識タスクは認識率40〜50％の性能範囲であり、工業レベルで使われているRGB-D Kinectシステムと同等レベルの競争力となった。


We present a unified deformation model for the markerless capture of multiple scales of human movement, including facial expressions, body motion, and hand gestures. An initial model is generated by locally stitching together models of the individual parts of the human body, which we refer to as the "Frankenstein" model. This model enables the full expression of part movements, including face and hands by a single seamless model. Using a large-scale capture of people wearing everyday clothes, we optimize the Frankenstein model to create "Adam". Adam is a calibrated model that shares the same skeleton hierarchy as the initial model but can express hair and clothing geometry, making it directly usable for fitting people as they normally appear in everyday life. Finally, we demonstrate the use of these models for total motion tracking, simultaneously capturing the large-scale body movements and the subtle face and hand motion of a social group of people.

顔の表情、身体の動き、手のジェスチャーなどをマークなしでキャプチャーできるモデルの提案。複数人も可能。 マーカレスはこれまでもあるが全身や解像度がどうしても小さくなる部位（顔、手）などはなかった。Youtube動画での紹介がわかりやすい。https://www.youtube.com/watch?v=5QzdXQSf-oY


We argue that robustness of explanations---i.e., that similar inputs should give rise to similar explanations---is a key desideratum for interpretability. We introduce metrics to quantify robustness and demonstrate that current methods do not perform well according to these metrics. Finally, we propose ways that robustness can be enforced on existing interpretability approaches.

LIME をはじめとした摂動に基づいた説明手法が，勾配に基づいたそれと比べ，入力が僅かに異なるだけで説明も大きく異なる，即ち，頑健でないことを示した研究．局所リプシッツの定義から，説明の頑健性を測る指標を導入した．いかにして既存の説明手法に頑健性を施すかについても議論している．


UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.

t-SNE/PCAのような、次元圧縮した表現を得る手法。論文だけでなく実装が公開されている。データをローカルな多様体の複合として表現し、データ(X)が次元圧縮後の表現(Y)とこの複合多様体上で一致するかを学習させることで圧縮を行う。


State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments. Prior work has varied the complexity and approach employed in these two steps, including the incorporation of multi-layer neural networks to perform boundary prediction, and the use of global optimizations during pixel clustering. We propose a unified and end-to-end trainable machine learning approach, flood-filling networks, in which a recurrent 3d convolutional network directly produces individual segments from a raw image. The proposed approach robustly segments images with an unknown and variable number of objects as well as highly variable object sizes. We demonstrate the approach on a challenging 3d image segmentation task, connectomic reconstruction from volume electron microscopy data, on which flood-filling neural networks substantially improve accuracy over other state-of-the-art methods. The proposed approach can replace complex multi-step segmentation pipelines with a single neural network that is learned end-to-end.

3Dのセグメンテーションを行う際に、3D空間の一部を切り取りマスクを推定＋マージ、という処理を繰り返すことで全体のセグメンテーションを行う研究。推定されたマスクは再度ネットワークへの入力となり、再帰的に処理される(Jordan型のRNNに近い)。


Advances in machine learning have led to broad deployment of systems with impressive performance on important problems. Nonetheless, these systems can be induced to make errors on data that are surprisingly similar to examples the learned system handles correctly. The existence of these errors raises a variety of questions about out-of-sample generalization and whether bad actors might use such examples to abuse deployed systems. As a result of these security concerns, there has been a flurry of recent papers proposing algorithms to defend against such malicious perturbations of correctly handled examples. It is unclear how such misclassifications represent a different kind of security problem than other errors, or even other attacker-produced examples that have no specific relationship to an uncorrupted input. In this paper, we argue that adversarial example defense papers have, to date, mostly considered abstract, toy games that do not relate to any specific security concern. Furthermore, defense papers have not yet precisely described all the abilities and limitations of attackers that would be relevant in practical security. Towards this end, we establish a taxonomy of motivations, constraints, and abilities for more plausible adversaries. Finally, we provide a series of recommendations outlining a path forward for future work to more clearly articulate the threat model and perform more meaningful evaluation.

Adversarial Exampleについて、実際のセキュリティ上の脅威を想定して問題設定を行おうという提言。攻撃者と防衛者それぞれについて、何がゴールで(分類ミスが起こればいいのか、特定の間違った分類にさせないといけないのか)どういう行動がとれるのか(どの程度の摂動までOKか等)を定義している。


Active learning (AL) is a widely-used training strategy for maximizing predictive performance subject to a fixed annotation budget. In AL one iteratively selects training examples for annotation, often those for which the current model is most uncertain (by some measure). The hope is that active sampling leads to better performance than would be achieved under independent and identically distributed (i.i.d.) random samples. While AL has shown promise in retrospective evaluations, these studies often ignore practical obstacles to its use. In this paper we show that while AL may provide benefits when used with specific models and for particular domains, the benefits of current approaches do not generalize reliably across models and tasks. This is problematic because in practice one does not have the opportunity to explore and compare alternative AL strategies. Moreover, AL couples the training dataset with the model used to guide its acquisition. We find that subsequently training a successor model with an actively-acquired dataset does not consistently outperform training on i.i.d. sampled data. Our findings raise the question of whether the downsides inherent to AL are worth the modest and inconsistent performance gains it tends to afford.

学習効果が高いサンプルを優先してラベル付けするアクティブラーニングの手法では、実際学習させるモデルで学習効果を予測する。この「学習効果が高い」とされたサンプルが他のモデルの学習でも有用かを検証した論文。同一のモデルでなければ、他のモデルにとってはランダムに選んだ方がましという結果


Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems. We also show how reasoning about ambiguity can also be used for downstream active learning problems.

Few-shot learningにおけるMAMLという手法は、少ないサンプルで学習できる「ベストな初期値」を探す手法となっている。この研究では「ベストな初期値」の分布を推定することで、(少ないサンプルしかない)タスクにとってベストな初期値を複数サンプリングできるようにしている。


Deep learning is an emerging research field that has proven its effectiveness towards deploying more efficient intelligent systems. Security, on the other hand, is one of the most essential issues in modern communication systems. Recently many papers have shown that using deep learning models can achieve promising results when applied to the security domain. In this work, we provide an overview for the recent studies that apply deep learning techniques to the field of security.

DNNをセキュリティに応用した研究のサーベイ。検知系を主体にまとめており、マルウェア・BotNet(乗っ取られたネットワーク)・悪意あるコードそれぞれの検知手法についてまとめている。


For evaluating generation systems, automatic metrics such as BLEU cost nothing to run but have been shown to correlate poorly with human judgment, leading to systematic bias against certain model improvements. On the other hand, averaging human judgments, the unbiased gold standard, is often too expensive. In this paper, we use control variates to combine automatic metrics with human evaluation to obtain an unbiased estimator with lower cost than human evaluation alone. In practice, however, we obtain only a 7-13% cost reduction on evaluating summarization and open-response question answering systems. We then prove that our estimator is optimal: there is no unbiased estimator with lower cost. Our theory further highlights the two fundamental bottlenecks---the automatic metric and the prompt shown to human evaluators---both of which need to be improved to obtain greater cost savings.

BLEUやROUGEといった自動評価指標が、人間の評価と相関がないことは知られていた。この研究では単に相関がないだけでなく人手評価との差異がタスクやモデル(手法)によっても変わってしまうことを示しており、その変動を除去するには完全な人手評価と同等のコストがかかるとしている。


Language communication plays an important role in human learning and knowledge acquisition. With the emergence of a new generation of cognitive robots, empowering these robots to learn directly from human partners becomes increasingly important. This paper gives a brief introduction to interactive task learning where humans can teach physical agents new tasks through natural language communication and action demonstration. It discusses research challenges and opportunities in language and communication grounding that are critical in this process. It further highlights the importance of commonsense knowledge, particularly the very basic physical causality knowledge, in grounding language to perception and action.

事前プログラムなしで人工エージェント（ロボット）に新タスクを教える、対話型タスク学習(ITL) の具体例の紹介。スムージーの作り方タスクでは、YOLOを用いて周囲を観察し、どのように意味的・コミュニケーション上の共通認識を持てるようにしたが紹介されている。


A very deep convolutional neural network (CNN) has recently achieved great success for image super-resolution (SR) and offered hierarchical features as well. However, most deep CNN based SR models do not make full use of the hierarchical features from the original low-resolution (LR) images, thereby achieving relatively-low performance. In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers. Specifically, we propose residual dense block (RDB) to extract abundant local features via dense connected convolutional layers. RDB further allows direct connections from the state of preceding RDB to all the layers of current RDB, leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network. After fully obtaining dense local features, we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way. Extensive experiments on benchmark datasets with different degradation models show that our RDN achieves favorable performance against state-of-the-art methods.

超解像（SR）のためのRDNを提案。RDNは階層的特徴を全て考慮できる方法。CNNベースのSRモデルが、オリジナルの低解像度（LR）画像から階層的特徴を全て使用するわけではないため、階層的特徴を考慮した。結果、最先端の方法に対して競争力のある性能を発揮した。


We propose a framework for general probabilistic multi-step time series regression. Specifically, we exploit the expressiveness and temporal nature of Sequence-to-Sequence Neural Networks (e.g. recurrent and convolutional structures), the nonparametric nature of Quantile Regression and the efficiency of Direct Multi-Horizon Forecasting. A new training scheme, *forking-sequences*, is designed for sequential nets to boost stability and performance. We show that the approach accommodates both temporal and static covariates, learning across multiple related series, shifting seasonality, future planned event spikes and cold-starts in real life large-scale forecasting. The performance of the framework is demonstrated in an application to predict the future demand of items sold on this http URL, and in a public probabilistic forecasting competition to predict electricity price and load.

MQ-RNN による確率的時系列回帰のフレームワークの提示。Amazon.comで販売商品の需要予測、電気料金負荷を予測し、ベストの予測結果(論文公表時点)。特に時系列データの大きなスパイクやディップの原因となる、季節性や既知のイベントの問題を考慮するようにした。(NIPS2017採用論文)


Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations. However, there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions. In this paper, we conduct a point-by-point comparative study between Simple Word-Embedding-based Models (SWEMs), consisting of parameter-free pooling operations, relative to word-embedding-based RNN/CNN models. Surprisingly, SWEMs exhibit comparable or even superior performance in the majority of cases considered. Based upon this understanding, we propose two additional pooling strategies over learned word embeddings: (i) a max-pooling operation for improved interpretability; and (ii) a hierarchical pooling operation, which preserves spatial (n-gram) information within text sequences. We present experiments on 17 datasets encompassing three tasks: (i) (long) document classification; (ii) text sequence matching; and (iii) short text tasks, including classification and tagging. The source code and datasets can be obtained from https:// this http URL.

自然言語処理において文章の構造をとらえるためにCNNやLSTMが利用されているが、それらがどれくらい役に立っているのかを検証した論文。分散表現＋重み無しのPooling処理のみというシンプルなモデル(SWEMs)と比較したが、分類・文選択といったタスクでCNN/LSTMを上回る結果


Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective.

近年提案されている抽出型QAタスク(SQuAD等)のモデルは、シンプルなニューラルベースのモデルと比較されることなく、様々なコンポーネントが提案され複雑化している。本論文では現状のモデルと同等精度のシンプルな構造のモデルを提案している。


When creating an outfit, style is a criterion in selecting each fashion item. This means that style can be regarded as a feature of the overall outfit. However, in various previous studies on outfit generation, there have been few methods focusing on global information obtained from an outfit. To address this deficiency, we have incorporated an unsupervised style extraction module into a model to learn outfits. Using the style information of an outfit as a whole, the proposed model succeeded in generating outfits more flexibly without requiring additional information. Moreover, the style information extracted by the proposed model is easy to interpret. The proposed model was evaluated on two human-generated outfit datasets. In a fashion item prediction task (missing prediction task), the proposed model outperformed a baseline method. In a style extraction task, the proposed model extracted some easily distinguishable styles. In an outfit generation task, the proposed model generated an outfit while controlling its styles. This capability allows us to generate fashionable outfits according to various preferences.

似合う服装スタイルの提案。人の服装やスタイルを、カジュアル、フォーマルの割合でラベリングすることを提案しており、そのラベルから服装コーディネートを生成した。これにより様々な好みに応じることができる。（ZOZOの研究）（KDD2018採択）


Recent work has made significant progress in improving spatial resolution for pixelwise labeling with Fully Convolutional Network (FCN) framework by employing Dilated/Atrous convolution, utilizing multi-scale features and refining boundaries. In this paper, we explore the impact of global contextual information in semantic segmentation by introducing the Context Encoding Module, which captures the semantic context of scenes and selectively highlights class-dependent featuremaps. The proposed Context Encoding Module significantly improves semantic segmentation results with only marginal extra computation cost over FCN. Our approach has achieved new state-of-the-art results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single model achieves a final score of 0.5567 on ADE20K test set, which surpass the winning entry of COCO-Place Challenge in 2017. In addition, we also explore how the Context Encoding Module can improve the feature representation of relatively shallow networks for the image classification on CIFAR-10 dataset. Our 14 layer network has achieved an error rate of 3.45%, which is comparable with state-of-the-art approaches with over 10 times more layers. The source code for the complete system are publicly available.

セマンティックエンコーディングロスを組み込んだコンテキストエンコーディングモジュールの紹介と、セマンティックセグメンテーションフレームワークContext Encoding Network（EncNet）の設計と実装。これにより、映像シーンの理解の精度をあげた。PASCAL VOC 2012で85.9％、PASCALで51.7％の結果。（Amazon.com関連の研究）


Occupant behavior (OB) and in particular window openings need to be considered in building performance simulation (BPS), in order to realistically model the indoor climate and energy consumption for heating ventilation and air conditioning (HVAC). However, the proposed OB window opening models are often biased towards the over-represented class where windows remained closed. In addition, they require tuning for each occupant which can not be efficiently scaled to the increased number of occupants. This paper presents a window opening model for commercial buildings using deep learning methods. The model is trained using data from occupants from an office building in Germany. In total the model is evaluated using almost 20 mio. data points from 3 independent buildings, located in Aachen, Frankfurt and Philadelphia. Eventually, the results of 3100 core hours of model development are summarized, which makes this study the largest of its kind in window states modeling. Additionally, the practical potential of the proposed model was tested by incorporating it in the Modelica-based thermal building simulation. The resulting evaluation accuracy and F1 scores on the office buildings ranged between 86-89 % and 0.53-0.65 respectively. The performance dropped around 15 % points in case of sparse input data, while the F1 score remained high.

商業ビルの暖房換気および空調（HVAC）の室内気候、エネルギー消費を、初めて窓の解放を考慮（多層パーセプトロンとハイパーパラメーターを使用）したモデル化をした研究。現実応用の可能性として、Modelicaベースの熱ビルディングテストを行い、オフィスの建物の評価精度とF1得点はそれぞれ86-89％と0.53-0.65、スパース入力データでは高いF1スコアを出せた。


This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to investigate procedural generation of instructions that teach players how to play video games. We present a representation of game rules and mechanics using a graph system as well as a tutorial generation method that uses said graph representation. We demonstrate the concept by testing it on games within the General Video Game Artificial Intelligence (GVG-AI) framework; the paper discusses tutorials generated for eight different games. Our findings suggest that a graph representation scheme works well for simple arcade style games such as Space Invaders and Pacman, but it appears that tutorials for more complex games might require higher-level understanding of the game than just single mechanics.

ゲーム内のルールをグラフで表現し、そこからゲームのチュートリアルを自動生成するという研究。


Learning autonomous-driving policies is one of the most challenging but promising tasks for computer vision. Most researchers believe that future research and applications should combine cameras, video recorders and laser scanners to obtain comprehensive semantic understanding of real traffic. However, current approaches only learn from large-scale videos, due to the lack of benchmarks that consist of precise laser-scanner data. In this paper, we are the first to propose a LiDAR-Video dataset, which provides large-scale high-quality point clouds scanned by a Velodyne laser, videos recorded by a dashboard camera and standard drivers’ behaviors. Extensive experiments demonstrate that extra depth information help networks to determine driving policies indeed.

これまで自動運転に向けた実験データセットというとビデオしかなかったため、レーザーによる奥行き情報データセット（LiDAR-Video Driving Dataset）の紹介する。片目しかないドライバーが駐車および車線変更作業が上手にできないように、奥行き情報の利用は予測性能の大幅な向上をもたらすことが分かった。


Few ideas have enjoyed as large an impact on deep learning as convolution. For any problem involving pixels or spatial representations, common intuition holds that convolutional neural networks may be appropriate. In this paper we show a striking counterexample to this intuition via the seemingly trivial coordinate transform problem, which simply requires learning a mapping between coordinates in (x,y) Cartesian space and one-hot pixel space. Although convolutional networks would seem appropriate for this task, we show that they fail spectacularly. We demonstrate and carefully analyze the failure first on a toy problem, at which point a simple fix becomes obvious. We call this solution CoordConv, which works by giving convolution access to its own input coordinates through the use of extra coordinate channels. Without sacrificing the computational and parametric efficiency of ordinary convolution, CoordConv allows networks to learn either complete translation invariance or varying degrees of translation dependence, as required by the end task. CoordConv solves the coordinate transform problem with perfect generalization and 150 times faster with 10--100 times fewer parameters than convolution. This stark contrast raises the question: to what extent has this inability of convolution persisted insidiously inside other tasks, subtly hampering performance from within? A complete answer to this question will require further investigation, but we show preliminary evidence that swapping convolution for CoordConv can improve models on a diverse set of tasks. Using CoordConv in a GAN produced less mode collapse as the transform between high-level spatial latents and pixels becomes easier to learn. A Faster R-CNN detection model trained on MNIST showed 24% better IOU when using CoordConv, and in the RL domain agents playing Atari games benefit significantly from the use of CoordConv layers.

CNNが位置情報の推定タスクに弱いことを突き止め、その解決方法を提案した研究。CNNでは座標をDeconvolutionして2次元マップ上の位置を復元するというタスク難しいことを確認。これはフィルターが位置情報を知らないためで、x/yの座標情報を追加すると上手くいくことを確認


While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In this work, we propose a universal unsupervised learning approach to extract useful representations from high-dimensional data, which we call Contrastive Predictive Coding. The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models. We use a probabilistic contrastive loss which induces the latent space to capture information that is maximally useful to predict future samples. It also makes the model tractable by using negative sampling. While most prior work has focused on evaluating representations for a particular modality, we demonstrate that our approach is able to learn useful representations achieving strong performance on four distinct domains: speech, images, text and reinforcement learning in 3D environments.

Encodeした潜在表現からさらに自己回帰モデルでコンテキストを抜き、そこから複数ステップ先の潜在表現を予測させることでより抽象化された、長期の推定に有用な表現を学習させる手法。学習では、コンテキスト/データの量的偏りを改善するためコンテキストの有り無しにおける確率比率を使用している。


The issue of disagreements amongst human experts is a ubiquitous one in both machine learning and medicine. In medicine, this often corresponds to doctor disagreements on a patient diagnosis. In this work, we show that machine learning models can be trained to give uncertainty scores to data instances that might result in high expert disagreements. In particular, they can identify patient cases that would benefit most from a medical second opinion. Our central methodological finding is that Direct Uncertainty Prediction (DUP), training a model to predict an uncertainty score directly from the raw patient features, works better than Uncertainty Via Classification, the two-step process of training a classifier and postprocessing the output distribution to give an uncertainty score. We show this both with a theoretical result, and on extensive evaluations on a large scale medical imaging application.

ラベル付けに際して意見が割れるようなケースがある場合、通常は意見の分布(複数人のアノテーターによるラベル付けの結果分布)を予測させる。そうではなく、通常のラベルに加えて意見が割れる/割れないを直接予測させる方が効果的だったという研究。


Consistently checking the statistical significance of experimental results is one of the mandatory methodological steps to address the so-called "reproducibility crisis" in deep reinforcement learning. In this tutorial paper, we explain how the number of random seeds relates to the probabilities of statistical errors. For both the t-test and the bootstrap confidence interval test, we recall theoretical guidelines to determine the number of random seeds one should use to provide a statistically significant comparison of the performance of two algorithms. Finally, we discuss the influence of deviations from the assumptions usually made by statistical tests. We show that they can lead to inaccurate evaluations of statistical errors and provide guidelines to counter these negative effects. We make our code available to perform the tests.

強化学習でランダムシードによって挙動が変わってしまう問題について、どれくらいシードを取れば十分なのか調査した研究。結論としては、AとB、2つのアルゴリズムを比較する際はブートストラップ法で信頼区間を求めた上で、有意水準0.05未満のウェルチt検定を行う。またシード数は20はほしいという


Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a challenging dataset to enable research on learning driver behavior in real-life environments. The dataset includes 104 hours of real human driving in the San Francisco Bay Area collected using an instrumented vehicle equipped with different sensors. We provide a detailed analysis of HDD with a comparison to other driving datasets. A novel annotation methodology is introduced to enable research on driver behavior understanding from untrimmed data sequences. As the first step, baseline algorithms for driver behavior detection are trained and tested to demonstrate the feasibility of the proposed task.

運転者の行動理解のためのホンダリサーチインスティチュートデータセット (HDD）の紹介。（行動４分類、１）目標指向、２）刺激、３）原因、４）気づきへと注釈する方法の提案）


Complex machine learning models for NLP are often brittle, making different predictions for input instances that are extremely similar semantically. To automatically detect this behavior for individual instances, we present semantically equivalent adversaries (SEAs) – semantic-preserving perturbations that induce changes in the model’s predictions. We generalize these adversaries into semantically equivalent adversarial rules (SEARs) – simple, universal replacement rules that induce adversaries on many instances. We demonstrate the usefulness and flexibility of SEAs and SEARs by detecting bugs in black-box state-of-the-art models for three domains: machine comprehension, visual question-answering, and sentiment analysis. Via user studies, we demonstrate that we generate high-quality local adversaries for more instances than humans, and that SEARs induce four times as many mistakes as the bugs discovered by human experts. SEARs are also actionable: retraining models using data augmentation significantly reduces bugs, while maintaining accuracy.

自然言語処理におけるAdversarialな変換の研究。単純な言いかえ(ルールで変換)で文書読解やQA、感情分類のモデルをだませることを確認。例えばmovieを同じ意味のfilmに変えるだけでセンチメントが反転したり、What isをWhat'sに変えるだけでQAが間違えるなどかなり衝撃的な結果。


Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typically simpler, more flexible to use, and thus more prevalent in modern deep RL than model-based approaches. However, empirical work has suggested that model-free algorithms may require more samples to learn [Deisenroth and Rasmussen 2011, Schulman et al. 2015]. The theoretical question of "whether model-free algorithms can be made sample efficient" is one of the most fundamental questions in RL, and remains unsolved even in the basic scenario with finitely many states and actions. We prove that, in an episodic MDP setting, Q-learning with UCB exploration achieves regret \tilde{O}(\sqrt{H^3 SAT}), where S and A are the numbers of states and actions, H is the number of steps per episode, and T is the total number of steps. This sample efficiency matches the optimal regret that can be achieved by any model-based approach, up to a single \sqrt{H} factor. To the best of our knowledge, this is the first analysis in the model-free setting that establishes \sqrt{T} regret without requiring access to a "simulator."

モデルフリーとモデルベースで、どれぐらいサンプル効率に差があるのかを分析した研究。モデルフリーにおいても、探索をUCB(未探索のところほど探索する手法)で行い、学習率を直近の経験を重く評価するよう調整することでモデルベースの下限と同等の性能が出せることを証明。


Flow-based generative models (Dinh et al., 2014) are conceptually attractive due to tractability of the exact log-likelihood, tractability of exact latent-variable inference, and parallelizability of both training and synthesis. In this paper we propose Glow, a simple type of generative flow using an invertible 1x1 convolution. Using our method we demonstrate a significant improvement in log-likelihood on standard benchmarks. Perhaps most strikingly, we demonstrate that a generative model optimized towards the plain log-likelihood objective is capable of efficient realistic-looking synthesis and manipulation of large images. The code for our model is available at this https URL

画像生成を可逆変換の関数を組み合わせ行う手法。1. データ各点に対する正確な尤度評価が可能、2. 潜在表現が得られる、3. 並列計算が行いやすいの3点を満たす(VAEだと1、GANだと2、自己回帰だと3がNGになる。可逆変換の場合逆変換で1、各関数(encoder)により2の獲得が可能)。高解像度の画像生成に成功


We address a learning-to-normalize problem by proposing Switchable Normalization (SN), which learns to select different normalizers for different normalization layers of a deep neural network. SN employs three distinct scopes to compute statistics (means and variances) including a channel, a layer, and a minibatch. SN switches between them by learning their importance weights in an end-to-end manner. It has several good properties. First, it adapts to various network architectures and tasks (see Fig.1). Second, it is robust to a wide range of batch sizes, maintaining high performance even when small minibatch is presented (e.g. 2 images/GPU). Third, SN does not have sensitive hyper-parameter, unlike group normalization that searches the number of groups as a hyper-parameter. Without bells and whistles, SN outperforms its counterparts on various challenging benchmarks, such as ImageNet, COCO, CityScapes, ADE20K, and Kinetics. Analyses of SN are also presented. We hope SN will help ease the usage and understand the normalization techniques in deep learning. The code of SN has been made available in this https URL.

正規化の手法で、Batch・Instance・Layerという3種のNormalizationをミックスする手法の提案。平均を引いて分散で割るという形式は同じだが、平均・分散を各手法における平均/分散の重みつき平均で算出する。この重みは学習可能であり、グループ数を決める必要があるGroup Normaliztionより優位性がある


In this paper, we propose a solution to transforming photos of real-world scenes into cartoon style images, which is valuable and challenging in computer vision and computer graphics. Our solution belongs to learning based methods, which have recently become popular to stylize images in artistic forms such as painting. However, existing methods do not produce satisfactory results for cartoonization, due to the fact that (1) cartoon styles have unique characteristics with high level simplification and abstraction, and (2) cartoon images tend to have clear edges, smooth color shading and relatively simple textures, which exhibit significant challenges for texture-descriptor-based loss functions used in existing methods. In this paper, we propose CartoonGAN, a generative adversarial network (GAN) framework for cartoon stylization. Our method takes unpaired photos and cartoon images for training, which is easy to use. Two novel losses suitable for cartoonization are proposed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of the VGG network to cope with substantial style variation between photos and cartoons, and (2) an edge-promoting adversarial loss for preserving clear edges. We further introduce an initialization phase, to improve the convergence of the network to the target manifold. Our method is also much more efficient to train than existing methods. Experimental results show that our method is able to generate high-quality cartoon images from real-world photos (i.e., following specific artists’ styles and with clear edges and smooth shading) and outperforms state-of-the-art methods.

写真を高品質マンガに置き換えるGANの提案。


Copying mechanism shows effectiveness in sequence-to-sequence based neural network models for text generation tasks, such as abstractive sentence summarization and question generation. However, existing works on modeling copying or pointing mechanism only considers single word copying from the source sentences. In this paper, we propose a novel copying framework, named Sequential Copying Networks (SeqCopyNet), which not only learns to copy single words, but also copies sequences from the input sentence. It leverages the pointer networks to explicitly select a sub-span from the source side to target side, and integrates this sequential copying mechanism to the generation process in the encoder-decoder paradigm. Experiments on abstractive sentence summarization and question generation tasks show that the proposed SeqCopyNet can copy meaningful spans and outperforms the baseline models.

従来のコピーメカニズムは、ソースから１単語をコピーするかどうかの制御だったが、単語系列をコピーする拡張をおこなった。系列をコピーできると、フレーズや固有表現をコピーできるようになる


Inspired by how humans summarize long documents, we propose an accurate and fast summarization model that first selects salient sentences and then rewrites them abstractively (i.e., compresses and paraphrases) to generate a concise overall summary. We use a novel sentence-level policy gradient method to bridge the non-differentiable computation between these two neural networks in a hierarchical way, while maintaining language fluency. Empirically, we achieve the new state-of-the-art on all metrics (including human evaluation) on the CNN/Daily Mail dataset, as well as significantly higher abstractiveness scores. Moreover, by first operating at the sentence-level and then the word-level, we enable parallel decoding of our neural generative model that results in substantially faster (10-20x) inference speed as well as 4x faster training convergence than previous long-paragraph encoder-decoder models. We also demonstrate the generalization of our model on the test-only DUC-2002 dataset, where we achieve higher scores than a state-of-the-art model.

要約を作成する際文を抽出してから書き換える形で作成する手法。文選択は単語分散表現をCNNで畳み込みBi-directionalにかけたものをRNNに入れ、ステップごと選択を行う。これをEncoder-Decoderに入れ書き換えを行うが、文選択が微分不可能なため強化学習で最適化を行っている


Common-sense and background knowledge is required to understand natural language, but in most neural natural language understanding (NLU) systems, this knowledge must be acquired from training corpora during learning, and then it is static at test time. We introduce a new architecture for the dynamic integration of explicit background knowledge in NLU models. A general-purpose reading module reads background knowledge in the form of free-text statements (together with task-specific text inputs) and yields refined word representations to a task-specific NLU architecture that reprocesses the task inputs with these representations. Experiments on document question answering (DQA) and recognizing textual entailment (RTE) demonstrate the effectiveness and flexibility of the approach. Analysis shows that our model learns to exploit knowledge in a semantically appropriate way.

自然言語処理において外部知識を獲得する機構を組み込む研究。外部知識は知識グラフのように構築が大変なものでなく、普通の文書であることを想定している。最初は単なる単語分散表現として格納するが、文章のEncode結果を使い徐々にコンテキスト依存の表現に更新していく。


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

「自然な」Adversarial Exampleを生成する研究。自然なというのは単なるノイズではなく実際のデータとしてあり得そうな変動を乗せるという意。一般的にはデータに直接ノイズを入れるが、こちらではGANの潜在空間上で入力に近い＋誤認識を誘う表現を探し生成する。これで自然言語でのExample生成も可能


We demonstrate that a very deep ResNet with stacked modules with one neuron per hidden layer and ReLU activation functions can uniformly approximate any Lebesgue integrable function in $d$ dimensions, i.e. $\ell_1(\mathbb{R}^d)$. Because of the identity mapping inherent to ResNets, our network has alternating layers of dimension one and $d$. This stands in sharp contrast to fully connected networks, which are not universal approximators if their width is the input dimension $d$ [Lu et al, 2017; Hanin and Sellke, 2017]. Hence, our result implies an increase in representational power for narrow deep networks by the ResNet architecture.

ResNetにおいて、ノードは一つだけで十分ではないかという研究。実験により全結合より単一ノードの方が決定境界を上手く学習することを確認。Dropoutがなぜ有効なのか？の証左にもなっているという。


Reading comprehension models are based on recurrent neural networks that sequentially process the document tokens. As interest turns to answering more complex questions over longer documents, sequential reading of large portions of text becomes a substantial bottleneck. Inspired by how humans use document structure, we propose a novel framework for reading comprehension. We represent documents as trees, and model an agent that learns to interleave quick navigation through the document tree with more expensive answer extraction. To encourage exploration of the document tree, we propose a new algorithm, based on Deep Q-Network (DQN), which strategically samples tree nodes at training time. Empirically we find our algorithm improves question answering performance compared to DQN and a strong information-retrieval (IR) baseline, and that ensembling our model with the IR baseline results in further gains in performance.

長い文書からの質問回答を、強化学習(DQNベース)で行う研究。ドキュメントの見出し(タイトル、セクションetc)をノード、その中の文をリーフに見立てツリー構造にして、回答がある文までの探索方法を学習させる。回答は文章前半にあることが多いため初期位置のノードは分布からサンプルしてスタートする


Convolutional Neural Networks (CNNs) are commonly assumed to be invariant to small image transformations: either because of the convolutional architecture or because they were trained using data augmentation. Recently, several authors have shown that this is not the case: small translations or rescalings of the input image can drastically change the network's prediction. In this paper, we quantify this phenomena and ask why neither the convolutional architecture nor data augmentation are sufficient to achieve the desired invariance. Specifically, we show that the convolutional architecture does not give invariance since architectures ignore the classical sampling theorem, and data augmentation does not give invariance because the CNNs learn to be invariant to transformations only for images that are very similar to typical images from the training set. We discuss two possible solutions to this problem: (1) antialiasing the intermediate representations and (2) increasing data augmentation and show that they provide only a partial solution at best. Taken together, our results indicate that the problem of insuring invariance to small image transformations in neural networks while preserving high accuracy remains unsolved.

CNNが微細な画像の変更に弱いことを検証した研究。畳み込みによる画像領域の「サンプリング」はサンプリングの基本原則を無視しており(端的にはアンチエイリアスをかけろ原則)、それが原因ではないかとしている(=Poolingを入れることを推奨している)。また、学習画像の偏りについても言及している。


Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners of machine learning and beyond to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges such as the difficulty in performing accurate inference and its generalizability, its sensitivity to prior knowledge, and the disproportionate growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions to traditional IRL methods for handling: inaccurate and incomplete perception, an incomplete model, multiple reward functions, and nonlinear reward functions. This survey concludes the discussion with some broad advances in the research area and currently open research questions.

逆強化学習のサーベイ論文。基本的な仕組みから活用用途までが書かれている。逆強化学習の代表的な手法(Max Margin/Max Entropy/Bayesian)をきちんと整理して書いている。


This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Our implementation has been made publicly available to facilitate further research on efficient architecture search algorithms.

ニューラルネットの構造探索を勾配法で学習する手法(実質的には構造全体でなくセル構造の探索)。ノードをつなぐ処理の選択確率と(ノード数は事前に決める)、処理に使用する重みを交互に学習していく。「処理を選択する」というのは微分不可能なので、実質的には各処理の結果にかける重みになっている。


We investigate the design challenges of constructing effective and efficient neural sequence labeling systems, by reproducing twelve neural sequence labeling models, which include most of the state-of-the-art structures, and conduct a systematic model comparison on three benchmarks (i.e. NER, Chunking, and POS tagging). Misconceptions and inconsistent conclusions in existing literature are examined and clarified under statistical experiments. In the comparison and analysis process, we reach several practical conclusions which can be useful to practitioners.

系列ラベリング(固有表現認識やPOSTaggingなど)で良好な精度を出している12のモデルについて、前処理やハイパーパラメーターなどの条件をそろえ同じデータセットで比較した研究。タスクによって効果のある手法(LSTM or CNN、文字特徴、CRF層etc)にばらつきがあるが、効果がある場合は1ptほど上がる


Humans and animals are capable of learning a new behavior by observing others perform the skill just once. We consider the problem of allowing a robot to do the same -- learning from a raw video pixels of a human, even when there is substantial domain shift in the perspective, environment, and embodiment between the robot and the observed human. Prior approaches to this problem have hand-specified how human and robot actions correspond and often relied on explicit human pose detection systems. In this work, we present an approach for one-shot learning from a video of a human by using human and robot demonstration data from a variety of previous tasks to build up prior knowledge through meta-learning. Then, combining this prior knowledge and only a single video demonstration from a human, the robot can perform the task that the human demonstrated. We show experiments on both a PR2 arm and a Sawyer arm, demonstrating that after meta-learning, the robot can learn to place, push, and pick-and-place new objects using just one video of a human performing the manipulation.

One-shotで模倣学習を、しかもドメインを変えて行う研究。具体的には(ロボットのでなく)人の動作を一回見て模倣する。事前学習として人とロボットの動作のペア画像を用意し、戦略の初期値を人の画像から、初期値からの学習をロボットの画像から行う。これで人の画像から良い初期値を得る関数を学習する


We propose cw2vec, a novel method for learning Chinese word embeddings. It is based on our observation that exploiting stroke-level information is crucial for improving the learning of Chinese word embeddings. Specifically, we design a minimalist approach to exploit such features, by using stroke n-grams, which capture semantic and morphological level information of Chinese words. Through qualitative analysis, we demonstrate that our model is able to extract semantic information that cannot be captured by existing methods. Empirical results on the word similarity, word analogy, text classification and named entity recognition tasks show that the proposed approach consistently outperforms state-of-the-art approaches such as word-based word2vec and GloVe, character-based CWE, component-based JWE and pixel-based GWE.

cw2vecは新しい単語分散表現方法。漢字は英語ワードと違う、漢字の形自体は情報がふくまれている。word2vec, gloveなどの手法は漢字の笔画（漢字を構成する点や線）を考えしていない。cw2vecは漢字を字画で表示し、n-gramの情報を学習する。cw2vecは今までの手法より、漢字の形態と構造情報（word morphological information）をよりよい学習した。


Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks. Comparing representations in neural networks is fundamentally difficult as the structure of representations varies greatly, even across groups of networks trained on identical tasks, and over the course of training. Here, we develop projection weighted CCA (Canonical Correlation Analysis) as a tool for understanding neural networks, building off of SVCCA, a recently proposed method (Raghu et al., 2017). We first improve the core method, showing how to differentiate between signal and noise, and then apply this technique to compare across a group of CNNs, demonstrating that networks which generalize converge to more similar representations than networks which memorize, that wider networks converge to more similar solutions than narrow networks, and that trained networks with identical topology but different learning rates converge to distinct clusters with diverse representations. We also investigate the representational dynamics of RNNs, across both training and sequential timesteps, finding that RNNs converge in a bottom-up pattern over the course of training and that the hidden state is highly variable over the course of a sequence, even when accounting for linear transforms. Together, these results provide new insights into the function of CNNs and RNNs, and demonstrate the utility of using CCA to understand representations.

ネットワーク間の類似性を調べるための手法の提案。類似度の手法は正準相関分析を基準としている(A, Bについて、相関(共分散)を最大化しつつA.Bそれぞれもよく説明するベクトルを発見する手法)。CNNでは汎化性能が高いほど似ている、RNNでは層が薄いほど入力=>出力の層が近づく速度が速いと言う結果


Modern deep transfer learning approaches have mainly focused on learning generic feature vectors from one task that are transferable to other tasks, such as word embeddings in language and pretrained convolutional features in vision. However, these approaches usually transfer unary features and largely ignore more structured graphical representations. This work explores the possibility of learning generic latent relational graphs that capture dependencies between pairs of data units (e.g., words or pixels) from large-scale unlabeled data and transferring the graphs to downstream tasks. Our proposed transfer learning framework improves performance on various tasks including question answering, natural language inference, sentiment analysis, and image classification. We also show that the learned graphs are generic enough to be transferred to different embeddings on which the graphs have not been trained (including GloVe embeddings, ELMo embeddings, and task-specific RNN hidden unit), or embedding-free units such as image pixels.

自然言語において、Attentionの転移を試みた研究。これにより単語分散表現＋Attentionの転移と、単語＋その「組み合わせ方」についても転移を行うという併用が可能になる。Attentionの機構はCNNを使ったSelf-Attentionを多段に組んだような形。性能はいまいちだが、併用によるブーストは確認。


This paper proposes Self-Imitation Learning (SIL), a simple off-policy actor-critic algorithm that learns to reproduce the agent's past good decisions. This algorithm is designed to verify our hypothesis that exploiting past good experiences can indirectly drive deep exploration. Our empirical results show that SIL significantly improves advantage actor-critic (A2C) on several hard exploration Atari games and is competitive to the state-of-the-art count-based exploration methods. We also show that SIL improves proximal policy optimization (PPO) on MuJoCo tasks.

強化学習で、過去の良かった行動を模倣することで精度を上げる研究。通常の学習に加え、推定よりも高い報酬が得られた経験(R > V_theta)をサンプルしての学習を追加する(R - V_thetaでActor/Criticの学習を行う)。ただ探索が足りないと局所最適な行動に陥ることがありバランス設定が重要になるとのこと


Despite the fast developmental pace of new sentence embedding methods, it is still challenging to find comprehensive evaluations of these different techniques. In the past years, we saw significant improvements in the field of sentence embeddings and especially towards the development of universal sentence encoders that could provide inductive transfer to a wide variety of downstream tasks. In this work, we perform a comprehensive evaluation of recent methods using a wide variety of downstream and linguistic feature probing tasks. We show that a simple approach using bag-of-words with a recently introduced language model for deep context-dependent word embeddings proved to yield better results in many tasks when compared to sentence encoders trained on entailment datasets. We also show, however, that we are still far away from a universal encoder that can perform consistently across several downstream tasks.

ELMo(#655)やUniversal Sentence Encoder(#699)といった文ベクトルを作成する各手法について、その性能を様々なタスクで比較した研究(Word2VecやGloVeの平均で作成したものも含む)。結果としてはELMo/USE(Transformer)強しというものだが、すべてのタスクで高いパフォーマンスのものはまだないという。


The success of neural network methods for computing word embeddings has motivated methods for generating semantic embeddings of longer pieces of text, such as sentences and paragraphs. Surprisingly, Wieting et al (ICLR'16) showed that such complicated methods are outperformed, especially in out-of-domain (transfer learning) settings, by simpler methods involving mild retraining of word embeddings and basic linear regression. The  method of Wieting et al. requires retraining with a substantial labeled dataset such as Paraphrase Database (Ganitkevitch et al., 2013).   The current paper goes further, showing that the following completely unsupervised sentence embedding is a formidable baseline: Use word embeddings computed using one of the popular methods on unlabeled corpus like Wikipedia, represent the sentence by a weighted average of the word vectors, and then modify them a bit using PCA/SVD. This weighting improves performance by about 10% to 30% in textual similarity tasks, and beats sophisticated supervised methods including RNN's and LSTM's. It even improves Wieting et al.'s embeddings.   This simple method should be used as the baseline to beat in future, especially when labeled training data is scarce or nonexistent.   The paper also gives a theoretical explanation of the success of the above unsupervised method using a latent variable generative model for sentences, which is a simple extension of the model in Arora et al. (TACL'16) with new "smoothing" terms that allow for  words occurring out of context, as well as high probabilities for words like and, not in all contexts.

単語ベクトルから文のベクトル表現を得る、シンプルだが強力な手法。単語ベクトルの平均を取るのが基本だが、TF-IDFライクに頻度が高い語については重みを下げる。そして、各文のベクトルが出そろった後全体(文数Xベクトルサイズ)の第一主成分要素をマイナスする。


We introduce Probabilistic FastText, a new model for word embeddings that can capture multiple word senses, sub-word structure, and uncertainty information. In particular, we represent each word with a Gaussian mixture density, where the mean of a mixture component is given by the sum of n-grams. This representation allows the model to share statistical strength across sub-word structures (e.g. Latin roots), producing accurate representations of rare, misspelt, or even unseen words. Moreover, each component of the mixture can capture a different word sense. Probabilistic FastText outperforms both FastText, which has no probabilistic model, and dictionary-level probabilistic embeddings, which do not incorporate subword structures, on several word-similarity benchmarks, including English RareWord and foreign language datasets. We also achieve state-of-art performance on benchmarks that measure ability to discern different meanings. Thus, the proposed model is the first to achieve multi-sense representations while having enriched semantics on rare words.

サブワードを分布で表現し、単語をその集合(混合ガウス分布)で表現するという手法(サブワードは文字n-gramで作成する)。式はサブワードの組み合わせのみでなく、単語ベクトルを補完する形で定義されている。単語類似度の精度が上がったほか、未知語対応の効果が確認できた。


Deep learning has improved performance on many natural language processing (NLP) tasks individually. However, general NLP models cannot emerge within a paradigm that focuses on the particularities of a single metric, dataset, and task. We introduce the Natural Language Decathlon (decaNLP), a challenge that spans ten tasks: question answering, machine translation, summarization, natural language inference, sentiment analysis, semantic role labeling, zero-shot relation extraction, goal-oriented dialogue, semantic parsing, and commonsense pronoun resolution. We cast all tasks as question answering over a context. Furthermore, we present a new Multitask Question Answering Network (MQAN) jointly learns all tasks in decaNLP without any task-specific modules or parameters in the multitask setting. MQAN shows improvements in transfer learning for machine translation and named entity recognition, domain adaptation for sentiment analysis and natural language inference, and zero-shot capabilities for text classification. We demonstrate that the MQAN's multi-pointer-generator decoder is key to this success and performance further improves with an anti-curriculum training strategy. Though designed for decaNLP, MQAN also achieves state of the art results on the WikiSQL semantic parsing task in the single-task setting. We also release code for procuring and processing data, training and evaluating models, and reproducing all experiments for decaNLP.

自然言語処理のタスク全10個を単一のモデルで解く新しい問題設定と評価手法の提案。タスクは要約・翻訳・質問回答など様々なものを含むが、フォーマットは質問回答の形に還元されている(要約なら、「この文の要約は？」になるなど)。マルチタスクで学習を行い、転移性能が確認できたとのこと。


We propose RUDDER, a novel reinforcement learning approach for delayed rewards in finite Markov decision processes (MDPs). In MDPs the Q-values are equal to the expected immediate reward plus the expected future rewards. The latter are related to bias problems in temporal difference (TD) learning and to high variance problems in Monte Carlo (MC) learning. Both problems are even more severe when rewards are delayed. RUDDER aims at making the expected future rewards zero, which simplifies Q-value estimation to computing the mean of the immediate reward. We propose the following two new concepts to push the expected future rewards toward zero. (i) Reward redistribution that leads to return-equivalent decision processes with the same optimal policies and, when optimal, zero expected future rewards. (ii) Return decomposition via contribution analysis which transforms the reinforcement learning task into a regression task at which deep learning excels. On artificial tasks with delayed rewards, RUDDER is significantly faster than MC and exponentially faster than Monte Carlo Tree Search (MCTS), TD({\lambda}), and reward shaping approaches. At Atari games, RUDDER on top of a Proximal Policy Optimization (PPO) baseline improves the scores, which is most prominent at games with delayed rewards. Source code is available at \url{this https URL} and demonstration videos at \url{this https URL}.

強化学習では通常報酬を予測する形で学習を行うが、最終報酬から各ステップの即時報酬を逆算する(Return Decomposition)という、逆方向の手法を取った研究。 LSTMとLRP(判断根拠の推定に使われる手法の一種)を使用し、最終報酬の根拠となるステップを推定し最終報酬を分配する。


Do visual tasks have a relationship, or are they unrelated? For instance, could having surface normals simplify estimating the depth of an image? Intuition answers these questions positively, implying existence of a structure among visual tasks. Knowing this structure has notable values; it is the concept underlying transfer learning and provides a principled way for identifying redundancies across tasks, e.g., to seamlessly reuse supervision among related tasks or solve many tasks in one system without piling up the complexity. We proposes a fully computational approach for modeling the structure of space of visual tasks. This is done via finding (first and higher-order) transfer learning dependencies across a dictionary of twenty six 2D, 2.5D, 3D, and semantic tasks in a latent space. The product is a computational taxonomic map for task transfer learning. We study the consequences of this structure, e.g. nontrivial emerged relationships, and exploit them to reduce the demand for labeled data. For example, we show that the total number of labeled datapoints needed for solving a set of 10 tasks can be reduced by roughly 2/3 (compared to training independently) while keeping the performance nearly the same. We provide a set of tools for computing and probing this taxonomical structure including a solver that users can employ to devise efficient supervision policies for their use cases.

様々な画像関連のタスクが、どれくらい相互に転移しやすいかを調べた研究。全26の画像関連タスク(セグメンテーションやエッジ推定など)をそれぞれ個別に学習した後、他のどのタスクに転移しやすいか、また他のどの特徴と組み合わせた場合精度が高まるかを検証。これにより、タスク関連マップを作成した


Deep neural networks have enabled progress in a wide variety of applications. Growing the size of the neural network typically results in improved accuracy. As model sizes grow, the memory and compute requirements for training these models also increases. We introduce a technique to train deep neural networks using half precision floating point numbers. In our technique, weights, activations and gradients are stored in IEEE half-precision format. Half-precision floating numbers have limited numerical range compared to single-precision numbers. We propose two techniques to handle this loss of information. Firstly, we recommend maintaining a single-precision copy of the weights that accumulates the gradients after each optimizer step. This single-precision copy is rounded to half-precision format during training. Secondly, we propose scaling the loss appropriately to handle the loss of information with half-precision gradients. We demonstrate that this approach works for a wide variety of models including convolution neural networks, recurrent neural networks and generative adversarial networks. This technique works for large scale models with more than 100 million parameters trained on large datasets. Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x. In future processors, we can also expect a significant computation speedup using half-precision hardware units.

計算速度の向上ために半精度(FP16)、学習精度維持のために単精度(FP32)をうまく組み合わせて学習する手法の提案。具体的には、重みはFP32で管理するが(勾配の値は小さいため、FP16だと消えてしまう可能性がある)、FP32をFP16に圧縮し、Forward/Backwardを行う。この時、lossの値が小さくFP16では消えてしまうことが多いため、スケーリングをした上でBackwardし、あとで戻す処理を行っている。


Music is an expressive form of communication often used to convey emotion in scenarios where "words are not enough". Part of this information lies in the musical composition where well-defined language exists. However, a significant amount of information is added during a performance as the musician interprets the composition. The performer injects expressiveness into the written score through variations of different musical properties such as dynamics and tempo. In this paper, we describe a model that can learn to perform sheet music. Our research concludes that the generated performances are indistinguishable from a human performance, thereby passing a test in the spirit of a "musical Turing test".

音楽のスタイル変換を試みた研究。Encoder-Decoderが基本モデルで、Encoderは音楽のジャンルによらない表現を学習し、Decoderは各ジャンルごとの音楽の生成を学習させるというスタイル。


Learning by contrasting positive and negative samples is a general strategy adopted by many methods. Noise contrastive estimation (NCE) for word embeddings and translating embeddings for knowledge graphs are examples in NLP employing this approach. In this work, we view contrastive learning as an abstraction of all such methods and augment the negative sampler into a mixture distribution containing an adversarially learned sampler. The resulting adaptive sampler finds harder negative examples, which forces the main model to learn a better representation of the data. We evaluate our proposal on learning word embeddings, order embeddings and knowledge graph embeddings and observe both faster convergence and improved results on multiple metrics.

単語埋め込みなどのパラメータ推定に用いられるNoise Contrastive Estimationの負例サンプリングにGANの仕組みを取り入れた。Conditional GANの一種と見ることができる。実験によってNCEと比較して早く収束することと応用タスクでの複数のメトリックが改善することが確認された。


We study how to effectively leverage expert feedback to learn sequential decision-making policies. We focus on problems with sparse rewards and long time horizons, which typically pose significant challenges in reinforcement learning. We propose an algorithmic framework, called hierarchical guidance, that leverages the hierarchical structure of the underlying problem to integrate different modes of expert interaction. Our framework can incorporate different combinations of imitation learning (IL) and reinforcement learning (RL) at different levels, leading to dramatic reductions in both expert effort and cost of exploration. Using long-horizon benchmarks, including Montezuma's Revenge, we demonstrate that our approach can learn significantly faster than hierarchical RL, and be significantly more label-efficient than standard IL. We also theoretically analyze labeling cost for certain instantiations of our framework.

模倣学習を行う際に、タスクを階層的に考える手法の提案。例えば「学校まで行く」というのが高次の行動で「右に一歩進む」が低次の行動(通常のアクション)になる。各ゴールへの到達が失敗した場合のみエキスパートの行動を取り入れることで、模倣対象であるエキスパートの負担を下げることができる。


Scene representation—the process of converting visual sensory data into concise descriptions—is a requirement for intelligent behavior. Recent work has shown that neural networks excel at this task when provided with large, labeled datasets. However, removing the reliance on human labeling remains an important open problem. To this end, we introduce the Generative Query Network (GQN), a framework within which machines learn to represent scenes using only their own sensors. The GQN takes as input images of a scene taken from different viewpoints, constructs an internal representation, and uses this representation to predict the appearance of that scene from previously unobserved viewpoints. The GQN demonstrates representation learning without human labels or domain knowledge, paving the way toward machines that autonomously learn to understand the world around them.

シーンを認識で、A地点/B地点からの画像から、C地点からの画像を推定するというタスクを解かせた研究。各地点からの画像をネットワークで潜在表現に圧縮し、クエリ(C地点)と潜在表現から画像を生成する。これにより2Dから3Dへのレンダリングや、強化学習での学習効率の向上が行えたとのこと。


Semantic segmentation benefits robotics related applications especially autonomous driving. Most of the research on semantic segmentation is only on increasing the accuracy of segmentation models with little attention to computationally efficient solutions. The few work conducted in this direction does not provide principled methods to evaluate the different design choices for segmentation. In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods. The framework is comprised of different network architectures for feature extraction such as VGG16, Resnet18, MobileNet, and ShuffleNet. It is also comprised of multiple meta-architectures for segmentation that define the decoding methodology. These include SkipNet, UNet, and Dilation Frontend. Experimental results are presented on the Cityscapes dataset for urban scenes. The modular design allows novel architectures to emerge, that lead to 143x GFLOPs reduction in comparison to SegNet. This benchmarking framework is publicly available at "this https URL".

セグメンテーションについては精度が注目されがちだが、速度の方に注目して各手法の比較を行ったサーベイ


Many real-world vision problems suffer from inherent ambiguities. In clinical applications for example, it might not be clear from a CT scan alone which particular region is cancer tissue. Therefore a group of graders typically produces a set of diverse but plausible segmentations. We consider the task of learning a distribution over segmentations given an input. To this end we propose a generative segmentation model based on a combination of a U-Net with a conditional variational autoencoder that is capable of efficiently producing an unlimited number of plausible hypotheses. We show on a lung abnormalities segmentation task and on a Cityscapes segmentation task that our model reproduces the possible segmentation variants as well as the frequencies with which they occur, doing so significantly better than published approaches. These models could have a high impact in real-world applications, such as being used as clinical decision-making algorithms accounting for multiple plausible semantic segmentation hypotheses to provide possible diagnoses and recommend further actions to resolve the present ambiguities.

画像のセグメンテーションでは「このピクセルはxx」と決定的なのが一般的だが、確率的な場合もある(医療画像における腫瘍かも？など)。そこでVAEの仕組みを用い、U-Netで特徴抽出＋潜在分布からのサンプリング結果で領域の推定を行う。潜在分布を入力/実領域から作成した事後分布とのKL距離で学習する


Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations, they are often susceptible to overfitting and therefore require proper regularization in order to generalize well. In this paper, we show that the simple regularization technique of randomly masking out square regions of input during training, which we call cutout, can be used to improve the robustness and overall performance of convolutional neural networks. Not only is this method extremely easy to implement, but we also demonstrate that it can be used in conjunction with existing forms of data augmentation and other regularizers to further improve model performance. We evaluate this method by applying it to current state-of-the-art architectures on the CIFAR-10, CIFAR-100, and SVHN datasets, yielding new state-of-the-art results of 2.56%, 15.20%, and 1.30% test error respectively. Code is available at this https URL

画像認識におけるData Augmentationの手法で、画像の一部(矩形領域)をマスクする(=Cutout)というとても簡単な方法(を既存のAugmentationに組み込むこと)でCIFAR10・100、SVHNでSOTAを更新したという話。組み合わせているのはzero-pad/random crop/水平のフリップ+今回のCutouのみ。


Deep learning for predicting or generating 3D human pose sequences is an active research area. Previous work regresses either joint rotations or joint positions. The former strategy is prone to error accumulation along the kinematic chain, as well as discontinuities when using Euler angle or exponential map parameterizations. The latter requires re-projection onto skeleton constraints to avoid bone stretching and invalid configurations. This work addresses both limitations. Our recurrent network, QuaterNet, represents rotations with quaternions and our loss function performs forward kinematics on a skeleton to penalize absolute position errors instead of angle errors. On short-term predictions, QuaterNet improves the state-of-the-art quantitatively. For long-term generation, our approach is qualitatively judged as realistic as recent neural strategies from the graphics literature.

RNNを使用して3次元のボーンの動きを学習させる研究。動きの角度表現(腕を30度傾けるなど)には3次元のオイラー角を使用するのが一般的だが、オイラー角は一周(2π)すると値が元にもどるため不連続であるという扱いにくさがあった。そこで4次元で3次元空間の回転を表現するQuaternionを使用している。


We propose a framework for solving combinatorial optimization problems of which the output can be represented as a sequence of input elements. As an alternative to the Pointer Network, we parameterize a policy by a model based entirely on (graph) attention layers, and train it efficiently using REINFORCE with a simple and robust baseline based on a deterministic (greedy) rollout of the best policy found during training. We significantly improve over state-of-the-art results for learning algorithms for the 2D Euclidean TSP, reducing the optimality gap for a single tour construction by more than 75% (to 0.33%) and 50% (to 2.28%) for instances with 20 and 50 nodes respectively.

巡回セールスマン問題をAttention+強化学習で解く手法。Encoderは周辺ノードの情報を畳み込み、DecoderはEncoder出力/前回ノード/初期ノードの3つを元に次のノードを選択する(Policyとして動作する)。Encoderで畳み込む際に、TransformerベースのAttentionを使用する。学習はPolicy Gradientの形。


Currently available methods for extracting saliency maps identify parts of the input which are the most important to a specific fixed classifier. We show that this strong dependence on a given classifier hinders their performance. To address this problem, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance. We observe that the proposed approach extracts higher quality saliency maps than prior work while being conceptually simple and easy to implement. The method sets the new state of the art result for localization task on the ImageNet data, outperforming all existing weakly-supervised localization techniques, despite not using the ground truth labels at the inference time. The code reproducing the results is available at this https URL .

通常のSaliency Map(モデルの着目点を示したマップ)は分類機とペアで作られるが、それはその分類機にとってのマップでしかない、という問題提起から分類機独立のマップを作る試み。ストレートに複数の分類機共通のマップを作るのは現実的でないため、学習過程の分類機を使用しモデルと同時に学習させる


Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds of millions of users remains a challenge. Here we describe a large-scale deep recommendation engine that we developed and deployed at Pinterest. We develop a data-efficient Graph Convolutional Network (GCN) algorithm PinSage, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Compared to prior GCN approaches, we develop a novel method based on highly efficient random walks to structure the convolutions and design a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model. We also develop an efficient MapReduce model inference algorithm to generate embeddings using a trained model. We deploy PinSage at Pinterest and train it on 7.5 billion examples on a graph with 3 billion nodes representing pins and boards, and 18 billion edges. According to offline metrics, user studies and A/B tests, PinSage generates higher-quality recommendations than comparable deep learning and graph-based alternatives. To our knowledge, this is the largest application of deep graph embeddings to date and paves the way for a new generation of web-scale recommender systems based on graph convolutional architectures.

Graph Convolutionをプロダクションレベル(30億ノード！)で使用したという金字塔的な論文(Pinterestで使われている)。グラフサイズが膨大なので近傍ノードをサンプリング(ランダムウォーク)で収集しており、収集はCPU・畳み込みはGPUで役割分担しMapReduceで分散処理して高速化している。


Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.

事前学習した言語モデルを使用し代名詞の解決問題に答える研究。パソコンを鞄に入れようとしたが「それ」が大きすぎて入らなかった、という時「それ」がパソコンか鞄かを回答する形で、言語モデルを使用し「それ」を回答候補に置き換えた場合の文全体/置き換えた以後の単語の出現確率の変動を見る