pwc-archive/papers-with-abstracts huggingface dataset:
Trajectory Optimization for Unknown Constrained Systems using Reinforcement Learning
Authors: Kei Ota, Devesh K. Jha, Tomoaki Oiki, Mamoru Miura, Takashi Nammoto, Daniel Nikovski, Toshisada Mariyama
Date: 2019-03-13 00:00:00
Tasks: Motion Planning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we propose a reinforcement learning-based algorithm for trajectory optimization for constrained dynamical systems. This problem is motivated by the fact that for most robotic systems, the dynamics may not always be known. Generating smooth, dynamically feasible trajectories could be difficult for such systems. Using sampling-based algorithms for motion planning may result in trajectories that are prone to undesirable control jumps. However, they can usually provide a good reference trajectory which a model-free reinforcement learning algorithm can then exploit by limiting the search domain and quickly finding a dynamically smooth trajectory. We use this idea to train a reinforcement learning agent to learn a dynamically smooth trajectory in a curriculum learning setting. Furthermore, for generalization, we parameterize the policies with goal locations, so that the agent can be trained for multiple goals simultaneously. We show result in both simulated environments as well as real experiments, for a $6$-DoF manipulator arm operated in position-controlled mode to validate the proposed idea. We compare the proposed ideas against a PID controller which is used to track a designed trajectory in configuration space. Our experiments show that our RL agent trained with a reference path outperformed a model-free PID controller of the type commonly used on many robotic platforms for trajectory tracking.
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Authors: Raunak P. Bhattacharyya, Derek J. Phillips, Changliu Liu, Jayesh K. Gupta, Katherine Driggs-Campbell, Mykel J. Kochenderfer
Date: 2019-03-14 00:00:00
Tasks: Imitation Learning

Recent developments in multi-agent imitation learning have shown promising
results for modeling the behavior of human drivers. However, it is challenging
to capture emergent traffic behaviors that are observed in real-world datasets.
Such behaviors arise due to the many local interactions between agents that are
not commonly accounted for in imitation learning. This paper proposes Reward
Augmented Imitation Learning (RAIL), which integrates reward augmentation into
the multi-agent imitation learning framework and allows the designer to specify
prior knowledge in a principled fashion. We prove that convergence guarantees
for the imitation learning process are preserved under the application of
reward augmentation. This method is validated in a driving scenario, where an
entire traffic scene is controlled by driving policies learned using our
proposed algorithm. Further, we demonstrate improved performance in comparison
to traditional imitation learning algorithms both in terms of the local actions
of a single agent and the behavior of emergent properties in complex,
multi-agent settings.
A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience
Authors: Aritra Mitra, John A. Richards, Shreyas Sundaram
Date: 2019-03-14 00:00:00
Tasks: Two-sample testing

We study a setting where a group of agents, each receiving partially
informative private observations, seek to collaboratively learn the true state
(among a set of hypotheses) that explains their joint observation profiles over
time. To solve this problem, we propose a distributed learning rule that
differs fundamentally from existing approaches, in the sense, that it does not
employ any form of "belief-averaging". Specifically, every agent maintains a
local belief (on each hypothesis) that is updated in a Bayesian manner without
any network influence, and an actual belief that is updated (up to
normalization) as the minimum of its own local belief and the actual beliefs of
its neighbors. Under minimal requirements on the signal structures of the
agents and the underlying communication graph, we establish consistency of the
proposed belief update rule, i.e., we show that the actual beliefs of the
agents asymptotically concentrate on the true state almost surely. As one of
the key benefits of our approach, we show that our learning rule can be
extended to scenarios that capture misbehavior on the part of certain agents in
the network, modeled via the Byzantine adversary model. In particular, we prove
that each non-adversarial agent can asymptotically learn the true state of the
world almost surely, under appropriate conditions on the observation model and
the network topology.
On power chi expansions of $f$-divergences
Authors: Frank Nielsen, Gaëtan Hadjeres
Date: 2019-03-14 00:00:00
Tasks: Form

We consider both finite and infinite power chi expansions of $f$-divergences
derived from Taylor's expansions of smooth generators, and elaborate on cases
where these expansions yield closed-form formula, bounded approximations, or
analytic divergence series expressions of $f$-divergences.
SimpleDet: A Simple and Versatile Distributed Framework for Object Detection and Instance Recognition
Authors: Yuntao Chen, Chenxia Han, Yanghao Li, Zehao Huang, Yi Jiang, Naiyan Wang, Zhao-Xiang Zhang
Date: 2019-03-14 00:00:00
Tasks: Autonomous Driving, Medical Image Analysis, Object, object-detection, Object Detection

A Simple and Versatile Framework for Object Detection and Instance Recognition
Learning Dependency Structures for Weak Supervision Models
Authors: Paroma Varma, Frederic Sala, Ann He, Alexander Ratner, Christopher Ré
Date: 2019-03-14 00:00:00
Tasks: image-classification, Image Classification, Relation Extraction

Labeling training data is a key bottleneck in the modern machine learning
pipeline. Recent weak supervision approaches combine labels from multiple noisy
sources by estimating their accuracies without access to ground truth labels;
however, estimating the dependencies among these sources is a critical
challenge. We focus on a robust PCA-based algorithm for learning these
dependency structures, establish improved theoretical recovery rates, and
outperform existing methods on various real-world tasks. Under certain
conditions, we show that the amount of unlabeled data needed can scale
sublinearly or even logarithmically with the number of sources $m$, improving
over previous efforts that ignore the sparsity pattern in the dependency
structure and scale linearly in $m$. We provide an information-theoretic lower
bound on the minimum sample complexity of the weak supervision setting. Our
method outperforms weak supervision approaches that assume
conditionally-independent sources by up to 4.64 F1 points and previous
structure learning approaches by up to 4.41 F1 points on real-world relation
extraction and image classification tasks.
MirrorGAN: Learning Text-to-image Generation by Redescription
Authors: Tingting Qiao, Jing Zhang, Duanqing Xu, DaCheng Tao
Date: 2019-03-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diversity, Image Generation, Image to text, Sentence, Text to Image Generation, Text-to-Image Generation

Generating an image from a given text description has two goals: visual
realism and semantic consistency. Although significant progress has been made
in generating high-quality and visually realistic images using generative
adversarial networks, guaranteeing semantic consistency between the text
description and visual content remains very challenging. In this paper, we
address this problem by proposing a novel global-local attentive and
semantic-preserving text-to-image-to-text framework called MirrorGAN. MirrorGAN
exploits the idea of learning text-to-image generation by redescription and
consists of three modules: a semantic text embedding module (STEM), a
global-local collaborative attentive module for cascaded image generation
(GLAM), and a semantic text regeneration and alignment module (STREAM). STEM
generates word- and sentence-level embeddings. GLAM has a cascaded architecture
for generating target images from coarse to fine scales, leveraging both local
word attention and global sentence attention to progressively enhance the
diversity and semantic consistency of the generated images. STREAM seeks to
regenerate the text description from the generated image, which semantically
aligns with the given text description. Thorough experiments on two public
benchmark datasets demonstrate the superiority of MirrorGAN over other
representative state-of-the-art methods.
Can User-Centered Reinforcement Learning Allow a Robot to Attract Passersby without Causing Discomfort?
Authors: Yasunori Ozaki, Tatsuya Ishihara, Narimune Matsumura, Tadashi Nunobiki
Date: 2019-03-14 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

The aim of our study was to develop a method by which a social robot can greet passersby and get their attention without causing them to suffer discomfort.A number of customer services have recently come to be provided by social robots rather than people, including, serving as receptionists, guides, and exhibitors. Robot exhibitors, for example, can explain products being promoted by the robot owners. However, a sudden greeting by a robot can startle passersby and cause discomfort to passersby.Social robots should thus adapt their mannerisms to the situation they face regarding passersby.We developed a method for meeting this requirement on the basis of the results of related work. Our proposed method, user-centered reinforcement learning, enables robots to greet passersby and get their attention without causing them to suffer discomfort (p<0.01) .The results of an experiment in the field, an office entrance, demonstrated that our method meets this requirement.
Recurrence is required to capture the representational dynamics of the human visual system
Authors: Tim C. Kietzmann, Courtney J Spoerer, Lynn Sörensen, Radoslaw M. Cichy, Olaf Hauk, Nikolaus Kriegeskorte
Date: 2019-03-14 00:00:00

The human visual system is an intricate network of brain regions that enables us to recognize the world around us. Despite its abundant lateral and feedback connections, object processing is commonly viewed and studied as a feedforward process. Here, we measure and model the rapid representational dynamics across multiple stages of the human ventral stream using time-resolved brain imaging and deep learning. We observe substantial representational transformations during the first 300 ms of processing within and across ventral-stream regions. Categorical divisions emerge in sequence, cascading forward and in reverse across regions, and Granger causality analysis suggests bidirectional information flow between regions. Finally, recurrent deep neural network models clearly outperform parameter-matched feedforward models in terms of their ability to capture the multi-region cortical dynamics. Targeted virtual cooling experiments on the recurrent deep network models further substantiate the importance of their lateral and top-down connections. These results establish that recurrent models are required to understand information processing in the human ventral stream.
Generative adversarial network-based glottal waveform model for statistical parametric speech synthesis
Authors: Bajibabu Bollepalli, Lauri Juvela, Paavo Alku
Date: 2019-03-14 00:00:00
Tasks: Generative Adversarial Network, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Recent studies have shown that text-to-speech synthesis quality can be
improved by using glottal vocoding. This refers to vocoders that parameterize
speech into two parts, the glottal excitation and vocal tract, that occur in
the human speech production apparatus. Current glottal vocoders generate the
glottal excitation waveform by using deep neural networks (DNNs). However, the
squared error-based training of the present glottal excitation models is
limited to generating conditional average waveforms, which fails to capture the
stochastic variation of the waveforms. As a result, shaped noise is added as
post-processing. In this study, we propose a new method for predicting glottal
waveforms by generative adversarial networks (GANs). GANs are generative models
that aim to embed the data distribution in a latent space, enabling generation
of new instances very similar to the original by randomly sampling the latent
distribution. The glottal pulses generated by GANs show a stochastic component
similar to natural glottal pulses. In our experiments, we compare synthetic
speech generated using glottal waveforms produced by both DNNs and GANs. The
results show that the newly proposed GANs achieve synthesis quality comparable
to that of widely-used DNNs, without using an additive noise component.
Empirical effect of graph embeddings on fraud detection/ risk mitigation
Authors: Sida Zhou
Date: 2019-03-05 00:00:00
Tasks: Fraud Detection, Graph Embedding, Graph Reconstruction, Link Prediction, Position

Graph embedding technics are studied with interest on public datasets, such
as BlogCatalog, with the common practice of maximizing scoring on graph
reconstruction, link prediction metrics etc. However, in the financial sector
the important metrics are often more business related, for example fraud
detection rates. With our privileged position of having large amount of
real-world non-public P2P-lending social data, we aim to study empirically
whether recent advances in graph embedding technics provide a useful signal for
metrics more closely related to business interests, such as fraud detection
rate.
Unsupervised Network Embedding for Graph Visualization, Clustering and Classification
Authors: Leonardo Gutiérrez-Gómez, Jean-Charles Delvenne
Date: 2019-02-25 00:00:00
Tasks: Classification, Clustering, General Classification, Graph Clustering, Graph Mining, Network Embedding

A main challenge in mining network-based data is finding effective ways to
represent or encode graph structures so that it can be efficiently exploited by
machine learning algorithms. Several methods have focused in network
representation at node/edge or substructure level. However, many real life
challenges such as time-varying, multilayer, chemical compounds and brain
networks involve analysis of a family of graphs instead of single one opening
additional challenges in graph comparison and representation. Traditional
approaches for learning representations relies on hand-crafting specialized
heuristics to extract meaningful information about the graphs, e.g statistical
properties, structural features, etc. as well as engineered graph distances to
quantify dissimilarity between networks. In this work we provide an
unsupervised approach to learn embedding representation for a collection of
graphs so that it can be used in numerous graph mining tasks. By using an
unsupervised neural network approach on input graphs, we aim to capture the
underlying distribution of the data in order to discriminate between different
class of networks. Our method is assessed empirically on synthetic and real
life datasets and evaluated in three different tasks: graph clustering,
visualization and classification. Results reveal that our method outperforms
well known graph distances and graph-kernels in clustering and classification
tasks, being highly efficient in runtime.
To Tune or Not to Tune? Adapting Pretrained Representations to Diverse Tasks
Authors: Matthew E. Peters, Sebastian Ruder, Noah A. Smith
Date: 2019-03-14 00:00:00
Proceeding: WS 2019 8
Tasks: Transfer Learning

While most previous work has focused on different pretraining objectives and architectures for transfer learning, we ask how to best adapt the pretrained model to a given target task. We focus on the two most common forms of adaptation, feature extraction (where the pretrained weights are frozen), and directly fine-tuning the pretrained model. Our empirical results across diverse NLP tasks with two state-of-the-art models show that the relative performance of fine-tuning vs. feature extraction depends on the similarity of the pretraining and target tasks. We explore possible explanations for this finding and provide a set of adaptation guidelines for the NLP practitioner.
$L^γ$-PageRank for Semi-Supervised Learning
Authors: Esteban Bautista, Patrice Abry, Paulo Gonçalves
Date: 2019-03-11 00:00:00
Tasks: Classification, General Classification

PageRank for Semi-Supervised Learning has shown to leverage data structures
and limited tagged examples to yield meaningful classification. Despite
successes, classification performance can still be improved, particularly in
cases of fuzzy graphs or unbalanced labeled data. To address such limitations,
a novel approach based on powers of the Laplacian matrix $L^\gamma$ ($\gamma >
0$), referred to as $L^\gamma$-PageRank, is proposed. Its theoretical study
shows that it operates on signed graphs, where nodes belonging to one same
class are more likely to share positive edges while nodes from different
classes are more likely to be connected with negative edges. It is shown that
by selecting an optimal $\gamma$, classification performance can be
significantly enhanced. A procedure for the automated estimation of the optimal
$\gamma$, from a unique observation of data, is devised and assessed.
Experiments on several datasets demonstrate the effectiveness of both
$L^\gamma$-PageRank classification and the optimal $\gamma$ estimation.
On Learning from Ghost Imaging without Imaging
Authors: Issei Sato
Date: 2019-03-14 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghost cytometry skips the reconstruction of cell images from signals and directly used signals for cell-classification because this reconstruction is what creates the bottleneck in the high-speed analysis. In this paper, we provide theoretical analysis for learning from ghost imaging without imaging.
Deep Distribution Regression
Authors: Rui Li, Howard D. Bondell, Brian J. Reich
Date: 2019-03-14 00:00:00
Tasks: Decision Making, General Classification, Multi-class Classification, regression

Due to their flexibility and predictive performance, machine-learning based
regression methods have become an important tool for predictive modeling and
forecasting. However, most methods focus on estimating the conditional mean or
specific quantiles of the target quantity and do not provide the full
conditional distribution, which contains uncertainty information that might be
crucial for decision making. In this article, we provide a general solution by
transforming a conditional distribution estimation problem into a constrained
multi-class classification problem, in which tools such as deep neural
networks. We propose a novel joint binary cross-entropy loss function to
accomplish this goal. We demonstrate its performance in various simulation
studies comparing to state-of-the-art competing methods. Additionally, our
method shows improved accuracy in a probabilistic solar energy forecasting
problem.
MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks
Authors: Animesh Karnewar, Oliver Wang
Date: 2019-03-14 00:00:00
Proceeding: CVPR 2020 6
Tasks: Generative Adversarial Network, Image Generation

While Generative Adversarial Networks (GANs) have seen huge successes in image synthesis tasks, they are notoriously difficult to adapt to different datasets, in part due to instability during training and sensitivity to hyperparameters. One commonly accepted reason for this instability is that gradients passing from the discriminator to the generator become uninformative when there isn't enough overlap in the supports of the real and fake distributions. In this work, we propose the Multi-Scale Gradient Generative Adversarial Network (MSG-GAN), a simple but effective technique for addressing this by allowing the flow of gradients from the discriminator to the generator at multiple scales. This technique provides a stable approach for high resolution image synthesis, and serves as an alternative to the commonly used progressive growing technique. We show that MSG-GAN converges stably on a variety of image datasets of different sizes, resolutions and domains, as well as different types of loss functions and architectures, all with the same set of fixed hyperparameters. When compared to state-of-the-art GANs, our approach matches or exceeds the performance in most of the cases we tried.
Attention-Based Structural-Plasticity
Authors: Soheil Kolouri, Nicholas Ketz, Xinyun Zou, Jeffrey Krichmar, Praveen Pilly
Date: 2019-03-02 00:00:00
Tasks: Lifelong learning, Permuted-MNIST, Split-MNIST

Catastrophic forgetting/interference is a critical problem for lifelong
learning machines, which impedes the agents from maintaining their previously
learned knowledge while learning new tasks. Neural networks, in particular,
suffer plenty from the catastrophic forgetting phenomenon. Recently there has
been several efforts towards overcoming catastrophic forgetting in neural
networks. Here, we propose a biologically inspired method toward overcoming
catastrophic forgetting. Specifically, we define an attention-based selective
plasticity of synapses based on the cholinergic neuromodulatory system in the
brain. We define synaptic importance parameters in addition to synaptic weights
and then use Hebbian learning in parallel with backpropagation algorithm to
learn synaptic importances in an online and seamless manner. We test our
proposed method on benchmark tasks including the Permuted MNIST and the Split
MNIST problems and show competitive performance compared to the
state-of-the-art methods.
Scalable Deep Convolutional Neural Networks for Sparse, Locally Dense Liquid Argon Time Projection Chamber Data
Authors: Laura Dominé, Kazuhiro Terao
Date: 2019-03-13 00:00:00
Tasks: 3D Semantic Segmentation, Clustering, Semantic Segmentation

Deep convolutional neural networks (CNNs) show strong promise for analyzing scientific data in many domains including particle imaging detectors such as a liquid argon time projection chamber (LArTPC). Yet the high sparsity of LArTPC data challenges traditional CNNs which were designed for dense data such as photographs. A naive application of CNNs on LArTPC data results in inefficient computations and a poor scalability to large LArTPC detectors such as the Short Baseline Neutrino Program and Deep Underground Neutrino Experiment. Recently Submanifold Sparse Convolutional Networks (SSCNs) have been proposed to address this challenge. We report their performance on a 3D semantic segmentation task on simulated LArTPC samples. In comparison with standard CNNs, we observe that the computation memory and wall-time cost for inference are reduced by factor of 364 and 33 respectively without loss of accuracy. The same factors for 2D samples are found to be 93 and 3.1 respectively. Using SSCN, we present the first machine learning-based approach to the reconstruction of Michel electrons using public 3D LArTPC samples. We find a Michel electron identification efficiency of 93.9% with 96.7% of true positive rate. Reconstructed Michel electron clusters yield 95.4% in average pixel clustering efficiency and 95.5% in purity. The results are compelling to show strong promise of scalable data reconstruction technique using deep neural networks for large scale LArTPC detectors.