pwc-archive/papers-with-abstracts huggingface dataset:
Theoretical properties of the global optimizer of two-layer Neural Network
Authors: Digvijay Boob, Guanghui Lan
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Vocal Bursts Valence Prediction

In this paper, we study the problem of optimizing a two-layer artificial neural network that best fits a training dataset. We look at this problem in the setting where the number of parameters is greater than the number of sampled points. We show that for a wide class of differentiable activation functions (this class involves most nonlinear functions and excludes piecewise linear functions), we have that arbitrary first-order optimal solutions satisfy global optimality provided the hidden layer is non-singular. We essentially show that these non-singular hidden layer matrix satisfy a ``"good" property for these big class of activation functions. Techniques involved in proving this result inspire us to look at a new algorithmic, where in between two gradient step of hidden layer, we add a stochastic gradient descent (SGD) step of the output layer. In this new algorithmic framework, we extend our earlier result and show that for all finite iterations the hidden layer satisfies the``good" property mentioned earlier therefore partially explaining success of noisy gradient methods and addressing the issue of data independency of our earlier result. Both of these results are easily extended to hidden layers given by a flat matrix from that of a square matrix. Results are applicable even if network has more than one hidden layer provided all inner hidden layers are arbitrary, satisfy non-singularity, all activations are from the given class of differentiable functions and optimization is only with respect to the outermost hidden layer. Separately, we also study the smoothness properties of the objective function and show that it is actually Lipschitz smooth, i.e., its gradients do not change sharply. We use smoothness properties to guarantee asymptotic convergence of $O(1/\text{number of iterations})$ to a first-order optimal solution.
Kernel Graph Convolutional Neural Nets
Authors: Giannis Nikolentzos, Polykarpos Meladianos, Antoine J-P Tixier, Konstantinos Skianis, Michalis Vazirgiannis
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Graph Classification

Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal. On the other hand, Convolutional Neural Networks (CNNs) have the capability to learn their own features directly from the raw data during training. Unfortunately, they cannot handle irregular data such as graphs. We address this challenge by using graph kernels to embed meaningful local neighborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets, and reaches comparable performance elsewhere. Code and data are publicly available.
Now I Remember! Episodic Memory For Reinforcement Learning
Authors: Ricky Loynd, Matthew Hausknecht, Lihong Li, Li Deng
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Humans rely on episodic memory constantly, in remembering the name of someone they met 10 minutes ago, the plot of a movie as it unfolds, or where they parked the car. Endowing reinforcement learning agents with episodic memory is a key step on the path toward replicating human-like general intelligence. We analyze why standard RL agents lack episodic memory today, and why existing RL tasks don't require it. We design a new form of external memory called Masked Experience Memory, or MEM, modeled after key features of human episodic memory. To evaluate episodic memory we define an RL task based on the common children's game of Concentration. We find that a MEM RL agent leverages episodic memory effectively to master Concentration, unlike the baseline agents we tested.
Global Convergence of Policy Gradient Methods for Linearized Control Problems
Authors: Maryam Fazel, Rong Ge, Sham M. Kakade, Mehran Mesbahi
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: continuous-control, Continuous Control, Policy Gradient Methods, Reinforcement Learning

Direct policy gradient methods for reinforcement learning and continuous control problems are a popular
approach for a variety of reasons: 
1) they are easy to implement without explicit knowledge of the underlying model;
2) they are an "end-to-end" approach, directly optimizing the performance metric of interest;
3) they inherently allow for richly parameterized policies.
A notable drawback is that even in the most basic continuous control problem (that of linear quadratic regulators), these methods must solve a non-convex optimization problem, where little is understood about their efficiency from both computational and statistical perspectives. In contrast, system identification and model based planning in optimal control theory have a much more solid theoretical footing, where much is known with regards to their computational and statistical properties.  This work bridges this gap showing that (model free) policy gradient methods globally converge to the optimal solution and are efficient (polynomially so in relevant problem dependent quantities) with regards to their sample and computational complexities.
A Bayesian Nonparametric Topic Model with Variational Auto-Encoders
Authors: Xuefei Ning, Yin Zheng, Zhuxi Jiang, Yu Wang, Huazhong Yang, Junzhou Huang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Representation Learning, Retrieval, Topic Models

Topic modeling of text documents is one of the most important tasks in representation learning. In this work, we propose iTM-VAE, which is a Bayesian nonparametric (BNP) topic model with variational auto-encoders. On one hand, as a BNP topic model, iTM-VAE potentially has infinite topics and can adapt the topic number to data automatically. On the other hand, different with the other BNP topic models, the inference of iTM-VAE is modeled by neural networks, which has rich representation capacity and can be computed in a simple feed-forward manner. Two variants of iTM-VAE are also proposed in this paper, where iTM-VAE-Prod models the generative process in products-of-experts fashion for better performance and iTM-VAE-G places a prior over the concentration parameter such that the model can adapt a suitable concentration parameter to data automatically. Experimental results on 20News and Reuters RCV1-V2 datasets show that the proposed models outperform the state-of-the-arts in terms of perplexity, topic coherence and document retrieval tasks. Moreover, the ability of adjusting the concentration parameter to data is also confirmed by experiments.
DropMax: Adaptive Stochastic Softmax
Authors: Hae Beom Lee, Juho Lee, Eunho Yang, Sung Ju Hwang
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, General Classification, Variational Inference

We propose DropMax, a stochastic version of softmax classifier which at each iteration drops non-target classes with some probability, for each instance. Specifically, we overlay binary masking variables over class output probabilities, which are learned based on the input via regularized variational inference. This stochastic regularization has an effect of building an ensemble classifier out of combinatorial number of classifiers with different decision boundaries. Moreover, the learning of dropout probabilities for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes. We validate our model on multiple public datasets for classification, on which it obtains improved accuracy over regular softmax classifier and other baselines. Further analysis of the learned dropout masks shows that our model indeed selects confusing classes more often when it performs classification.
Semi-supervised Outlier Detection using Generative And Adversary Framework
Authors: Jindong Gu, Matthias Schubert, Volker Tresp
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Multi-class Classification, One-Class Classification, One-class classifier, Outlier Detection

In a conventional binary/multi-class classification task, the decision boundary is supported by data from two or more classes. However, in one-class classification task, only data from one class are available. To build an robust outlier detector using only data from a positive class, we propose a corrupted GAN(CorGAN), a deep convolutional Generative Adversary Network requiring no convergence during training. In the adversarial process of training CorGAN, the Generator is supposed to generate outlier samples for negative class, and the Discriminator as an one-class classifier is trained to distinguish data from training datasets (i.e. positive class) and generated data from the Generator (i.e. negative class). To improve the performance of the Discriminator (one-class classifier), we also propose a lot of techniques to improve the performance of the model. The proposed model outperforms the traditional method PCA + PSVM and the solution based on Autoencoder.
Cross-Corpus Training with TreeLSTM for the Extraction of Biomedical Relationships from Text
Authors: Legrand Joël, Yannick Toussaint, Chedy Raïssi, Adrien Coulet
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Cross-corpus, Relationship Extraction (Distant Supervised)

A bottleneck problem in machine learning-based relationship extraction (RE) algorithms, and particularly of deep learning-based ones, is the availability of training data in the form of annotated corpora. For specific domains, such as biomedicine, the long time and high expertise required for the development of manually annotated corpora explain that most of the existing one are relatively small (i.e., hundreds of sentences). Beside, larger corpora focusing on general or domain-specific relationships (such as citizenship or drug-drug interactions) have been developed. In this paper, we study how large annotated corpora developed for alternative tasks may improve the performances on biomedicine related tasks, for which few annotated resources are available. We experiment two deep learning-based models to extract relationships from biomedical texts with high performance. The first one combine locally extracted features using a Convolutional Neural Network (CNN) model, while the second exploit the syntactic structure of sentences using a Recursive Neural Network (RNN) architecture. Our experiments show that, contrary to the former, the latter benefits from a cross-corpus learning strategy to improve the performance of relationship extraction tasks. Indeed our approach leads to the best published performances for two biomedical RE tasks, and to state-of-the-art results for two other biomedical RE tasks, for which few annotated resources are available (less than 400 manually annotated sentences). This may be particularly impactful in specialized domains in which training resources are scarce, because they would benefit from the training data of other domains for which large annotated corpora does exist.
Learning to navigate by distilling visual information and natural language instructions
Authors: Abhishek Sinha, Akilesh B, Mausoom Sarkar, Balaji Krishnamurthy
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Navigate, Zero-shot Generalization

In this work, we focus on the problem of grounding language by training an agent
to follow a set of natural language instructions and navigate to a target object
in a 2D grid environment. The agent receives visual information through raw
pixels and a natural language instruction telling what task needs to be achieved.
Other than these two sources of information, our model does not have any prior
information of both the visual and textual modalities and is end-to-end trainable.
We develop an attention mechanism for multi-modal fusion of visual and textual
modalities that allows the agent to learn to complete the navigation tasks and also
achieve language grounding. Our experimental results show that our attention
mechanism outperforms the existing multi-modal fusion mechanisms proposed in
order to solve the above mentioned navigation task. We demonstrate through the
visualization of attention weights that our model learns to correlate attributes of
the object referred in the instruction with visual representations and also show
that the learnt textual representations are semantically meaningful as they follow
vector arithmetic and are also consistent enough to induce translation between instructions
in different natural languages. We also show that our model generalizes
effectively to unseen scenarios and exhibit zero-shot generalization capabilities.
In order to simulate the above described challenges, we introduce a new 2D environment
for an agent to jointly learn visual and textual modalities
Statestream: A toolbox to explore layerwise-parallel deep neural networks
Authors: Volker Fischer
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Building deep neural networks to control autonomous agents which have to interact in real-time with the physical world, such as robots or automotive vehicles, requires a seamless integration of time into a network’s architecture. The central question of this work is, how the temporal nature of reality should be reflected in the execution of a deep neural network and its components. Most artificial deep neural networks are partitioned into a directed graph of connected modules or layers and the layers themselves consist of elemental building blocks, such as single units. For most deep neural networks, all units of a layer are processed synchronously and in parallel, but layers themselves are processed in a sequential manner. In contrast, all elements of a biological neural network are processed in parallel. In this paper, we define a class of networks between these two extreme cases. These networks are executed in a streaming or synchronous layerwise-parallel manner, unlocking the layers of such networks for parallel processing. Compared to the standard layerwise-sequential deep networks, these new layerwise-parallel networks show a fundamentally different temporal behavior and flow of information, especially for networks with skip or recurrent connections. We argue that layerwise-parallel deep networks are better suited for future challenges of deep neural network design, such as large functional modularized and/or recurrent architectures as well as networks allocating different network capacities dependent on current stimulus and/or task complexity. We layout basic properties and discuss major challenges for layerwise-parallel networks. Additionally, we provide a toolbox to design, train, evaluate, and online-interact with layerwise-parallel networks.
Training RNNs as Fast as CNNs
Authors: Tao Lei, Yu Zhang, Yoav Artzi
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Language Modeling, Language Modelling, Question Answering, speech-recognition, Speech Recognition, Translation

Common recurrent neural network architectures scale poorly due to the intrinsic difficulty in parallelizing their state computations. In this work, we propose the Simple Recurrent Unit (SRU) architecture, a recurrent unit that simplifies the computation and exposes more parallelism. In SRU, the majority of computation for each step is independent of the recurrence and can be easily parallelized. SRU is as fast as a convolutional layer and 5-10x faster than an optimized LSTM implementation. We study SRUs on a wide range of applications,  including classification, question answering, language modeling, translation and speech recognition. Our experiments demonstrate the effectiveness of SRU and the trade-off it enables between speed and performance.
Revisiting Bayes by Backprop
Authors: Meire Fortunato, Charles Blundell, Oriol Vinyals
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Captioning, Language Modelling

In this work we explore a straightforward variational Bayes scheme for Recurrent Neural Networks.
Firstly, we show that a simple adaptation of truncated backpropagation through time can yield good quality uncertainty estimates and superior regularisation at only a small extra computational cost during training, also reducing the amount of parameters by 80\%.
Secondly, we demonstrate how a novel kind of posterior approximation yields further improvements to the performance of Bayesian RNNs. We incorporate local gradient information into the approximate posterior to sharpen it around the current batch statistics. We show how this technique is not exclusive to recurrent neural networks and can be applied more widely to train Bayesian neural networks.
We also empirically demonstrate how Bayesian RNNs are superior to traditional RNNs on a language modelling benchmark and an image captioning task, as well as showing how each of these methods improve our model over a variety of other schemes for training them. We also introduce a new benchmark for studying uncertainty for language models so future methods can be easily compared.
PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
Authors: Jiechao Xiong, Qing Wang, Zhuoran Yang, Peng Sun, Yang Zheng, Lei Han, Haobo Fu, Xiangru Lian, Carson Eisenach, Haichuan Yang, Emmanuel Ekwedike, Bei Peng, Haoyue Gao, Tong Zhang, Ji Liu, Han Liu
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Most existing deep reinforcement learning (DRL) frameworks consider action spaces that are either
discrete or continuous space. Motivated by the project of design Game AI for King of Glory
(KOG), one the world’s most popular mobile game, we consider the scenario with the discrete-continuous
hybrid action space. To directly apply existing DLR frameworks, existing approaches
either approximate the hybrid space by a discrete set or relaxing it into a continuous set, which is
usually less efficient and robust. In this paper, we propose a parametrized deep Q-network (P-DQN)
for the hybrid action space without approximation or relaxation. Our algorithm combines DQN and
DDPG and can be viewed as an extension of the DQN to hybrid actions. The empirical study on the
game KOG validates the efficiency and effectiveness of our method.
Towards Building Affect sensitive Word Distributions
Authors: Kushal Chawla, Sopan Khosla, Niyati Chhaya, Kokil Jaidka
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Outlier Detection, Word Similarity

Learning word representations from large available corpora relies on the distributional hypothesis that words present in similar contexts tend to have similar meanings. Recent work has shown that word representations learnt in this manner lack sentiment information which, fortunately, can be leveraged using external knowledge. Our work addresses the question: can affect lexica improve the word representations learnt from a corpus? In this work, we propose techniques to incorporate affect lexica, which capture fine-grained information about a word's psycholinguistic and emotional orientation, into the training process of Word2Vec SkipGram, Word2Vec CBOW and GloVe methods using a joint learning approach. We use affect scores from Warriner's affect lexicon to regularize the vector representations learnt from an unlabelled corpus. Our proposed method outperforms previously proposed methods on standard tasks for word similarity detection, outlier detection and sentiment detection. We also demonstrate the usefulness of our approach for a new task related to the prediction of formality, frustration and politeness in corporate communication.
Unsupervised Learning of Entailment-Vector Word Embeddings
Authors: James Henderson
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Word Embeddings

Entailment vectors are a principled way to encode in a vector what information is known and what is unknown.  They are designed to model relations where one vector should include all the information in another vector, called entailment.  This paper investigates the unsupervised learning of entailment vectors for the semantics of words.  Using simple entailment-based models of the semantics of words in text (distributional semantics), we induce entailment-vector word embeddings which outperform the best previous results for predicting entailment between words, in unsupervised and semi-supervised experiments on hyponymy.
Graph Topological Features via GAN
Authors: Weiyi Liu, Hal Cooper, Min-hwan Oh
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1

Inspired by the success of generative adversarial networks (GANs) in image domains, we introduce a novel hierarchical architecture for learning characteristic topological features from a single arbitrary input graph via GANs. The hierarchical architecture consisting of multiple GANs preserves both local and global topological features, and automatically partitions the input graph into representative stages for feature learning. The stages facilitate reconstruction and can be used as indicators of the importance of the associated topological structures. Experiments show that our method produces subgraphs retaining a wide range of topological features, even in early reconstruction stages. This paper contains original research on combining the use of GANs and graph topological analysis.
Improve Training Stability of Semi-supervised Generative Adversarial Networks with Collaborative Training
Authors: Dalei Wu, Xiaohua Liu
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: General Classification, Generative Adversarial Network

Improved generative adversarial network (Improved GAN) is a successful method of using generative adversarial models to solve the problem of semi-supervised learning. However, it suffers from the problem of unstable training. In this paper, we found that the instability is mostly due to the vanishing gradients on the generator. To remedy this issue, we propose a new method to use collaborative training to improve the stability of semi-supervised GAN with the combination of Wasserstein GAN. The experiments have shown that our proposed method is more stable than the original Improved GAN and achieves comparable classification accuracy on different data sets.
Disentangled activations in deep networks
Authors: Mikael Kågebäck, Olof Mogren
Date: 2018-01-01 00:00:00
Proceeding: ICLR 2018 1
Tasks: Disentanglement

Deep neural networks have been tremendously successful in a number of tasks.
One of the main reasons for this is their capability to automatically
learn representations of data in levels of abstraction,
increasingly disentangling the data as the internal transformations are applied.
In this paper we propose a novel regularization method that penalize covariance between dimensions of the hidden layers in a network, something that benefits the disentanglement.
This makes the network learn nonlinear representations that are linearly uncorrelated, yet allows the model to obtain good results on a number of tasks, as demonstrated by our experimental evaluation.
The proposed technique can be used to find the dimensionality of the underlying data, because it effectively disables dimensions that aren't needed.
Our approach is simple and computationally cheap, as it can be applied as a regularizer to any gradient-based learning model.