pwc-archive/papers-with-abstracts huggingface dataset:
I Know the Feeling: Learning to Converse with Empathy
Authors: Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Dialogue Generation

Beyond understanding what is being discussed, human communication requires an awareness of what someone is feeling. One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill that is trivial for humans. Research in this area is made difficult by the paucity of suitable publicly available datasets both for emotion and dialogues. This work proposes a new task for empathetic dialogue generation and EmpatheticDialogues, a dataset of 25k conversations grounded in emotional situations to facilitate training and evaluating dialogue systems. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, while improving on other metrics as well (e.g. perceived relevance of responses, BLEU scores), compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of several ways to improve the performance of a given model by leveraging existing models or datasets without requiring lengthy re-training of the full model.
Neural MMO: A massively multiplayer game environment for intelligent agents
Authors: Joseph Suarez, Yilun Du, Phillip Isola, Igor Mordatch
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We present an artificial intelligence research platform inspired by the human game genre of MMORPGs (Massively Multiplayer Online Role-Playing Games, a.k.a. MMOs). We demonstrate how this platform can be used to study behavior and learning in large populations of neural agents. Unlike currently popular game environments, our platform supports persistent environments, with variable number of agents, and open-ended task descriptions. The emergence of complex life on Earth is often attributed to the arms race that ensued from a huge number of organisms all competing for finite resources. Our platform aims to simulate this setting in microcosm: we conduct a series of experiments to test how large-scale multiagent competition can incentivize the development of skillful behavior. We find that population size magnifies the complexity of the behaviors that emerge and results in agents that out-compete agents trained in smaller populations.
LEARNING GENERATIVE MODELS FOR DEMIXING OF STRUCTURED SIGNALS FROM THEIR SUPERPOSITION USING GANS
Authors: Mohammadreza Soltani, Swayambhoo Jain, Abhinav V. Sambasivan
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Compressive Sensing, Denoising

Recently, Generative Adversarial Networks (GANs) have emerged as a popular alternative for modeling complex high dimensional distributions. Most of the existing works implicitly assume that the clean samples from the target distribution are easily available. However, in many applications, this assumption is violated. In this paper, we consider the problem of learning GANs under the observation setting when the samples from target distribution are given by the superposition of two structured components. We propose two novel frameworks: denoising-GAN and demixing-GAN. The denoising-GAN assumes access to clean samples from the second component and try to learn the other distribution, whereas demixing-GAN learns the distribution of the components at the same time. Through comprehensive numerical experiments, we demonstrate that proposed frameworks can generate clean samples from unknown distributions, and provide competitive performance in tasks such as denoising, demixing, and compressive sensing.
Encoding Category Trees Into Word-Embeddings Using Geometric Approach
Authors: Tiansi Dong, Olaf Cremers, Hailong Jin, Juanzi Li, Chrisitan Bauckhage, Armin B. Cremers, Daniel Speicher, Joerg Zimmermann
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Word Embeddings

We present a novel method to implicitly encode a tree-structured category information into word-embeddings, resulting in super-dimensional ball representations ($n$-ball embedding for short). Inclusion relations among $n$-balls precisely encode subordinate relations among categories. The cosine  similarity function is enriched by category information. A large $n$-ball dataset is constructed using geometrical method, which achieves zero energy cost in embedding tree structures into word embedding. A new benchmark dataset is created for predicting the category of unknown words. Experiments show that $n$-ball embeddings, carried with category information, significantly out-perform word-embeddings in the neighbourhood test, while only slightly change the original word-embeddings. Experiment results also show that $n$-ball embeddings demonstrate surprisingly good performance in validating the category of unknown word. Source codes and data-sets are free for public access \url{https://github.com/gnodisnait/nball4tree.git} and \url{https://github.com/gnodisnait/bp94nball.git}.
Probabilistic Semantic Embedding
Authors: Yue Jiao, Jonathon Hare, Adam Prügel-Bennett
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, General Classification, Image Generation

We present an extension of a variational auto-encoder that creates semantically richcoupled probabilistic latent representations that capture the semantics of multiplemodalities of data. We demonstrate this model through experiments using imagesand textual descriptors as inputs and images as outputs. Our latent representationsare not only capable of driving a decoder to generate novel data, but can also be useddirectly for annotation or classification. Using the MNIST and Fashion-MNISTdatasets we show that the embedding not only provides better reconstruction andclassification performance than the current state-of-the-art, but it also allows us toexploit the semantic content of the pretrained word embedding spaces to do taskssuch as image generation from labels outside of those seen during training.
Pix2Scene: Learning Implicit 3D Representations from Images
Authors: Sai Rajeswar, Fahim Mannan, Florian Golemo, David Vazquez, Derek Nowrouzezahrai, Aaron Courville
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Spatial Reasoning

Modelling 3D scenes from 2D images is a long-standing problem in computer vision with implications in, e.g., simulation and robotics. We propose pix2scene, a deep generative-based approach that implicitly models the geometric properties of a scene from images. Our method learns the depth and orientation of scene points visible in images. Our model can then predict the structure of a scene from various, previously unseen view points. It relies on a bi-directional adversarial learning mechanism to generate scene representations from a latent code, inferring the 3D representation of the underlying scene geometry. We showcase a novel differentiable renderer to train the 3D model in an end-to-end fashion, using only images. We demonstrate the generative ability of our model qualitatively on both a custom dataset and on ShapeNet. Finally, we evaluate the effectiveness of the learned 3D scene representation in supporting a 3D spatial reasoning.
COCO-GAN: Conditional Coordinate Generative Adversarial Network
Authors: Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Generative Adversarial Network, Image Generation, Scene Generation

Recent advancements on Generative Adversarial Network (GAN) have inspired a wide range of works that generate synthetic images. However, the current processes have to generate an entire image at once, and therefore resolutions are limited by memory or computational constraints. In this work, we propose COnditional COordinate GAN (COCO-GAN), which generates a specific patch of an image conditioned on a spatial position rather than the entire image at a time. The generated patches are later combined together to form a globally coherent full-image. With this process, we show that the generated image can achieve competitive quality to state-of-the-arts and the generated patches are locally smooth between consecutive neighbors. One direct implication of the COCO-GAN is that it can be applied onto any coordinate systems including the cylindrical systems which makes it feasible for generating panorama images. The fact that the patch generation process is independent to each other inspires a wide range of new applications: firstly, "Patch-Inspired Image Generation" enables us to generate the entire image based on a single patch. Secondly, "Partial-Scene Generation" allows us to generate images within a customized target region. Finally, thanks to COCO-GAN's patch generation and massive parallelism, which enables combining patches for generating a full-image with higher resolution than state-of-the-arts.
Learning Entropic Wasserstein Embeddings
Authors: Charlie Frogner, Farzaneh Mirzazadeh, Justin Solomon
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Dimensionality Reduction

Despite their prevalence, Euclidean embeddings of data are fundamentally limited in their ability to capture latent semantic structures, which need not conform to Euclidean spatial assumptions. Here we consider an alternative, which embeds data as discrete probability distributions in a Wasserstein space, endowed with an optimal transport metric. Wasserstein spaces are much larger and more flexible than Euclidean spaces, in that they can successfully embed a wider variety of metric structures. We propose to exploit this flexibility by learning an embedding that captures the semantic information in the Wasserstein distance between embedded distributions. We examine empirically the representational capacity of such learned Wasserstein embeddings, showing that they can embed a wide variety of complex metric structures with smaller distortion than an equivalent Euclidean embedding. We also investigate an application to word embedding, demonstrating a unique advantage of Wasserstein embeddings: we can directly visualize the high-dimensional embedding, as it is a probability distribution on a low-dimensional space. This obviates the need for dimensionality reduction techniques such as t-SNE for visualization.
Backplay: 'Man muss immer umkehren'
Authors: Cinjon Resnick, Roberta Raileanu, Sanyam Kapoor, Alexander Peysakhovich, Kyunghyun Cho, Joan Bruna
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Model-free reinforcement learning (RL) requires a large number of trials to learn a good policy, especially in environments with sparse rewards. We explore a method to improve the sample efficiency when we have access to demonstrations. Our approach, Backplay, uses a single demonstration to construct a curriculum for a given task. Rather than starting each training episode in the environment's fixed initial state, we start the agent near the end of the demonstration and move the starting point backwards during the course of training until we reach the initial state. Our contributions are that we analytically characterize the types of environments where Backplay can improve training speed, demonstrate the effectiveness of Backplay both in large grid worlds and a complex four player zero-sum game (Pommerman), and show that Backplay compares favorably to other competitive methods known to improve sample efficiency. This includes reward shaping, behavioral cloning, and reverse curriculum generation.
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
Authors: Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: counterfactual, Counterfactual Reasoning, Inductive Bias, Multi-agent Reinforcement Learning, Reinforcement Learning

We derive a new intrinsic social motivation for multi-agent reinforcement learning (MARL), in which agents are rewarded for having causal influence over another agent's actions, where causal influence is assessed using counterfactual reasoning. The reward does not depend on observing another agent's reward function, and is thus a more realistic approach to MARL than taken in previous work. We show that the causal influence reward is related to maximizing the mutual information between agents' actions. We test the approach in challenging social dilemma environments, where it consistently leads to enhanced cooperation between agents and higher collective reward. Moreover, we find that rewarding influence can lead agents to develop emergent communication protocols. Therefore, we also employ influence to train agents to use an explicit communication channel, and find that it leads to more effective communication and higher collective reward. Finally, we show that influence can be computed by equipping each agent with an internal model that predicts the actions of other agents. This allows the social influence reward to be computed without the use of a centralised controller, and as such represents a significantly more general and scalable inductive bias for MARL with independent agents.
Robustness Certification with Refinement
Authors: Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

We present a novel approach for verification of neural networks which combines scalable over-approximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state of the art verifiers on feed forward neural networks with piecewise linear activation functions.
Using Ontologies To Improve Performance In Massively Multi-label Prediction
Authors: Ethan Steinberg, Peter J. Liu
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Disease Prediction, General Classification, Prediction, Protein Function Prediction

Massively multi-label prediction/classification problems arise in environments like health-care or biology where it is useful to make very precise predictions. One challenge with massively multi-label problems is that there is often a long-tailed frequency distribution for the labels, resulting in few positive examples for the rare labels. We propose a solution to this problem by modifying the output layer of a neural network to create a Bayesian network of sigmoids which takes advantage of ontology relationships between the labels to help share information between the rare and the more common labels.  We apply this method to the two massively multi-label tasks of disease prediction (ICD-9 codes) and protein function prediction (Gene Ontology terms) and obtain significant improvements in per-label AUROC and average precision.
Overfitting Detection of Deep Neural Networks without a Hold Out Set
Authors: Konrad Groh
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5

Overfitting is an ubiquitous problem in neural network training and usually mitigated using a holdout data set.
Here we challenge this rationale and investigate criteria for overfitting without using a holdout data set.
Specifically, we train a model for a fixed number of epochs multiple times with varying fractions of randomized labels and for a range of regularization strengths. 
A properly trained model should not be able to attain an accuracy greater than the fraction of properly labeled data points. Otherwise the model overfits. 
We introduce two criteria for detecting overfitting and one to detect underfitting. We analyze early stopping, the regularization factor, and network depth.
In safety critical applications we are interested in models and parameter settings which perform well and are not likely to overfit. The methods of this paper allow characterizing and identifying such models.
Minimum Divergence vs. Maximum Margin: an Empirical Comparison on Seq2Seq Models
Authors: Huan Zhang, Hai Zhao
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, Sentence, Sentence Summarization, Translation

Sequence to sequence (seq2seq) models have become a popular framework for neural sequence prediction. While traditional seq2seq models are trained by Maximum Likelihood Estimation (MLE), much recent work has made various attempts to optimize evaluation scores directly to solve the mismatch between training and evaluation, since model predictions are usually evaluated by a task specific evaluation metric like BLEU or ROUGE scores instead of perplexity. This paper for the first time puts this existing work into two categories, a) minimum divergence, and b) maximum margin. We introduce a new training criterion based on the analysis of existing work, and empirically compare models in the two categories. Our experimental results show that our new training criterion can usually work better than existing methods, on both the tasks of machine translation and sentence summarization.
DATNet: Dual Adversarial Transfer for Low-resource Named Entity Recognition
Authors: Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Rick Siow Mong Goh, Kenneth Kwok
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Low Resource Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

We propose a new architecture termed Dual Adversarial Transfer Network (DATNet) for addressing low-resource Named Entity Recognition (NER). Specifically, two variants of DATNet, i.e., DATNet-F and DATNet-P, are proposed to explore effective feature fusion between high and low resource. To address the noisy and imbalanced training data, we propose a novel Generalized Resource-Adversarial Discriminator (GRAD). Additionally, adversarial training is adopted to boost model generalization. We examine the effects of different components in DATNet across domains and languages and show that significant improvement can be obtained especially for low-resource data. Without augmenting any additional hand-crafted features, we achieve new state-of-the-art performances on CoNLL and Twitter NER---88.16% F1 for Spanish, 53.43% F1 for WNUT-2016, and 42.83% F1 for WNUT-2017.
Soft Q-Learning with Mutual-Information Regularization
Authors: Jordi Grau-Moya, Felix Leibfried, Peter Vrancx
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decision Making, Q-Learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

We propose a reinforcement learning (RL) algorithm that uses mutual-information regularization to optimize a prior action distribution for better performance and exploration. Entropy-based regularization has previously been shown to improve both exploration and robustness in challenging sequential decision-making tasks. It does so by encouraging policies to put probability mass on all actions. However, entropy regularization might be undesirable when actions have significantly different importance. In this paper, we propose a theoretically motivated framework that dynamically weights the importance of actions by using the mutual-information. In particular, we express the RL problem as an inference problem where the prior probability distribution over actions is subject to optimization. We show that the prior optimization introduces a mutual-information regularizer in the RL objective. This regularizer encourages the policy to be close to a non-uniform distribution that assigns higher probability mass to more important actions. We empirically demonstrate that our method significantly improves over entropy regularization methods and unregularized methods.
Adversarial Attacks on Node Embeddings
Authors: Aleksandar Bojchevski, Stephan Günnemann
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Representation Learning

The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despite the proliferation of such methods there is currently no study of their robustness to adversarial attacks. We provide the first adversarial vulnerability analysis on the widely used family of methods based on random walks. We derive efficient adversarial perturbations that poison the network structure and have a negative effect on both the quality of the embeddings and the downstream tasks. We further show that our attacks are transferable since they generalize to many models, and are successful even when the attacker is restricted.
Training Hard-Threshold Networks with Combinatorial Search in a Discrete Target Propagation Setting
Authors: Lukas Nabergall, Justin Toth, Leah Cousins
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Combinatorial Optimization

Learning deep neural networks with hard-threshold activation has recently become an important problem due to the proliferation of resource-constrained computing devices. In order to circumvent the inability to train with backpropagation in the present of hard-threshold activations, \cite{friesen2017} introduced a discrete target propagation framework for training hard-threshold networks in a layer-by-layer fashion. Rather than using a gradient-based target heuristic, we explore the use of search methods for solving the target setting problem. Building on both traditional combinatorial optimization algorithms and gradient-based techniques, we develop a novel search algorithm Guided Random Local Search (GRLS). We demonstrate the effectiveness of our algorithm in training small networks on several datasets and evaluate our target-setting algorithm compared to simpler search methods and gradient-based techniques. Our results indicate that combinatorial optimization is a viable method for training hard-threshold networks that may have the potential to eventually surpass gradient-based methods in many settings.
Projective Subspace Networks For Few-Shot Learning
Authors: Christian Simon, Piotr Koniusz, Mehrtash Harandi
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Few-Shot Learning, General Classification, Lifelong learning, Meta-Learning

Generalization from limited examples, usually studied under the umbrella of meta-learning, equips learning techniques with the ability to adapt quickly in dynamical environments and proves to be an essential aspect of lifelong learning. In this paper, we introduce the Projective Subspace Networks (PSN), a deep learning paradigm that learns non-linear embeddings from limited supervision. In contrast to previous studies, the embedding in PSN deems samples of a given class to form an affine subspace. We will show that such modeling leads to robust solutions, yielding competitive results on supervised and semi-supervised few-shot classification. Moreover, our PSN approach has the ability of end-to-end learning. In contrast to previous works, our projective subspace can be thought of as a richer representation capturing higher-order information datapoints for modeling new concepts.