pwc-archive/papers-with-abstracts huggingface dataset:
Exploration of object recognition from 3D point cloud
Authors: Lin Duan
Date: 2017-07-05 00:00:00
Tasks: Object, Object Recognition

We present our latest experiment results of object recognition from 3D point
cloud data collected through moving car.
Learning Geometric Concepts with Nasty Noise
Authors: Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
Date: 2017-07-05 00:00:00
Tasks: LEMMA, Outlier Detection, PAC learning

We study the efficient learnability of geometric concept classes -
specifically, low-degree polynomial threshold functions (PTFs) and
intersections of halfspaces - when a fraction of the data is adversarially
corrupted. We give the first polynomial-time PAC learning algorithms for these
concept classes with dimension-independent error guarantees in the presence of
nasty noise under the Gaussian distribution. In the nasty noise model, an
omniscient adversary can arbitrarily corrupt a small fraction of both the
unlabeled data points and their labels. This model generalizes well-studied
noise models, including the malicious noise model and the agnostic (adversarial
label noise) model. Prior to our work, the only concept class for which
efficient malicious learning algorithms were known was the class of
origin-centered halfspaces.
  Specifically, our robust learning algorithm for low-degree PTFs succeeds
under a number of tame distributions -- including the Gaussian distribution
and, more generally, any log-concave distribution with (approximately) known
low-degree moments. For LTFs under the Gaussian distribution, we give a
polynomial-time algorithm that achieves error $O(\epsilon)$, where $\epsilon$
is the noise rate. At the core of our PAC learning results is an efficient
algorithm to approximate the low-degree Chow-parameters of any bounded function
in the presence of nasty noise. To achieve this, we employ an iterative
spectral method for outlier detection and removal, inspired by recent work in
robust unsupervised learning. Our aforementioned algorithm succeeds for a range
of distributions satisfying mild concentration bounds and moment assumptions.
The correctness of our robust learning algorithm for intersections of
halfspaces makes essential use of a novel robust inverse independence lemma
that may be of broader interest.
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
Authors: David Salinas, Valentin Flunkert, Jan Gasthaus
Date: 2017-04-13 00:00:00
Tasks: Multivariate Time Series Forecasting, Probabilistic Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting

Probabilistic forecasting, i.e. estimating the probability distribution of a
time series' future given its past, is a key enabler for optimizing business
processes. In retail businesses, for example, forecasting demand is crucial for
having the right inventory available at the right time at the right place. In
this paper we propose DeepAR, a methodology for producing accurate
probabilistic forecasts, based on training an auto regressive recurrent network
model on a large number of related time series. We demonstrate how by applying
deep learning techniques to forecasting, one can overcome many of the
challenges faced by widely-used classical approaches to the problem. We show
through extensive empirical evaluation on several real-world forecasting data
sets accuracy improvements of around 15% compared to state-of-the-art methods.
Copy-move Forgery Detection based on Convolutional Kernel Network
Authors: Yaqi Liu, Qingxiao Guan, Xianfeng Zhao
Date: 2017-07-05 00:00:00
Tasks: GPU

In this paper, a copy-move forgery detection method based on Convolutional
Kernel Network is proposed. Different from methods based on conventional
hand-crafted features, Convolutional Kernel Network is a kind of data-driven
local descriptor with the deep convolutional structure. Thanks to the
development of deep learning theories and widely available datasets, the
data-driven methods can achieve competitive performance on different conditions
for its excellent discriminative capability. Besides, our Convolutional Kernel
Network is reformulated as a series of matrix computations and convolutional
operations which are easy to parallelize and accelerate by GPU, leading to high
efficiency. Then, appropriate preprocessing and postprocessing for
Convolutional Kernel Network are adopted to achieve copy-move forgery
detection. Particularly, a segmentation-based keypoints distribution strategy
is proposed and a GPU-based adaptive oversegmentation method is adopted.
Numerous experiments are conducted to demonstrate the effectiveness and
robustness of the GPU version of Convolutional Kernel Network, and the
state-of-the-art performance of the proposed copy-move forgery detection method
based on Convolutional Kernel Network.
Model compression as constrained optimization, with application to neural nets. Part I: general framework
Authors: Miguel Á. Carreira-Perpiñán
Date: 2017-07-05 00:00:00
Tasks: Model Compression, Object Recognition, Quantization

Compressing neural nets is an active research problem, given the large size
of state-of-the-art nets for tasks such as object recognition, and the
computational limits imposed by mobile devices. We give a general formulation
of model compression as constrained optimization. This includes many types of
compression: quantization, low-rank decomposition, pruning, lossless
compression and others. Then, we give a general algorithm to optimize this
nonconvex problem based on the augmented Lagrangian and alternating
optimization. This results in a "learning-compression" algorithm, which
alternates a learning step of the uncompressed model, independent of the
compression type, with a compression step of the model parameters, independent
of the learning task. This simple, efficient algorithm is guaranteed to find
the best compressed model for the task in a local sense under standard
assumptions.
  We present separately in several companion papers the development of this
general framework into specific algorithms for model compression based on
quantization, pruning and other variations, including experimental results on
compressing neural nets and other models.
Multi-Scale Saliency Detection using Dictionary Learning
Authors: Shubham Pachori
Date: 2016-11-19 00:00:00
Tasks: Dictionary Learning, General Classification, Motion Detection, Object, object-detection, Object Detection, Object Recognition, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Semantic Segmentation, Video Compression

Saliency detection has drawn a lot of attention of researchers in various
fields over the past several years. Saliency is the perceptual quality that
makes an object, person to draw the attention of humans at the very sight.
Salient object detection in an image has been used centrally in many
computational photography and computer vision applications like video
compression, object recognition and classification, object segmentation,
adaptive content delivery, motion detection, content aware resizing, camouflage
images and change blindness images to name a few. We propose a method to detect
saliency in the objects using multimodal dictionary learning which has been
recently used in classification and image fusion. The multimodal dictionary
that we are learning is task driven which gives improved performance over its
counterpart (the one which is not task specific).
A Survey of Recent Advances in CNN-based Single Image Crowd Counting and Density Estimation
Authors: Vishwanath A. Sindagi, Vishal M. Patel
Date: 2017-07-05 00:00:00
Tasks: Crowd Counting, Density Estimation

Estimating count and density maps from crowd images has a wide range of
applications such as video surveillance, traffic monitoring, public safety and
urban planning. In addition, techniques developed for crowd counting can be
applied to related tasks in other fields of study such as cell microscopy,
vehicle counting and environmental survey. The task of crowd counting and
density map estimation is riddled with many challenges such as occlusions,
non-uniform density, intra-scene and inter-scene variations in scale and
perspective. Nevertheless, over the last few years, crowd count analysis has
evolved from earlier methods that are often limited to small variations in
crowd density and scales to the current state-of-the-art methods that have
developed the ability to perform successfully on a wide range of scenarios. The
success of crowd counting methods in the recent years can be largely attributed
to deep learning and publications of challenging datasets. In this paper, we
provide a comprehensive survey of recent Convolutional Neural Network (CNN)
based approaches that have demonstrated significant improvements over earlier
methods that rely largely on hand-crafted representations. First, we briefly
review the pioneering methods that use hand-crafted representations and then we
delve in detail into the deep learning-based approaches and recently published
datasets. Furthermore, we discuss the merits and drawbacks of existing
CNN-based approaches and identify promising avenues of research in this rapidly
evolving field.
Exact Clustering of Weighted Graphs via Semidefinite Programming
Authors: Aleksis Pirinen, Brendan Ames
Date: 2016-03-16 00:00:00
Tasks: Clustering

As a model problem for clustering, we consider the densest k-disjoint-clique
problem of partitioning a weighted complete graph into k disjoint subgraphs
such that the sum of the densities of these subgraphs is maximized. We
establish that such subgraphs can be recovered from the solution of a
particular semidefinite relaxation with high probability if the input graph is
sampled from a distribution of clusterable graphs. Specifically, the
semidefinite relaxation is exact if the graph consists of k large disjoint
subgraphs, corresponding to clusters, with weight concentrated within these
subgraphs, plus a moderate number of outliers. Further, we establish that if
noise is weakly obscuring these clusters, i.e, the between-cluster edges are
assigned very small weights, then we can recover significantly smaller
clusters. For example, we show that in approximately sparse graphs, where the
between-cluster weights tend to zero as the size n of the graph tends to
infinity, we can recover clusters of size polylogarithmic in n. Empirical
evidence from numerical simulations is also provided to support these
theoretical phase transitions to perfect recovery of the cluster structure.
Stochastic Configuration Networks Ensemble for Large-Scale Data Analytics
Authors: Dianhui Wang, Caihao Cui
Date: 2017-07-02 00:00:00
Tasks: Ensemble Learning

This paper presents a fast decorrelated neuro-ensemble with heterogeneous
features for large-scale data analytics, where stochastic configuration
networks (SCNs) are employed as base learner models and the well-known negative
correlation learning (NCL) strategy is adopted to evaluate the output weights.
By feeding a large number of samples into the SCN base models, we obtain a huge
sized linear equation system which is difficult to be solved by means of
computing a pseudo-inverse used in the least squares method. Based on the group
of heterogeneous features, the block Jacobi and Gauss-Seidel methods are
employed to iteratively evaluate the output weights, and a convergence analysis
is given with a demonstration on the uniqueness of these iterative solutions.
Experiments with comparisons on two large-scale datasets are carried out, and
the system robustness with respect to the regularizing factor used in NCL is
given. Results indicate that the proposed ensemble learning techniques have
good potential for resolving large-scale data modelling problems.
Causal Falling Rule Lists
Authors: Fulton Wang, Cynthia Rudin
Date: 2015-10-18 00:00:00
Tasks: Model Selection

A causal falling rule list (CFRL) is a sequence of if-then rules that
specifies heterogeneous treatment effects, where (i) the order of rules
determines the treatment effect subgroup a subject belongs to, and (ii) the
treatment effect decreases monotonically down the list. A given CFRL
parameterizes a hierarchical bayesian regression model in which the treatment
effects are incorporated as parameters, and assumed constant within
model-specific subgroups. We formulate the search for the CFRL best supported
by the data as a Bayesian model selection problem, where we perform a search
over the space of CFRL models, and approximate the evidence for a given CFRL
model using standard variational techniques. We apply CFRL to a census wage
dataset to identify subgroups of differing wage inequalities between men and
women.
Sentiment Identification in Code-Mixed Social Media Text
Authors: Souvick Ghosh, Satanu Ghosh, Dipankar Das
Date: 2017-07-04 00:00:00
Tasks: Sentiment Analysis, Subjectivity Analysis

Sentiment analysis is the Natural Language Processing (NLP) task dealing with
the detection and classification of sentiments in texts. While some tasks deal
with identifying the presence of sentiment in the text (Subjectivity analysis),
other tasks aim at determining the polarity of the text categorizing them as
positive, negative and neutral. Whenever there is a presence of sentiment in
the text, it has a source (people, group of people or any entity) and the
sentiment is directed towards some entity, object, event or person. Sentiment
analysis tasks aim to determine the subject, the target and the polarity or
valence of the sentiment. In our work, we try to automatically extract
sentiment (positive or negative) from Facebook posts using a machine learning
approach.While some works have been done in code-mixed social media data and in
sentiment analysis separately, our work is the first attempt (as of now) which
aims at performing sentiment analysis of code-mixed social media text. We have
used extensive pre-processing to remove noise from raw text. Multilayer
Perceptron model has been used to determine the polarity of the sentiment. We
have also developed the corpus for this task by manually labeling Facebook
posts with their associated sentiments.
Complexity Metric for Code-Mixed Social Media Text
Authors: Souvick Ghosh, Satanu Ghosh, Dipankar Das
Date: 2017-07-04 00:00:00
Tasks: Sentence

An evaluation metric is an absolute necessity for measuring the performance
of any system and complexity of any data. In this paper, we have discussed how
to determine the level of complexity of code-mixed social media texts that are
growing rapidly due to multilingual interference. In general, texts written in
multiple languages are often hard to comprehend and analyze. At the same time,
in order to meet the demands of analysis, it is also necessary to determine the
complexity of a particular document or a text segment. Thus, in the present
paper, we have discussed the existing metrics for determining the code-mixing
complexity of a corpus, their advantages, and shortcomings as well as proposed
several improvements on the existing metrics. The new index better reflects the
variety and complexity of a multilingual document. Also, the index can be
applied to a sentence and seamlessly extended to a paragraph or an entire
document. We have employed two existing code-mixed corpora to suit the
requirements of our study.
Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting
Authors: Sercan O. Arik, Markus Kliegl, Rewon Child, Joel Hestness, Andrew Gibiansky, Chris Fougner, Ryan Prenger, Adam Coates
Date: 2017-03-15 00:00:00
Tasks: Keyword Spotting, Small-Footprint Keyword Spotting, speech-recognition, Speech Recognition

Keyword spotting (KWS) constitutes a major component of human-technology
interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate,
while minimizing the footprint size, latency and complexity are the goals for
KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks
(CRNNs). Inspired by large-scale state-of-the-art speech recognition systems,
we combine the strengths of convolutional layers and recurrent layers to
exploit local structure and long-range context. We analyze the effect of
architecture parameters, and propose training strategies to improve
performance. With only ~230k parameters, our CRNN model yields acceptably low
latency, and achieves 97.71% accuracy at 0.5 FA/hour for 5 dB signal-to-noise
ratio.
Kernel Feature Selection via Conditional Covariance Minimization
Authors: Jianbo Chen, Mitchell Stern, Martin J. Wainwright, Michael. I. Jordan
Date: 2017-07-04 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Dimensionality Reduction, feature selection

We propose a method for feature selection that employs kernel-based measures
of independence to find a subset of covariates that is maximally predictive of
the response. Building on past work in kernel dimension reduction, we show how
to perform feature selection via a constrained optimization problem involving
the trace of the conditional covariance operator. We prove various consistency
results for this procedure, and also demonstrate that our method compares
favorably with other state-of-the-art algorithms on a variety of synthetic and
real data sets.
UPSET and ANGRI : Breaking High Performance Image Classifiers
Authors: Sayantan Sarkar, Ankan Bansal, Upal Mahbub, Rama Chellappa
Date: 2017-07-04 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, targeted fooling of high performance image classifiers is
achieved by developing two novel attack methods. The first method generates
universal perturbations for target classes and the second generates image
specific perturbations. Extensive experiments are conducted on MNIST and
CIFAR10 datasets to provide insights about the proposed algorithms and show
their effectiveness.
Stochastic, Distributed and Federated Optimization for Machine Learning
Authors: Jakub Konečný
Date: 2017-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Distributed Optimization

We study optimization algorithms for the finite sum problems frequently
arising in machine learning applications. First, we propose novel variants of
stochastic gradient descent with a variance reduction property that enables
linear convergence for strongly convex objectives. Second, we study distributed
setting, in which the data describing the optimization problem does not fit
into a single computing node. In this case, traditional methods are
inefficient, as the communication costs inherent in distributed optimization
become the bottleneck. We propose a communication-efficient framework which
iteratively forms local subproblems that can be solved with arbitrary local
optimization algorithms. Finally, we introduce the concept of Federated
Optimization/Learning, where we try to solve the machine learning problems
without having data stored in any centralized manner. The main motivation comes
from industry when handling user-generated data. The current prevalent practice
is that companies collect vast amounts of user data and store them in
datacenters. An alternative we propose is not to collect the data in first
place, and instead occasionally use the computational power of users' devices
to solve the very same optimization problems, while alleviating privacy
concerns at the same time. In such setting, minimization of communication
rounds is the primary goal, and we demonstrate that solving the optimization
problems in such circumstances is conceptually tractable.
Interpretable & Explorable Approximations of Black Box Models
Authors: Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec
Date: 2017-07-04 00:00:00

We propose Black Box Explanations through Transparent Approximations (BETA),
a novel model agnostic framework for explaining the behavior of any black-box
classifier by simultaneously optimizing for fidelity to the original model and
interpretability of the explanation. To this end, we develop a novel objective
function which allows us to learn (with optimality guarantees), a small number
of compact decision sets each of which explains the behavior of the black box
model in unambiguous, well-defined regions of feature space. Furthermore, our
framework also is capable of accepting user input when generating these
approximations, thus allowing users to interactively explore how the black-box
model behaves in different subspaces that are of interest to the user. To the
best of our knowledge, this is the first approach which can produce global
explanations of the behavior of any given black box model through joint
optimization of unambiguity, fidelity, and interpretability, while also
allowing users to explore model behavior based on their preferences.
Experimental evaluation with real-world datasets and user studies demonstrates
that our approach can generate highly compact, easy-to-understand, yet accurate
approximations of various kinds of predictive models compared to
state-of-the-art baselines.
ToolNet: Holistically-Nested Real-Time Segmentation of Robotic Surgical Tools
Authors: Luis C. Garcia-Peraza-Herrera, Wenqi Li, Lucas Fidon, Caspar Gruijthuijsen, Alain Devreker, George Attilakos, Jan Deprest, Emmanuel Vander Poorten, Danail Stoyanov, Tom Vercauteren, Sebastien Ourselin
Date: 2017-06-25 00:00:00
Tasks: Segmentation

Real-time tool segmentation from endoscopic videos is an essential part of
many computer-assisted robotic surgical systems and of critical importance in
robotic surgical data science. We propose two novel deep learning architectures
for automatic segmentation of non-rigid surgical instruments. Both methods take
advantage of automated deep-learning-based multi-scale feature extraction while
trying to maintain an accurate segmentation quality at all resolutions. The two
proposed methods encode the multi-scale constraint inside the network
architecture. The first proposed architecture enforces it by cascaded
aggregation of predictions and the second proposed network does it by means of
a holistically-nested architecture where the loss at each scale is taken into
account for the optimization process. As the proposed methods are for real-time
semantic labeling, both present a reduced number of parameters. We propose the
use of parametric rectified linear units for semantic labeling in these small
architectures to increase the regularization ability of the design and maintain
the segmentation accuracy without overfitting the training sets. We compare the
proposed architectures against state-of-the-art fully convolutional networks.
We validate our methods using existing benchmark datasets, including ex vivo
cases with phantom tissue and different robotic surgical instruments present in
the scene. Our results show a statistically significant improved Dice
Similarity Coefficient over previous instrument segmentation methods. We
analyze our design choices and discuss the key drivers for improving accuracy.