pwc-archive/papers-with-abstracts huggingface dataset:
Wasserstein-Bounded Generative Adversarial Networks
Authors: Peng Zhou, Bingbing Ni, Lingxi Xie, Xiaopeng Zhang, Hang Wang, Cong Geng, Qi Tian
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

In the field of Generative Adversarial Networks (GANs), how to design a stable training strategy remains an open problem. Wasserstein GANs have largely promoted the stability over the original GANs by introducing Wasserstein distance, but still remain unstable and are prone to a variety of failure modes. In this paper, we present a general framework named Wasserstein-Bounded GAN (WBGAN), which improves a large family of WGAN-based approaches by simply adding an upper-bound constraint to the Wasserstein term. Furthermore, we show that WBGAN can reasonably measure the difference of distributions which almost have no intersection. Experiments demonstrate that WBGAN can stabilize as well as accelerate convergence in the training processes of a series of WGAN-based variants.
BEYOND SUPERVISED LEARNING: RECOGNIZING UNSEEN ATTRIBUTE-OBJECT PAIRS WITH VISION-LANGUAGE FUSION AND ATTRACTOR NETWORKS
Authors: Hui Chen, Zhixiong Nan, Nanning Zheng
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Attribute, Object

This paper handles a challenging problem, unseen attribute-object pair recognition, which asks a model to simultaneously recognize the attribute type and the object type of a given image while this attribute-object pair is not included in the training set. In the past years, the conventional classifier-based methods, which recognize unseen attribute-object pairs by composing separately-trained attribute classifiers and object classifiers, are strongly frustrated. Different from conventional methods, we propose a generative model with a visual pathway and a linguistic pathway. In each pathway, the attractor network is involved to learn the intrinsic feature representation to explore the inner relationship between the attribute and the object. With the learned features in both pathways, the unseen attribute-object pair is recognized by finding out the pair whose linguistic feature closely matches the visual feature of the given image. On two public datasets, our model achieves impressive experiment results, notably outperforming the state-of-the-art methods.
Out-of-Distribution Detection Using Layerwise Uncertainty in Deep Neural Networks
Authors: Hirono Okamoto, Masahiro Suzuki, Yutaka Matsuo
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Classification, General Classification, Out-of-Distribution Detection

In this paper, we tackle the problem of detecting samples that are not drawn from the training distribution, i.e., out-of-distribution (OOD) samples, in classification. Many previous studies have attempted to solve this problem by regarding samples with low classification confidence as OOD examples using deep neural networks (DNNs). However, on difficult datasets or models with low classification ability, these methods incorrectly regard in-distribution samples close to the decision boundary as OOD samples. This problem arises because their approaches use only the features close to the output layer and disregard the uncertainty of the features. Therefore, we propose a method that extracts the uncertainties of features in each layer of DNNs using a reparameterization trick and combines them. In experiments, our method outperforms the existing methods by a large margin, achieving state-of-the-art detection performance on several datasets and classification models. For example, our method increases the AUROC score of prior work (83.8%) to 99.8% in DenseNet on the CIFAR-100 and Tiny-ImageNet datasets.
MODiR: Multi-Objective Dimensionality Reduction for Joint Data Visualisation
Authors: Tim Repke, Ralf Krestel
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Dimensionality Reduction

Many large text collections exhibit graph structures, either inherent to the content itself or encoded in the metadata of the individual documents.
Example graphs extracted from document collections are co-author networks, citation networks, or named-entity-cooccurrence networks.
Furthermore, social networks can be extracted from email corpora, tweets, or social media. 
When it comes to visualising these large corpora, either the textual content or the network graph are used.

In this paper, we propose to incorporate both, text and graph, to not only visualise the semantic information encoded in the documents' content but also the relationships expressed by the inherent network structure.
To this end, we introduce a novel algorithm based on multi-objective optimisation to jointly position embedded documents and graph nodes in a two-dimensional landscape.
We illustrate the effectiveness of our approach with real-world datasets and show that we can capture the semantics of large document collections better than other visualisations based on either the content or the network information.
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches
Authors: George H. Chen, Linhong Li, Ren Zuo, Amanda Coston, Jeremy C. Weiss
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Survival Analysis, Time-to-Event Prediction, Topic Models

We introduce two approaches to topic modeling supervised by survival analysis. Both approaches predict time-to-event outcomes while simultaneously learning topics over features that help prediction. The high-level idea is to represent each data point as a distribution over topics using some underlying topic model. Then each data point's distribution over topics is fed as input to a survival model. The topic and survival models are jointly learned. The two approaches we propose differ in the generality of topic models they can learn. The first approach finds topics via archetypal analysis, a nonnegative matrix factorization method that optimizes over a wide class of topic models encompassing latent Dirichlet allocation (LDA), correlated topic models, and topic models based on the ``anchor word'' assumption; the resulting survival-supervised variant solves an alternating minimization problem. Our second approach builds on recent work that approximates LDA in a neural net framework. We add a survival loss layer to this neural net to form an approximation to survival-supervised LDA. Both of our approaches can be combined with a variety of survival models. We demonstrate our approach on two survival datasets, showing that survival-supervised topic models can achieve competitive time-to-event prediction accuracy while outputting clinically interpretable topics.
Way Off-Policy Batch Deep Reinforcement Learning of Human Preferences in Dialog
Authors: Natasha Jaques, Asma Ghandeharioun, Judy Hanwen Shen, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Deep Reinforcement Learning, OpenAI Gym, Open-Domain Dialog, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Most deep reinforcement learning (RL) systems are not able to learn effectively from off-policy data, especially if they cannot explore online in the environment. This is a critical shortcoming for applying RL to real-world problems where collecting data is expensive, and models must be tested offline before being deployed to interact with the environment -- e.g. systems that learn from human interaction. Thus, we develop a novel class of off-policy batch RL algorithms which use KL-control to penalize divergence from a pre-trained prior model of probable actions. This KL-constraint reduces extrapolation error, enabling effective offline learning, without exploration, from a fixed batch of data. We also use dropout-based uncertainty estimates to lower bound the target Q-values as a more efficient alternative to Double Q-Learning. This Way Off-Policy (WOP) algorithm is tested on both traditional RL tasks from OpenAI Gym, and on the problem of open-domain dialog generation; a challenging reinforcement learning problem with a 20,000 dimensional action space. WOP allows for the extraction of multiple different reward functions post-hoc from collected human interaction data, and can learn effectively from all of these. We test real-world generalization by deploying dialog models live to converse with humans in an open-domain setting, and demonstrate that WOP achieves significant improvements over state-of-the-art prior methods in batch deep RL.
Discovering Motor Programs by Recomposing Demonstrations
Authors: Tanmay Shankar, Shubham Tulsiani, Lerrel Pinto, Abhinav Gupta
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Hierarchical Reinforcement Learning, Reinforcement Learning

In this paper, we present an approach to learn recomposable motor primitives across large-scale and diverse manipulation demonstrations. Current approaches to decomposing demonstrations into primitives often assume manually defined primitives and bypass the difficulty of discovering these primitives. On the other hand, approaches in primitive discovery put restrictive assumptions on the complexity of a primitive, which limit applicability to narrow tasks. Our approach attempts to circumvent these challenges by jointly learning both the underlying motor primitives and recomposing these primitives to form the original demonstration. Through constraints on both the parsimony of primitive decomposition and the simplicity of a given primitive, we are able to learn a diverse set of motor primitives, as well as a coherent latent representation for these primitives. We demonstrate, both qualitatively and quantitatively, that our learned primitives capture semantically meaningful aspects of a demonstration. This allows us to compose these primitives in a hierarchical reinforcement learning setup to efficiently solve robotic manipulation tasks like reaching and pushing.
Improving the Generalization of Visual Navigation Policies using Invariance Regularization
Authors: Michel Aractingi, Christopher Dance, Julien Perez, Tomi Silander
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Visual Navigation

Training agents to operate in one environment often yields overfitted models that are unable to generalize to the changes in that environment. However, due to the numerous variations that can occur in the real-world, the agent is often required to be robust in order to be useful. This has not been the case for agents trained with reinforcement learning (RL) algorithms. In this paper, we investigate the overfitting of RL agents to the training environments in visual navigation tasks. Our experiments show that deep RL agents can overfit even when trained on multiple environments simultaneously. 
We propose a regularization method which combines RL with supervised learning methods by adding a term to the RL objective that would encourage the invariance of a policy to variations in the observations that ought not to affect the action taken. The results of this method, called invariance regularization, show an improvement in the generalization of policies to environments not seen during training.
Stagnant zone segmentation with U-net
Authors: Selam Waktola, Laurent Babout, Krzysztof Grudzien
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Computed Tomography (CT), Image Segmentation, Segmentation, Semantic Segmentation

Silo discharging and monitoring the process for
industrial or research application depend on computerized
segmentation of different parts of images such as stagnant and
flowing zones which is the toughest task. X-ray Computed
Tomography (CT) is one of a powerful non-destructive technique
for cross-sectional images of a 3D object based on X-ray
absorption. CT is the most proficient for investigating different
granular flow phenomena and segmentation of the stagnant zone
as compared to other imaging techniques. In any case, manual
segmentation is tiresome and erroneous for further investigations.
Hence, automatic and precise strategies are required. In the
present work, a U-net architecture is used for segmenting the
stagnant zone during silo discharging process. This proposed
image segmentation method provides fast and effective outcomes
by exploiting a convolutional neural networks technique with an
accuracy of 97 percent
LDMGAN: Reducing Mode Collapse in GANs with Latent Distribution Matching
Authors: Zhiwen Zuo, Lei Zhao, Huiming Zhang, Qihang Mo, Haibo Chen, Zhizhong Wang, Ailin Li, Lihong Qiu, Wei Xing, Dongming Lu
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Diversity

Generative Adversarial Networks (GANs) have shown impressive results in modeling distributions over complicated manifolds such as those of natural images. However, GANs often suffer from mode collapse, which means they are prone to characterize only a single or a few modes of the data distribution. In order to address this problem, we propose a novel framework called LDMGAN. We ﬁrst introduce Latent Distribution Matching (LDM) constraint which regularizes the generator by aligning distribution of generated samples with that of real samples in latent space. To make use of such latent space, we propose a regularized AutoEncoder (AE) that maps the data distribution to prior distribution in encoded space. Extensive experiments on synthetic data and real world datasets show that our proposed framework signiﬁcantly improves GAN’s stability and diversity.
Optimising Neural Network Architectures for Provable Adversarial Robustness
Authors: Henry Gouk, Timothy M. Hospedales
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Adversarial Robustness

Existing Lipschitz-based provable defences to adversarial examples only cover the L2 threat model. We introduce the first bound that makes use of Lipschitz continuity to provide a more general guarantee for threat models based on any p-norm. Additionally, a new strategy is proposed for designing network architectures that exhibit superior provable adversarial robustness over conventional convolutional neural networks. Experiments are conducted to validate our theoretical contributions, show that the assumptions made during the design of our novel architecture hold in practice, and quantify the empirical robustness of several Lipschitz-based adversarial defence methods.
AN EFFICIENT HOMOTOPY TRAINING ALGORITHM FOR NEURAL NETWORKS
Authors: Qipin Chen, Wenrui Hao
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

We present a Homotopy Training Algorithm (HTA) to solve optimization problems arising from neural networks. The HTA starts with several decoupled systems with low dimensional structure and tracks the solution to the high dimensional coupled system. The decoupled systems are easy to solve due to the low dimensionality but can be connected to the original system via a continuous homotopy path guided by the HTA. We have proved the convergence of HTA for the non-convex case and existence of the homotopy solution path for the convex case. The HTA has provided a better accuracy on several examples including VGG models on CIFAR-10. Moreover, the HTA would be combined with the dropout technique to provide an alternative way to train the neural networks.
Improving End-to-End Object Tracking Using Relational Reasoning
Authors: Fabian B. Fuchs, Adam R. Kosiorek, Li Sun, Oiwi Parker Jones, Ingmar Posner
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Multi-Object Tracking, Object, Object Tracking, Prediction, Relational Reasoning, Trajectory Prediction

Relational reasoning, the ability to model interactions and relations between objects, is valuable for robust multi-object tracking and pivotal for trajectory prediction. In this paper, we propose MOHART, a class-agnostic, end-to-end multi-object tracking and trajectory prediction algorithm, which explicitly accounts for permutation invariance in its relational reasoning. We explore a number of permutation invariant architectures and show that multi-headed self-attention outperforms the provided baselines and better accounts for complex physical interactions in a challenging toy experiment. We show on three real-world tracking datasets that adding relational reasoning capabilities in this way increases the tracking and trajectory prediction performance, particularly in the presence of ego-motion, occlusions, crowded scenes, and faulty sensor inputs. To the best of our knowledge, MOHART is the first fully end-to-end multi-object tracking from vision approach applied to real-world data reported in the literature.
Scaling Laws for the Principled Design, Initialization, and Preconditioning of ReLU Networks
Authors: Aaron Defazio, Leon Bottou
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1

Abstract In this work, we describe a set of rules for the design and initialization of well-conditioned neural networks, guided by the goal of naturally balancing the diagonal blocks of the Hessian at the start of training. We show how our measure of conditioning of a block relates to another natural measure of conditioning, the ratio of weight gradients to the weights. We prove that for a ReLU-based deep multilayer perceptron, a simple initialization scheme using the geometric mean of the fan-in and fan-out satisfies our scaling rule. For more sophisticated architectures, we show how our scaling principle can be used to guide design choices to produce well-conditioned neural networks, reducing guess-work.
Parallel Neural Text-to-Speech
Authors: Kainan Peng, Wei Ping, Zhao Song, Kexin Zhao
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: text-to-speech, Text to Speech

In this work, we first propose ParaNet, a non-autoregressive seq2seq model that converts text to spectrogram. It is fully convolutional and obtains 46.7 times speed-up over Deep Voice 3 at synthesis while maintaining comparable speech quality using a WaveNet vocoder. ParaNet also produces stable alignment between text and speech on the challenging test sentences by iteratively improving the attention in a layer-by-layer manner. Based on ParaNet, we build the first fully parallel neural text-to-speech system using parallel neural vocoders, which can synthesize speech from text through a single feed-forward pass.  We investigate several parallel vocoders within the TTS system, including variants of IAF vocoders and bipartite flow vocoder.
Cross-Dimensional Self-Attention for Multivariate, Geo-tagged Time Series Imputation
Authors: Jiawei Ma*, Zheng Shou*, Alireza Zareian, Hassan Mansour, Anthony Vetro, Shih-Fu Chang
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Imputation, Machine Translation, Missing Values, Time Series, Time Series Analysis

Many real-world applications involve multivariate, geo-tagged time series data: at each location, multiple sensors record corresponding measurements. For example, air quality monitoring system records PM2.5, CO, etc. The resulting time-series data often has missing values due to device outages or communication errors. In order to impute the missing values, state-of-the-art methods are built on Recurrent Neural Networks (RNN), which process each time stamp sequentially, prohibiting the direct modeling of the relationship between distant time stamps. Recently, the self-attention mechanism has been proposed for sequence modeling tasks such as machine translation, significantly outperforming RNN because the relationship between each two time stamps can be modeled explicitly. In this paper, we are the first to adapt the self-attention mechanism for multivariate, geo-tagged time series data. In order to jointly capture the self-attention across different dimensions (i.e. time, location and sensor measurements) while keep the size of attention maps reasonable, we propose a novel approach called Cross-Dimensional Self-Attention (CDSA) to process each dimension sequentially, yet in an order-independent manner. On three real-world datasets, including one our newly collected NYC-traffic dataset, extensive experiments demonstrate the superiority of our approach compared to state-of-the-art methods for both imputation and forecasting tasks.
Deep Reinforcement Learning with Implicit Human Feedback
Authors: Duo Xu, Mohit Agarwal, Raghupathy Sivakumar, Faramarz Fekri
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Atari Games, Deep Reinforcement Learning, EEG, Electroencephalogram (EEG), reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the following central question in the field of Deep Reinforcement Learning (DRL): How can we use implicit human feedback to accelerate and optimize the training of a DRL algorithm? State-of-the-art methods rely on any human feedback to be provided explicitly, requiring the active participation of humans (e.g., expert labeling, demonstrations, etc.). In this work, we investigate an alternative paradigm, where non-expert humans are silently observing (and assessing) the agent interacting with the environment. The human's intrinsic reactions to the agent's behavior is sensed as implicit feedback by placing electrodes on the human scalp and monitoring what are known as event-related electric potentials. The implicit feedback is then used to augment the agent's learning in the RL tasks. We develop a system to obtain and accurately decode the implicit human feedback (specifically error-related event potentials) for state-action pairs in an Atari-type environment. As a baseline contribution, we demonstrate the feasibility of capturing error-potentials  of a human observer watching an agent learning to play several different Atari-games using an electroencephalogram (EEG) cap, and then decoding the signals appropriately and using them as an auxiliary reward function to a DRL algorithm with the intent of accelerating its learning of the game. Building atop the baseline, we then make the following novel contributions in our work:
(i) We argue that the definition of error-potentials is generalizable across different environments; specifically we show that error-potentials of an observer can be learned for a specific game, and the definition used as-is for another game without requiring re-learning of the error-potentials.  
(ii) We propose two different frameworks to combine recent advances in DRL into the error-potential based feedback system in a sample-efficient manner, allowing humans to provide implicit feedback while training in the loop, or prior to the training of the RL agent.
(iii) Finally, we scale the implicit human feedback (via ErrP) based RL to reasonably complex environments (games) and demonstrate the significance of our approach through synthetic and real user experiments.
Towards understanding the true loss surface of deep neural networks using random matrix theory and iterative spectral methods
Authors: Diego Granziol, Timur Garipov, Dmitry Vetrov, Stefan Zohren, Stephen Roberts, Andrew Gordon Wilson
Date: 2020-01-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: GPU

The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning. The Hessian is often used to understand these geometric properties. We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian. Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian. We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16. To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature. This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.