pwc-archive/papers-with-abstracts huggingface dataset:
Model-Agnostic Meta-Learning for Multimodal Task Distributions
Authors: Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim
Date: 2018-09-27 00:00:00
Tasks: Diversity, Few-Shot Image Classification, image-classification, Image Classification, Meta-Learning, model

Gradient-based meta-learners such as MAML (Finn et al., 2017) are able to learn a meta-prior from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. One important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML algorithm that is able to modulate its meta-learned prior according to the identified task, allowing faster adaptation. We evaluate the proposed model on a diverse set of problems including regression, few-shot image classification, and reinforcement learning. The results demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks sampled from a multimodal distribution.
Teaching to Teach by Structured Dark Knowledge
Authors: Ziliang Chen, Keze Wang, Liang Lin
Date: 2018-09-27 00:00:00
Tasks: Diversity

To educate hyper deep learners, \emph{Curriculum Learnings} (CLs) require either human heuristic participation or self-deciding the difficulties of training instances. These coaching manners are blind to the coherent structures among examples, categories, and tasks, which are pregnant with more knowledgeable curriculum-routed teachers. In this paper, we propose a general methodology \emph{Teaching to Teach} (T2T). T2T is facilitated by \emph{Structured Dark Knowledge} (SDK) that constitutes a communication protocol between structured knowledge prior and teaching strategies. On one hand, SDK adaptively extracts structured knowledge by selecting a training subset consistent with the previous teaching decisions. On the other hand, SDK teaches curriculum-agnostic teachers by transferring this knowledge to update their teaching policy. This virtuous cycle can be flexibly-deployed in most existing CL platforms and more importantly, very generic across various structured knowledge characteristics, e.g., diversity, complementarity, and causality. We evaluate T2T across different learners, teachers, and tasks, which significantly demonstrates that structured knowledge can be inherited by the teachers to further benefit learners' training.
Transfer Value or Policy? A Value-centric Framework Towards Transferrable Continuous Reinforcement Learning
Authors: Xingchao Liu, Tongzhou Mu, Hao Su
Date: 2018-09-27 00:00:00
Tasks: continuous-control, Continuous Control, Reinforcement Learning (RL), Transfer Learning

Transferring learned knowledge from one environment to another is an important step towards practical reinforcement learning (RL). In this paper, we investigate the problem of transfer learning across environments with different dynamics while accomplishing the same task in the continuous control domain. We start by illustrating the limitations of policy-centric methods (policy gradient, actor- critic, etc.) when transferring knowledge across environments. We then propose a general model-based value-centric (MVC) framework for continuous RL. MVC learns a dynamics approximator and a value approximator simultaneously in the source domain, and makes decision based on both of them. We evaluate MVC against popular baselines on 5 benchmark control tasks in a training from scratch setting and a transfer learning setting. Our experiments demonstrate MVC achieves comparable performance with the baselines when it is trained from scratch, while it significantly surpasses them when it is used in the transfer setting.
Understanding GANs via Generalization Analysis for Disconnected Support
Authors: Masaaki Imaizumi, Kenji Fukumizu
Date: 2018-09-27 00:00:00

This paper provides theoretical analysis of generative adversarial networks (GANs) to explain its advantages over other standard methods of learning probability measures. GANs learn a probability through observations, using the objective function with a generator and a discriminator. While many empirical results indicate that GANs can generate realistic samples, the reason for such successful performance remains unelucidated. This paper focuses the situation where the target probability measure satisfies the disconnected support property, which means a separate support of a probability, and relates it with the advantage of GANs.  It is theoretically shown that, unlike other popular models, GANs do not suffer from the decrease of generalization performance caused by the disconnected support property.  We rigorously quantify the generalization performance of GANs of a given architecture, and compare it with the performance of the other models. Based on the theory, we also provide a guideline for selecting deep network architecture for GANs.  We demonstrate some numerical examples which support our results.
Ergodic Measure Preserving Flows
Authors: Yichuan Zhang, José Miguel Hernández-Lobato, Zoubin Ghahramani
Date: 2018-09-27 00:00:00
Tasks: Variational Inference

Training probabilistic models with neural network components is intractable in most cases and requires to use approximations such as Markov chain Monte Carlo (MCMC), which is not scalable and requires significant hyper-parameter tuning, or mean-field variational inference (VI), which is biased. While there has been attempts at combining both approaches, the resulting methods have some important limitations in theory and in practice. As an alternative, we propose a novel method which is scalable, like mean-field VI, and, due to its theoretical foundation in ergodic theory, is also asymptotically accurate, like MCMC. We test our method on popular benchmark problems with deep generative models and Bayesian neural networks. Our results show that we can outperform existing approximate inference methods.
S-System, Geometry, Learning, and Optimization: A Theory of Neural Networks
Authors: Shuai Li, Kui Jia
Date: 2018-09-27 00:00:00
Tasks: Diversity

We present a formal measure-theoretical theory of neural networks (NN) built on {\it probability coupling theory}. Particularly, we present an algorithm framework, Hierarchical Measure Group and Approximate System (HMGAS), nicknamed S-System, of which NNs are special cases. In addition to many other results, the framework enables us to prove that 1) NNs implement {\it renormalization group (RG)} using information geometry, which points out that the large scale property to renormalize is dual Bregman divergence and completes the analog between NNs and RG; 2) and under a set of {\it realistic} boundedness and diversity conditions, for {\it large size nonlinear deep} NNs with a class of losses, including the hinge loss, all local minima are global minima with zero loss errors, using random matrix theory.
Lorentzian Distance Learning
Authors: Marc T Law, Jake Snell, Richard S Zemel
Date: 2018-09-27 00:00:00
Tasks: Representation Learning, Retrieval

This paper introduces an approach to learn representations based on the Lorentzian distance in hyperbolic geometry. Hyperbolic geometry is especially suited to hierarchically-structured datasets, which are prevalent in the real world. Current hyperbolic representation learning methods compare examples with the Poincar\'e distance metric. They formulate the problem as minimizing the distance of each node in a hierarchy with its descendants while maximizing its distance with other nodes. This formulation produces node representations close to the centroid of their descendants. We exploit the fact that the centroid w.r.t the squared Lorentzian distance can be written in closed-form. We show that the Euclidean norm of such a centroid decreases as the curvature of the hyperbolic space decreases. This property makes it appropriate to represent hierarchies where parent nodes minimize the distances to their descendants and have smaller Euclidean norm than their children. Our approach obtains state-of-the-art results in retrieval and classification tasks on different datasets.
W2GAN: RECOVERING AN OPTIMAL TRANSPORT MAP WITH A GAN
Authors: Leygonie Jacob*, Jennifer She*, Amjad Almahairi, Sai Rajeswar, Aaron Courville
Date: 2018-09-27 00:00:00

Understanding and improving Generative Adversarial Networks (GAN) using notions from Optimal Transport (OT) theory has been a successful area of study, originally established by the introduction of the Wasserstein GAN (WGAN). An increasing number of GANs incorporate OT for improving their discriminators, but that is so far the sole way for the two domains to cross-fertilize. In this work we address the converse question: is it possible to recover an optimal map in a GAN fashion? To achieve this, we build a new model relying on the second Wasserstein distance. This choice enables the use of many results from OT community. In particular, we may completely describe the dynamics of the generator during training. In addition, experiments show that practical uses of our model abide by the rule of evolution we describe. As an application, our generator may be considered as a new way of computing an optimal transport map. It is competitive in low-dimension with standard and deterministic ways to approach the same problem. In high dimension, the fact it is a GAN-style method makes it more powerful than other methods.
A Differentiable Self-disambiguated Sense Embedding Model via Scaled Gumbel Softmax
Authors: Fenfei Guo, Mohit Iyyer, Leah Findlater, Jordan Boyd-Graber
Date: 2018-09-27 00:00:00
Tasks: Hard Attention, Sentence, Word Similarity

We present a differentiable multi-prototype word representation model that disentangles senses of polysemous words and produces meaningful sense-specific embeddings without external resources. It jointly learns how to disambiguate senses given local context and how to represent senses using hard attention. Unlike previous multi-prototype models, our model approximates discrete sense selection in a differentiable manner via a modified Gumbel softmax. We also propose a novel human evaluation task that quantitatively measures (1) how meaningful the learned sense groups are to humans and (2) how well the model is able to disambiguate senses given a context sentence. Our model outperforms competing approaches on both human evaluations and multiple word similarity tasks.
Three continual learning scenarios and a case for generative replay
Authors: Gido M. van de Ven, Andreas S. Tolias
Date: 2018-09-27 00:00:00
Tasks: Continual Learning, Lifelong learning, Permuted-MNIST

Standard artificial neural networks suffer from the well-known issue of catastrophic forgetting, making continual or lifelong learning problematic. Recently, numerous methods have been proposed for continual learning, but due to differences in evaluation protocols it is difficult to directly compare their performance. To enable more meaningful comparisons, we identified three distinct continual learning scenarios based on whether task identity is known and, if it is not, whether it needs to be inferred. Performing the split and permuted MNIST task protocols according to each of these scenarios, we found that regularization-based approaches (e.g., elastic weight consolidation) failed when task identity needed to be inferred. In contrast, generative replay combined with distillation (i.e., using class probabilities as “soft targets”) achieved superior performance in all three scenarios. In addition, we reduced the computational cost of generative replay by integrating the generative model into the main model.
Conscious Inference for Object Detection
Authors: Jiahuan Zhou, Nikolaos Karianakis, Ying Wu, Gang Hua
Date: 2018-09-27 00:00:00
Tasks: 2D Object Detection, 6D Pose Estimation using RGB, Object, object-detection, Object Detection, Pose Estimation

Current Convolutional Neural Network (CNN)-based object detection models adopt strictly feedforward inference to predict the final detection results. However, the widely used one-way inference is agnostic to the global image context and the interplay between input image and task semantics. In this work, we present a general technique to improve off-the-shelf CNN-based object detection models in the inference stage without re-training, architecture modification or ground-truth requirements. We propose an iterative, bottom-up and top-down inference mechanism, which is named conscious inference, as it is inspired by prevalent models for human consciousness with top-down guidance and temporal persistence. While the downstream pass accumulates category-specific evidence over time, it subsequently affects the proposal calculation and the final detection. Feature activations are updated in line with no additional memory cost. Our approach advances the state of the art using popular detection models (Faster-RCNN, YOLOv2, YOLOv3) on 2D object detection and 6D object pose estimation.
Learning Gibbs-regularized GANs with variational discriminator reparameterization
Authors: Nicholas Rhinehart, Anqi Liu, Kihyuk Sohn, Paul Vernaza
Date: 2018-09-27 00:00:00
Tasks: Trajectory Forecasting

 We propose a novel approach to regularizing generative adversarial networks (GANs) leveraging learned {\em structured Gibbs distributions}.  Our method consists of reparameterizing the discriminator to be an explicit function of two densities: the generator PDF $q$ and a structured Gibbs distribution $\nu$.  Leveraging recent work on invertible pushforward density estimators, this reparameterization is made possible by assuming the generator is invertible, which enables the analytic evaluation of the generator PDF $q$.  We further propose optimizing the Jeffrey divergence, which balances mode coverage with sample quality.  The combination of this loss and  reparameterization allows us to effectively regularize the generator by imposing structure from domain knowledge on $\nu$, as in classical graphical models. Applying our method to a vehicle trajectory forecasting task, we observe that we are able to obtain quantitatively superior mode coverage as well as better-quality samples compared to traditional methods.
DynCNN: An Effective Dynamic Architecture on Convolutional Neural Network for Surveillance Videos
Authors: De-Qin Gao, Ping-Chen Tsai, Shanq-Jang Ruan
Date: 2018-09-27 00:00:00

The large-scale surveillance video analysis becomes important as the development of intelligent city. The heavy computation resources neccessary for state-of-the-art deep learning model makes the real-time processing hard to be implemented. This paper exploits the characteristic of high scene similarity generally existing in surveillance videos and proposes dynamic convolution reusing the previous feature map to reduce the computation amount. We tested the proposed method on 45 surveillance videos with various scenes. The experimental results show that dynamic convolution can reduce up to 75.7% of FLOPs while preserving the precision within 0.7% mAP. Furthermore, the dynamic convolution can enhance the processing time up to 2.2 times.
Expressiveness in Deep Reinforcement Learning
Authors: Xufang Luo, Qi Meng, Di He, Wei Chen, Yunhong Wang, Tie-Yan Liu
Date: 2018-09-27 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Representation learning in reinforcement learning (RL) algorithms focuses on extracting useful features for choosing good actions. Expressive representations are essential for learning well-performed policies. In this paper, we study the relationship between the state representation assigned by the state extractor and the performance of the RL agent. We observe that representations assigned by the better state extractor are more scattered than which assigned by the worse one. Moreover, RL agents achieving high performances always have high rank matrices which are composed by their representations. Based on our observations, we formally define expressiveness of the state extractor as the rank of the matrix composed by representations. Therefore, we propose to promote expressiveness so as to improve algorithm performances, and we call it Expressiveness Promoted DRL. We apply our method on both policy gradient and value-based algorithms, and experimental results on 55 Atari games show the superiority of our proposed method.
Exploring Curvature Noise in Large-Batch Stochastic Optimization
Authors: Yeming Wen, Kevin Luk, Maxime Gazeau, Guodong Zhang, Harris Chan, Jimmy Ba
Date: 2018-09-27 00:00:00
Tasks: Stochastic Optimization

Using stochastic gradient descent (SGD) with large batch-sizes to train deep neural networks is an increasingly popular technique. By doing so, one can improve parallelization by scaling to multiple workers (GPUs) and hence leading to significant reductions in training time. Unfortunately, a major drawback is the so-called generalization gap: large-batch training typically leads to a degradation in generalization performance of the model as compared to small-batch training. In this paper, we propose to correct this generalization gap by adding diagonal Fisher curvature noise to large-batch gradient updates. We provide a theoretical analysis of our method in the convex quadratic setting. Our empirical study with state-of-the-art deep learning models shows that our method not only improves the generalization performance in large-batch training but furthermore, does so in a way where the training convergence remains desirable and the training duration is not elongated. We additionally connect our method to recent works on loss surface landscape in the experimental section.
Layerwise Recurrent Autoencoder for General Real-world Traffic Flow Forecasting
Authors: Peize Zhao, Danfeng Cai, Shaokun Zhang, Feng Chen, Zhemin Zhang, Cheng Wang, Jonathan Li
Date: 2018-09-27 00:00:00
Tasks: Management

Accurate spatio-temporal traffic forecasting is a fundamental task that has wide applications in city management, transportation area and financial domain. There are many factors that make this significant task also challenging, like: (1) maze-like road network makes the spatial dependency complex; (2) the traffic-time relationships bring non-linear temporal complication; (3) with the larger road network, the difficulty of flow forecasting grows. The prevalent and state-of-the-art methods have mainly been discussed on datasets covering relatively small districts and short time span, e.g., the dataset that is collected within a city during months. To forecast the traffic flow across a wide area and overcome the mentioned challenges, we design and propose a promising forecasting model called Layerwise Recurrent Autoencoder (LRA), in which a three-layer stacked autoencoder (SAE) architecture is used to obtain temporal traffic correlations and a recurrent neural networks (RNNs) model for prediction. The convolutional neural networks (CNNs) model is also employed to extract spatial traffic information within the transport topology for more accurate prediction. To the best of our knowledge, there is no general and effective method for traffic flow prediction in large area which covers a group of cities. The experiment is completed on such large scale real-world traffic datasets to show superiority. And a smaller dataset is exploited to prove universality of the proposed model. And evaluations show that our model outperforms the state-of-the-art baselines by 6% - 15%.
An experimental study of layer-level training speed and its impact on generalization
Authors: Simon Carbonnelle, Christophe De Vleeschouwer
Date: 2018-09-27 00:00:00

How optimization influences the generalization ability of a DNN is still an active area of research. This work aims to unveil and study a factor of influence: the speed at which each layer trains. In our preliminary work, we develop a visualization technique and an optimization algorithm to monitor and control the layer rotation rate, a tentative measure of layer-level training speed, and show that it has a remarkably consistent and substantial impact on generalization. Our experiments further suggest that weight decay's and adaptive gradients methods' impact on both generalization performance and speed of convergence are solely due to layer rotation rate changes compared to vanilla SGD, offering a novel interpretation of these widely used techniques, and providing supplementary evidence that layer-level training speed indeed impacts generalization. Besides these fundamental findings, we also expect that on a practical level, the tools we introduce will reduce the meta-parameter tuning required to get the best generalization out of a deep network.
DEEP ADVERSARIAL FORWARD MODEL
Authors: Morgan Funtowicz, Tomi Silander, Arnaud Sors, Julien Perez
Date: 2018-09-27 00:00:00
Tasks: Image Generation, model, Reinforcement Learning (RL)

Learning world dynamics has recently been investigated as a way to make reinforcement
learning (RL) algorithms to be more sample efficient and interpretable.
In this paper, we propose to capture an environment dynamics with a novel forward
model that leverages recent works on adversarial learning and visual control. Such
a model estimates future observations conditioned on the current ones and other
input variables such as actions taken by an RL-agent. We focus on image generation
which is a particularly challenging topic but our method can be adapted to
other modalities. More precisely, our forward model is trained to produce realistic
observations of the future while a discriminator model is trained to distinguish
between real images and the model’s prediction of the future. This approach overcomes
the need to define an explicit loss function for the forward model which is currently
used for solving such a class of problem. As a consequence, our learning protocol
does not have to rely on an explicit distance such as Euclidean distance which
tends to produce unsatisfactory predictions. To illustrate our method, empirical
qualitative and quantitative results are presented on a real driving scenario, along
with qualitative results on Atari game Frostbite.
End-to-End Learning of Video Compression Using Spatio-Temporal Autoencoders
Authors: Jorge Pessoa, Helena Aidos, Pedro Tomás, Mário A. T. Figueiredo
Date: 2018-09-27 00:00:00
Tasks: Image Compression, Motion Estimation, Video Compression

Deep learning (DL) is having a revolutionary impact in image processing, with DL-based approaches now holding the state of the art in many tasks, including image compression. However, video compression has so far resisted the DL revolution, with the very few proposed approaches being based on complex and impractical architectures with multiple networks. This paper proposes what we believe is the first approach to end-to-end learning of a single network for video compression. We tackle the problem in a novel way, avoiding explicit motion estimation/prediction, by formalizing it as the rate-distortion optimization of a single spatio-temporal autoencoder; i.e., we jointly learn a latent-space projection transform and a synthesis transform for low bitrate video compression. The quantizer uses a rounding scheme, which is relaxed during training, and an entropy estimation technique to enforce an information bottleneck, inspired by recent advances in image compression. We compare the obtained video compression networks with standard widely-used codecs, showing better performance than the MPEG-4 standard, being competitive with H.264/AVC for low bitrates.