pwc-archive/papers-with-abstracts huggingface dataset:
Denoising Weak Lensing Mass Maps with Deep Learning
Authors: Masato Shirasaki, Naoki Yoshida, Shiro Ikeda
Date: 2018-12-14 00:00:00
Tasks: Clustering, Deep Learning, Denoising, Image-to-Image Translation

Weak gravitational lensing is a powerful probe of the large-scale cosmic matter distribution. Wide-field galaxy surveys allow us to generate the so-called weak lensing maps, but actual observations suffer from noise due to imperfect measurement of galaxy shape distortions and to the limited number density of the source galaxies. In this paper, we explore a deep-learning approach to reduce the noise. We develop an image-to-image translation method with conditional adversarial networks (CANs), which learn efficient mapping from an input noisy weak lensing map to the underlying noise field. We train the CANs using $30000$ image pairs obtained from $1000$ ray-tracing simulations of weak gravitational lensing. We show that the trained CANs reproduce the true one-point probability distribution function (PDF) of the noiseless lensing map with a bias less than $1\sigma$ on average, where $\sigma$ is the statistical error. We perform a Fisher analysis to make forecast for cosmological parameter inference with the one-point lensing PDF. By our denoising method using CANs, the first derivative of the PDF with respect to the cosmic mean matter density and the amplitude of the primordial curvature perturbations becomes larger by $\sim50\%$. This allows us to improve the cosmological constraints by $\sim30-40\%$ with using observational data from ongoing and upcoming galaxy imaging surveys.
Don't Classify, Translate: Multi-Level E-Commerce Product Categorization Via Machine Translation
Authors: Maggie Yundi Li, Stanley Kok, Liling Tan
Date: 2018-12-14 00:00:00
Tasks: General Classification, Machine Translation, Product Categorization, Translation

E-commerce platforms categorize their products into a multi-level taxonomy
tree with thousands of leaf categories. Conventional methods for product
categorization are typically based on machine learning classification
algorithms. These algorithms take product information as input (e.g., titles
and descriptions) to classify a product into a leaf category. In this paper, we
propose a new paradigm based on machine translation. In our approach, we
translate a product's natural language description into a sequence of tokens
representing a root-to-leaf path in a product taxonomy. In our experiments on
two large real-world datasets, we show that our approach achieves better
predictive accuracy than a state-of-the-art classification system for product
categorization. In addition, we demonstrate that our machine translation models
can propose meaningful new paths between previously unconnected nodes in a
taxonomy tree, thereby transforming the taxonomy into a directed acyclic graph
(DAG). We discuss how the resultant taxonomy DAG promotes user-friendly
navigation, and how it is more adaptable to new products.
Action Machine: Rethinking Action Recognition in Trimmed Videos
Authors: Jiagang Zhu, Wei Zou, Liang Xu, Yiming Hu, Zheng Zhu, Manyu Chang, Jun-Jie Huang, Guan Huang, Dalong Du
Date: 2018-12-14 00:00:00
Tasks: Action Recognition, Multimodal Activity Recognition, Pose Estimation, Skeleton Based Action Recognition, Temporal Action Localization

Existing methods in video action recognition mostly do not distinguish human
body from the environment and easily overfit the scenes and objects. In this
work, we present a conceptually simple, general and high-performance framework
for action recognition in trimmed videos, aiming at person-centric modeling.
The method, called Action Machine, takes as inputs the videos cropped by person
bounding boxes. It extends the Inflated 3D ConvNet (I3D) by adding a branch for
human pose estimation and a 2D CNN for pose-based action recognition, being
fast to train and test. Action Machine can benefit from the multi-task training
of action recognition and pose estimation, the fusion of predictions from RGB
images and poses. On NTU RGB-D, Action Machine achieves the state-of-the-art
performance with top-1 accuracies of 97.2% and 94.3% on cross-view and
cross-subject respectively. Action Machine also achieves competitive
performance on another three smaller action recognition datasets: Northwestern
UCLA Multiview Action3D, MSR Daily Activity3D and UTD-MHAD. Code will be made
available.
Using Detailed Access Trajectories for Learning Behavior Analysis
Authors: Yanbang Wang, Nancy Law, Erik Hemberg, Una-May O'Reilly
Date: 2018-12-14 00:00:00
Tasks: Time Series, Time Series Analysis

Student learning activity in MOOCs can be viewed from multiple perspectives.
We present a new organization of MOOC learner activity data at a resolution
that is in between the fine granularity of the clickstream and coarse
organizations that count activities, aggregate students or use long duration
time units. A detailed access trajectory (DAT) consists of binary values and is
two dimensional with one axis that is a time series, e.g. days and the other
that is a chronologically ordered list of a MOOC component type's instances,
e.g. videos in instructional order. Most popular MOOC platforms generate data
that can be organized as detailed access trajectories (DATs).We explore the
value of DATs by conducting four empirical mini-studies. Our studies suggest
DATs contain rich information about students' learning behaviors and facilitate
MOOC learning analyses.
Helix: Holistic Optimization for Accelerating Iterative Machine Learning
Authors: Doris Xin, Stephen Macke, Litian Ma, Jialin Liu, Shuchen Song, Aditya Parameswaran
Date: 2018-12-14 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning workflow development is a process of trial-and-error:
developers iterate on workflows by testing out small modifications until the
desired accuracy is achieved. Unfortunately, existing machine learning systems
focus narrowly on model training---a small fraction of the overall development
time---and neglect to address iterative development. We propose Helix, a
machine learning system that optimizes the execution across
iterations---intelligently caching and reusing, or recomputing intermediates as
appropriate. Helix captures a wide variety of application needs within its
Scala DSL, with succinct syntax defining unified processes for data
preprocessing, model specification, and learning. We demonstrate that the reuse
problem can be cast as a Max-Flow problem, while the caching problem is
NP-Hard. We develop effective lightweight heuristics for the latter. Empirical
evaluation shows that Helix is not only able to handle a wide variety of use
cases in one unified workflow but also much faster, providing run time
reductions of up to 19x over state-of-the-art systems, such as DeepDive or
KeystoneML, on four real-world applications in natural language processing,
computer vision, social and natural sciences.
On Stacked Denoising Autoencoder based Pre-training of ANN for Isolated Handwritten Bengali Numerals Dataset Recognition
Authors: Al Mehdi Saadat Chowdhury, M. Shahidur Rahman, Asia Khanom, Tamanna Islam Chowdhury, Afaz Uddin
Date: 2018-12-14 00:00:00
Tasks: Denoising

This work attempts to find the most optimal parameter setting of a deep
artificial neural network (ANN) for Bengali digit dataset by pre-training it
using stacked denoising autoencoder (SDA). Although SDA based recognition is
hugely popular in image, speech and language processing related tasks among the
researchers, it was never tried in Bengali dataset recognition. For this work,
a dataset of 70000 handwritten samples were used from (Chowdhury and Rahman,
2016) and was recognized using several settings of network architecture. Among
all these settings, the most optimal setting being found to be five or more
deeper hidden layers with sigmoid activation and one output layer with softmax
activation. We proposed the optimal number of neurons that can be used in the
hidden layer is 1500 or more. The minimum validation error found from this work
is 2.34% which is the lowest error rate on handwritten Bengali dataset proposed
till date.
Pay Voice: Point of Sale Recognition for Visually Impaired People
Authors: Guilherme Folego, Filipe Costa, Bruno Costa, Alan Godoy, Luiz Pita
Date: 2018-12-14 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR), POS

Millions of visually impaired people depend on relatives and friends to
perform their everyday tasks. One relevant step towards self-sufficiency is to
provide them with means to verify the value and operation presented in payment
machines. In this work, we developed and released a smartphone application,
named Pay Voice, that uses image processing, optical character recognition
(OCR) and voice synthesis to recognize the value and operation presented in POS
and PIN pad machines, and thus informing the user with auditive and visual
feedback. The proposed approach presented significant results for value and
operation recognition, especially for POS, due to the higher display quality.
Importantly, we achieved the key performance indicators, namely, more than 80%
of accuracy in a real-world scenario, and less than $5$ seconds of processing
time for recognition. Pay Voice is publicly available on Google Play and App
Store for free.
Effectiveness of Hierarchical Softmax in Large Scale Classification Tasks
Authors: Abdul Arfat Mohammed, Venkatesh Umaashankar
Date: 2018-12-13 00:00:00
Tasks: Classification, General Classification

Typically, Softmax is used in the final layer of a neural network to get a
probability distribution for output classes. But the main problem with Softmax
is that it is computationally expensive for large scale data sets with large
number of possible outputs. To approximate class probability efficiently on
such large scale data sets we can use Hierarchical Softmax. LSHTC datasets were
used to study the performance of the Hierarchical Softmax. LSHTC datasets have
large number of categories. In this paper we evaluate and report the
performance of normal Softmax Vs Hierarchical Softmax on LSHTC datasets. This
evaluation used macro f1 score as a performance measure. The observation was
that the performance of Hierarchical Softmax degrades as the number of classes
increase.
Detecting unseen visual relations using analogies
Authors: Julia Peyre, Ivan Laptev, Cordelia Schmid, Josef Sivic
Date: 2018-12-13 00:00:00
Proceeding: ICCV 2019 10
Tasks: Retrieval, Triplet

We seek to detect visual relations in images of the form of triplets t = (subject, predicate, object), such as "person riding dog", where training examples of the individual entities are available but their combinations are unseen at training. This is an important set-up due to the combinatorial nature of visual relations : collecting sufficient training data for all possible triplets would be very hard. The contributions of this work are three-fold. First, we learn a representation of visual relations that combines (i) individual embeddings for subject, object and predicate together with (ii) a visual phrase embedding that represents the relation triplet. Second, we learn how to transfer visual phrase embeddings from existing training triplets to unseen test triplets using analogies between relations that involve similar objects. Third, we demonstrate the benefits of our approach on three challenging datasets : on HICO-DET, our model achieves significant improvement over a strong baseline for both frequent and unseen triplets, and we observe similar improvement for the retrieval of unseen triplets with out-of-vocabulary predicates on the COCO-a dataset as well as the challenging unusual triplets in the UnRel dataset.
Stochastic Gradient Descent for Spectral Embedding with Implicit Orthogonality Constraint
Authors: Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
Date: 2018-12-13 00:00:00
Tasks: Clustering, Graph Clustering, Stochastic Optimization

In this paper, we propose a scalable algorithm for spectral embedding. The
latter is a standard tool for graph clustering. However, its computational
bottleneck is the eigendecomposition of the graph Laplacian matrix, which
prevents its application to large-scale graphs. Our contribution consists of
reformulating spectral embedding so that it can be solved via stochastic
optimization. The idea is to replace the orthogonality constraint with an
orthogonalization matrix injected directly into the criterion. As the gradient
can be computed through a Cholesky factorization, our reformulation allows us
to develop an efficient algorithm based on mini-batch gradient descent.
Experimental results, both on synthetic and real data, confirm the efficiency
of the proposed method in term of execution speed with respect to similar
existing techniques.
Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem
Authors: Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: General Classification

Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear classifier function fail in this regard as they produce almost always high confidence predictions far away from the training data. For bounded domains like images we propose a new robust optimization technique similar to adversarial training which enforces low confidence predictions far away from the training data. We show that this technique is surprisingly effective in reducing the confidence of predictions far away from the training data while maintaining high confidence predictions and test error on the original classification task compared to standard training.
Multilayer Network Model of Movie Script
Authors: Youssef Mourchid, Benjamin Renoust, Hocine Cherifi, Mohammed El Hassouni
Date: 2018-12-13 00:00:00
Tasks: model

Network models have been increasingly used in the past years to support
summarization and analysis of narratives, such as famous TV series, books and
news. Inspired by social network analysis, most of these models focus on the
characters at play. The network model well captures all characters
interactions, giving a broad picture of the narration's content. A few works
went beyond by introducing additional semantic elements, always captured in a
single layer network. In contrast, we introduce in this work a multilayer
network model to capture more elements of the narration of a movie from its
script: people, locations, and other semantic elements. This model enables new
measures and insights on movies. We demonstrate this model on two very popular
movies.
Exploring Embedding Methods in Binary Hyperdimensional Computing: A Case Study for Motor-Imagery based Brain-Computer Interfaces
Authors: Michael Hersche, José del R. Millán, Luca Benini, Abbas Rahimi
Date: 2018-12-13 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), Motor Imagery, Quantization

Key properties of brain-inspired hyperdimensional (HD) computing make it a
prime candidate for energy-efficient and fast learning in biosignal processing.
The main challenge is however to formulate embedding methods that map biosignal
measures to a binary HD space. In this paper, we explore variety of such
embedding methods and examine them with a challenging application of motor
imagery brain-computer interface (MI-BCI) from electroencephalography (EEG)
recordings. We explore embedding methods including random projections,
quantization based thermometer and Gray coding, and learning HD representations
using end-to-end training. All these methods, differing in complexity, aim to
represent EEG signals in binary HD space, e.g. with 10,000 bits. This leads to
development of a set of HD learning and classification methods that can be
selectively chosen (or configured) based on accuracy and/or computational
complexity requirements of a given task. We compare them with state-of-the-art
linear support vector machine (SVM) on an NVIDIA TX2 board using the 4-class
BCI competition IV-2a dataset as well as a new 3-class dataset. Compared to
SVM, results on 3-class dataset show that simple thermometer embedding achieves
moderate average accuracy (79.56% vs. 82.67%) with 26.8$\times$ faster training
time and 22.3$\times$ lower energy; on the other hand, switching to end-to-end
training with learned HD representations wipes out these training benefits
while boosting the accuracy to 84.22% (1.55% higher than SVM). Similar trend is
observed on the 4-class dataset where SVM achieves on average 74.29%: the
thermometer embedding achieves 89.9$\times$ faster training time and
58.7$\times$ lower energy, but a lower accuracy (67.09%) than the learned
representation of 72.54%.
Ablation of a Robot's Brain: Neural Networks Under a Knife
Authors: Peter E. Lillian, Richard Meyes, Tobias Meisen
Date: 2018-12-13 00:00:00

It is still not fully understood exactly how neural networks are able to
solve the complex tasks that have recently pushed AI research forward. We
present a novel method for determining how information is structured inside a
neural network. Using ablation (a neuroscience technique for cutting away parts
of a brain to determine their function), we approach several neural network
architectures from a biological perspective. Through an analysis of this
method's results, we examine important similarities between biological and
artificial neural networks to search for the implicit knowledge locked away in
the network's weights.
A Probe Towards Understanding GAN and VAE Models
Authors: Lu Mi, Macheng Shen, Jingzhao Zhang
Date: 2018-12-13 00:00:00
Tasks: Generative Adversarial Network

This project report compares some known GAN and VAE models proposed prior to
2017. There has been significant progress after we finished this report. We
upload this report as an introduction to generative models and provide some
personal interpretations supported by empirical evidence. Both generative
adversarial network models and variational autoencoders have been widely used
to approximate probability distributions of data sets. Although they both use
parametrized distributions to approximate the underlying data distribution,
whose exact inference is intractable, their behaviors are very different. We
summarize our experiment results that compare these two categories of models in
terms of fidelity and mode collapse. We provide a hypothesis to explain their
different behaviors and propose a new model based on this hypothesis. We
further tested our proposed model on MNIST dataset and CelebA dataset.
Sequence Prediction using Spectral RNNs
Authors: Moritz Wolter, Juergen Gall, Angela Yao
Date: 2018-12-13 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Fourier methods have a long and proven track record as an excellent tool in data processing. As memory and computational constraints gain importance in embedded and mobile applications, we propose to combine Fourier methods and recurrent neural network architectures. The short-time Fourier transform allows us to efficiently process multiple samples at a time. Additionally, weight reductions trough low pass filtering is possible. We predict time series data drawn from the chaotic Mackey-Glass differential equation and real-world power load and motion capture data.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception
Authors: Yue Meng, Yongxi Lu, Aman Raj, Samuel Sunarjo, Rui Guo, Tara Javidi, Gaurav Bansal, Dinesh Bharadia
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, 3D Geometry Perception, Depth Estimation, Depth Prediction, Monocular Depth Estimation, Optical Flow Estimation, Prediction

Unsupervised learning for geometric perception (depth, optical flow, etc.) is
of great interest to autonomous systems. Recent works on unsupervised learning
have made considerable progress on perceiving geometry; however, they usually
ignore the coherence of objects and perform poorly under scenarios with dark
and noisy environments. In contrast, supervised learning algorithms, which are
robust, require large labeled geometric dataset. This paper introduces SIGNet,
a novel framework that provides robust geometry perception without requiring
geometrically informative labels. Specifically, SIGNet integrates semantic
information to make depth and flow predictions consistent with objects and
robust to low lighting conditions. SIGNet is shown to improve upon the
state-of-the-art unsupervised learning for depth prediction by 30% (in squared
relative error). In particular, SIGNet improves the dynamic object class
performance by 39% in depth prediction and 29% in flow prediction. Our code
will be made available at https://github.com/mengyuest/SIGNet
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition
Authors: Hao Huang, Luowei Zhou, Wei zhang, Jason J. Corso, Chenliang Xu
Date: 2018-12-13 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Activity Recognition, Object, Temporal Action Localization, Video Understanding

Video action recognition, a critical problem in video understanding, has been gaining increasing attention. To identify actions induced by complex object-object interactions, we need to consider not only spatial relations among objects in a single frame, but also temporal relations among different or the same objects across multiple frames. However, existing approaches that model video representations and non-local features are either incapable of explicitly modeling relations at the object-object level or unable to handle streaming videos. In this paper, we propose a novel dynamic hidden graph module to model complex object-object interactions in videos, of which two instantiations are considered: a visual graph that captures appearance/motion changes among objects and a location graph that captures relative spatiotemporal position changes among objects. Additionally, the proposed graph module allows us to process streaming videos, setting it apart from existing methods. Experimental results on benchmark datasets, Something-Something and ActivityNet, show the competitive performance of our method.
Adversarial Inference for Multi-Sentence Video Description
Authors: Jae Sung Park, Marcus Rohrbach, Trevor Darrell, Anna Rohrbach
Date: 2018-12-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Diversity, Image Captioning, Sentence, Video Description

While significant progress has been made in the image captioning task, video
description is still in its infancy due to the complex nature of video data.
Generating multi-sentence descriptions for long videos is even more
challenging. Among the main issues are the fluency and coherence of the
generated descriptions, and their relevance to the video. Recently,
reinforcement and adversarial learning based methods have been explored to
improve the image captioning models; however, both types of methods suffer from
a number of issues, e.g. poor readability and high redundancy for RL and
stability issues for GANs. In this work, we instead propose to apply
adversarial techniques during inference, designing a discriminator which
encourages better multi-sentence video description. In addition, we find that a
multi-discriminator "hybrid" design, where each discriminator targets one
aspect of a description, leads to the best results. Specifically, we decouple
the discriminator to evaluate on three criteria: 1) visual relevance to the
video, 2) language diversity and fluency, and 3) coherence across sentences.
Our approach results in more accurate, diverse, and coherent multi-sentence
video descriptions, as shown by automatic as well as human evaluation on the
popular ActivityNet Captions dataset.