pwc-archive/papers-with-abstracts huggingface dataset:
Latent Domain Transfer: Crossing modalities with Bridging Autoencoders
Authors: Yingtao Tian, Jesse Engel
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network

Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels. 
However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video), 
utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes. 
Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces. 
We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network). 
We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space. 
The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.
Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.
Deep Bayesian Convolutional Networks with Many Channels are Gaussian Processes
Authors: Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Gaussian Processes

There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be computed without ever instantiating the FCN, but by instead evaluating the corresponding GP. In this work, we derive an analogous equivalence for multi-layer convolutional neural networks (CNNs) both with and without pooling layers, and achieve state of the art results on CIFAR10 for GPs without trainable kernels. We also introduce a Monte Carlo method to estimate the GP corresponding to a given neural network architecture, even in cases where the analytic form has too many terms to be computationally feasible. 

Surprisingly, in the absence of pooling layers, the GPs corresponding to CNNs with and without weight sharing are identical. As a consequence, translation equivariance in finite channel CNNs trained with stochastic gradient descent (SGD) has no corresponding property in the Bayesian treatment of the infinite channel limit -- a qualitative difference between the two regimes that is not present in the FCN case. We confirm experimentally, that while in some scenarios the performance of SGD-trained finite CNNs approaches that of the corresponding GPs as the channel count increases, with careful tuning SGD-trained CNNs can significantly outperform their corresponding GPs.
How Important is a Neuron
Authors: Kedar Dhamdhere, Mukund Sundararajan, Qiqi Yan
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: feature selection

The problem of attributing a deep network’s prediction to its input/base features is
well-studied (cf. Simonyan et al. (2013)). We introduce the notion of conductance
to extend the notion of attribution to understanding the importance of hidden units.
Informally, the conductance of a hidden unit of a deep network is the flow of attribution
via this hidden unit. We can use conductance to understand the importance of
a hidden unit to the prediction for a specific input, or over a set of inputs. We justify
conductance in multiple ways via a qualitative comparison with other methods,
via some axiomatic results, and via an empirical evaluation based on a feature
selection task. The empirical evaluations are done using the Inception network
over ImageNet data, and a convolutinal network over text data. In both cases, we
demonstrate the effectiveness of conductance in identifying interesting insights
about the internal workings of these networks.
TENSOR RING NETS ADAPTED DEEP MULTI-TASK LEARNING
Authors: Xinqi Chen, Ming Hou, Guoxu Zhou, Qibin Zhao
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Multi-Task Learning

Recent deep multi-task learning (MTL) has been witnessed its success in alleviating data scarcity of some task by utilizing domain-specific knowledge from related tasks. Nonetheless, several major issues of deep MTL, including the effectiveness of sharing mechanisms, the efficiency of model complexity and the flexibility of network architectures, still remain largely unaddressed. To this end, we propose a novel generalized latent-subspace based knowledge sharing mechanism for linking task-specific models, namely tensor ring multi-task learning (TRMTL). TRMTL has a highly compact representation, and it is very effective in transferring task-invariant knowledge while being super flexible in learning task-specific features, successfully mitigating the dilemma of both negative-transfer in lower layers and under-transfer in higher layers. Under our TRMTL, it is feasible for each task to have heterogenous input data dimensionality or distinct feature sizes at different hidden layers. Experiments on a variety of datasets demonstrate our model is capable of significantly improving each single task’s performance, particularly favourable in scenarios where some of the tasks have insufficient data.
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
Authors: Avraham Ruderman, Richard Everett, Bristy Sikder, Hubert Soyer, Jonathan Uesato, Ananya Kumar, Charlie Beattie, Pushmeet Kohli
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings. But does the distribution over environment settings contain important biases, and do these lead to agents that fail in certain cases despite high average-case performance? In this work, we consider worst-case analysis of agents over environment settings in order to detect whether there are directions in which agents may have failed to generalize. Specifically, we consider a 3D first-person task where agents must navigate procedurally generated mazes, and where reinforcement learning agents have recently achieved human-level average-case performance. By optimizing over the structure of mazes, we find that agents can suffer from catastrophic failures, failing to find the goal even on surprisingly simple mazes, despite their impressive average-case performance. Additionally, we find that these failures transfer between different agents and even significantly different architectures. We believe our findings highlight an important role for worst-case analysis in identifying whether there are directions in which agents have failed to generalize. Our hope is that the ability to automatically identify failures of generalization will facilitate development of more general and robust agents. To this end, we report initial results on enriching training with settings causing failure.
Unsupervised classification into unknown number of classes
Authors: Sungyeob Han, Daeyoung Kim, Jungwoo Lee
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Classification, Clustering, General Classification

We propose a novel unsupervised classification method based on graph Laplacian. Unlike the widely used classification method, this architecture does not require the labels of data and the number of classes. Our key idea is to introduce a approximate linear map and a spectral clustering theory on the dimension reduced spaces into generative adversarial networks. Inspired by the human visual recognition system, the proposed framework can classify and also generate images as the human brains do. We build an approximate linear connector network $C$ analogous to the cerebral cortex, between the discriminator $D$ and the generator $G$. The connector network allows us to estimate the unknown number of classes. Estimating the number of classes is one of the challenging researches in the unsupervised learning, especially in spectral clustering. The proposed method can also classify the images by using the estimated number of classes. Therefore, we define our method as an unsupervised classification method.
Sample-efficient policy learning in multi-agent Reinforcement Learning via meta-learning
Authors: Jialian Li, Hang Su, Jun Zhu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Meta-Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

To gain high rewards in muti-agent scenes, it is sometimes necessary to understand other agents and make corresponding optimal decisions. We can solve these tasks by first building models for other agents and then finding the optimal policy with these models. To get an accurate model, many observations are needed and this can be sample-inefficient. What's more, the learned model and policy can overfit to current agents and cannot generalize if the other agents are replaced by new agents. In many practical situations, each agent we face can be considered as a sample from a population with a fixed but unknown distribution. Thus we can treat the task against some specific agents as a task sampled from a task distribution. We apply meta-learning method to build models and learn policies. Therefore when new agents come, we can adapt to them efficiently. Experiments on grid games show that our method can quickly get high rewards.
UaiNets: From Unsupervised to Active Deep Anomaly Detection
Authors: Tiago Pimentel, Marianne Monteiro, Juliano Viana, Adriano Veloso, Nivio Ziviani
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Anomaly Detection, Deep Learning, Unsupervised Anomaly Detection

This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection. We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection. We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.
LSH Microbatches for Stochastic Gradients: Value in Rearrangement
Authors: Eliav Buchnik, Edith Cohen, Avinatan Hassidim, Yossi Matias
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

   Metric embeddings are   immensely useful representations of associations between entities   (images, users, search queries, words, and more).  Embeddings are learned by  optimizing a loss objective of the general form of a sum over example associations. Typically, the optimization uses stochastic gradient updates over minibatches of examples that are arranged  independently at random. In this work, we propose the use of {\em structured arrangements} through randomized {\em microbatches} of examples that are more likely to include similar ones. We make a principled argument for the properties of our arrangements  that accelerate the training and present efficient algorithms to generate microbatches that respect the marginal  distribution of training examples.  Finally, we observe experimentally that our structured arrangements accelerate training by 3-20\%. Structured arrangements emerge as a powerful and novel performance knob for SGD that is independent and complementary to other SGD  hyperparameters and thus is a candidate for wide deployment.
Learning to Control Visual Abstractions for Structured Exploration in Deep Reinforcement Learning
Authors: catalin ionescu, tejas kulkarni, aaron van de oord, andriy mnih, Vlad Mnih
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Atari Games, Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Unsupervised Reinforcement Learning

Exploration in environments with sparse rewards is a key challenge for reinforcement learning. How do we design agents with generic inductive biases so that they can explore in a consistent manner instead of just using local exploration schemes like epsilon-greedy? We propose an unsupervised reinforcement learning agent which learns a discrete pixel grouping model that preserves spatial geometry of the sensors and implicitly of the environment as well. We use this representation to derive geometric intrinsic reward functions, like centroid coordinates and area, and learn policies to control each one of them with off-policy learning. These policies form a basis set of behaviors (options) which allows us explore in a consistent way and use them in a hierarchical reinforcement learning setup to solve for extrinsically defined rewards. We show that our approach can scale to a variety of domains with competitive performance, including navigation in 3D environments and Atari games with sparse rewards.
Graph Classification with Geometric Scattering
Authors: Feng Gao, Guy Wolf, Matthew Hirn
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Classification, General Classification, Graph Classification, image-classification, Image Classification

One of the most notable contributions of deep learning is the application of convolutional neural networks (ConvNets) to structured signal classification, and in particular image classification. Beyond their impressive performances in supervised learning, the structure of such networks inspired the development of deep filter banks referred to as scattering transforms. These transforms apply a cascade of wavelet transforms and complex modulus operators to extract features that are invariant to group operations and stable to deformations. Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades. We further advance these lines of research by proposing a geometric scattering transform using graph wavelets defined in terms of random walks on the graph. We demonstrate the utility of features extracted with this designed deep filter bank in graph classification of biochemistry and social network data (incl. state of the art results in the latter case), and in data exploration, where they enable inference of EC exchange preferences in enzyme evolution.
Capsule Graph Neural Network
Authors: Zhang Xinyi, Lihui Chen
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Graph Classification, Graph Neural Network

The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance. However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings.

Inspired by the Capsule Neural Network (CapsNet), we propose the Capsule Graph Neural Network (CapsGNN), which adopts the concept of capsules to address the weakness in existing GNN-based graph embeddings algorithms. By extracting node features in the form of capsules, routing mechanism can be utilized to capture important information at the graph level. As a result, our model generates multiple embeddings for each graph to capture graph properties from different aspects. The attention module incorporated in CapsGNN is used to tackle graphs with various sizes which also enables the model to focus on critical parts of the graphs.

Our extensive evaluations with 10 graph-structured datasets demonstrate that CapsGNN has a powerful mechanism that operates to capture macroscopic properties of the whole graph by data-driven. It outperforms other SOTA techniques on several graph classification tasks, by virtue of the new instrument.
Multi-Task Learning for Semantic Parsing with Cross-Domain Sketch
Authors: Huan Wang, Yuxiang Hu, Li Dong, Feijun Jiang, Zaiqing Nie
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Multi-Task Learning, Semantic Parsing, Sentence

Semantic parsing which maps a natural language sentence into a formal machine-readable representation of its meaning, is highly constrained by the limited annotated training data. Inspired by the idea of coarse-to-fine, we propose a general-to-detailed neural network(GDNN) by incorporating cross-domain sketch(CDS) among utterances and their logic forms. For utterances in different domains, the General Network will extract CDS using an encoder-decoder model in a multi-task learning setup. Then for some utterances in a specific domain, the Detailed Network will generate the detailed target parts using sequence-to-sequence architecture with advanced attention to both utterance and generated CDS. Our experiments show that compared to direct multi-task learning, CDS has improved the performance in semantic parsing task which converts users' requests into meaning representation language(MRL). We also use experiments to illustrate that CDS works by adding some constraints to the target decoding process, which further proves the effectiveness and rationality of CDS.
GEOMETRIC AUGMENTATION FOR ROBUST NEURAL NETWORK CLASSIFIERS
Authors: Robert M. Taylor, Yusong Tan
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: General Classification, Time Series, Time Series Analysis

We introduce a novel geometric perspective and unsupervised model augmentation framework for transforming traditional deep (convolutional) neural networks into adversarially robust classifiers. Class-conditional probability densities based on Bayesian nonparametric mixtures of factor analyzers (BNP-MFA) over the input space are used to design soft decision labels for feature to label isometry. Classconditional distributions over features are also learned using BNP-MFA to develop plug-in maximum a posterior (MAP) classifiers to replace the traditional multinomial logistic softmax classification layers. This novel unsupervised augmented framework, which we call geometrically robust networks (GRN), is applied to CIFAR-10, CIFAR-100, and to Radio-ML (a time series dataset for radio modulation recognition). We demonstrate the robustness of GRN models to adversarial attacks from fast gradient sign method, Carlini-Wagner, and projected gradient descent.
A Guider Network for Multi-Dual Learning
Authors: Wenpeng Hu, Zhengwei Tao, Zhanxing Zhu, Bing Liu, Zhou Lin, Jinwen Ma, Dongyan Zhao, Rui Yan
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, NMT, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

A large amount of parallel data is needed to train a strong neural machine translation (NMT) system. This is a major challenge for low-resource languages. Building on recent work on unsupervised and semi-supervised methods, we propose a multi-dual learning framework to improve the performance of NMT by using an almost infinite amount of available monolingual data and some parallel data of other languages. Since our framework involves multiple languages and components, we further propose a timing optimization method that uses reinforcement learning (RL) to optimally schedule the different components in order to avoid imbalanced training. Experimental results  demonstrate the validity of our model, and confirm its superiority to existing dual learning methods.
Dual Learning: Theoretical Study and Algorithmic Extensions
Authors: Zhibing Zhao, Yingce Xia, Tao Qin, Tie-Yan Liu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, Translation

Dual learning has been successfully applied in many machine learning applications, including machine translation, image-to-image  transformation, etc. The high-level idea of dual learning is very intuitive: if we map an x from one domain to another and then map it back, we should recover the original x. Although its effectiveness has been empirically verified, theoretical understanding of dual learning is still missing. In this paper, we conduct a theoretical study to understand why and when dual learning can improve a mapping function. Based on the theoretical discoveries, we extend dual learning by introducing more related mappings and propose highly symmetric frameworks, cycle dual learning and multipath dual learning, in both of which we can leverage the feedback signals from additional domains to improve the qualities of the mappings. We prove that both cycle dual learning and multipath dual learning can boost the performance of standard dual learning under mild conditions. Experiments on WMT 14 English↔German and MultiUN English↔French translations verify our theoretical findings on dual learning, and the results on the translations among English, French, and Spanish of MultiUN demonstrate the efficacy of cycle dual learning and multipath dual learning.
TabNN: A Universal Neural Network Solution for Tabular Data
Authors: Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, Tie-Yan Liu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Neural Network (NN) has achieved state-of-the-art performances in many tasks within image, speech, and text domains. Such great success is mainly due to special structure design to fit the particular data patterns, such as CNN capturing spatial locality and RNN modeling sequential dependency. Essentially, these specific NNs achieve good performance by leveraging the prior knowledge over corresponding domain data. Nevertheless, there are many applications with all kinds of tabular data in other domains. Since there are no shared patterns among these diverse tabular data, it is hard to design specific structures to fit them all. Without careful architecture design based on domain knowledge, it is quite challenging for NN to reach satisfactory performance in these tabular data domains. To fill the gap of NN in tabular data learning, we propose a universal neural network solution, called TabNN, to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: \emph{to explicitly leverages expressive feature combinations} and \emph{to reduce model complexity}. Since GBDT has empirically proven its strength in modeling tabular data, we use GBDT to power the implementation of TabNN. Comprehensive experimental analysis on a variety of tabular datasets demonstrate that TabNN can achieve much better performance than many baseline solutions.