pwc-archive/papers-with-abstracts huggingface dataset:
Momen(e)t: Flavor the Moments in Learning to Classify Shapes
Authors: Mor Joseph-Rivlin, Alon Zvirin, Ron Kimmel
Date: 2018-12-18 00:00:00

A fundamental question in learning to classify 3D shapes is how to treat the data in a way that would allow us to construct efficient and accurate geometric processing and analysis procedures. Here, we restrict ourselves to networks that operate on point clouds. There were several attempts to treat point clouds as non-structured data sets by which a neural network is trained to extract discriminative properties. The idea of using 3D coordinates as class identifiers motivated us to extend this line of thought to that of shape classification by comparing attributes that could easily account for the shape moments. Here, we propose to add polynomial functions of the coordinates allowing the network to account for higher order moments of a given shape. Experiments on two benchmarks show that the suggested network is able to provide state of the art results and at the same token learn more efficiently in terms of memory and computational complexity.
A cortical-inspired model for orientation-dependent contrast perception: a link with Wilson-Cowan equations
Authors: Marcelo Bertalmío, Luca Calatroni, Valentina Franceschi, Benedetta Franceschiello, Dario Prandi
Date: 2018-12-18 00:00:00

We consider a differential model describing neuro-physiological contrast
perception phenomena induced by surrounding orientations. The mathematical
formulation relies on a cortical-inspired modelling [10] largely used over the
last years to describe neuron interactions in the primary visual cortex (V1)
and applied to several image processing problems [12,19,13]. Our model connects
to Wilson-Cowan-type equations [23] and it is analogous to the one used in
[3,2,14] to describe assimilation and contrast phenomena, the main novelty
being its explicit dependence on local image orientation. To confirm the
validity of the model, we report some numerical tests showing its ability to
explain orientation-dependent phenomena (such as grating induction) and
geometric-optical illusions [21,16] classically explained only by
filtering-based techniques [6,18].
TWINs: Two Weighted Inconsistency-reduced Networks for Partial Domain Adaptation
Authors: Toshihiko Matsuura, Kuniaki Saito, Tatsuya Harada
Date: 2018-12-18 00:00:00
Tasks: Domain Adaptation, General Classification, Partial Domain Adaptation, Unsupervised Domain Adaptation, Vocal Bursts Valence Prediction

The task of unsupervised domain adaptation is proposed to transfer the
knowledge of a label-rich domain (source domain) to a label-scarce domain
(target domain). Matching feature distributions between different domains is a
widely applied method for the aforementioned task. However, the method does not
perform well when classes in the two domains are not identical. Specifically,
when the classes of the target correspond to a subset of those of the source,
target samples can be incorrectly aligned with the classes that exist only in
the source. This problem setting is termed as partial domain adaptation (PDA).
In this study, we propose a novel method called Two Weighted
Inconsistency-reduced Networks (TWINs) for PDA. We utilize two classification
networks to estimate the ratio of the target samples in each class with which a
classification loss is weighted to adapt the classes present in the target
domain. Furthermore, to extract discriminative features for the target, we
propose to minimize the divergence between domains measured by the classifiers'
inconsistency on target samples. We empirically demonstrate that reducing the
inconsistency between two networks is effective for PDA and that our method
outperforms other existing methods with a large margin in several datasets.
Improving Face Detection Performance with 3D-Rendered Synthetic Data
Authors: Jian Han, Sezer Karaoglu, Hoang-An Le, Theo Gevers
Date: 2018-12-18 00:00:00
Tasks: Data Augmentation, Face Detection

In this paper, we provide a synthetic data generator methodology with fully controlled, multifaceted variations based on a new 3D face dataset (3DU-Face). We customized synthetic datasets to address specific types of variations (scale, pose, occlusion, blur, etc.), and systematically investigate the influence of different variations on face detection performances. We examine whether and how these factors contribute to better face detection performances. We validate our synthetic data augmentation for different face detectors (Faster RCNN, SSH and HR) on various face datasets (MAFA, UFDD and Wider Face).
Non-parametric clustering over user features and latent behavioral functions with dual-view mixture models
Authors: Alberto Lumbreras, Julien Velcin, Marie Guégan, Bertrand Jouve
Date: 2018-12-18 00:00:00
Tasks: Clustering

We present a dual-view mixture model to cluster users based on their features
and latent behavioral functions. Every component of the mixture model
represents a probability density over a feature view for observed user
attributes and a behavior view for latent behavioral functions that are
indirectly observed through user actions or behaviors. Our task is to infer the
groups of users as well as their latent behavioral functions. We also propose a
non-parametric version based on a Dirichlet Process to automatically infer the
number of clusters. We test the properties and performance of the model on a
synthetic dataset that represents the participation of users in the threads of
an online forum. Experiments show that dual-view models outperform single-view
ones when one of the views lacks information.
A Novel Variational Autoencoder with Applications to Generative Modelling, Classification, and Ordinal Regression
Authors: Joel Jaskari, Jyri J. Kivinen
Date: 2018-12-18 00:00:00
Tasks: Classification, General Classification, Ordinal Classification, regression

We develop a novel probabilistic generative model based on the variational
autoencoder approach. Notable aspects of our architecture are: a novel way of
specifying the latent variables prior, and the introduction of an ordinality
enforcing unit. We describe how to do supervised, unsupervised and
semi-supervised learning, and nominal and ordinal classification, with the
model. We analyze generative properties of the approach, and the classification
effectiveness under nominal and ordinal classification, using two benchmark
datasets. Our results show that our model can achieve comparable results with
relevant baselines in both of the classification tasks.
Predicting user intent from search queries using both CNNs and RNNs
Authors: Mihai Cristian Pîrvu, Alexandra Anghel, Ciprian Borodescu, Alexandru Constantin
Date: 2018-12-18 00:00:00

Predicting user behaviour on a website is a difficult task, which requires
the integration of multiple sources of information, such as geo-location, user
profile or web surfing history. In this paper we tackle the problem of
predicting the user intent, based on the queries that were used to access a
certain webpage. We make no additional assumptions, such as domain detection,
device used or location, and only use the word information embedded in the
given query. In order to build competitive classifiers, we label a small
fraction of the EDI query intent prediction dataset
\cite{edi-challenge-dataset}, which is used as ground truth. Then, using
various rule-based approaches, we automatically label the rest of the dataset,
train the classifiers and evaluate the quality of the automatic labeling on the
ground truth dataset. We used both recurrent and convolutional networks as the
models, while representing the words in the query with multiple embedding
methods.
Evaluating the squared-exponential covariance function in Gaussian processes with integral observations
Authors: J. N. Hendriks, C. Jidling, A. Wills, T. B. Schön
Date: 2018-12-18 00:00:00
Tasks: Gaussian Processes

This paper deals with the evaluation of double line integrals of the squared
exponential covariance function. We propose a new approach in which the double
integral is reduced to a single integral using the error function. This single
integral is then computed with efficiently implemented numerical techniques.
The performance is compared against existing state of the art methods and the
results show superior properties in numerical robustness and accuracy per
computation time.
Continual Match Based Training in Pommerman: Technical Report
Authors: Peng Peng, Liang Pang, Yufeng Yuan, Chao GAO
Date: 2018-12-18 00:00:00
Tasks: Continual Learning

Continual learning is the ability of agents to improve their capacities
throughout multiple tasks continually. While recent works in the literature of
continual learning mostly focused on developing either particular loss
functions or specialized structures of neural network explaining the episodic
memory or neural plasticity, we study continual learning from the perspective
of the training mechanism. Specifically, we propose a COnitnual Match BAsed
Training (COMBAT) framework for training a population of advantage-actor-critic
(A2C) agents in Pommerman, a partially observable multi-agent environment with
no communication. Following the COMBAT framework, we trained an agent, namely,
Navocado, that won the title of the top 1 learning agent in the NeurIPS 2018
Pommerman Competition. Two critical features of our agent are worth mentioning.
Firstly, our agent did not learn from any demonstrations. Secondly, our agent
is highly reproducible. As a technical report, we articulate the design of
state space, action space, reward, and most importantly, the COMBAT framework
for our Pommerman agent. We show in the experiments that Pommerman is a perfect
environment for studying continual learning, and the agent can improve its
performance by continually learning new skills without forgetting the old ones.
Finally, the result in the Pommerman Competition verifies the robustness of our
agent when competing with various opponents.
SwipeCut: Interactive Segmentation with Diversified Seed Proposals
Authors: Ding-Jie Chen, Hwann-Tzong Chen, Long-Wen Chang
Date: 2018-12-18 00:00:00
Tasks: Diversity, Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

Interactive image segmentation algorithms rely on the user to provide
annotations as the guidance. When the task of interactive segmentation is
performed on a small touchscreen device, the requirement of providing precise
annotations could be cumbersome to the user. We design an efficient seed
proposal method that actively proposes annotation seeds for the user to label.
The user only needs to check which ones of the query seeds are inside the
region of interest (ROI). We enforce the sparsity and diversity criteria on the
selection of the query seeds. At each round of interaction the user is only
presented with a small number of informative query seeds that are far apart
from each other. As a result, we are able to derive a user friendly interaction
mechanism for annotation on small touchscreen devices. The user merely has to
swipe through on the ROI-relevant query seeds, which should be easy since those
gestures are commonly used on a touchscreen. The performance of our algorithm
is evaluated on six publicly available datasets. The evaluation results show
that our algorithm achieves high segmentation accuracy, with short response
time and less user feedback.
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Authors: Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, Dmitry Kalashnikov, Alex Irpan, Julian Ibarz, Sergey Levine, Raia Hadsell, Konstantinos Bousmalis
Date: 2018-12-18 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Reinforcement Learning, Robotic Grasping

Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation to produce large amounts of labelled data. However, training models on simulated images does not readily transfer to real-world ones. Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power. In this paper, we present Randomized-to-Canonical Adaptation Networks (RCANs), a novel approach to crossing the visual reality gap that uses no real-world data. Our method learns to translate randomized rendered images into their equivalent non-randomized, canonical versions. This in turn allows for real images to also be translated into canonical sim images. We demonstrate the effectiveness of this sim-to-real approach by training a vision-based closed-loop grasping reinforcement learning agent in simulation, and then transferring it to the real world to attain 70% zero-shot grasp success on unseen objects, a result that almost doubles the success of learning the same task directly on domain randomization alone. Additionally, by joint finetuning in the real-world with only 5,000 real-world grasps, our method achieves 91%, attaining comparable performance to a state-of-the-art system trained with 580,000 real-world grasps, resulting in a reduction of real-world data by more than 99%.
Attend, Copy, Parse -- End-to-end information extraction from documents
Authors: Rasmus Berg Palm, Florian Laws, Ole Winther
Date: 2018-12-18 00:00:00
Tasks: Classification, General Classification

Document information extraction tasks performed by humans create data consisting of a PDF or document image input, and extracted string outputs. This end-to-end data is naturally consumed and produced when performing the task because it is valuable in and of itself. It is naturally available, at no additional cost. Unfortunately, state-of-the-art word classification methods for information extraction cannot use this data, instead requiring word-level labels which are expensive to create and consequently not available for many real life tasks. In this paper we propose the Attend, Copy, Parse architecture, a deep neural network model that can be trained directly on end-to-end data, bypassing the need for word-level labels. We evaluate the proposed architecture on a large diverse set of invoices, and outperform a state-of-the-art production system based on word classification. We believe our proposed architecture can be used on many real life information extraction tasks where word classification cannot be used due to a lack of the required word-level labels.
Sparsity in Variational Autoencoders
Authors: Andrea Asperti
Date: 2018-12-18 00:00:00

Working in high-dimensional latent spaces, the internal encoding of data in
Variational Autoencoders becomes naturally sparse. We discuss this known but
controversial phenomenon sometimes refereed to as overpruning, to emphasize the
under-use of the model capacity. In fact, it is an important form of
self-regularization, with all the typical benefits associated with sparsity: it
forces the model to focus on the really important features, highly reducing the
risk of overfitting. Especially, it is a major methodological guide for the
correct tuning of the model capacity, progressively augmenting it to attain
sparsity, or conversely reducing the dimension of the network removing links to
zeroed out neurons. The degree of sparsity crucially depends on the network
architecture: for instance, convolutional networks typically show less
sparsity, likely due to the tighter relation of features to different spatial
regions of the input.
Continuous Trajectory Planning Based on Learning Optimization in High Dimensional Input Space for Serial Manipulators
Authors: Shiyu Zhang, Shuling Dai
Date: 2018-12-18 00:00:00
Tasks: Motion Planning, Trajectory Planning

To continuously generate trajectories for serial manipulators with high
dimensional degrees of freedom (DOF) in the dynamic environment, a real-time
optimal trajectory generation method based on machine learning aiming at high
dimensional inputs is presented in this paper. First, a learning optimization
(LO) framework is established, and implementations with different sub-methods
are discussed. Additionally, multiple criteria are defined to evaluate the
performance of LO models. Furthermore, aiming at high dimensional inputs, a
database generation method based on input space dimension-reducing mapping is
proposed. At last, this method is validated on motion planning for haptic
feedback manipulators (HFM) in virtual reality systems. Results show that the
input space dimension-reducing method can significantly elevate the efficiency
and quality of database generation and consequently improve the performance of
the LO. Moreover, using this LO method, real-time trajectory generation with
high dimensional inputs can be achieved, which lays a foundation for continuous
trajectory planning for high-DOF-robots in complex environments.
Interpretable Optimal Stopping
Authors: Dragos Florin Ciocan, Velibor V. Mišić
Date: 2018-12-18 00:00:00
Tasks: Marketing

Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the greatest reward, arising in numerous application areas such as finance, healthcare and marketing. State-of-the-art methods for high-dimensional optimal stopping involve approximating the value function or the continuation value, and then using that approximation within a greedy policy. Although such policies can perform very well, they are generally not guaranteed to be interpretable; that is, a decision maker may not be able to easily see the link between the current system state and the policy's action. In this paper, we propose a new approach to optimal stopping, wherein the policy is represented as a binary tree, in the spirit of naturally interpretable tree models commonly used in machine learning. We show that the class of tree policies is rich enough to approximate the optimal policy. We formulate the problem of learning such policies from observed trajectories of the stochastic system as a sample average approximation (SAA) problem. We prove that the SAA problem converges under mild conditions as the sample size increases, but that computationally even immediate simplifications of the SAA problem are theoretically intractable. We thus propose a tractable heuristic for approximately solving the SAA problem, by greedily constructing the tree from the top down. We demonstrate the value of our approach by applying it to the canonical problem of option pricing, using both synthetic instances and instances using real S&P-500 data. Our method obtains policies that (1) outperform state-of-the-art non-interpretable methods, based on simulation-regression and martingale duality, and (2) possess a remarkably simple and intuitive structure.
Expanding the Reach of Federated Learning by Reducing Client Resource Requirements
Authors: Sebastian Caldas, Jakub Konečny, H. Brendan McMahan, Ameet Talwalkar
Date: 2018-12-18 00:00:00
Proceeding: ICLR 2019 5
Tasks: Federated Learning

Communication on heterogeneous edge networks is a fundamental bottleneck in
Federated Learning (FL), restricting both model capacity and user
participation. To address this issue, we introduce two novel strategies to
reduce communication costs: (1) the use of lossy compression on the global
model sent server-to-client; and (2) Federated Dropout, which allows users to
efficiently train locally on smaller subsets of the global model and also
provides a reduction in both client-to-server communication and local
computation. We empirically show that these strategies, combined with existing
compression approaches for client-to-server communication, collectively provide
up to a $14\times$ reduction in server-to-client communication, a $1.7\times$
reduction in local computation, and a $28\times$ reduction in upload
communication, all without degrading the quality of the final model. We thus
comprehensively reduce FL's impact on client device resources, allowing higher
capacity models to be trained, and a more diverse set of users to be reached.
Constrained speaker diarization of TV series based on visual patterns
Authors: Xavier Bost, Georges Linares
Date: 2018-12-18 00:00:00
Tasks: Clustering, speaker-diarization, Speaker Diarization

Speaker diarization, usually denoted as the ''who spoke when'' task, turns
out to be particularly challenging when applied to fictional films, where many
characters talk in various acoustic conditions (background music, sound
effects...). Despite this acoustic variability , such movies exhibit specific
visual patterns in the dialogue scenes. In this paper, we introduce a two-step
method to achieve speaker diarization in TV series: a speaker diarization is
first performed locally in the scenes detected as dialogues; then, the
hypothesized local speakers are merged in a second agglomerative clustering
process, with the constraint that speakers locally hypothesized to be distinct
must not be assigned to the same cluster. The performances of our approach are
compared to those obtained by standard speaker diarization tools applied to the
same data.
Audiovisual speaker diarization of TV series
Authors: Xavier Bost, Georges Linarès, Serigne Gueye
Date: 2018-12-18 00:00:00
Tasks: speaker-diarization, Speaker Diarization

Speaker diarization may be difficult to achieve when applied to narrative
films, where speakers usually talk in adverse acoustic conditions: background
music, sound effects, wide variations in intonation may hide the inter-speaker
variability and make audio-based speaker diarization approaches error prone. On
the other hand, such fictional movies exhibit strong regularities at the image
level, particularly within dialogue scenes. In this paper, we propose to
perform speaker diarization within dialogue scenes of TV series by combining
the audio and video modalities: speaker diarization is first performed by using
each modality, the two resulting partitions of the instance set are then
optimally matched, before the remaining instances, corresponding to cases of
disagreement between both modalities, are finally processed. The results
obtained by applying such a multi-modal approach to fictional films turn out to
outperform those obtained by relying on a single modality.
Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE
Authors: Santhosh Kelathodi Kumaran, Debi Prosad Dogra, Partha Pratim Roy, Adway Mitra
Date: 2018-12-18 00:00:00
Tasks: Anomaly Detection, Classification, Clustering, General Classification, Object, Time Series, Time Series Analysis

Classifying time series data using neural networks is a challenging problem
when the length of the data varies. Video object trajectories, which are key to
many of the visual surveillance applications, are often found to be of varying
length. If such trajectories are used to understand the behavior (normal or
anomalous) of moving objects, they need to be represented correctly. In this
paper, we propose video object trajectory classification and anomaly detection
using a hybrid Convolutional Neural Network (CNN) and Variational Autoencoder
(VAE) architecture. First, we introduce a high level representation of object
trajectories using color gradient form. In the next stage, a semi-supervised
way to annotate moving object trajectories extracted using Temporal Unknown
Incremental Clustering (TUIC), has been applied for trajectory class labeling.
Anomalous trajectories are separated using t-Distributed Stochastic Neighbor
Embedding (t-SNE). Finally, a hybrid CNN-VAE architecture has been used for
trajectory classification and anomaly detection. The results obtained using
publicly available surveillance video datasets reveal that the proposed method
can successfully identify some of the important traffic anomalies such as
vehicles not following lane driving, sudden speed variations, abrupt
termination of vehicle movement, and vehicles moving in wrong directions. The
proposed method is able to detect above anomalies at higher accuracy as
compared to existing anomaly detection methods.