pwc-archive/papers-with-abstracts huggingface dataset:
A Multi-view RGB-D Approach for Human Pose Estimation in Operating Rooms
Authors: Abdolrahim Kadkhodamohammadi, Afshin Gangi, Michel de Mathelin, Nicolas Padoy
Date: 2017-01-25 00:00:00
Tasks: 3D Human Pose Estimation, Multi-view 3D Human Pose Estimation, Pose Estimation

Many approaches have been proposed for human pose estimation in single and
multi-view RGB images. However, some environments, such as the operating room,
are still very challenging for state-of-the-art RGB methods. In this paper, we
propose an approach for multi-view 3D human pose estimation from RGB-D images
and demonstrate the benefits of using the additional depth channel for pose
refinement beyond its use for the generation of improved features. The proposed
method permits the joint detection and estimation of the poses without knowing
a priori the number of persons present in the scene. We evaluate this approach
on a novel multi-view RGB-D dataset acquired during live surgeries and
annotated with ground truth 3D poses.
Grad-CAM: Why did you say that?
Authors: Ramprasaath R. Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, Dhruv Batra
Date: 2016-11-22 00:00:00
Tasks: Image Captioning, Visual Question Answering, Visual Question Answering (VQA)

We propose a technique for making Convolutional Neural Network (CNN)-based
models more transparent by visualizing input regions that are 'important' for
predictions -- or visual explanations. Our approach, called Gradient-weighted
Class Activation Mapping (Grad-CAM), uses class-specific gradient information
to localize important regions. These localizations are combined with existing
pixel-space visualizations to create a novel high-resolution and
class-discriminative visualization called Guided Grad-CAM. These methods help
better understand CNN-based models, including image captioning and visual
question answering (VQA) models. We evaluate our visual explanations by
measuring their ability to discriminate between classes, to inspire trust in
humans, and their correlation with occlusion maps. Grad-CAM provides a new way
to understand CNN-based models.
  We have released code, an online demo hosted on CloudCV, and a full version
of this extended abstract.
Photographic dataset: playing cards
Authors: David Villacis, Santeri Kaupinmäki, Samuli Siltanen, Teemu Helenius
Date: 2017-01-25 00:00:00

This is a photographic dataset collected for testing image processing
algorithms. The idea is to have images that can exploit the properties of total
variation, therefore a set of playing cards was distributed on the scene. The
dataset is made available at www.fips.fi/photographic_dataset2.php
Kernel Mean Embedding of Distributions: A Review and Beyond
Authors: Krikamol Muandet, Kenji Fukumizu, Bharath Sriperumbudur, Bernhard Schölkopf
Date: 2016-05-31 00:00:00
Tasks: Causal Discovery, Reinforcement Learning, Survey, Two-sample testing

A Hilbert space embedding of a distribution---in short, a kernel mean embedding---has recently emerged as a powerful tool for machine learning and inference. The basic idea behind this framework is to map distributions into a reproducing kernel Hilbert space (RKHS) in which the whole arsenal of kernel methods can be extended to probability measures. It can be viewed as a generalization of the original "feature map" common to support vector machines (SVMs) and other kernel methods. While initially closely associated with the latter, it has meanwhile found application in fields ranging from kernel machines and probabilistic modeling to statistical inference, causal discovery, and deep learning. The goal of this survey is to give a comprehensive review of existing work and recent advances in this research area, and to discuss the most challenging issues and open problems that could lead to new research directions. The survey begins with a brief introduction to the RKHS and positive definite kernels which forms the backbone of this survey, followed by a thorough discussion of the Hilbert space embedding of marginal distributions, theoretical guarantees, and a review of its applications. The embedding of distributions enables us to apply RKHS methods to probability measures which prompts a wide range of applications such as kernel two-sample testing, independent testing, and learning on distributional data. Next, we discuss the Hilbert space embedding for conditional distributions, give theoretical insights, and review some applications. The conditional mean embedding enables us to perform sum, product, and Bayes' rules---which are ubiquitous in graphical model, probabilistic inference, and reinforcement learning---in a non-parametric way. We then discuss relationships between this framework and other related areas. Lastly, we give some suggestions on future research directions.
Overcoming catastrophic forgetting in neural networks
Authors: James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell
Date: 2016-12-02 00:00:00
Tasks: Atari Games, class-incremental learning, Class Incremental Learning, Continual Learning, General Classification, Incremental Learning

The ability to learn tasks in a sequential fashion is crucial to the
development of artificial intelligence. Neural networks are not, in general,
capable of this and it has been widely thought that catastrophic forgetting is
an inevitable feature of connectionist models. We show that it is possible to
overcome this limitation and train networks that can maintain expertise on
tasks which they have not experienced for a long time. Our approach remembers
old tasks by selectively slowing down learning on the weights important for
those tasks. We demonstrate our approach is scalable and effective by solving a
set of classification tasks based on the MNIST hand written digit dataset and
by learning several Atari 2600 games sequentially.
Universal representations:The missing link between faces, text, planktons, and cat breeds
Authors: Hakan Bilen, Andrea Vedaldi
Date: 2017-01-25 00:00:00
Tasks: Continual Learning

With the advent of large labelled datasets and high-capacity models, the
performance of machine vision systems has been improving rapidly. However, the
technology has still major limitations, starting from the fact that different
vision problems are still solved by different models, trained from scratch or
fine-tuned on the target data. The human visual system, in stark contrast,
learns a universal representation for vision in the early life of an
individual. This representation works well for an enormous variety of vision
problems, with little or no change, with the major advantage of requiring
little training data to solve any of them.
k*-Nearest Neighbors: From Global to Local
Authors: Oren Anava, Kfir. Y. Levy
Date: 2017-01-25 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: General Classification

The weighted k-nearest neighbors algorithm is one of the most fundamental
non-parametric methods in pattern recognition and machine learning. The
question of setting the optimal number of neighbors as well as the optimal
weights has received much attention throughout the years, nevertheless this
problem seems to have remained unsettled. In this paper we offer a simple
approach to locally weighted regression/classification, where we make the
bias-variance tradeoff explicit. Our formulation enables us to phrase a notion
of optimal weights, and to efficiently find these weights as well as the
optimal number of neighbors efficiently and adaptively, for each data point
whose value we wish to estimate. The applicability of our approach is
demonstrated on several datasets, showing superior performance over standard
locally weighted methods.
Matching-Based Selection with Incomplete Lists for Decomposition Multi-Objective Optimization
Authors: Mengyuan Wu, Ke Li, Sam Kwong, Yu Zhou, Qingfu Zhang
Date: 2016-08-30 00:00:00
Tasks: Diversity

The balance between convergence and diversity is a key issue of evolutionary
multi-objective optimization. The recently proposed stable matching-based
selection provides a new perspective to handle this balance under the framework
of decomposition multi-objective optimization. In particular, the stable
matching between subproblems and solutions, which achieves an equilibrium
between their mutual preferences, implicitly strikes a balance between the
convergence and diversity. Nevertheless, the original stable matching model has
a high risk of matching a solution with a unfavorable subproblem which finally
leads to an imbalanced selection result. In this paper, we propose an adaptive
two-level stable matching-based selection for decomposition multi-objective
optimization. Specifically, borrowing the idea of stable matching with
incomplete lists, we match each solution with one of its favorite subproblems
by restricting the length of its preference list during the first-level stable
matching. During the second-level stable matching, the remaining subproblems
are thereafter matched with their favorite solutions according to the classic
stable matching model. In particular, we develop an adaptive mechanism to
automatically set the length of preference list for each solution according to
its local competitiveness. The performance of our proposed method is validated
and compared with several state-of-the-art evolutionary multi-objective
optimization algorithms on 62 benchmark problem instances. Empirical results
fully demonstrate the competitive performance of our proposed method on
problems with complicated Pareto sets and those with more than three
objectives.
Decoding Epileptogenesis in a Reduced State Space
Authors: François G. Meyer, Alexander M. Benison, Zachariah Smith, Daniel S. Barth
Date: 2017-01-25 00:00:00

We describe here the recent results of a multidisciplinary effort to design a
biomarker that can actively and continuously decode the progressive changes in
neuronal organization leading to epilepsy, a process known as epileptogenesis.
Using an animal model of acquired epilepsy, wechronically record hippocampal
evoked potentials elicited by an auditory stimulus. Using a set of reduced
coordinates, our algorithm can identify universal smooth low-dimensional
configurations of the auditory evoked potentials that correspond to distinct
stages of epileptogenesis. We use a hidden Markov model to learn the dynamics
of the evoked potential, as it evolves along these smooth low-dimensional
subsets. We provide experimental evidence that the biomarker is able to exploit
subtle changes in the evoked potential to reliably decode the stage of
epileptogenesis and predict whether an animal will eventually recover from the
injury, or develop spontaneous seizures.
Learn&Fuzz: Machine Learning for Input Fuzzing
Authors: Patrice Godefroid, Hila Peleg, Rishabh Singh
Date: 2017-01-25 00:00:00
Tasks: BIG-bench Machine Learning

Fuzzing consists of repeatedly testing an application with modified, or
fuzzed, inputs with the goal of finding security vulnerabilities in
input-parsing code. In this paper, we show how to automate the generation of an
input grammar suitable for input fuzzing using sample inputs and
neural-network-based statistical machine-learning techniques. We present a
detailed case study with a complex input format, namely PDF, and a large
complex security-critical parser for this format, namely, the PDF parser
embedded in Microsoft's new Edge browser. We discuss (and measure) the tension
between conflicting learning and fuzzing goals: learning wants to capture the
structure of well-formed inputs, while fuzzing wants to break that structure in
order to cover unexpected code paths and find bugs. We also present a new
algorithm for this learn&fuzz challenge which uses a learnt input probability
distribution to intelligently guide where to fuzz inputs.
Learning from Label Proportions in Brain-Computer Interfaces: Online Unsupervised Learning with Guarantees
Authors: D Hübner, T Verhoeven, K Schmid, K-R Müller, M Tangermann, P-J Kindermans
Date: 2017-01-25 00:00:00
Tasks: Brain Computer Interface, Decoder, ERP, General Classification

Objective: Using traditional approaches, a Brain-Computer Interface (BCI)
requires the collection of calibration data for new subjects prior to online
use. Calibration time can be reduced or eliminated e.g.~by transfer of a
pre-trained classifier or unsupervised adaptive classification methods which
learn from scratch and adapt over time. While such heuristics work well in
practice, none of them can provide theoretical guarantees. Our objective is to
modify an event-related potential (ERP) paradigm to work in unison with the
machine learning decoder to achieve a reliable calibration-less decoding with a
guarantee to recover the true class means.
  Method: We introduce learning from label proportions (LLP) to the BCI
community as a new unsupervised, and easy-to-implement classification approach
for ERP-based BCIs. The LLP estimates the mean target and non-target responses
based on known proportions of these two classes in different groups of the
data. We modified a visual ERP speller to meet the requirements of the LLP. For
evaluation, we ran simulations on artificially created data sets and conducted
an online BCI study with N=13 subjects performing a copy-spelling task.
  Results: Theoretical considerations show that LLP is guaranteed to minimize
the loss function similarly to a corresponding supervised classifier. It
performed well in simulations and in the online application, where 84.5% of
characters were spelled correctly on average without prior calibration.
  Significance: The continuously adapting LLP classifier is the first
unsupervised decoder for ERP BCIs guaranteed to find the true class means. This
makes it an ideal solution to avoid a tedious calibration and to tackle
non-stationarities in the data. Additionally, LLP works on complementary
principles compared to existing unsupervised methods, allowing for their
further enhancement when combined with LLP.
Mapping and Localization from Planar Markers
Authors: Rafael Muñoz-Salinas, Manuel J. Marín-Jimenez, Enrique Yeguas-Bolivar, Rafael Medina-Carnicer
Date: 2016-06-01 00:00:00
Tasks: Camera Localization

Squared planar markers are a popular tool for fast, accurate and robust
camera localization, but its use is frequently limited to a single marker, or
at most, to a small set of them for which their relative pose is known
beforehand. Mapping and localization from a large set of planar markers is yet
a scarcely treated problem in favour of keypoint-based approaches. However,
while keypoint detectors are not robust to rapid motion, large changes in
viewpoint, or significant changes in appearance, fiducial markers can be
robustly detected under a wider range of conditions. This paper proposes a
novel method to simultaneously solve the problems of mapping and localization
from a set of squared planar markers. First, a quiver of pairwise relative
marker poses is created, from which an initial pose graph is obtained. The pose
graph may contain small pairwise pose errors, that when propagated, leads to
large errors. Thus, we distribute the rotational and translational error along
the basis cycles of the graph so as to obtain a corrected pose graph. Finally,
we perform a global pose optimization by minimizing the reprojection errors of
the planar markers in all observed frames. The experiments conducted show that
our method performs better than Structure from Motion and visual SLAM
techniques.
Advances in All-Neural Speech Recognition
Authors: G. Zweig, C. Yu, J. Droppo, A. Stolcke
Date: 2016-09-19 00:00:00
Tasks: All, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

This paper advances the design of CTC-based all-neural (or end-to-end) speech
recognizers. We propose a novel symbol inventory, and a novel iterated-CTC
method in which a second system is used to transform a noisy initial output
into a cleaner version. We present a number of stabilization and initialization
methods we have found useful in training these networks. We evaluate our system
on the commonly used NIST 2000 conversational telephony test set, and
significantly exceed the previously published performance of similar systems,
both with and without the use of an external language model and decoding
technology.
The Microsoft 2016 Conversational Speech Recognition System
Authors: W. Xiong, J. Droppo, X. Huang, F. Seide, M. Seltzer, A. Stolcke, D. Yu, G. Zweig
Date: 2016-09-12 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition

We describe Microsoft's conversational speech recognition system, in which we
combine recent developments in neural-network-based acoustic and language
modeling to advance the state of the art on the Switchboard recognition task.
Inspired by machine learning ensemble techniques, the system uses a range of
convolutional and recurrent neural networks. I-vector modeling and lattice-free
MMI training provide significant gains for all acoustic model architectures.
Language model rescoring with multiple forward and backward running RNNLMs, and
word posterior-based system combination provide a 20% boost. The best single
system uses a ResNet architecture acoustic model with RNNLM rescoring, and
achieves a word error rate of 6.9% on the NIST 2000 Switchboard task. The
combined system has an error rate of 6.2%, representing an improvement over
previously reported results on this benchmark task.
Privileged Multi-label Learning
Authors: Shan You, Chang Xu, Yunhe Wang, Chao Xu, DaCheng Tao
Date: 2017-01-25 00:00:00
Tasks: Multi-Label Learning

This paper presents privileged multi-label learning (PrML) to explore and
exploit the relationship between labels in multi-label learning problems. We
suggest that for each individual label, it cannot only be implicitly connected
with other labels via the low-rank constraint over label predictors, but also
its performance on examples can receive the explicit comments from other labels
together acting as an \emph{Oracle teacher}. We generate privileged label
feature for each example and its individual label, and then integrate it into
the framework of low-rank based multi-label learning. The proposed algorithm
can therefore comprehensively explore and exploit label relationships by
inheriting all the merits of privileged information and low-rank constraints.
We show that PrML can be efficiently solved by dual coordinate descent
algorithm using iterative optimization strategy with cheap updates. Experiments
on benchmark datasets show that through privileged label features, the
performance can be significantly improved and PrML is superior to several
competing methods in most cases.
Understanding the Effective Receptive Field in Deep Convolutional Neural Networks
Authors: Wenjie Luo, Yujia Li, Raquel Urtasun, Richard Zemel
Date: 2017-01-15 00:00:00
Proceeding: NeurIPS 2016 12

We study characteristics of receptive fields of units in deep convolutional
networks. The receptive field size is a crucial issue in many visual tasks, as
the output must respond to large enough areas in the image to capture
information about large objects. We introduce the notion of an effective
receptive field, and show that it both has a Gaussian distribution and only
occupies a fraction of the full theoretical receptive field. We analyze the
effective receptive field in several architecture designs, and the effect of
nonlinear activations, dropout, sub-sampling and skip connections on it. This
leads to suggestions for ways to address its tendency to be too small.
Towards End-to-End Face Recognition through Alignment Learning
Authors: Yuanyi Zhong, Jiansheng Chen, Bo Huang
Date: 2017-01-25 00:00:00
Tasks: Face Alignment, Face Recognition

Plenty of effective methods have been proposed for face recognition during
the past decade. Although these methods differ essentially in many aspects, a
common practice of them is to specifically align the facial area based on the
prior knowledge of human face structure before feature extraction. In most
systems, the face alignment module is implemented independently. This has
actually caused difficulties in the designing and training of end-to-end face
recognition models. In this paper we study the possibility of alignment
learning in end-to-end face recognition, in which neither prior knowledge on
facial landmarks nor artificially defined geometric transformations are
required. Specifically, spatial transformer layers are inserted in front of the
feature extraction layers in a Convolutional Neural Network (CNN) for face
recognition. Only human identity clues are used for driving the neural network
to automatically learn the most suitable geometric transformation and the most
appropriate facial area for the recognition task. To ensure reproducibility,
our model is trained purely on the publicly available CASIA-WebFace dataset,
and is tested on the Labeled Face in the Wild (LFW) dataset. We have achieved a
verification accuracy of 99.08\% which is comparable to state-of-the-art single
model based methods.
Query Complexity of Tournament Solutions
Authors: Arnab Maiti, Palash Dey
Date: 2016-11-18 00:00:00

A directed graph where there is exactly one edge between every pair of vertices is called a {\em tournament}. Finding the "best" set of vertices of a tournament is a well studied problem in social choice theory. A {\em tournament solution} takes a tournament as input and outputs a subset of vertices of the input tournament. However, in many applications, for example, choosing the best set of drugs from a given set of drugs, the edges of the tournament are given only implicitly and knowing the orientation of an edge is costly. In such scenarios, we would like to know the best set of vertices (according to some tournament solution) by "querying" as few edges as possible. We, in this paper, precisely study this problem for commonly used tournament solutions: given an oracle access to the edges of a tournament T, find $f(T)$ by querying as few edges as possible, for a tournament solution f. We first show that the set of Condorcet non-losers in a tournament can be found by querying $2n-\lfloor \log n \rfloor -2$ edges only and this is tight in the sense that every algorithm for finding the set of Condorcet non-losers needs to query at least $2n-\lfloor \log n \rfloor -2$ edges in the worst case, where $n$ is the number of vertices in the input tournament. We then move on to study other popular tournament solutions and show that any algorithm for finding the Copeland set, the Slater set, the Markov set, the bipartisan set, the uncovered set, the Banks set, and the top cycle must query $\Omega(n^2)$ edges in the worst case. On the positive side, we are able to circumvent our strong query complexity lower bound results by proving that, if the size of the top cycle of the input tournament is at most $k$, then we can find all the tournament solutions mentioned above by querying $O(nk + \frac{n\log n}{\log(1-\frac{1}{k})})$ edges only.