pwc-archive/papers-with-abstracts huggingface dataset:
A Software-equivalent SNN Hardware using RRAM-array for Asynchronous Real-time Learning
Authors: Aditya Shukla, Vinay Kumar, Udayan Ganguly
Date: 2017-04-06 00:00:00
Tasks: Management

Spiking Neural Network (SNN) naturally inspires hardware implementation as it
is based on biology. For learning, spike time dependent plasticity (STDP) may
be implemented using an energy efficient waveform superposition on memristor
based synapse. However, system level implementation has three challenges.
First, a classic dilemma is that recognition requires current reading for short
voltage$-$spikes which is disturbed by large voltage$-$waveforms that are
simultaneously applied on the same memristor for real$-$time learning i.e. the
simultaneous read$-$write dilemma. Second, the hardware needs to exactly
replicate software implementation for easy adaptation of algorithm to hardware.
Third, the devices used in hardware simulations must be realistic. In this
paper, we present an approach to address the above concerns. First, the
learning and recognition occurs in separate arrays simultaneously in
real$-$time, asynchronously $-$ avoiding non$-$biomimetic clocking based
complex signal management. Second, we show that the hardware emulates software
at every stage by comparison of SPICE (circuit$-$simulator) with MATLAB
(mathematical SNN algorithm implementation in software) implementations. As an
example, the hardware shows 97.5 per cent accuracy in classification which is
equivalent to software for a Fisher$-$Iris dataset. Third, the STDP is
implemented using a model of synaptic device implemented using HfO2 memristor.
We show that an increasingly realistic memristor model slightly reduces the
hardware performance (85 per cent), which highlights the need to engineer RRAM
characteristics specifically for SNN.
DIMM-SC: A Dirichlet mixture model for clustering droplet-based single cell transcriptomic data
Authors: Zhe Sun, Ting Wang, Ke Deng, Xiao-Feng Wang, Robert Lafyatis, Ying Ding, Ming Hu, Wei Chen
Date: 2017-04-06 00:00:00
Tasks: Clustering

Motivation: Single cell transcriptome sequencing (scRNA-Seq) has become a
revolutionary tool to study cellular and molecular processes at single cell
resolution. Among existing technologies, the recently developed droplet-based
platform enables efficient parallel processing of thousands of single cells
with direct counting of transcript copies using Unique Molecular Identifier
(UMI). Despite the technology advances, statistical methods and computational
tools are still lacking for analyzing droplet-based scRNA-Seq data.
Particularly, model-based approaches for clustering large-scale single cell
transcriptomic data are still under-explored. Methods: We developed DIMM-SC, a
Dirichlet Mixture Model for clustering droplet-based Single Cell transcriptomic
data. This approach explicitly models UMI count data from scRNA-Seq experiments
and characterizes variations across different cell clusters via a Dirichlet
mixture prior. An expectation-maximization algorithm is used for parameter
inference. Results: We performed comprehensive simulations to evaluate DIMM-SC
and compared it with existing clustering methods such as K-means, CellTree and
Seurat. In addition, we analyzed public scRNA-Seq datasets with known cluster
labels and in-house scRNA-Seq datasets from a study of systemic sclerosis with
prior biological knowledge to benchmark and validate DIMM-SC. Both simulation
studies and real data applications demonstrated that overall, DIMM-SC achieves
substantially improved clustering accuracy and much lower clustering
variability compared to other existing clustering methods. More importantly, as
a model-based approach, DIMM-SC is able to quantify the clustering uncertainty
for each single cell, facilitating rigorous statistical inference and
biological interpretations, which are typically unavailable from existing
clustering methods.
On Generalization and Regularization in Deep Learning
Authors: Pirmin Lemberger
Date: 2017-04-05 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, image-classification, Image Classification, speech-recognition, Speech Recognition

Why do large neural network generalize so well on complex tasks such as image
classification or speech recognition? What exactly is the role regularization
for them? These are arguably among the most important open questions in machine
learning today. In a recent and thought provoking paper [C. Zhang et al.]
several authors performed a number of numerical experiments that hint at the
need for novel theoretical concepts to account for this phenomenon. The paper
stirred quit a lot of excitement among the machine learning community but at
the same time it created some confusion as discussions on OpenReview.net
testifies. The aim of this pedagogical paper is to make this debate accessible
to a wider audience of data scientists without advanced theoretical knowledge
in statistical learning. The focus here is on explicit mathematical definitions
and on a discussion of relevant concepts, not on proofs for which we provide
references.
From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards
Authors: Henrique Santos, Victor Dantas, Vasco Furtado, Paulo Pinheiro, Deborah L. McGuinness
Date: 2017-04-06 00:00:00
Tasks: Data Visualization, Knowledge Graphs

In the context of Smart Cities, indicator definitions have been used to
calculate values that enable the comparison among different cities. The
calculation of an indicator values has challenges as the calculation may need
to combine some aspects of quality while addressing different levels of
abstraction. Knowledge graphs (KGs) have been used successfully to support
flexible representation, which can support improved understanding and data
analysis in similar settings. This paper presents an operational description
for a city KG, an indicator ontology that support indicator discovery and data
visualization and an application capable of performing metadata analysis to
automatically build and display dashboards according to discovered indicators.
We describe our implementation in an urban mobility setting.
Training Triplet Networks with GAN
Authors: Maciej Zieba, Lei Wang
Date: 2017-04-06 00:00:00
Tasks: General Classification, Representation Learning, Retrieval, Triplet

Triplet networks are widely used models that are characterized by good
performance in classification and retrieval tasks. In this work we propose to
train a triplet network by putting it as the discriminator in Generative
Adversarial Nets (GANs). We make use of the good capability of representation
learning of the discriminator to increase the predictive quality of the model.
We evaluated our approach on Cifar10 and MNIST datasets and observed
significant improvement on the classification performance using the simple k-nn
method.
The Interplay of Semantics and Morphology in Word Embeddings
Authors: Oded Avraham, Yoav Goldberg
Date: 2017-04-06 00:00:00
Proceeding: EACL 2017 4
Tasks: LEMMA, TAG, Word Embeddings

We explore the ability of word embeddings to capture both semantic and
morphological similarity, as affected by the different types of linguistic
properties (surface form, lemma, morphological tag) used to compose the
representation of each word. We train several models, where each uses a
different subset of these properties to compose its representations. By
evaluating the models on semantic and morphological measures, we reveal some
useful insights on the relationship between semantics and morphology.
Semantically-Guided Video Object Segmentation
Authors: Sergi Caelles, Yu-Hua Chen, Jordi Pont-Tuset, Luc van Gool
Date: 2017-04-06 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

This paper tackles the problem of semi-supervised video object segmentation,
that is, segmenting an object in a sequence given its mask in the first frame.
One of the main challenges in this scenario is the change of appearance of the
objects of interest. Their semantics, on the other hand, do not vary. This
paper investigates how to take advantage of such invariance via the
introduction of a semantic prior that guides the appearance model.
Specifically, given the segmentation mask of the first frame of a sequence, we
estimate the semantics of the object of interest, and propagate that knowledge
throughout the sequence to improve the results based on an appearance model. We
present Semantically-Guided Video Object Segmentation (SGV), which improves
results over previous state of the art on two different datasets using a
variety of evaluation metrics, while running in half a second per frame.
Automated Latent Fingerprint Recognition
Authors: Kai Cao, Anil K. Jain
Date: 2017-04-06 00:00:00

Latent fingerprints are one of the most important and widely used evidence in
law enforcement and forensic agencies worldwide. Yet, NIST evaluations show
that the performance of state-of-the-art latent recognition systems is far from
satisfactory. An automated latent fingerprint recognition system with high
accuracy is essential to compare latents found at crime scenes to a large
collection of reference prints to generate a candidate list of possible mates.
In this paper, we propose an automated latent fingerprint recognition algorithm
that utilizes Convolutional Neural Networks (ConvNets) for ridge flow
estimation and minutiae descriptor extraction, and extract complementary
templates (two minutiae templates and one texture template) to represent the
latent. The comparison scores between the latent and a reference print based on
the three templates are fused to retrieve a short candidate list from the
reference database. Experimental results show that the rank-1 identification
accuracies (query latent is matched with its true mate in the reference
database) are 64.7% for the NIST SD27 and 75.3% for the WVU latent databases,
against a reference database of 100K rolled prints. These results are the best
among published papers on latent recognition and competitive with the
performance (66.7% and 70.8% rank-1 accuracies on NIST SD27 and WVU DB,
respectively) of a leading COTS latent Automated Fingerprint Identification
System (AFIS). By score-level (rank-level) fusion of our system with the
commercial off-the-shelf (COTS) latent AFIS, the overall rank-1 identification
performance can be improved from 64.7% and 75.3% to 73.3% (74.4%) and 76.6%
(78.4%) on NIST SD27 and WVU latent databases, respectively.
Encoder Based Lifelong Learning
Authors: Amal Rannen Triki, Rahaf Aljundi, Mathew B. Blaschko, Tinne Tuytelaars
Date: 2017-04-06 00:00:00
Proceeding: ICCV 2017 10
Tasks: image-classification, Image Classification, Lifelong learning

This paper introduces a new lifelong learning solution where a single model
is trained for a sequence of tasks. The main challenge that vision systems face
in this context is catastrophic forgetting: as they tend to adapt to the most
recently seen task, they lose performance on the tasks that were learned
previously. Our method aims at preserving the knowledge of the previous tasks
while learning a new one by using autoencoders. For each task, an
under-complete autoencoder is learned, capturing the features that are crucial
for its achievement. When a new task is presented to the system, we prevent the
reconstructions of the features with these autoencoders from changing, which
has the effect of preserving the information on which the previous tasks are
mainly relying. At the same time, the features are given space to adjust to the
most recent environment as only their projection into a low dimension
submanifold is controlled. The proposed system is evaluated on image
classification tasks and shows a reduction of forgetting over the
state-of-the-art
Online Hashing
Authors: Long-Kai Huang, Qiang Yang, Wei-Shi Zheng
Date: 2017-04-06 00:00:00

Although hash function learning algorithms have achieved great success in
recent years, most existing hash models are off-line, which are not suitable
for processing sequential or online data. To address this problem, this work
proposes an online hash model to accommodate data coming in stream for online
learning. Specifically, a new loss function is proposed to measure the
similarity loss between a pair of data samples in hamming space. Then, a
structured hash model is derived and optimized in a passive-aggressive way.
Theoretical analysis on the upper bound of the cumulative loss for the proposed
online hash model is provided. Furthermore, we extend our online hashing from a
single-model to a multi-model online hashing that trains multiple models so as
to retain diverse online hashing models in order to avoid biased update. The
competitive efficiency and effectiveness of the proposed online hash models are
verified through extensive experiments on several large-scale datasets as
compared to related hashing methods.
Conformative Filtering for Implicit Feedback Data
Authors: Farhan Khawar, Nevin L. Zhang
Date: 2017-04-06 00:00:00
Tasks: Clustering

Implicit feedback is the simplest form of user feedback that can be used for
item recommendation. It is easy to collect and is domain independent. However,
there is a lack of negative examples. Previous work tackles this problem by
assuming that users are not interested or not as much interested in the
unconsumed items. Those assumptions are often severely violated since
non-consumption can be due to factors like unawareness or lack of resources.
Therefore, non-consumption by a user does not always mean disinterest or
irrelevance. In this paper, we propose a novel method called Conformative
Filtering (CoF) to address the issue. The motivating observation is that if
there is a large group of users who share the same taste and none of them have
consumed an item before, then it is likely that the item is not of interest to
the group. We perform multidimensional clustering on implicit feedback data
using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups
and make recommendations for a user based on her memberships in the groups and
on the past behavior of the groups. Experiments on two real-world datasets from
different domains show that CoF has superior performance compared to several
common baselines.
Landmark Guided Probabilistic Roadmap Queries
Authors: Brian Paden, Yannik Nager, Emilio Frazzoli
Date: 2017-04-06 00:00:00
Tasks: Motion Planning

A landmark based heuristic is investigated for reducing query phase run-time
of the probabilistic roadmap (\PRM) motion planning method. The heuristic is
generated by storing minimum spanning trees from a small number of vertices
within the \PRM graph and using these trees to approximate the cost of a
shortest path between any two vertices of the graph. The intermediate step of
preprocessing the graph increases the time and memory requirements of the
classical motion planning technique in exchange for speeding up individual
queries making the method advantageous in multi-query applications. This paper
investigates these trade-offs on \PRM graphs constructed in randomized
environments as well as a practical manipulator simulation.We conclude that the
method is preferable to Dijkstra's algorithm or the ${\rm A}^*$ algorithm with
conventional heuristics in multi-query applications.
A Convolution Tree with Deconvolution Branches: Exploiting Geometric Relationships for Single Shot Keypoint Detection
Authors: Amit Kumar, Rama Chellappa
Date: 2017-04-06 00:00:00
Tasks: Face Alignment, Keypoint Detection

Recently, Deep Convolution Networks (DCNNs) have been applied to the task of
face alignment and have shown potential for learning improved feature
representations. Although deeper layers can capture abstract concepts like
pose, it is difficult to capture the geometric relationships among the
keypoints in DCNNs. In this paper, we propose a novel convolution-deconvolution
network for facial keypoint detection. Our model predicts the 2D locations of
the keypoints and their individual visibility along with 3D head pose, while
exploiting the spatial relationships among different keypoints. Different from
existing approaches of modeling these relationships, we propose learnable
transform functions which captures the relationships between keypoints at
feature level. However, due to extensive variations in pose, not all of these
relationships act at once, and hence we propose, a pose-based routing function
which implicitly models the active relationships. Both transform functions and
the routing function are implemented through convolutions in a multi-task
framework. Our approach presents a single-shot keypoint detection method,
making it different from many existing cascade regression-based methods. We
also show that learning these relationships significantly improve the accuracy
of keypoint detections for in-the-wild face images from challenging datasets
such as AFW and AFLW.
Learning Detailed Face Reconstruction from a Single Image
Authors: Elad Richardson, Matan Sela, Roy Or-El, Ron Kimmel
Date: 2016-11-15 00:00:00
Proceeding: CVPR 2017 7
Tasks: Face Reconstruction, Object Recognition

Reconstructing the detailed geometric structure of a face from a given image
is a key to many computer vision and graphics applications, such as motion
capture and reenactment. The reconstruction task is challenging as human faces
vary extensively when considering expressions, poses, textures, and intrinsic
geometries. While many approaches tackle this complexity by using additional
data to reconstruct the face of a single subject, extracting facial surface
from a single image remains a difficult problem. As a result, single-image
based methods can usually provide only a rough estimate of the facial geometry.
In contrast, we propose to leverage the power of convolutional neural networks
to produce a highly detailed face reconstruction from a single image. For this
purpose, we introduce an end-to-end CNN framework which derives the shape in a
coarse-to-fine fashion. The proposed architecture is composed of two main
blocks, a network that recovers the coarse facial geometry (CoarseNet),
followed by a CNN that refines the facial features of that geometry (FineNet).
The proposed networks are connected by a novel layer which renders a depth
image given a mesh in 3D. Unlike object recognition and detection problems,
there are no suitable datasets for training CNNs to perform face geometry
reconstruction. Therefore, our training regime begins with a supervised phase,
based on synthetic images, followed by an unsupervised phase that uses only
unconstrained facial images. The accuracy and robustness of the proposed model
is demonstrated by both qualitative and quantitative evaluation tests.
Person Re-identification in the Wild
Authors: Liang Zheng, Hengheng Zhang, Shaoyan Sun, Manmohan Chandraker, Yi Yang, Qi Tian
Date: 2016-04-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Benchmarking, Pedestrian Detection, Person Recognition, Person Re-Identification

We present a novel large-scale dataset and comprehensive baselines for
end-to-end pedestrian detection and person recognition in raw video frames. Our
baselines address three issues: the performance of various combinations of
detectors and recognizers, mechanisms for pedestrian detection to help improve
overall re-identification accuracy and assessing the effectiveness of different
detectors for re-identification. We make three distinct contributions. First, a
new dataset, PRW, is introduced to evaluate Person Re-identification in the
Wild, using videos acquired through six synchronized cameras. It contains 932
identities and 11,816 frames in which pedestrians are annotated with their
bounding box positions and identities. Extensive benchmarking results are
presented on this dataset. Second, we show that pedestrian detection aids
re-identification through two simple yet effective improvements: a
discriminatively trained ID-discriminative Embedding (IDE) in the person
subspace using convolutional neural network (CNN) features and a Confidence
Weighted Similarity (CWS) metric that incorporates detection scores into
similarity measurement. Third, we derive insights in evaluating detector
performance for the particular scenario of accurate person re-identification.
Massive Data Clustering in Moderate Dimensions from the Dual Spaces of Observation and Attribute Data Clouds
Authors: Fionn Murtagh
Date: 2017-04-06 00:00:00
Tasks: Attribute, Clustering

Cluster analysis of very high dimensional data can benefit from the
properties of such high dimensionality. Informally expressed, in this work, our
focus is on the analogous situation when the dimensionality is moderate to
small, relative to a massively sized set of observations. Mathematically
expressed, these are the dual spaces of observations and attributes. The point
cloud of observations is in attribute space, and the point cloud of attributes
is in observation space. In this paper, we begin by summarizing various
perspectives related to methodologies that are used in multivariate analytics.
We draw on these to establish an efficient clustering processing pipeline, both
partitioning and hierarchical clustering.
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Authors: Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Date: 2017-04-06 00:00:00
Tasks: Causal Discovery

Causal effect estimation from observational data is an important and much
studied research topic. The instrumental variable (IV) and local causal
discovery (LCD) patterns are canonical examples of settings where a closed-form
expression exists for the causal effect of one variable on another, given the
presence of a third variable. Both rely on faithfulness to infer that the
latter only influences the target effect via the cause variable. In reality, it
is likely that this assumption only holds approximately and that there will be
at least some form of weak interaction. This brings about the paradoxical
situation that, in the large-sample limit, no predictions are made, as
detecting the weak edge invalidates the setting. We introduce an alternative
approach by replacing strict faithfulness with a prior that reflects the
existence of many 'weak' (irrelevant) and 'strong' interactions. We obtain a
posterior distribution over the target causal effect estimator which shows
that, in many cases, we can still make good estimates. We demonstrate the
approach in an application on a simple linear-Gaussian setting, using the
MultiNest sampling algorithm, and compare it with established techniques to
show our method is robust even when strict faithfulness is violated.
Tackling Dynamic Vehicle Routing Problem with Time Windows by means of Ant Colony System
Authors: Raluca Necula, Mihaela Breaban, Madalina Raschip
Date: 2017-04-06 00:00:00

The Dynamic Vehicle Routing Problem with Time Windows (DVRPTW) is an
extension of the well-known Vehicle Routing Problem (VRP), which takes into
account the dynamic nature of the problem. This aspect requires the vehicle
routes to be updated in an ongoing manner as new customer requests arrive in
the system and must be incorporated into an evolving schedule during the
working day. Besides the vehicle capacity constraint involved in the classical
VRP, DVRPTW considers in addition time windows, which are able to better
capture real-world situations. Despite this, so far, few studies have focused
on tackling this problem of greater practical importance. To this end, this
study devises for the resolution of DVRPTW, an ant colony optimization based
algorithm, which resorts to a joint solution construction mechanism, able to
construct in parallel the vehicle routes. This method is coupled with a local
search procedure, aimed to further improve the solutions built by ants, and
with an insertion heuristics, which tries to reduce the number of vehicles used
to service the available customers. The experiments indicate that the proposed
algorithm is competitive and effective, and on DVRPTW instances with a higher
dynamicity level, it is able to yield better results compared to existing
ant-based approaches.