pwc-archive/papers-with-abstracts huggingface dataset:
Self-supervised Multi-view Person Association and Its Applications
Authors: Minh Vo, Ersin Yumer, Kalyan Sunkavalli, Sunil Hadap, Yaser Sheikh, Srinivasa Narasimhan
Date: 2018-05-22 00:00:00
Tasks: Clustering

Reliable markerless motion tracking of people participating in a complex group activity from multiple moving cameras is challenging due to frequent occlusions, strong viewpoint and appearance variations, and asynchronous video streams. To solve this problem, reliable association of the same person across distant viewpoints and temporal instances is essential. We present a self-supervised framework to adapt a generic person appearance descriptor to the unlabeled videos by exploiting motion tracking, mutual exclusion constraints, and multi-view geometry. The adapted discriminative descriptor is used in a tracking-by-clustering formulation. We validate the effectiveness of our descriptor learning on WILDTRACK [14] and three new complex social scenes captured by multiple cameras with up to 60 people "in the wild". We report significant improvement in association accuracy (up to 18%) and stable and coherent 3D human skeleton tracking (5 to 10 times) over the baseline. Using the reconstructed 3D skeletons, we cut the input videos into a multi-angle video where the image of a specified person is shown from the best visible front-facing camera. Our algorithm detects inter-human occlusion to determine the camera switching moment while still maintaining the flow of the action well.
Normalization of Transliterated Words in Code-Mixed Data Using Seq2Seq Model & Levenshtein Distance
Authors: Soumil Mandal, Karthick Nanmaran
Date: 2018-05-22 00:00:00
Proceeding: WS 2018 11
Tasks: Transliteration

Building tools for code-mixed data is rapidly gaining popularity in the NLP
research community as such data is exponentially rising on social media.
Working with code-mixed data contains several challenges, especially due to
grammatical inconsistencies and spelling variations in addition to all the
previous known challenges for social media scenarios. In this article, we
present a novel architecture focusing on normalizing phonetic typing
variations, which is commonly seen in code-mixed data. One of the main features
of our architecture is that in addition to normalizing, it can also be utilized
for back-transliteration and word identification in some cases. Our model
achieved an accuracy of 90.27% on the test data.
Learning to Repair Software Vulnerabilities with Generative Adversarial Networks
Authors: Jacob Harer, Onur Ozdemir, Tomo Lazovich, Christopher P. Reale, Rebecca L. Russell, Louis Y. Kim, Peter Chin
Date: 2018-05-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Code Repair, Generative Adversarial Network, Translation

Motivated by the problem of automated repair of software vulnerabilities, we
propose an adversarial learning approach that maps from one discrete source
domain to another target domain without requiring paired labeled examples or
source and target domains to be bijections. We demonstrate that the proposed
adversarial learning approach is an effective technique for repairing software
vulnerabilities, performing close to seq2seq approaches that require labeled
pairs. The proposed Generative Adversarial Network approach is
application-agnostic in that it can be applied to other problems similar to
code repair, such as grammar correction or sentiment translation.
Dealing with Categorical and Integer-valued Variables in Bayesian Optimization with Gaussian Processes
Authors: Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato
Date: 2018-05-09 00:00:00
Tasks: Bayesian Optimization, Gaussian Processes

Bayesian Optimization (BO) methods are useful for optimizing functions that
are expen- sive to evaluate, lack an analytical expression and whose
evaluations can be contaminated by noise. These methods rely on a probabilistic
model of the objective function, typically a Gaussian process (GP), upon which
an acquisition function is built. The acquisition function guides the
optimization process and measures the expected utility of performing an
evaluation of the objective at a new point. GPs assume continous input
variables. When this is not the case, for example when some of the input
variables take categorical or integer values, one has to introduce extra
approximations. Consider a suggested input location taking values in the real
line. Before doing the evaluation of the objective, a common approach is to use
a one hot encoding approximation for categorical variables, or to round to the
closest integer, in the case of integer-valued variables. We show that this can
lead to problems in the optimization process and describe a more principled
approach to account for input variables that are categorical or integer-valued.
We illustrate in both synthetic and a real experiments the utility of our
approach, which significantly improves the results of standard BO methods using
Gaussian processes on problems with categorical or integer-valued variables.
Aesthetics Assessment of Images Containing Faces
Authors: Simone Bianco, Luigi Celona, Raimondo Schettini
Date: 2018-05-22 00:00:00

Recent research has widely explored the problem of aesthetics assessment of
images with generic content. However, few approaches have been specifically
designed to predict the aesthetic quality of images containing human faces,
which make up a massive portion of photos in the web. This paper introduces a
method for aesthetic quality assessment of images with faces. We exploit three
different Convolutional Neural Networks to encode information regarding
perceptual quality, global image aesthetics, and facial attributes; then, a
model is trained to combine these features to explicitly predict the aesthetics
of images containing faces. Experimental results show that our approach
outperforms existing methods for both binary, i.e. low/high, and continuous
aesthetic score prediction on four different databases in the state-of-the-art.
Convexity Shape Prior for Level Set based Image Segmentation Method
Authors: Shi Yan, Xue-Cheng Tai, Jun Liu, Hai-yang Huang
Date: 2018-05-22 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

We propose a geometric convexity shape prior preservation method for
variational level set based image segmentation methods. Our method is built
upon the fact that the level set of a convex signed distanced function must be
convex. This property enables us to transfer a complicated geometrical
convexity prior into a simple inequality constraint on the function. An active
set based Gauss-Seidel iteration is used to handle this constrained
minimization problem to get an efficient algorithm. We apply our method to
region and edge based level set segmentation models including Chan-Vese (CV)
model with guarantee that the segmented region will be convex. Experimental
results show the effectiveness and quality of the proposed model and algorithm.
Information Constraints on Auto-Encoding Variational Bayes
Authors: Romain Lopez, Jeffrey Regier, Michael. I. Jordan, Nir Yosef
Date: 2018-05-22 00:00:00
Proceeding: NeurIPS 2018 12

Parameterizing the approximate posterior of a generative model with neural
networks has become a common theme in recent machine learning research. While
providing appealing flexibility, this approach makes it difficult to impose or
assess structural constraints such as conditional independence. We propose a
framework for learning representations that relies on Auto-Encoding Variational
Bayes and whose search space is constrained via kernel-based measures of
independence. In particular, our method employs the $d$-variable
Hilbert-Schmidt Independence Criterion (dHSIC) to enforce independence between
the latent representations and arbitrary nuisance factors. We show how to apply
this method to a range of problems, including the problems of learning
invariant representations and the learning of interpretable representations. We
also present a full-fledged application to single-cell RNA sequencing
(scRNA-seq). In this setting the biological signal is mixed in complex ways
with sequencing errors and sampling effects. We show that our method
out-performs the state-of-the-art in this domain.
Learning Rules-First Classifiers
Authors: Deborah Cohen, Amit Daniely, Amir Globerson, Gal Elidan
Date: 2018-03-08 00:00:00
Tasks: General Classification, Sentiment Analysis

Complex classifiers may exhibit "embarassing" failures in cases where humans can easily provide a justified classification. Avoiding such failures is obviously of key importance. In this work, we focus on one such setting, where a label is perfectly predictable if the input contains certain features, or rules, and otherwise it is predictable by a linear classifier. We define a hypothesis class that captures this notion and determine its sample complexity. We also give evidence that efficient algorithms cannot achieve this sample complexity. We then derive a simple and efficient algorithm and show that its sample complexity is close to optimal, among efficient algorithms. Experiments on synthetic and sentiment analysis data demonstrate the efficacy of the method, both in terms of accuracy and interpretability.
Adding One Neuron Can Eliminate All Bad Local Minima
Authors: Shiyu Liang, Ruoyu Sun, Jason D. Lee, R. Srikant
Date: 2018-05-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: All, Binary Classification, General Classification

One of the main difficulties in analyzing neural networks is the
non-convexity of the loss function which may have many bad local minima.
  In this paper, we study the landscape of neural networks for binary
classification tasks. Under mild assumptions, we prove that after adding one
special neuron with a skip connection to the output, or one special neuron per
layer, every local minimum is a global minimum.
Structured Bayesian Gaussian process latent variable model
Authors: Steven Atkinson, Nicholas Zabaras
Date: 2018-05-22 00:00:00
Tasks: Imputation, model, Super-Resolution, Time Series, Time Series Analysis

We introduce a Bayesian Gaussian process latent variable model that
explicitly captures spatial correlations in data using a parameterized spatial
kernel and leveraging structure-exploiting algebra on the model covariance
matrices for computational tractability. Inference is made tractable through a
collapsed variational bound with similar computational complexity to that of
the traditional Bayesian GP-LVM. Inference over partially-observed test cases
is achieved by optimizing a "partially-collapsed" bound. Modeling
high-dimensional time series systems is enabled through use of a dynamical GP
latent variable prior. Examples imputing missing data on images and
super-resolution imputation of missing video frames demonstrate the model.
COCO-CN for Cross-Lingual Image Tagging, Captioning and Retrieval
Authors: Xirong Li, Chaoxi Xu, Xiaoxu Wang, Weiyu Lan, Zhengxiong Jia, Gang Yang, Jieping Xu
Date: 2018-05-22 00:00:00
Tasks: Retrieval

This paper contributes to cross-lingual image annotation and retrieval in
terms of data and baseline methods. We propose COCO-CN, a novel dataset
enriching MS-COCO with manually written Chinese sentences and tags. For more
effective annotation acquisition, we develop a recommendation-assisted
collective annotation system, automatically providing an annotator with several
tags and sentences deemed to be relevant with respect to the pictorial content.
Having 20,342 images annotated with 27,218 Chinese sentences and 70,993 tags,
COCO-CN is currently the largest Chinese-English dataset that provides a
unified and challenging platform for cross-lingual image tagging, captioning
and retrieval. We develop conceptually simple yet effective methods per task
for learning from cross-lingual resources. Extensive experiments on the three
tasks justify the viability of the proposed dataset and methods. Data and code
are publicly available at https://github.com/li-xirong/coco-cn
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment
Authors: Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic
Date: 2018-05-22 00:00:00
Proceeding: ACL 2018 7

Multimodal affective computing, learning to recognize and interpret human
affects and subjective information from multiple data sources, is still
challenging because: (i) it is hard to extract informative features to
represent human affects from heterogeneous inputs; (ii) current fusion
strategies only fuse different modalities at abstract level, ignoring
time-dependent interactions between modalities. Addressing such issues, we
introduce a hierarchical multimodal architecture with attention and word-level
fusion to classify utter-ance-level sentiment and emotion from text and audio
data. Our introduced model outperforms the state-of-the-art approaches on
published datasets and we demonstrated that our model is able to visualize and
interpret the synchronized attention over modalities.
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
Authors: Kai Zhang, WangMeng Zuo, Lei Zhang
Date: 2017-10-11 00:00:00
Tasks: Color Image Denoising, CPU, Denoising, Image Denoising

Due to the fast inference and good performance, discriminative learning
methods have been widely studied in image denoising. However, these methods
mostly learn a specific model for each noise level, and require multiple models
for denoising images with different noise levels. They also lack flexibility to
deal with spatially variant noise, limiting their applications in practical
denoising. To address these issues, we present a fast and flexible denoising
convolutional neural network, namely FFDNet, with a tunable noise level map as
the input. The proposed FFDNet works on downsampled sub-images, achieving a
good trade-off between inference speed and denoising performance. In contrast
to the existing discriminative denoisers, FFDNet enjoys several desirable
properties, including (i) the ability to handle a wide range of noise levels
(i.e., [0, 75]) effectively with a single network, (ii) the ability to remove
spatially variant noise by specifying a non-uniform noise level map, and (iii)
faster speed than benchmark BM3D even on CPU without sacrificing denoising
performance. Extensive experiments on synthetic and real noisy images are
conducted to evaluate FFDNet in comparison with state-of-the-art denoisers. The
results show that FFDNet is effective and efficient, making it highly
attractive for practical denoising applications.
Robust Conditional Generative Adversarial Networks
Authors: Grigorios G. Chrysos, Jean Kossaifi, Stefanos Zafeiriou
Date: 2018-05-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Conditional Image Generation, Image Generation

Conditional generative adversarial networks (cGAN) have led to large
improvements in the task of conditional image generation, which lies at the
heart of computer vision. The major focus so far has been on performance
improvement, while there has been little effort in making cGAN more robust to
noise. The regression (of the generator) might lead to arbitrarily large errors
in the output, which makes cGAN unreliable for real-world applications. In this
work, we introduce a novel conditional GAN model, called RoCGAN, which
leverages structure in the target space of the model to address the issue. Our
model augments the generator with an unsupervised pathway, which promotes the
outputs of the generator to span the target manifold even in the presence of
intense noise. We prove that RoCGAN share similar theoretical properties as GAN
and experimentally verify that our model outperforms existing state-of-the-art
cGAN architectures by a large margin in a variety of domains including images
from natural scenes and faces.
LMKL-Net: A Fast Localized Multiple Kernel Learning Solver via Deep Neural Networks
Authors: Ziming Zhang
Date: 2018-05-22 00:00:00

In this paper we propose solving localized multiple kernel learning (LMKL)
using LMKL-Net, a feedforward deep neural network. In contrast to previous
works, as a learning principle we propose {\em parameterizing} both the gating
function for learning kernel combination weights and the multiclass classifier
in LMKL using an attentional network (AN) and a multilayer perceptron (MLP),
respectively. In this way we can learn the (nonlinear) decision function in
LMKL (approximately) by sequential applications of AN and MLP. Empirically on
benchmark datasets we demonstrate that overall LMKL-Net can not only outperform
the state-of-the-art MKL solvers in terms of accuracy, but also be trained
about {\em two orders of magnitude} faster with much smaller memory footprint
for large-scale learning.
Universal discriminative quantum neural networks
Authors: Hongxiang Chen, Leonard Wossnig, Simone Severini, Hartmut Neven, Masoud Mohseni
Date: 2018-05-22 00:00:00
Proceeding: ICLR 2019 5
Tasks: Quantum Machine Learning, Stochastic Optimization

Quantum mechanics fundamentally forbids deterministic discrimination of
quantum states and processes. However, the ability to optimally distinguish
various classes of quantum data is an important primitive in quantum
information science. In this work, we train near-term quantum circuits to
classify data represented by non-orthogonal quantum probability distributions
using the Adam stochastic optimization algorithm. This is achieved by iterative
interactions of a classical device with a quantum processor to discover the
parameters of an unknown non-unitary quantum circuit. This circuit learns to
simulates the unknown structure of a generalized quantum measurement, or
Positive-Operator-Value-Measure (POVM), that is required to optimally
distinguish possible distributions of quantum inputs. Notably we use universal
circuit topologies, with a theoretically motivated circuit design, which
guarantees that our circuits can in principle learn to perform arbitrary
input-output mappings. Our numerical simulations show that shallow quantum
circuits could be trained to discriminate among various pure and mixed quantum
states exhibiting a trade-off between minimizing erroneous and inconclusive
outcomes with comparable performance to theoretically optimal POVMs. We train
the circuit on different classes of quantum data and evaluate the
generalization error on unseen mixed quantum states. This generalization power
hence distinguishes our work from standard circuit optimization and provides an
example of quantum machine learning for a task that has inherently no classical
analogue.
Visual Explanation by Interpretation: Improving Visual Feedback Capabilities of Deep Neural Networks
Authors: Jose Oramas, Kaili Wang, Tinne Tuytelaars
Date: 2017-12-18 00:00:00
Proceeding: ICLR 2019 5

Interpretation and explanation of deep models is critical towards wide
adoption of systems that rely on them. In this paper, we propose a novel scheme
for both interpretation as well as explanation in which, given a pretrained
model, we automatically identify internal features relevant for the set of
classes considered by the model, without relying on additional annotations. We
interpret the model through average visualizations of this reduced set of
features. Then, at test time, we explain the network prediction by accompanying
the predicted class label with supporting visualizations derived from the
identified features. In addition, we propose a method to address the artifacts
introduced by stridded operations in deconvNet-based visualizations. Moreover,
we introduce an8Flower, a dataset specifically designed for objective
quantitative evaluation of methods for visual explanation.Experiments on the
MNIST,ILSVRC12,Fashion144k and an8Flower datasets show that our method produces
detailed explanations with good coverage of relevant features of the classes of
interest
Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach
Authors: Grant M. Rotskoff, Eric Vanden-Eijnden
Date: 2018-05-02 00:00:00
Tasks: BIG-bench Machine Learning

Neural networks, a central tool in machine learning, have demonstrated remarkable, high fidelity performance on image recognition and classification tasks. These successes evince an ability to accurately represent high dimensional functions, but rigorous results about the approximation error of neural networks after training are few. Here we establish conditions for global convergence of the standard optimization algorithm used in machine learning applications, stochastic gradient descent (SGD), and quantify the scaling of its error with the size of the network. This is done by reinterpreting SGD as the evolution of a particle system with interactions governed by a potential related to the objective or "loss" function used to train the network. We show that, when the number $n$ of units is large, the empirical distribution of the particles descends on a convex landscape towards the global minimum at a rate independent of $n$, with a resulting approximation error that universally scales as $O(n^{-1})$. These properties are established in the form of a Law of Large Numbers and a Central Limit Theorem for the empirical distribution. Our analysis also quantifies the scale and nature of the noise introduced by SGD and provides guidelines for the step size and batch size to use when training a neural network. We illustrate our findings on examples in which we train neural networks to learn the energy function of the continuous 3-spin model on the sphere. The approximation error scales as our analysis predicts in as high a dimension as $d=25$.
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Authors: Aapo Hyvarinen, Hiroaki Sasaki, Richard E. Turner
Date: 2018-05-22 00:00:00
Tasks: Contrastive Learning, Representation Learning, Time Series, Time Series Analysis

Nonlinear ICA is a fundamental problem for unsupervised representation
learning, emphasizing the capacity to recover the underlying latent variables
generating the data (i.e., identifiability). Recently, the very first
identifiability proofs for nonlinear ICA have been proposed, leveraging the
temporal structure of the independent components. Here, we propose a general
framework for nonlinear ICA, which, as a special case, can make use of temporal
structure. It is based on augmenting the data by an auxiliary variable, such as
the time index, the history of the time series, or any other available
information. We propose to learn nonlinear ICA by discriminating between true
augmented data, or data in which the auxiliary variable has been randomized.
This enables the framework to be implemented algorithmically through logistic
regression, possibly in a neural network. We provide a comprehensive proof of
the identifiability of the model as well as the consistency of our estimation
method. The approach not only provides a general theoretical framework
combining and generalizing previously proposed nonlinear ICA models and
algorithms, but also brings practical advantages.
Constructing Compact Brain Connectomes for Individual Fingerprinting
Authors: Vikram Ravindra, Petros Drineas, Ananth Grama
Date: 2018-05-22 00:00:00

Recent neuroimaging studies have shown that functional connectomes are unique to individuals, i.e., two distinct fMRIs taken over different sessions of the same subject are more similar in terms of their connectomes than those from two different subjects. In this study, we present significant new results that identify, for the first time, specific parts of resting-state and task-specific connectomes that code the unique signatures. We show that a very small part of the connectome codes the signatures. A network of these features is shown to achieve excellent training and test accuracy in matching imaging datasets. We show that these features are statistically significant, robust to perturbations, invariant across populations, and are localized to a small number of structural regions of the brain. Furthermore, we show that for task-specific connectomes, the regions identified by our method are consistent with their known functional characterization. We present a new matrix sampling technique to derive computationally efficient and accurate methods for identifying the discriminating sub-connectome and support all of our claims using state-of-the-art statistical tests and computational techniques.