pwc-archive/papers-with-abstracts huggingface dataset:
Model-based Adversarial Imitation Learning
Authors: Nir Baram, Oron Anschel, Shie Mannor
Date: 2016-12-07 00:00:00
Tasks: Imitation Learning, model, MuJoCo

Generative adversarial learning is a popular new approach to training
generative models which has been proven successful for other related problems
as well. The general idea is to maintain an oracle $D$ that discriminates
between the expert's data distribution and that of the generative model $G$.
The generative model is trained to capture the expert's distribution by
maximizing the probability of $D$ misclassifying the data it generates.
Overall, the system is \emph{differentiable} end-to-end and is trained using
basic backpropagation. This type of learning was successfully applied to the
problem of policy imitation in a model-free setup. However, a model-free
approach does not allow the system to be differentiable, which requires the use
of high-variance gradient estimations. In this paper we introduce the Model
based Adversarial Imitation Learning (MAIL) algorithm. A model-based approach
for the problem of adversarial imitation learning. We show how to use a forward
model to make the system fully differentiable, which enables us to train
policies using the (stochastic) gradient of $D$. Moreover, our approach
requires relatively few environment interactions, and fewer hyper-parameters to
tune. We test our method on the MuJoCo physics simulator and report initial
results that surpass the current state-of-the-art.
Re-identification of Humans in Crowds using Personal, Social and Environmental Constraints
Authors: Shayan Modiri Assari, Haroon Idrees, Mubarak Shah
Date: 2016-12-07 00:00:00
Tasks: Collision Avoidance, Person Re-Identification

This paper addresses the problem of human re-identification across
non-overlapping cameras in crowds.Re-identification in crowded scenes is a
challenging problem due to large number of people and frequent occlusions,
coupled with changes in their appearance due to different properties and
exposure of cameras. To solve this problem, we model multiple Personal, Social
and Environmental (PSE) constraints on human motion across cameras. The
personal constraints include appearance and preferred speed of each individual
assumed to be similar across the non-overlapping cameras. The social influences
(constraints) are quadratic in nature, i.e. occur between pairs of individuals,
and modeled through grouping and collision avoidance. Finally, the
environmental constraints capture the transition probabilities between gates
(entrances / exits) in different cameras, defined as multi-modal distributions
of transition time and destination between all pairs of gates. We incorporate
these constraints into an energy minimization framework for solving human
re-identification. Assigning $1-1$ correspondence while modeling PSE
constraints is NP-hard. We present a stochastic local search algorithm to
restrict the search space of hypotheses, and obtain $1-1$ solution in the
presence of linear and quadratic PSE constraints. Moreover, we present an
alternate optimization using Frank-Wolfe algorithm that solves the convex
approximation of the objective function with linear relaxation on binary
variables, and yields an order of magnitude speed up over stochastic local
search with minor drop in performance. We evaluate our approach using
Cumulative Matching Curves as well $1-1$ assignment on several thousand frames
of Grand Central, PRID and DukeMTMC datasets, and obtain significantly better
results compared to existing re-identification methods.
Revisiting Winner Take All (WTA) Hashing for Sparse Datasets
Authors: Beidi Chen, Anshumali Shrivastava
Date: 2016-12-06 00:00:00
Tasks: All, General Classification, image-classification, Image Classification, Retrieval

WTA (Winner Take All) hashing has been successfully applied in many large
scale vision applications. This hashing scheme was tailored to take advantage
of the comparative reasoning (or order based information), which showed
significant accuracy improvements. In this paper, we identify a subtle issue
with WTA, which grows with the sparsity of the datasets. This issue limits the
discriminative power of WTA. We then propose a solution for this problem based
on the idea of Densification which provably fixes the issue. Our experiments
show that Densified WTA Hashing outperforms Vanilla WTA both in image
classification and retrieval tasks consistently and significantly.
A Multi-Pass Approach to Large-Scale Connectomics
Authors: Yaron Meirovitch, Alexander Matveev, Hayk Saribekyan, David Budden, David Rolnick, Gergely Odor, Seymour Knowles-Barley, Thouis Raymond Jones, Hanspeter Pfister, Jeff William Lichtman, Nir Shavit
Date: 2016-12-07 00:00:00

The field of connectomics faces unprecedented "big data" challenges. To
reconstruct neuronal connectivity, automated pixel-level segmentation is
required for petabytes of streaming electron microscopy data. Existing
algorithms provide relatively good accuracy but are unacceptably slow, and
would require years to extract connectivity graphs from even a single cubic
millimeter of neural tissue. Here we present a viable real-time solution, a
multi-pass pipeline optimized for shared-memory multicore systems, capable of
processing data at near the terabyte-per-hour pace of multi-beam electron
microscopes. The pipeline makes an initial fast-pass over the data, and then
makes a second slow-pass to iteratively correct errors in the output of the
fast-pass. We demonstrate the accuracy of a sparse slow-pass reconstruction
algorithm and suggest new methods for detecting morphological errors. Our
fast-pass approach provided many algorithmic challenges, including the design
and implementation of novel shallow convolutional neural nets and the
parallelization of watershed and object-merging techniques. We use it to
reconstruct, from image stack to skeletons, the full dataset of Kasthuri et al.
(463 GB capturing 120,000 cubic microns) in a matter of hours on a single
multicore machine rather than the weeks it has taken in the past on much larger
distributed systems.
Chinese Poetry Generation with Planning based Neural Network
Authors: Zhe Wang, wei he, Hua Wu, Haiyang Wu, Wei Li, Haifeng Wang, Enhong Chen
Date: 2016-10-31 00:00:00
Proceeding: COLING 2016 12
Tasks: Decoder

Chinese poetry generation is a very challenging task in natural language
processing. In this paper, we propose a novel two-stage poetry generating
method which first plans the sub-topics of the poem according to the user's
writing intent, and then generates each line of the poem sequentially, using a
modified recurrent neural network encoder-decoder framework. The proposed
planning-based method can ensure that the generated poem is coherent and
semantically consistent with the user's intent. A comprehensive evaluation with
human judgments demonstrates that our proposed approach outperforms the
state-of-the-art poetry generating methods and the poem quality is somehow
comparable to human poets.
Distributed Gaussian Learning over Time-varying Directed Graphs
Authors: Angelia Nedić, Alex Olshevsky, César A. Uribe
Date: 2016-12-06 00:00:00
Tasks: parameter estimation

We present a distributed (non-Bayesian) learning algorithm for the problem of
parameter estimation with Gaussian noise. The algorithm is expressed as
explicit updates on the parameters of the Gaussian beliefs (i.e. means and
precision). We show a convergence rate of $O(1/k)$ with the constant term
depending on the number of agents and the topology of the network. Moreover, we
show almost sure convergence to the optimal solution of the estimation problem
for the general case of time-varying directed graphs.
Online Multi-Target Tracking Using Recurrent Neural Networks
Authors: Anton Milan, Seyed Hamid Rezatofighi, Anthony Dick, Ian Reid, Konrad Schindler
Date: 2016-04-13 00:00:00
Tasks: CPU, State Estimation

We present a novel approach to online multi-target tracking based on
recurrent neural networks (RNNs). Tracking multiple objects in real-world
scenes involves many challenges, including a) an a-priori unknown and
time-varying number of targets, b) a continuous state estimation of all present
targets, and c) a discrete combinatorial problem of data association. Most
previous methods involve complex models that require tedious tuning of
parameters. Here, we propose for the first time, an end-to-end learning
approach for online multi-target tracking. Existing deep learning methods are
not designed for the above challenges and cannot be trivially applied to the
task. Our solution addresses all of the above points in a principled way.
Experiments on both synthetic and real data show promising results obtained at
~300 Hz on a standard CPU, and pave the way towards future research in this
direction.
Statistical and Computational Guarantees of Lloyd's Algorithm and its Variants
Authors: Yu Lu, Harrison H. Zhou
Date: 2016-12-07 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Community Detection

Clustering is a fundamental problem in statistics and machine learning.
Lloyd's algorithm, proposed in 1957, is still possibly the most widely used
clustering algorithm in practice due to its simplicity and empirical
performance. However, there has been little theoretical investigation on the
statistical and computational guarantees of Lloyd's algorithm. This paper is an
attempt to bridge this gap between practice and theory. We investigate the
performance of Lloyd's algorithm on clustering sub-Gaussian mixtures. Under an
appropriate initialization for labels or centers, we show that Lloyd's
algorithm converges to an exponentially small clustering error after an order
of $\log n$ iterations, where $n$ is the sample size. The error rate is shown
to be minimax optimal. For the two-mixture case, we only require the
initializer to be slightly better than random guess.
  In addition, we extend the Lloyd's algorithm and its analysis to community
detection and crowdsourcing, two problems that have received a lot of attention
recently in statistics and machine learning. Two variants of Lloyd's algorithm
are proposed respectively for community detection and crowdsourcing. On the
theoretical side, we provide statistical and computational guarantees of the
two algorithms, and the results improve upon some previous signal-to-noise
ratio conditions in literature for both problems. Experimental results on
simulated and real data sets demonstrate competitive performance of our
algorithms to the state-of-the-art methods.
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Authors: Haoqiang Fan, Hao Su, Leonidas Guibas
Date: 2016-12-02 00:00:00
Proceeding: CVPR 2017 7
Tasks: 3D Object Reconstruction, 3D Object Reconstruction From A Single Image, 3D Reconstruction, Object Reconstruction

Generation of 3D data by deep neural network has been attracting increasing
attention in the research community. The majority of extant works resort to
regular representations such as volumetric grids or collection of images;
however, these representations obscure the natural invariance of 3D shapes
under geometric transformations and also suffer from a number of other issues.
In this paper we address the problem of 3D reconstruction from a single image,
generating a straight-forward form of output -- point cloud coordinates. Along
with this problem arises a unique and interesting issue, that the groundtruth
shape for an input image may be ambiguous. Driven by this unorthodox output
form and the inherent ambiguity in groundtruth, we design architecture, loss
function and learning paradigm that are novel and effective. Our final solution
is a conditional shape sampler, capable of predicting multiple plausible 3D
point clouds from an input image. In experiments not only can our system
outperform state-of-the-art methods on single image based 3d reconstruction
benchmarks; but it also shows a strong performance for 3d shape completion and
promising ability in making multiple plausible predictions.
Revealing Fundamental Physics from the Daya Bay Neutrino Experiment using Deep Neural Networks
Authors: Evan Racah, Seyoon Ko, Peter Sadowski, Wahid Bhimji, Craig Tull, Sang-Yun Oh, Pierre Baldi, Prabhat
Date: 2016-01-28 00:00:00
Tasks: Clustering, General Classification

Experiments in particle physics produce enormous quantities of data that must
be analyzed and interpreted by teams of physicists. This analysis is often
exploratory, where scientists are unable to enumerate the possible types of
signal prior to performing the experiment. Thus, tools for summarizing,
clustering, visualizing and classifying high-dimensional data are essential. In
this work, we show that meaningful physical content can be revealed by
transforming the raw data into a learned high-level representation using deep
neural networks, with measurements taken at the Daya Bay Neutrino Experiment as
a case study. We further show how convolutional deep neural networks can
provide an effective classification filter with greater than 97% accuracy
across different classes of physics events, significantly better than other
machine learning approaches.
Geometric deep learning on graphs and manifolds using mixture model CNNs
Authors: Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein
Date: 2016-11-25 00:00:00
Proceeding: CVPR 2017 7
Tasks: Deep Learning, Document Classification, Graph Classification, Graph Regression, Node Classification, object-detection, Object Detection, speech-recognition, Speech Recognition, Superpixel Image Classification

Deep learning has achieved a remarkable performance breakthrough in several
fields, most notably in speech recognition, natural language processing, and
computer vision. In particular, convolutional neural network (CNN)
architectures currently produce state-of-the-art performance on a variety of
image analysis tasks such as object detection and recognition. Most of deep
learning research has so far focused on dealing with 1D, 2D, or 3D
Euclidean-structured data such as acoustic signals, images, or videos.
Recently, there has been an increasing interest in geometric deep learning,
attempting to generalize deep learning methods to non-Euclidean structured data
such as graphs and manifolds, with a variety of applications from the domains
of network analysis, computational social science, or computer graphics. In
this paper, we propose a unified framework allowing to generalize CNN
architectures to non-Euclidean domains (graphs and manifolds) and learn local,
stationary, and compositional task-specific features. We show that various
non-Euclidean CNN methods previously proposed in the literature can be
considered as particular instances of our framework. We test the proposed
method on standard tasks from the realms of image-, graph- and 3D shape
analysis and show that it consistently outperforms previous approaches.
Diverse Sampling for Self-Supervised Learning of Semantic Segmentation
Authors: Mohammadreza Mostajabi, Nicholas Kolkin, Gregory Shakhnarovich
Date: 2016-12-06 00:00:00
Tasks: Classification, General Classification, Segmentation, Self-Supervised Learning, Semantic Segmentation

We propose an approach for learning category-level semantic segmentation
purely from image-level classification tags indicating presence of categories.
It exploits localization cues that emerge from training classification-tasked
convolutional networks, to drive a "self-supervision" process that
automatically labels a sparse, diverse training set of points likely to belong
to classes of interest. Our approach has almost no hyperparameters, is modular,
and allows for very fast training of segmentation in less than 3 minutes. It
obtains competitive results on the VOC 2012 segmentation benchmark. More,
significantly the modularity and fast training of our framework allows new
classes to efficiently added for inference.
Core Sampling Framework for Pixel Classification
Authors: Manohar Karki, Robert DiBiano, Saikat Basu, Supratik Mukhopadhyay
Date: 2016-12-06 00:00:00
Tasks: Classification, General Classification, Transfer Learning

The intermediate map responses of a Convolutional Neural Network (CNN)
contain information about an image that can be used to extract contextual
knowledge about it. In this paper, we present a core sampling framework that is
able to use these activation maps from several layers as features to another
neural network using transfer learning to provide an understanding of an input
image. Our framework creates a representation that combines features from the
test data and the contextual knowledge gained from the responses of a
pretrained network, processes it and feeds it to a separate Deep Belief
Network. We use this representation to extract more information from an image
at the pixel level, hence gaining understanding of the whole image. We
experimentally demonstrate the usefulness of our framework using a pretrained
VGG-16 model to perform segmentation on the BAERI dataset of Synthetic Aperture
Radar(SAR) imagery and the CAMVID dataset.
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
Authors: Tobias Pohlen, Alexander Hermans, Markus Mathias, Bastian Leibe
Date: 2016-11-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Autonomous Driving, Image Segmentation, Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation, Thermal Image Segmentation

Semantic image segmentation is an essential component of modern autonomous
driving systems, as an accurate understanding of the surrounding scene is
crucial to navigation and action planning. Current state-of-the-art approaches
in semantic image segmentation rely on pre-trained networks that were initially
developed for classifying images as a whole. While these networks exhibit
outstanding recognition performance (i.e., what is visible?), they lack
localization accuracy (i.e., where precisely is something located?). Therefore,
additional processing steps have to be performed in order to obtain
pixel-accurate segmentation masks at the full image resolution. To alleviate
this problem we propose a novel ResNet-like architecture that exhibits strong
localization and recognition performance. We combine multi-scale context with
pixel-level accuracy by using two processing streams within our network: One
stream carries information at the full image resolution, enabling precise
adherence to segment boundaries. The other stream undergoes a sequence of
pooling operations to obtain robust features for recognition. The two streams
are coupled at the full image resolution using residuals. Without additional
processing steps and without pre-training, our approach achieves an
intersection-over-union score of 71.8% on the Cityscapes dataset.
Memory Efficient Multi-Scale Line Detector Architecture for Retinal Blood Vessel Segmentation
Authors: Hamza Bendaoudi, Farida Cheriet, J. M. Pierre Langlois
Date: 2016-12-06 00:00:00
Tasks: Vessel Detection

This paper presents a memory efficient architecture that implements the
Multi-Scale Line Detector (MSLD) algorithm for real-time retinal blood vessel
detection in fundus images on a Zynq FPGA. This implementation benefits from
the FPGA parallelism to drastically reduce the memory requirements of the MSLD
from two images to a few values. The architecture is optimized in terms of
resource utilization by reusing the computations and optimizing the bit-width.
The throughput is increased by designing fully pipelined functional units. The
architecture is capable of achieving a comparable accuracy to its software
implementation but 70x faster for low resolution images. For high resolution
images, it achieves an acceleration by a factor of 323x.
Segmental Convolutional Neural Networks for Detection of Cardiac Abnormality With Noisy Heart Sound Recordings
Authors: Yuhao Zhang, Sandeep Ayyar, Long-Huei Chen, Ethan J. Li
Date: 2016-12-06 00:00:00
Tasks: Classification, General Classification

Heart diseases constitute a global health burden, and the problem is
exacerbated by the error-prone nature of listening to and interpreting heart
sounds. This motivates the development of automated classification to screen
for abnormal heart sounds. Existing machine learning-based systems achieve
accurate classification of heart sound recordings but rely on expert features
that have not been thoroughly evaluated on noisy recordings. Here we propose a
segmental convolutional neural network architecture that achieves automatic
feature learning from noisy heart sound recordings. Our experiments show that
our best model, trained on noisy recording segments acquired with an existing
hidden semi-markov model-based approach, attains a classification accuracy of
87.5% on the 2016 PhysioNet/CinC Challenge dataset, compared to the 84.6%
accuracy of the state-of-the-art statistical classifier trained and evaluated
on the same dataset. Our results indicate the potential of using neural
network-based methods to increase the accuracy of automated classification of
heart sound recordings for improved screening of heart diseases.
Semi-Supervised Learning with the Deep Rendering Mixture Model
Authors: Tan Nguyen, Wanjia Liu, Ethan Perez, Richard G. Baraniuk, Ankit B. Patel
Date: 2016-12-06 00:00:00
Tasks: Variational Inference

Semi-supervised learning algorithms reduce the high cost of acquiring labeled
training data by using both labeled and unlabeled data during learning. Deep
Convolutional Networks (DCNs) have achieved great success in supervised tasks
and as such have been widely employed in the semi-supervised learning. In this
paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a
probabilistic generative model that models latent nuisance variation, and whose
inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to
learn from both labeled and unlabeled data. Guided by the theory of the DRMM,
we introduce a novel non-negativity constraint and a variational inference
term. We report state-of-the-art performance on MNIST and SVHN and competitive
results on CIFAR10. We also probe deeper into how a DRMM trained in a
semi-supervised setting represents latent nuisance variation using
synthetically rendered images. Taken together, our work provides a unified
framework for supervised, unsupervised, and semi-supervised learning.
Coactive Critiquing: Elicitation of Preferences and Features
Authors: Stefano Teso, Paolo Dragone, Andrea Passerini
Date: 2016-12-06 00:00:00

When faced with complex choices, users refine their own preference criteria
as they explore the catalogue of options. In this paper we propose an approach
to preference elicitation suited for this scenario. We extend Coactive
Learning, which iteratively collects manipulative feedback, to optionally query
example critiques. User critiques are integrated into the learning model by
dynamically extending the feature space. Our formulation natively supports
constructive learning tasks, where the option catalogue is generated
on-the-fly. We present an upper bound on the average regret suffered by the
learner. Our empirical analysis highlights the promise of our approach.