mtybilly/Instruct2DS huggingface dataset:
MacNet: Transferring Knowledge from Machine Comprehension to Sequence-to-Sequence Models
Authors: Yazheng Yang, Hao Li, Zhou Zhao, Yueting Zhuang, Deng Cai, Xiaofei He Boyuan Pan
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Machine Comprehension (MC) is one of the core problems in natural language processing, requiring both understanding of the natural language and knowledge about the world. Rapid progress has been made since the release of several benchmark datasets, and recently the state-of-the-art models even surpass human performance on the well-known SQuAD evaluation. In this paper, we transfer knowledge learned from machine comprehension to the sequence-to-sequence tasks to deepen the understanding of the text. We propose MacNet: a novel encoder-decoder supplementary architecture to the widely used attention-based sequence-to-sequence models. Experiments on neural machine translation (NMT) and abstractive text summarization show that our proposed framework can significantly improve the performance of the baseline models, and our method for the abstractive text summarization achieves the state-of-the-art results on the Gigaword dataset.
Multiplicative Weights Updates with Constant Step-Size in Graphical Constant-Sum Games
Authors: Yun Kuen Cheung
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Since Multiplicative Weights (MW) updates are the discrete analogue of the continuous Replicator Dynamics (RD), some researchers had expected their qualitative behaviours would be similar. We show that this is false in the context of graphical constant-sum games, which include two-person zero-sum games as special cases. In such games which have a fully-mixed Nash Equilibrium (NE), it was known that RD satisfy the permanence and Poincare recurrence properties, but we show that MW updates with any constant step-size eps > 0 converge to the boundary of the state space, and thus do not satisfy the two properties. Using this result, we show that MW updates have a regret lower bound of Omega( 1 / (eps T) ), while it was known that the regret of RD is upper bounded by O( 1 / T ).
Interestingly, the regret perspective can be useful for better understanding of the behaviours of MW updates. In a two-person zero-sum game, if it has a unique NE which is fully mixed, then we show, via regret, that for any sufficiently small eps, there exist at least two probability densities and a constant Z > 0, such that for any arbitrarily small z > 0, each of the two densities fluctuates above Z and below z infinitely often.
Efficient Online Portfolio with Logarithmic Regret
Authors: Chen-Yu Wei, Kai Zheng Haipeng Luo
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We study the decades-old problem of online portfolio management and propose the first algorithm with logarithmic regret that is not based on Cover's Universal Portfolio algorithm and admits much faster implementation. Specifically Universal Portfolio enjoys optimal regret $\mathcal{O}(N\ln T)$ for $N$ financial instruments over $T$ rounds, but requires log-concave sampling and has a large polynomial running time. Our algorithm, on the other hand, ensures a slightly larger but still logarithmic regret of $\mathcal{O}(N^2(\ln T)^4)$, and is based on the well-studied Online Mirror Descent framework with a novel regularizer that can be implemented via standard optimization methods in time $\mathcal{O}(TN^{2.5})$ per round. The regret of all other existing works is either polynomial in $T$ or has a potentially unbounded factor such as the inverse of the smallest price relative.
Banach Wasserstein GAN
Authors: Sebastian Lunz Jonas Adler
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Wasserstein Generative Adversarial Networks (WGANs) can be used to generate realistic samples from complicated image distributions. The Wasserstein metric used in WGANs is based on a notion of distance between individual images, which  induces a notion of distance between probability distributions of images. So far the community has considered $\ell^2$ as the underlying distance. We generalize the theory of WGAN with gradient penalty to Banach spaces, allowing practitioners to select the features to emphasize in the generator. We further discuss the effect of some particular choices of underlying norms, focusing on Sobolev norms. Finally, we demonstrate a boost in performance for an appropriate choice of norm on CIFAR-10 and CelebA.
SplineNets: Continuous Neural Decision Graphs
Authors: Shahram Izadi Cem Keskin
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We present SplineNets, a practical and novel approach for using conditioning in convolutional neural networks (CNNs). SplineNets are continuous generalizations of neural decision graphs, and they can dramatically reduce runtime complexity and computation costs of CNNs, while maintaining or even increasing accuracy. Functions of SplineNets are both dynamic (i.e., conditioned on the input) and hierarchical (i.e.,conditioned on the computational path). SplineNets employ a unified loss function with a desired level of smoothness over both the network and decision parameters, while allowing for sparse activation of a subset of nodes for individual samples. In particular, we embed infinitely many function weights (e.g. filters) on smooth, low dimensional manifolds parameterized by compact B-splines, which are indexed by a position parameter. Instead of sampling from a categorical distribution to pick a branch, samples choose a continuous position to pick a function weight. We further show that by maximizing the mutual information between spline positions and class labels, the network can be optimally utilized and specialized for classification tasks. Experiments show that our approach can significantly increase the accuracy of ResNets with negligible cost in speed, matching the precision of a 110 level ResNet with a 32 level SplineNet.
Post: Device Placement with Cross-Entropy Minimization and Proximal Policy Optimization
Authors: Li Chen, Baochun Li Yuanxiang Gao
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Training deep neural networks requires an exorbitant amount of computation resources, including a heterogeneous mix of GPU and CPU devices. It is critical to place operations in a neural network on these devices in an optimal way, so that the training process can complete within the shortest amount of time. The state-of-the-art uses reinforcement learning to learn placement skills by repeatedly performing Monte-Carlo experiments. However, due to its equal treatment of placement samples, we argue that there remains ample room for significant improvements. In this paper, we propose a new joint learning algorithm, called Post, that integrates cross-entropy minimization and proximal policy optimization to achieve theoretically guaranteed optimal efficiency. In order to incorporate the cross-entropy method as a sampling technique, we propose to represent placements using discrete probability distributions, which allows us to estimate an optimal probability mass by maximal likelihood estimation, a powerful tool with the best possible efficiency. We have implemented Post in the Google Cloud platform, and our extensive experiments with several popular neural network training benchmarks have demonstrated clear evidence of superior performance: with the same amount of learning time, it leads to placements that have training times up to 63.7% shorter over the state-of-the-art.
Implicit Reparameterization Gradients
Authors: Shakir Mohamed, Andriy Mnih Mikhail Figurnov
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for training a variety of latent variable models. However, it is not applicable to a number of important continuous distributions.  We introduce an alternative approach to computing reparameterization gradients based on implicit differentiation and demonstrate its broader applicability by applying it to Gamma, Beta, Dirichlet, and von Mises distributions, which cannot be used with the classic reparameterization trick. Our experiments show that the proposed approach is faster and more accurate than the existing gradient estimators for these distributions.
Visual Object Networks: Image Generation with Disentangled 3D Representations
Authors: Zhoutong Zhang, Chengkai Zhang, Jiajun Wu, Antonio Torralba, Josh Tenenbaum, Bill Freeman Jun-Yan Zhu
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Recent progress in deep generative models has led to tremendous breakthroughs in image generation. While being able to synthesize photorealistic images, existing models lack an understanding of our underlying 3D world. Different from previous works built on 2D datasets and models, we present a new generative model, Visual Object Networks (VONs), synthesizing natural images of objects with a disentangled 3D representation. Inspired by classic graphics rendering pipelines, we unravel the image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3D shape and 2D texture. Our model first learns to synthesize 3D shapes that are indistinguishable from real shapes. It then renders the object's 2.5D sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. Finally, it learns to add realistic textures to these 2.5D sketches to generate realistic images. The VON not only generates images that are more realistic than the state-of-the-art 2D image synthesis methods but also enables many 3D operations such as changing the viewpoint of a generated image,  shape and texture editing, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.
Neural Architecture Optimization
Authors: Fei Tian, Tao Qin, Enhong Chen, Tie-Yan Liu Renqian Luo
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Automatic neural architecture design has shown its potential in discovering powerful neural network architectures. Existing methods, no matter based on reinforcement learning or evolutionary algorithms (EA), conduct architecture search in a discrete space, which is highly inefficient. In this paper, we propose a simple and efficient method to automatic neural architecture design based on continuous optimization. We call this new approach neural architecture optimization (NAO). There are three key components in our proposed approach: (1) An encoder embeds/maps neural network architectures into a continuous space. (2) A predictor takes the continuous representation of a network as input and predicts its accuracy. (3) A decoder maps a continuous representation of a network back to its architecture. The performance predictor and the encoder enable us to perform gradient based optimization in the continuous space to find the embedding of a new architecture with potentially better accuracy. Such a better embedding is then decoded to a network by the decoder. Experiments show that the architecture discovered by our method is very competitive for image classification task on CIFAR-10 and language modeling task on PTB, outperforming or on par with the best results of previous architecture search methods with a significantly reduction of computational resources. Specifically we obtain $2.11\%$ test set error rate for CIFAR-10 image classification task and $56.0$ test set perplexity of PTB language modeling task. The best discovered architectures on both tasks are successfully transferred to other tasks such as CIFAR-100 and WikiText-2. Furthermore, combined with the recent proposed weight sharing mechanism, we discover powerful architecture on CIFAR-10 (with error rate $3.53\%$) and on PTB (with test set perplexity $56.6$), with very limited computational resources (less than $10$ GPU hours) for both tasks.
MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare
Authors: Cao Xiao, Walter Stewart, Jimeng Sun Edward Choi
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Deep learning models exhibit state-of-the-art performance for many predictive healthcare tasks using electronic health records (EHR) data, but these models typically require training data volume that exceeds the capacity of most healthcare systems.
External resources such as medical ontologies are used to bridge the data volume constraint, but this approach is often not directly applicable or useful because of inconsistencies with terminology.
To solve the data insufficiency challenge, we leverage the inherent multilevel structure of EHR data and, in particular, the encoded relationships among medical codes.
We propose Multilevel Medical Embedding (MiME) which learns the multilevel embedding of EHR data while jointly performing auxiliary prediction tasks that rely on this inherent EHR structure without the need for external labels. 
We conducted two prediction tasks, heart failure prediction and sequential disease prediction, where MiME outperformed baseline methods in diverse evaluation settings.
In particular, MiME consistently outperformed all baselines when predicting heart failure on datasets of different volumes, especially demonstrating the greatest performance improvement (15% relative gain in PR-AUC over the best baseline) on the smallest dataset, demonstrating its ability to effectively model the multilevel structure of EHR data.
Learning Optimal Reserve Price against Non-myopic Bidders
Authors: Zhiyi Huang, Xiangning Wang Jinyan Liu
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bid strategically in order to gain in future rounds even if the single-round auctions are truthful. Previous algorithms, e.g., empirical pricing, do not provide non-trivial regret rounds in this setting in general. We introduce algorithms that obtain small regret against non-myopic bidders either when the market is large, i.e., no bidder appears in a constant fraction of the rounds, or when the bidders are impatient, i.e., they discount future utility by some factor mildly bounded away from one. Our approach carefully controls what information is revealed to each bidder, and builds on techniques from differentially private online learning as well as the recent line of works on jointly differentially private algorithms.
A Bayesian Approach to Generative Adversarial Imitation Learning
Authors: Seokin Seo, Kee-Eung Kim Wonseok Jeon
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Generative adversarial training for imitation learning has shown promising results on high-dimensional and continuous control tasks. This paradigm is based on reducing the imitation learning problem to the density matching problem, where the agent iteratively refines the policy to match the empirical state-action visitation frequency of the expert demonstration. Although this approach has shown to robustly learn to imitate even with scarce demonstration, one must still address the inherent challenge that collecting trajectory samples in each iteration is a costly operation. To address this issue, we first propose a Bayesian formulation of generative adversarial imitation learning (GAIL), where the imitation policy and the cost function are represented as stochastic neural networks. Then, we show that we can significantly enhance the sample efficiency of GAIL leveraging the predictive density of the cost, on an extensive set of imitation learning tasks with high-dimensional states and actions.
Credit Assignment For Collective Multiagent RL With Global Rewards
Authors: Akshat Kumar, Hoong Chuin Lau Duc Thien Nguyen
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Scaling decision theoretic planning to large multiagent systems is challenging due to uncertainty and partial observability in the environment. We focus on a multiagent planning model subclass, relevant to urban settings, where agent interactions are dependent on their ``collective influence'' on each other, rather than their identities. Unlike previous work, we address a general setting where system reward is not decomposable among agents. We develop collective actor-critic RL approaches for this setting, and address the problem of multiagent credit assignment, and computing low variance policy gradient estimates that result in faster convergence to high quality solutions. We also develop difference rewards based credit assignment methods for the collective setting. Empirically our new approaches provide significantly better solutions than previous methods in the presence of global rewards on two real world problems modeling taxi fleet optimization and multiagent patrolling, and a synthetic grid navigation domain.
Knowledge Distillation by On-the-Fly Native Ensemble
Authors: Xiatian Zhu, Shaogang Gong xu lan
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Knowledge distillation is effective to train the small and generalisable network models for meeting the low-memory and fast running requirements. Existing offline distillation methods rely on a strong pre-trained teacher, which enables favourable knowledge discovery and transfer but requires a complex two-phase training procedure. Online counterparts address this limitation at the price of lacking a high-capacity teacher. In this work, we present an On-the-fly Native Ensemble (ONE) learning strategy for one-stage online distillation. Specifically, ONE only trains a single multi-branch network while simultaneously establishing a strong teacher on-the-fly to enhance the learning of target network. Extensive evaluations show that ONE improves the generalisation performance of a variety of deep neural networks more significantly than alternative methods on four image classification dataset: CIFAR10, CIFAR100, SVHN, and ImageNet, whilst having the computational efficiency advantages.
Flexible and accurate inference and learning for deep generative models
Authors: Maneesh Sahani Eszter Vértes
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We introduce a new approach to learning in hierarchical latent-variable generative
models called the “distributed distributional code Helmholtz machine”, which
emphasises flexibility and accuracy in the inferential process. Like the original
Helmholtz machine and later variational autoencoder algorithms (but unlike adver-
sarial methods) our approach learns an explicit inference or “recognition” model
to approximate the posterior distribution over the latent variables. Unlike these
earlier methods, it employs a posterior representation that is not limited to a narrow
tractable parametrised form (nor is it represented by samples). To train the genera-
tive and recognition models we develop an extended wake-sleep algorithm inspired
by the original Helmholtz machine. This makes it possible to learn hierarchical
latent models with both discrete and continuous variables, where an accurate poste-
rior representation is essential. We demonstrate that the new algorithm outperforms
current state-of-the-art methods on synthetic, natural image patch and the MNIST
data sets.
A loss framework for calibrated anomaly detection
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Given samples from a probability distribution, anomaly detection is the problem of determining if a given point lies in a low-density region. This paper concerns calibrated anomaly detection, which is the practically relevant extension where we additionally wish to produce a confidence score for a point being anomalous. Building on a classification framework for anomaly detection, we show how minimisation of a suitably modified proper loss produces density estimates only for anomalous instances. We then show how to incorporate quantile control by relating our objective to a generalised version of the pinball loss. Finally, we show how to efficiently optimise the objective with kernelised scorer, by leveraging a recent result from the point process literature. The resulting objective captures a close relative of the one-class SVM as a special case.
Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence Diagrams
Authors: Makoto Yamada Tam Le
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Algebraic topology methods have recently played an important role for statistical analysis with complicated geometric structured data such as shapes, linked twist maps, and material data. Among them, \textit{persistent homology} is a well-known tool to extract robust topological features, and outputs as \textit{persistence diagrams} (PDs). However, PDs are point multi-sets which can not be used in machine learning algorithms for vector data. To deal with it, an emerged approach is to use kernel methods, and an appropriate geometry for PDs is an important factor to measure the similarity of PDs. A popular geometry for PDs is the \textit{Wasserstein metric}. However, Wasserstein distance is not \textit{negative definite}. Thus, it is limited to build positive definite kernels upon the Wasserstein distance \textit{without approximation}. In this work, we rely upon the alternative \textit{Fisher information geometry} to propose a positive definite kernel for PDs \textit{without approximation}, namely the Persistence Fisher (PF) kernel. Then, we analyze eigensystem of the integral operator induced by the proposed kernel for kernel machines. Based on that, we derive generalization error bounds via covering numbers and Rademacher averages for kernel machines with the PF kernel. Additionally, we show some nice properties such as stability and infinite divisibility for the proposed kernel. Furthermore, we also propose a linear time complexity over the number of points in PDs for an approximation of our proposed kernel with a bounded error. Throughout experiments with many different tasks on various benchmark datasets, we illustrate that the PF kernel compares favorably with other baseline kernels for PDs.
Constructing Deep Neural Networks by Bayesian Network Structure Learning
Authors: Shami Nisimov, Yaniv Gurwicz, Guy Koren, Gal Novik Raanan Y. Rohekar
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We introduce a principled approach for unsupervised structure learning of deep neural networks. We propose a new interpretation for depth and inter-layer connectivity where conditional independencies in the input distribution are encoded hierarchically in the network structure. Thus, the depth of the network is determined inherently. The proposed method casts the problem of neural network structure learning as a problem of Bayesian network structure learning. Then, instead of directly learning the discriminative structure, it learns a generative graph, constructs its stochastic inverse, and then constructs a discriminative graph. We prove that conditional-dependency relations among the latent variables in the generative graph are preserved in the class-conditional discriminative graph. We demonstrate on image classification benchmarks that the deepest layers (convolutional and dense) of common networks can be replaced by significantly smaller learned structures, while maintaining classification accuracy---state-of-the-art on tested benchmarks. Our structure learning algorithm requires a small computational cost and runs efficiently on a standard desktop CPU.
Training Deep Models Faster with Robust, Approximate Importance Sampling
Authors: Carlos Guestrin Tyler B Johnson
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: In theory, importance sampling speeds up stochastic gradient algorithms for supervised learning by prioritizing training examples. In practice, the cost of computing importances greatly limits the impact of importance sampling. We propose a robust, approximate importance sampling procedure (RAIS) for stochastic gradient de- scent. By approximating the ideal sampling distribution using robust optimization, RAIS provides much of the benefit of exact importance sampling with drastically reduced overhead. Empirically, we find RAIS-SGD and standard SGD follow similar learning curves, but RAIS moves faster through these paths, achieving speed-ups of at least 20% and sometimes much more.
Learning Beam Search Policies via Imitation Learning
Authors: Matthew Gormley, Geoffrey J. Gordon Renato Negrinho
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do not explicitly learn how to use the beam. We develop an unifying meta-algorithm for learning beam search policies using imitation learning. In our setting, the beam is part of the model and not just an artifact of approximate decoding. Our meta-algorithm captures existing learning algorithms and suggests new ones. It also lets us show novel no-regret guarantees for learning beam search policies.
Multivariate Time Series Imputation with Generative Adversarial Networks
Authors: Xiangrui Cai, Ying ZHANG, Jun Xu, Yuan xiaojie Yonghong Luo
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Multivariate time series usually contain a large number of missing values, which hinders the application of advanced analysis methods on multivariate time series data. Conventional approaches to addressing the challenge of missing values, including mean/zero imputation, case deletion, and matrix factorization-based imputation, are all incapable of modeling the temporal dependencies and the nature of complex distribution in multivariate time series. In this paper, we treat the problem of missing value imputation as data generation.  Inspired by the success of Generative Adversarial Networks (GAN) in image generation, we propose to learn the overall distribution of a multivariate time series dataset with GAN, which is further used to generate the missing values for each sample. Different from the image data, the time series data are usually incomplete due to the nature of data recording process. A modified Gate Recurrent Unit is employed in GAN to model the temporal irregularity of the incomplete time series. Experiments on two multivariate time series datasets show that the proposed model outperformed the baselines in terms of accuracy of imputation. Experimental results also showed that a simple model on the imputed data can achieve state-of-the-art results on the prediction tasks, demonstrating the benefits of our model in downstream applications.
An Efficient Pruning Algorithm for Robust Isotonic Regression
Authors: Cong Han Lim
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We study a generalization of the classic isotonic regression problem  where we allow separable nonconvex objective functions, focusing on the case of estimators used in robust regression. A simple dynamic programming approach allows us to solve this problem to within ε-accuracy (of the global minimum) in time linear in 1/ε and the dimension. We can combine techniques from the convex case with branch-and-bound ideas to form a new algorithm for this problem that naturally exploits the shape of the objective function. Our algorithm achieves the best bounds for both the general nonconvex and convex case (linear in log (1/ε)), while performing much faster in practice than a straightforward dynamic programming approach, especially as the desired accuracy increases.
Bilinear Attention Networks
Authors: Jaehyun Jun, Byoung-Tak Zhang Jin-Hwa Kim
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Attention networks in multimodal learning provide an efficient way to utilize given visual information selectively.  However, the computational cost to learn attention distributions for every pair of multimodal input channels is prohibitively expensive. To solve this problem, co-attention builds two separate attention distributions for each modality neglecting the interaction between multimodal inputs. In this paper, we propose bilinear attention networks (BAN) that find bilinear attention distributions to utilize given vision-language information seamlessly. BAN considers bilinear interactions among two groups of input channels, while low-rank bilinear pooling extracts the joint representations for each pair of channels. Furthermore, we propose a variant of multimodal residual networks to exploit eight-attention maps of the BAN efficiently. We quantitatively and qualitatively evaluate our model on visual question answering (VQA 2.0) and Flickr30k Entities datasets, showing that BAN significantly outperforms previous methods and achieves new state-of-the-arts on both datasets.
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
Authors: Jameson Weng, Pascal Poupart Vikash Goel
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We present a new technique for deep reinforcement learning that automatically detects moving objects and uses the relevant information for action selection. The detection of moving objects is done in an unsupervised way by exploiting structure from motion. Instead of directly learning a policy from raw images, the agent first learns to detect and segment moving objects by exploiting flow information in video sequences. The learned representation is then used to focus the policy of the agent on the moving objects. Over time, the agent identifies which objects are critical for decision making and gradually builds a policy based on relevant moving objects. This approach, which we call Motion-Oriented REinforcement Learning (MOREL), is demonstrated on a suite of Atari games where the ability to detect moving objects reduces the amount of interaction needed with the environment to obtain a good policy. Furthermore, the resulting policy is more interpretable than policies that directly map images to actions or values with a black box neural network. We can gain insight into the policy by inspecting the segmentation and motion of each object detected by the agent. This allows practitioners to confirm whether a policy is making decisions based on sensible information. Our code is available at https://github.com/vik-goel/MOREL.
Constructing Fast Network through Deconstruction of Convolution
Authors: Junmo Kim Yunho Jeon
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Convolutional neural networks have achieved great success in various vision tasks; however, they incur heavy resource costs. By using deeper and wider networks, network accuracy can be improved rapidly. However, in an environment with limited resources (e.g., mobile applications), heavy networks may not be usable. This study shows that naive convolution can be deconstructed into a shift operation and pointwise convolution. To cope with various convolutions, we propose a new shift operation called active shift layer (ASL) that formulates the amount of shift as a learnable function with shift parameters. This new layer can be optimized end-to-end through backpropagation and it can provide optimal shift values. Finally, we apply this layer to a light and fast network that surpasses existing state-of-the-art networks.
Improving Simple Models with Confidence Profiles
Authors: Karthikeyan Shanmugam, Ronny Luss, Peder A. Olsen Amit Dhurandhar
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: In this paper, we propose a new method called ProfWeight for transferring information from a pre-trained deep neural network that has a high test accuracy to a simpler interpretable model or a very shallow network of low complexity and a priori low test accuracy. We are motivated by applications in interpretability and model deployment in severely memory constrained environments (like sensors). Our method uses linear probes to generate confidence scores through flattened intermediate representations. Our transfer method involves a theoretically justified weighting of samples during the training of the simple model using  confidence scores of these intermediate layers. The value of our method is first demonstrated on CIFAR-10, where our weighting method significantly  improves (3-4\%) networks with only a fraction of the number of Resnet blocks of a complex Resnet model. We further demonstrate operationally significant results on a real manufacturing problem, where we dramatically increase the test accuracy of a CART model (the domain standard) by roughly $13\%$.
Turbo Learning for CaptionBot and DrawingBot
Authors: Pengchuan Zhang, Dapeng Wu, Lei Zhang Qiuyuan Huang
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We study in this paper the problems of both image captioning and
text-to-image generation, and present a novel turbo learning
approach to jointly training an image-to-text generator (a.k.a.
CaptionBot) and a text-to-image generator (a.k.a. DrawingBot). The
key idea behind the joint training is that image-to-text
generation and text-to-image generation as dual problems can form
a closed loop to provide informative feedback to each other. Based
on such feedback, we introduce a new loss metric by comparing the
original input with the output produced by the closed loop. In
addition to the old loss metrics used in CaptionBot and
DrawingBot, this extra loss metric makes the jointly trained
CaptionBot and DrawingBot better than the separately trained
CaptionBot and DrawingBot. Furthermore, the turbo-learning
approach enables semi-supervised learning since the closed loop
can provide peudo-labels for unlabeled samples. Experimental
results on the COCO dataset demonstrate that the proposed turbo
learning can significantly improve the performance of both
CaptionBot and DrawingBot by a large margin.
Online Reciprocal Recommendation with Theoretical Performance Guarantees
Authors: Nikos Parotsidis, Claudio Gentile Fabio Vitale
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply different from the more traditional items-to-users recommendation, since a good match requires meeting the preferences of both users. We initiate a rigorous theoretical investigation of the reciprocal recommendation task in a specific framework of sequential learning. We point out general limitations, formulate reasonable assumptions enabling effective learning and, under these assumptions, we design and analyze a computationally efficient algorithm that uncovers mutual likes at a pace comparable to those achieved by a clairvoyant algorithm knowing all user preferences in advance. Finally, we validate our algorithm against synthetic and real-world datasets, showing improved empirical performance over simple baselines.
Learning semantic similarity in a continuous space
Authors: Michel Deudon
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We address the problem of learning semantic representation of questions to measure similarity between pairs as a continuous distance metric. Our work naturally extends Word Mover’s Distance (WMD) [1] by representing text documents as normal distributions instead of bags of embedded words. Our learned metric measures the dissimilarity between two questions as the minimum amount of distance the intent (hidden representation) of one question needs to "travel" to match the intent of another question. We first learn to repeat, reformulate questions to infer intents as normal distributions with a deep generative model [2] (variational auto encoder). Semantic similarity between pairs is then learned discriminatively as an optimal transport distance metric (Wasserstein 2) with our novel variational siamese framework. Among known models that can read sentences individually, our proposed framework achieves competitive results on Quora duplicate questions dataset. Our work sheds light on how deep generative models can approximate distributions (semantic representations) to effectively measure semantic similarity with meaningful distance metrics from Information Theory.
Representation Balancing MDPs for Off-policy Policy Evaluation
Authors: Omer Gottesman, Aniruddh Raghu, Matthieu Komorowski, Aldo A Faisal, Finale Doshi-Velez, Emma Brunskill Yao Liu
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We study the problem of off-policy policy evaluation (OPPE) in RL. In contrast to prior work, we consider how to estimate both the individual policy value and average policy value accurately. We draw inspiration from recent work in causal reasoning, and propose a new finite sample generalization error bound for value estimates from MDP models. Using this upper bound as an objective, we develop a learning algorithm of an MDP model with a balanced representation, and show that our approach can yield substantially lower MSE in common synthetic benchmarks and a HIV treatment simulation domain.
See and Think: Disentangling Semantic Scene Completion
Authors: YU HU, Yiming Zeng, Qiankun Tang, Beibei Jin, Yinhe Han, Xiaowei Li Shice Liu
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Semantic scene completion predicts volumetric occupancy and object category of a 3D scene, which helps intelligent agents to understand and interact with the surroundings. In this work, we propose a disentangled framework, sequentially carrying out 2D semantic segmentation, 2D-3D reprojection and 3D semantic scene completion. This three-stage framework has three advantages: (1) explicit semantic segmentation significantly boosts performance; (2) flexible fusion ways of sensor data bring good extensibility; (3) progress in any subtask will promote the holistic performance. Experimental results show that regardless of inputing a single depth or RGB-D, our framework can generate high-quality semantic scene completion, and outperforms state-of-the-art approaches on both synthetic and real datasets.
L4: Practical loss-based stepsize adaptation for deep learning
Authors: Georg Martius Michal Rolinek
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: We propose a stepsize adaptation scheme for stochastic gradient descent.
It operates directly with the loss function and rescales the gradient in order to make fixed predicted progress on the loss.
We demonstrate its capabilities by conclusively improving the performance of Adam and Momentum optimizers.
The enhanced optimizers with default hyperparameters
 consistently outperform their constant stepsize counterparts, even the best ones,
 without a measurable increase in computational cost.
The performance is validated on multiple architectures including dense nets, CNNs, ResNets, and the recurrent Differential Neural Computer on classical datasets MNIST, fashion MNIST, CIFAR10 and others.
Generalisation of structural knowledge in the hippocampal-entorhinal system
Authors: Timothy Muller, Shirely Mark, Caswell Barry, Tim Behrens James Whittington
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities. We take inspiration from neuroscience, specifically the hippocampal-entorhinal system known to be important for generalisation. We propose that to generalise structural knowledge, the representations of the structure of the world, i.e. how entities in the world relate to each other, need to be separated from representations of the entities themselves. We show, under these principles, artificial neural networks embedded with hierarchy and fast Hebbian memory, can learn the statistics of memories and generalise structural knowledge. Spatial neuronal representations mirroring those found in the brain emerge, suggesting spatial cognition is an instance of more general organising principles. We further unify many entorhinal cell types as basis functions for constructing transition graphs, and show these representations effectively utilise memories. We experimentally support model assumptions, showing a preserved relationship between entorhinal grid and hippocampal place cells across environments.
Pelee: A Real-Time Object Detection System on Mobile Devices
Authors: Xiang Li, Charles X. Ling Robert J. Wang
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: An increasing need of running Convolutional Neural Network (CNN) models on mobile devices with limited computing power and memory resource encourages studies on efficient model design. A number of efficient architectures have been proposed in recent years, for example, MobileNet, ShuffleNet, and MobileNetV2. However, all these models are heavily dependent on depthwise separable convolution which lacks efficient implementation in most deep learning frameworks. In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead. On ImageNet ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy and 1.8 times faster speed than MobileNet and MobileNetV2 on NVIDIA TX2. Meanwhile, PeleeNet is only 66% of the model size of MobileNet. We then propose a real-time object detection system by combining PeleeNet with Single Shot MultiBox Detector (SSD) method and optimizing the architecture for fast speed. Our proposed detection system, named Pelee, achieves 76.4% mAP (mean average precision) on PASCAL VOC2007 and 22.4 mAP on MS COCO dataset at the speed of 23.6 FPS on iPhone 8 and 125 FPS on NVIDIA TX2. The result on COCO outperforms YOLOv2 in consideration of a higher precision, 13.6 times lower computational cost and 11.3 times smaller model size. The code and models are open sourced.
Co-regularized Alignment for Unsupervised Domain Adaptation
Authors: Prasanna Sattigeri, Kahini Wadhawan, Leonid Karlinsky, Rogerio Feris, Bill Freeman, Gregory Wornell Abhishek Kumar
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Deep neural networks, trained with large amount of labeled data, can fail to
generalize well when tested with examples from a target domain whose distribution differs from the training data distribution, referred as the source domain. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. 
Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature  spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples.
The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and 
observe that it provides significant performance improvements on several domain adaptation benchmarks.
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD
Authors: Zeyuan Allen-Zhu
Published in: Advances in Neural Information Processing Systems 31  (NeurIPS 2018) (Main Conference Track), 2018

Abstract: Stochastic gradient descent (SGD) gives an optimal convergence rate when minimizing convex stochastic objectives $f(x)$. However, in terms of making the gradients small, the original SGD does not give an optimal rate, even when $f(x)$ is convex.

If $f(x)$ is convex, to find a point with gradient norm $\varepsilon$, we design an algorithm SGD3 with a near-optimal rate $\tilde{O}(\varepsilon^{-2})$, improving the best known rate $O(\varepsilon^{-8/3})$. If $f(x)$ is nonconvex, to find its $\varepsilon$-approximate local minimum, we design an algorithm SGD5 with rate $\tilde{O}(\varepsilon^{-3.5})$, where previously SGD variants only achieve $\tilde{O}(\varepsilon^{-4})$. This is no slower than the best known stochastic version of Newton's method in all parameter regimes.