pwc-archive/papers-with-abstracts huggingface dataset:
COCO-GAN: Generation by Parts via Conditional Coordinating
Authors: Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen, Da-Cheng Juan, Wei Wei, Hwann-Tzong Chen
Date: 2019-03-30 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Generation, Image Generation

Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observations to piece together the surrounding environment. Inspired by such behavior and the fact that machines also have computational constraints, we propose \underline{CO}nditional \underline{CO}ordinate GAN (COCO-GAN) of which the generator generates images by parts based on their spatial coordinates as the condition. On the other hand, the discriminator learns to justify realism across multiple assembled patches by global coherence, local appearance, and edge-crossing continuity. Despite the full images are never generated during training, we show that COCO-GAN can produce \textbf{state-of-the-art-quality} full images during inference. We further demonstrate a variety of novel applications enabled by teaching the network to be aware of coordinates. First, we perform extrapolation to the learned coordinate manifold and generate off-the-boundary patches. Combining with the originally generated full image, COCO-GAN can produce images that are larger than training samples, which we called "beyond-boundary generation". We then showcase panorama generation within a cylindrical coordinate system that inherently preserves horizontally cyclic topology. On the computation side, COCO-GAN has a built-in divide-and-conquer paradigm that reduces memory requisition during training and inference, provides high-parallelism, and can generate parts of images on-demand.
Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting
Authors: Xilai Li, Yingbo Zhou, Tianfu Wu, Richard Socher, Caiming Xiong
Date: 2019-03-31 00:00:00
Tasks: Continual Learning, Neural Architecture Search, parameter estimation, Permuted-MNIST

Addressing catastrophic forgetting is one of the key challenges in continual learning where machine learning systems are trained with sequential or streaming tasks. Despite recent remarkable progress in state-of-the-art deep learning, deep neural networks (DNNs) are still plagued with the catastrophic forgetting problem. This paper presents a conceptually simple yet general and effective framework for handling catastrophic forgetting in continual learning with DNNs. The proposed method consists of two components: a neural structure optimization component and a parameter learning and/or fine-tuning component. By separating the explicit neural structure learning and the parameter estimation, not only is the proposed method capable of evolving neural structures in an intuitively meaningful way, but also shows strong capabilities of alleviating catastrophic forgetting in experiments. Furthermore, the proposed method outperforms all other baselines on the permuted MNIST dataset, the split CIFAR100 dataset and the Visual Domain Decathlon dataset in continual learning setting.
Variational Adversarial Active Learning
Authors: Samarth Sinha, Sayna Ebrahimi, Trevor Darrell
Date: 2019-03-31 00:00:00
Proceeding: ICCV 2019 10
Tasks: Active Learning, image-classification, Image Classification, Semantic Segmentation

Active learning aims to develop label-efficient algorithms by sampling the most representative queries to be labeled by an oracle. We describe a pool-based semi-supervised active learning algorithm that implicitly learns this sampling mechanism in an adversarial manner. Unlike conventional active learning algorithms, our approach is task agnostic, i.e., it does not depend on the performance of the task for which we are trying to acquire labeled data. Our method learns a latent space using a variational autoencoder (VAE) and an adversarial network trained to discriminate between unlabeled and labeled data. The mini-max game between the VAE and the adversarial network is played such that while the VAE tries to trick the adversarial network into predicting that all data points are from the labeled pool, the adversarial network learns how to discriminate between dissimilarities in the latent space. We extensively evaluate our method on various image classification and semantic segmentation benchmark datasets and establish a new state of the art on $\text{CIFAR10/100}$, $\text{Caltech-256}$, $\text{ImageNet}$, $\text{Cityscapes}$, and $\text{BDD100K}$. Our results demonstrate that our adversarial approach learns an effective low dimensional latent space in large-scale settings and provides for a computationally efficient sampling method. Our code is available at https://github.com/sinhasam/vaal.
Optimal Fusion of Elliptic Extended Target Estimates based on the Wasserstein Distance
Authors: Kolja Thormann, Marcus Baum
Date: 2019-04-01 00:00:00
Tasks: Object

This paper considers the fusion of multiple estimates of a spatially extended object, where the object extent is modeled as an ellipse parameterized by the orientation and semiaxes lengths. For this purpose, we propose a novel systematic approach that employs a distance measure for ellipses, i.e., the Gaussian Wasserstein distance, as a cost function. We derive an explicit approximate expression for the Minimum Mean Gaussian Wasserstein distance (MMGW) estimate. Based on the concept of a MMGW estimator, we develop efficient methods for the fusion of extended target estimates. The proposed fusion methods are evaluated in a simulated experiment and the benefits of the novel methods are discussed.
Netherlands Dataset: A New Public Dataset for Machine Learning in Seismic Interpretation
Authors: Reinaldo Mozart Silva, Lais Baroni, Rodrigo S. Ferreira, Daniel Civitarese, Daniela Szwarcman, Emilio Vital Brazil
Date: 2019-03-26 00:00:00
Tasks: BIG-bench Machine Learning, POS, Seismic Interpretation

Machine learning and, more specifically, deep learning algorithms have seen
remarkable growth in their popularity and usefulness in the last years. This is
arguably due to three main factors: powerful computers, new techniques to train
deeper networks and larger datasets. Although the first two are readily
available in modern computers and ML libraries, the last one remains a
challenge for many domains. It is a fact that big data is a reality in almost
all fields nowadays, and geosciences are not an exception. However, to achieve
the success of general-purpose applications such as ImageNet - for which there
are +14 million labeled images for 1000 target classes - we not only need more
data, we need more high-quality labeled data. When it comes to the Oil&Gas
industry, confidentiality issues hamper even more the sharing of datasets. In
this work, we present the Netherlands interpretation dataset, a contribution to
the development of machine learning in seismic interpretation. The Netherlands
F3 dataset acquisition was carried out in the North Sea, Netherlands offshore.
The data is publicly available and contains pos-stack data, 8 horizons and well
logs of 4 wells. For the purposes of our machine learning tasks, the original
dataset was reinterpreted, generating 9 horizons separating different seismic
facies intervals. The interpreted horizons were used to generate approximatelly
190,000 labeled images for inlines and crosslines. Finally, we present two deep
learning applications in which the proposed dataset was employed and produced
compelling results.
Non-linear aggregation of filters to improve image denoising
Authors: Benjamin Guedj, Juliette Rengot
Date: 2019-04-01 00:00:00
Tasks: Denoising, Image Denoising

We introduce a novel aggregation method to efficiently perform image denoising. Preliminary filters are aggregated in a non-linear fashion, using a new metric of pixel proximity based on how the pool of filters reaches a consensus. We provide a theoretical bound to support our aggregation scheme, its numerical performance is illustrated and we show that the aggregate significantly outperforms each of the preliminary filters.
The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
Authors: Fabio Cermelli, Massimiliano Mancini, Elisa Ricci, Barbara Caputo
Date: 2019-04-01 00:00:00
Tasks: object-detection, Object Detection, Pose Estimation, Scene Segmentation

Deep networks have brought significant advances in robot perception, enabling
to improve the capabilities of robots in several visual tasks, ranging from
object detection and recognition to pose estimation, semantic scene
segmentation and many others. Still, most approaches typically address visual
tasks in isolation, resulting in overspecialized models which achieve strong
performances in specific applications but work poorly in other (often related)
tasks. This is clearly sub-optimal for a robot which is often required to
perform simultaneously multiple visual recognition tasks in order to properly
act and interact with the environment. This problem is exacerbated by the
limited computational and memory resources typically available onboard to a
robotic platform. The problem of learning flexible models which can handle
multiple tasks in a lightweight manner has recently gained attention in the
computer vision community and benchmarks supporting this research have been
proposed. In this work we study this problem in the robot vision context,
proposing a new benchmark, the RGB-D Triathlon, and evaluating state of the art
algorithms in this novel challenging scenario. We also define a new evaluation
protocol, better suited to the robot vision setting. Results shed light on the
strengths and weaknesses of existing approaches and on open issues, suggesting
directions for future research.
A Gaussian process latent force model for joint input-state estimation in linear structural systems
Authors: Rajdip Nayek, Souvik Chakraborty, Sriram Narasimhan
Date: 2019-03-29 00:00:00
Tasks: Gaussian Processes, State Estimation, State Space Models

The problem of combined state and input estimation of linear structural
systems based on measured responses and a priori knowledge of structural model
is considered. A novel methodology using Gaussian process latent force models
is proposed to tackle the problem in a stochastic setting. Gaussian process
latent force models (GPLFMs) are hybrid models that combine differential
equations representing a physical system with data-driven non-parametric
Gaussian process models. In this work, the unknown input forces acting on a
structure are modelled as Gaussian processes with some chosen covariance
functions which are combined with the mechanistic differential equation
representing the structure to construct a GPLFM. The GPLFM is then conveniently
formulated as an augmented stochastic state-space model with additional states
representing the latent force components, and the joint input and state
inference of the resulting model is implemented using Kalman filter. The
augmented state-space model of GPLFM is shown as a generalization of the class
of input-augmented state-space models, is proven observable, and is robust
compared to conventional augmented formulations in terms of numerical
stability. The hyperparameters governing the covariance functions are estimated
using maximum likelihood optimization based on the observed data, thus
overcoming the need for manual tuning of the hyperparameters by
trial-and-error. To assess the performance of the proposed GPLFM method,
several cases of state and input estimation are demonstrated using numerical
simulations on a 10-dof shear building and a 76-storey ASCE benchmark office
tower. Results obtained indicate the superior performance of the proposed
approach over conventional Kalman filter based approaches.
EE-AE: An Exclusivity Enhanced Unsupervised Feature Learning Approach
Authors: Jingcai Guo, Song Guo
Date: 2019-03-30 00:00:00
Tasks: Decoder

Unsupervised learning is becoming more and more important recently. As one of
its key components, the autoencoder (AE) aims to learn a latent feature
representation of data which is more robust and discriminative. However, most
AE based methods only focus on the reconstruction within the encoder-decoder
phase, which ignores the inherent relation of data, i.e., statistical and
geometrical dependence, and easily causes overfitting. In order to deal with
this issue, we propose an Exclusivity Enhanced (EE) unsupervised feature
learning approach to improve the conventional AE. To the best of our knowledge,
our research is the first to utilize such exclusivity concept to cooperate with
feature extraction within AE. Moreover, in this paper we also make some
improvements to the stacked AE structure especially for the connection of
different layers from decoders, this could be regarded as a weight
initialization trial. The experimental results show that our proposed approach
can achieve remarkable performance compared with other related methods.
Symmetry-Based Disentangled Representation Learning requires Interaction with Environments
Authors: Hugo Caselles-Dupré, Michael Garcia-Ortiz, David Filliat
Date: 2019-03-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Representation Learning

Finding a generally accepted formal definition of a disentangled representation in the context of an agent behaving in an environment is an important challenge towards the construction of data-efficient autonomous agents. Higgins et al. recently proposed Symmetry-Based Disentangled Representation Learning, a definition based on a characterization of symmetries in the environment using group theory. We build on their work and make observations, theoretical and empirical, that lead us to argue that Symmetry-Based Disentangled Representation Learning cannot only be based on static observations: agents should interact with the environment to discover its symmetries. Our experiments can be reproduced in Colab and the code is available on GitHub.
Prediction Model for Semitransparent Watercolor Pigment Mixtures Using Deep Learning with a Dataset of Transmittance and Reflectance
Authors: Mei-Yun Chen, Ya-Bo Huang, Sheng-Ping Chang, Ming Ouhyoung
Date: 2019-03-30 00:00:00

Learning color mixing is difficult for novice painters. In order to support
novice painters in learning color mixing, we propose a prediction model for
semitransparent pigment mixtures and use its prediction results to create a
Smart Palette system. Such a system is constructed by first building a
watercolor dataset with two types of color mixing data, indicated by
transmittance and reflectance: incrementation of the same primary pigment and a
mixture of two different pigments. Next, we apply the collected data to a deep
neural network to train a model for predicting the results of semitransparent
pigment mixtures. Finally, we constructed a Smart Palette that provides
easily-followable instructions on mixing a target color with two primary
pigments in real life: when users pick a pixel, an RGB color, from an image,
the system returns its mixing recipe which indicates the two primary pigments
being used and their quantities.
Molecular geometry prediction using a deep generative graph neural network
Authors: Elman Mansimov, Omar Mahmood, Seokho Kang, Kyunghyun Cho
Date: 2019-03-31 00:00:00
Tasks: Graph Neural Network

A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations.
SysML'19 demo: customizable and reusable Collective Knowledge pipelines to automate and reproduce machine learning experiments
Authors: Grigori Fursin
Date: 2019-03-31 00:00:00
Tasks: Benchmarking, BIG-bench Machine Learning

Reproducing, comparing and reusing results from machine learning and systems
papers is a very tedious, ad hoc and time-consuming process. I will demonstrate
how to automate this process using open-source, portable, customizable and
CLI-based Collective Knowledge workflows and pipelines developed by the
community. I will help participants run several real-world non-virtualized CK
workflows from the SysML'19 conference, companies (General Motors, Arm) and
MLPerf benchmark to automate benchmarking and co-design of efficient
software/hardware stacks for machine learning workloads. I hope that our
approach will help authors reduce their effort when sharing reusable and
extensible research artifacts while enabling artifact evaluators to
automatically validate experimental results from published papers in a standard
and portable way.
Fourier Transform Approach to Machine Learning I: Fourier Regression
Authors: Soheil Mehrabkhani
Date: 2019-03-31 00:00:00
Tasks: BIG-bench Machine Learning, regression

We propose a supervised learning algorithm for machine learning applications. Contrary to the model developing in the classical methods, which treat training, validation, and test as separate steps, in the presented approach, there is a unified training and evaluating procedure based on an iterative band filtering by the use of a fast Fourier transform. The presented approach does not apply the method of least squares, thus, basically typical ill-conditioned matrices do not occur at all. The optimal model results from the convergence of the performance metric, which automatically prevents the usual underfitting and overfitting problems. The algorithm capability is investigated for noisy data, and the obtained result demonstrates a reliable and powerful machine learning approach beyond the typical limits of the classical methods.
Clique pooling for graph classification
Authors: Enxhell Luzhnica, Ben Day, Pietro Lio'
Date: 2019-03-31 00:00:00
Tasks: Classification, General Classification, Graph Classification

We propose a novel graph pooling operation using cliques as the unit pool. As
this approach is purely topological, rather than featural, it is more readily
interpretable, a better analogue to image coarsening than filtering or pruning
techniques, and entirely nonparametric. The operation is implemented within
graph convolution network (GCN) and GraphSAGE architectures and tested against
standard graph classification benchmarks. In addition, we explore the backwards
compatibility of the pooling to regular graphs, demonstrating competitive
performance when replacing two-by-two pooling in standard convolutional neural
networks (CNNs) with our mechanism.
Robust Low-Rank Tensor Ring Completion
Authors: Huyan Huang, Yipeng Liu, Ce Zhu
Date: 2019-03-31 00:00:00
Tasks: Shadow Removal

Low-rank tensor completion recovers missing entries based on different tensor decompositions. Due to its outstanding performance in exploiting some higher-order data structure, low rank tensor ring has been applied in tensor completion. To further deal with its sensitivity to sparse component as it does in tensor principle component analysis, we propose robust tensor ring completion (RTRC), which separates latent low-rank tensor component from sparse component with limited number of measurements. The low rank tensor component is constrained by the weighted sum of nuclear norms of its balanced unfoldings, while the sparse component is regularized by its l1 norm. We analyze the RTRC model and gives the exact recovery guarantee. The alternating direction method of multipliers is used to divide the problem into several sub-problems with fast solutions. In numerical experiments, we verify the recovery condition of the proposed method on synthetic data, and show the proposed method outperforms the state-of-the-art ones in terms of both accuracy and computational complexity in a number of real-world data based tasks, i.e., light-field image recovery, shadow removal in face images, and background extraction in color video.
Understanding Neural Architecture Search Techniques
Authors: George Adam, Jonathan Lorraine
Date: 2019-03-31 00:00:00
Tasks: Computational Efficiency, Graph Similarity, Neural Architecture Search

Automatic methods for generating state-of-the-art neural network architectures without human experts have generated significant attention recently. This is because of the potential to remove human experts from the design loop which can reduce costs and decrease time to model deployment. Neural architecture search (NAS) techniques have improved significantly in their computational efficiency since the original NAS was proposed. This reduction in computation is enabled via weight sharing such as in Efficient Neural Architecture Search (ENAS). However, recently a body of work confirms our discovery that ENAS does not do significantly better than random search with weight sharing, contradicting the initial claims of the authors. We provide an explanation for this phenomenon by investigating the interpretability of the ENAS controller's hidden state. We find models sampled from identical controller hidden states have no correlation with various graph similarity metrics, so no notion of structural similarity is learned. This failure mode implies the RNN controller does not condition on past architecture choices. Lastly, we propose a solution to this failure mode by forcing the controller's hidden state to encode pasts decisions by training it with a memory buffer of previously sampled architectures. Doing this improves hidden state interpretability by increasing the correlation between controller hidden states and graph similarity metrics.