pwc-archive/papers-with-abstracts huggingface dataset:
Snapshot Ensembles: Train 1, get M for free
Authors: Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E. Hopcroft, Kilian Q. Weinberger
Date: 2017-04-01 00:00:00

Ensembles of neural networks are known to be much more robust and accurate
than individual networks. However, training multiple deep networks for model
averaging is computationally expensive. In this paper, we propose a method to
obtain the seemingly contradictory goal of ensembling multiple neural networks
at no additional training cost. We achieve this goal by training a single
neural network, converging to several local minima along its optimization path
and saving the model parameters. To obtain repeated rapid convergence, we
leverage recent work on cyclic learning rate schedules. The resulting
technique, which we refer to as Snapshot Ensembling, is simple, yet
surprisingly effective. We show in a series of experiments that our approach is
compatible with diverse network architectures and learning tasks. It
consistently yields lower error rates than state-of-the-art single models at no
additional training cost, and compares favorably with traditional network
ensembles. On CIFAR-10 and CIFAR-100 our DenseNet Snapshot Ensembles obtain
error rates of 3.4% and 17.4% respectively.
Assortment Optimization under Unknown MultiNomial Logit Choice Models
Authors: Wang Chi Cheung, David Simchi-Levi
Date: 2017-04-01 00:00:00
Tasks: Assortment Optimization

Motivated by e-commerce, we study the online assortment optimization problem.
The seller offers an assortment, i.e. a subset of products, to each arriving
customer, who then purchases one or no product from her offered assortment. A
customer's purchase decision is governed by the underlying MultiNomial Logit
(MNL) choice model. The seller aims to maximize the total revenue in a finite
sales horizon, subject to resource constraints and uncertainty in the MNL
choice model. We first propose an efficient online policy which incurs a regret
$\tilde{O}(T^{2/3})$, where $T$ is the number of customers in the sales
horizon. Then, we propose a UCB policy that achieves a regret
$\tilde{O}(T^{1/2})$. Both regret bounds are sublinear in the number of
assortments.
SRN: Side-output Residual Network for Object Symmetry Detection in the Wild
Authors: Wei Ke, Jie Chen, Jianbin Jiao, Guoying Zhao, Qixiang Ye
Date: 2017-03-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Diversity, Object, Symmetry Detection

In this paper, we establish a baseline for object symmetry detection in
complex backgrounds by presenting a new benchmark and an end-to-end deep
learning approach, opening up a promising direction for symmetry detection in
the wild. The new benchmark, named Sym-PASCAL, spans challenges including
object diversity, multi-objects, part-invisibility, and various complex
backgrounds that are far beyond those in existing datasets. The proposed
symmetry detection approach, named Side-output Residual Network (SRN),
leverages output Residual Units (RUs) to fit the errors between the object
symmetry groundtruth and the outputs of RUs. By stacking RUs in a
deep-to-shallow manner, SRN exploits the 'flow' of errors among multiple scales
to ease the problems of fitting complex outputs with limited layers,
suppressing the complex backgrounds, and effectively matching object symmetry
of different scales. Experimental results validate both the benchmark and its
challenging aspects related to realworld images, and the state-of-the-art
performance of our symmetry detection approach. The benchmark and the code for
SRN are publicly available at https://github.com/KevinKecc/SRN.
Customizing First Person Image Through Desired Actions
Authors: Shan Su, Jianbo Shi, Hyun Soo Park
Date: 2017-04-01 00:00:00
Tasks: Generative Adversarial Network

This paper studies a problem of inverse visual path planning: creating a
visual scene from a first person action. Our conjecture is that the spatial
arrangement of a first person visual scene is deployed to afford an action, and
therefore, the action can be inversely used to synthesize a new scene such that
the action is feasible. As a proof-of-concept, we focus on linking visual
experiences induced by walking.
  A key innovation of this paper is a concept of ActionTunnel---a 3D virtual
tunnel along the future trajectory encoding what the wearer will visually
experience as moving into the scene. This connects two distinctive first person
images through similar walking paths. Our method takes a first person image
with a user defined future trajectory and outputs a new image that can afford
the future motion. The image is created by combining present and future
ActionTunnels in 3D where the missing pixels in adjoining area are computed by
a generative adversarial network. Our work can provide a travel across
different first person experiences in diverse real world scenes.
Efficient Asymmetric Co-Tracking using Uncertainty Sampling
Authors: Kourosh Meshgi, Maryam Sadat Mirzaei, Shigeyuki Oba, Shin Ishii
Date: 2017-03-31 00:00:00

Adaptive tracking-by-detection approaches are popular for tracking arbitrary
objects. They treat the tracking problem as a classification task and use
online learning techniques to update the object model. However, these
approaches are heavily invested in the efficiency and effectiveness of their
detectors. Evaluating a massive number of samples for each frame (e.g.,
obtained by a sliding window) forces the detector to trade the accuracy in
favor of speed. Furthermore, misclassification of borderline samples in the
detector introduce accumulating errors in tracking. In this study, we propose a
co-tracking based on the efficient cooperation of two detectors: a rapid
adaptive exemplar-based detector and another more sophisticated but slower
detector with a long-term memory. The sampling labeling and co-learning of the
detectors are conducted by an uncertainty sampling unit, which improves the
speed and accuracy of the system. We also introduce a budgeting mechanism which
prevents the unbounded growth in the number of examples in the first detector
to maintain its rapid response. Experiments demonstrate the efficiency and
effectiveness of the proposed tracker against its baselines and its superior
performance against state-of-the-art trackers on various benchmark videos.
Geodesic Distance Histogram Feature for Video Segmentation
Authors: Hieu Le, Vu Nguyen, Chen-Ping Yu, Dimitris Samaras
Date: 2017-03-31 00:00:00
Tasks: Segmentation, Superpixels, Video Segmentation, Video Semantic Segmentation

This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorporate adaptive voting weights and spatial pyramid configurations to
include spatial information into the geodesic histogram feature and show that
this further improves results. The feature is generic and can be used as part
of various algorithms. In experiments, we test the geodesic histogram feature
by incorporating it into two existing video segmentation frameworks. This leads
to significantly better performance in 3D video segmentation benchmarks on two
datasets.
Membership Inference Attacks against Machine Learning Models
Authors: Reza Shokri, Marco Stronati, Congzheng Song, Vitaly Shmatikov
Date: 2016-10-18 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Inference Attack, Membership Inference Attack

We quantitatively investigate how machine learning models leak information
about the individual data records on which they were trained. We focus on the
basic membership inference attack: given a data record and black-box access to
a model, determine if the record was in the model's training dataset. To
perform membership inference against a target model, we make adversarial use of
machine learning and train our own inference model to recognize differences in
the target model's predictions on the inputs that it trained on versus the
inputs that it did not train on.
  We empirically evaluate our inference techniques on classification models
trained by commercial "machine learning as a service" providers such as Google
and Amazon. Using realistic datasets and classification tasks, including a
hospital discharge dataset whose membership is sensitive from the privacy
perspective, we show that these models can be vulnerable to membership
inference attacks. We then investigate the factors that influence this leakage
and evaluate mitigation strategies.
One-Shot Neural Cross-Lingual Transfer for Paradigm Completion
Authors: Katharina Kann, Ryan Cotterell, Hinrich Schütze
Date: 2017-03-31 00:00:00
Proceeding: ACL 2017 7
Tasks: Cross-Lingual Transfer, Decoder, LEMMA, One-Shot Learning

We present a novel cross-lingual transfer method for paradigm completion, the
task of mapping a lemma to its inflected forms, using a neural encoder-decoder
model, the state of the art for the monolingual task. We use labeled data from
a high-resource language to increase performance on a low-resource language. In
experiments on 21 language pairs from four different language families, we
obtain up to 58% higher accuracy than without transfer and show that even
zero-shot and one-shot learning are possible. We further find that the degree
of language relatedness strongly influences the ability to transfer
morphological knowledge.
Neural networks based EEG-Speech Models
Authors: Pengfei Sun, Jun Qin
Date: 2016-12-16 00:00:00
Tasks: Binary Classification, EEG, Electroencephalogram (EEG), General Classification, Language Modeling, Language Modelling, Translation

In this paper, we propose an end-to-end neural network (NN) based EEG-speech
(NES) modeling framework, in which three network structures are developed to
map imagined EEG signals to phonemes. The proposed NES models incorporate a
language model based EEG feature extraction layer, an acoustic feature mapping
layer, and a restricted Boltzmann machine (RBM) based the feature learning
layer. The NES models can jointly realize the representation of multichannel
EEG signals and the projection of acoustic speech signals. Among three proposed
NES models, two augmented networks utilize spoken EEG signals as either bias or
gate information to strengthen the feature learning and translation of imagined
EEG signals. Experimental results show that all three proposed NES models
outperform the baseline support vector machine (SVM) method on EEG-speech
classification. With respect to binary classification, our approach achieves
comparable results relative to deep believe network approach.
Efficient Registration of Pathological Images: A Joint PCA/Image-Reconstruction Approach
Authors: Xu Han, Xiao Yang, Stephen Aylward, Roland Kwitt, Marc Niethammer
Date: 2017-03-31 00:00:00
Tasks: Image Reconstruction

Registration involving one or more images containing pathologies is
challenging, as standard image similarity measures and spatial transforms
cannot account for common changes due to pathologies. Low-rank/Sparse (LRS)
decomposition removes pathologies prior to registration; however, LRS is
memory-demanding and slow, which limits its use on larger data sets.
Additionally, LRS blurs normal tissue regions, which may degrade registration
performance. This paper proposes an efficient alternative to LRS: (1) normal
tissue appearance is captured by principal component analysis (PCA) and (2)
blurring is avoided by an integrated model for pathology removal and image
reconstruction. Results on synthetic and BRATS 2015 data demonstrate its
utility.
Transfer of View-manifold Learning to Similarity Perception of Novel Objects
Authors: Xingyu Lin, Hao Wang, Zhihao LI, Yimeng Zhang, Alan Yuille, Tai Sing Lee
Date: 2017-03-31 00:00:00
Tasks: Metric Learning, Object

We develop a model of perceptual similarity judgment based on re-training a
deep convolution neural network (DCNN) that learns to associate different views
of each 3D object to capture the notion of object persistence and continuity in
our visual experience. The re-training process effectively performs distance
metric learning under the object persistency constraints, to modify the
view-manifold of object representations. It reduces the effective distance
between the representations of different views of the same object without
compromising the distance between those of the views of different objects,
resulting in the untangling of the view-manifolds between individual objects
within the same category and across categories. This untangling enables the
model to discriminate and recognize objects within the same category,
independent of viewpoints. We found that this ability is not limited to the
trained objects, but transfers to novel objects in both trained and untrained
categories, as well as to a variety of completely novel artificial synthetic
objects. This transfer in learning suggests the modification of distance
metrics in view- manifolds is more general and abstract, likely at the levels
of parts, and independent of the specific objects or categories experienced
during training. Interestingly, the resulting transformation of feature
representation in the deep networks is found to significantly better match
human perceptual similarity judgment than AlexNet, suggesting that object
persistence could be an important constraint in the development of perceptual
similarity judgment in biological neural networks.
On the Reliable Detection of Concept Drift from Streaming Unlabeled Data
Authors: Tegjyot Singh Sethi, Mehmed Kantardzic
Date: 2017-03-31 00:00:00
Tasks: Change Detection, Drift Detection

Classifiers deployed in the real world operate in a dynamic environment,
where the data distribution can change over time. These changes, referred to as
concept drift, can cause the predictive performance of the classifier to drop
over time, thereby making it obsolete. To be of any real use, these classifiers
need to detect drifts and be able to adapt to them, over time. Detecting drifts
has traditionally been approached as a supervised task, with labeled data
constantly being used for validating the learned model. Although effective in
detecting drifts, these techniques are impractical, as labeling is a difficult,
costly and time consuming activity. On the other hand, unsupervised change
detection techniques are unreliable, as they produce a large number of false
alarms. The inefficacy of the unsupervised techniques stems from the exclusion
of the characteristics of the learned classifier, from the detection process.
In this paper, we propose the Margin Density Drift Detection (MD3) algorithm,
which tracks the number of samples in the uncertainty region of a classifier,
as a metric to detect drift. The MD3 algorithm is a distribution independent,
application independent, model independent, unsupervised and incremental
algorithm for reliably detecting drifts from data streams. Experimental
evaluation on 6 drift induced datasets and 4 additional datasets from the
cybersecurity domain demonstrates that the MD3 approach can reliably detect
drifts, with significantly fewer false alarms compared to unsupervised feature
based drift detectors. The reduced false alarms enables the signaling of drifts
only when they are most likely to affect classification performance. As such,
the MD3 approach leads to a detection scheme which is credible, label efficient
and general in its applicability.
Detecção de comunidades em redes complexas para identificar gargalos e desperdício de recursos em sistemas de ônibus
Authors: Carlos Caminha, Vasco Furtado, Vládia Pinheiro, Caio Ponte
Date: 2016-06-12 00:00:00

We propose here a methodology to help to understand the shortcomings of
public transportation in a city via the mining of complex networks representing
the supply and demand of public transport. We show how to build these networks
based upon data on smart card use in buses via the application of algorithms
that estimate an OD and reconstruct the complete itinerary of the passengers.
The overlapping of the two networks sheds light in potential overload and waste
in the offer of resources that can be mitigated with strategies for balancing
supply and demand.
Learning Deep Features via Congenerous Cosine Loss for Person Recognition
Authors: Yu Liu, Hongyang Li, Xiaogang Wang
Date: 2017-02-22 00:00:00
Tasks: Person Recognition

Person recognition aims at recognizing the same identity across time and
space with complicated scenes and similar appearance. In this paper, we propose
a novel method to address this task by training a network to obtain robust and
representative features. The intuition is that we directly compare and optimize
the cosine distance between two features - enlarging inter-class distinction as
well as alleviating inner-class variance. We propose a congenerous cosine loss
by minimizing the cosine distance between samples and their cluster centroid in
a cooperative way. Such a design reduces the complexity and could be
implemented via softmax with normalized inputs. Our method also differs from
previous work in person recognition that we do not conduct a second training on
the test subset. The identity of a person is determined by measuring the
similarity from several body regions in the reference set. Experimental results
show that the proposed approach achieves better classification accuracy against
previous state-of-the-arts.
Who is Mistaken?
Authors: Benjamin Eysenbach, Carl Vondrick, Antonio Torralba
Date: 2016-12-04 00:00:00
Tasks: Action Understanding

Recognizing when people have false beliefs is crucial for understanding their
actions. We introduce the novel problem of identifying when people in abstract
scenes have incorrect beliefs. We present a dataset of scenes, each visually
depicting an 8-frame story in which a character has a mistaken belief. We then
create a representation of characters' beliefs for two tasks in human action
understanding: predicting who is mistaken, and when they are mistaken.
Experiments suggest that our method for identifying mistaken characters
performs better on these tasks than simple baselines. Diagnostics on our model
suggest it learns important cues for recognizing mistaken beliefs, such as
gaze. We believe models of people's beliefs will have many applications in
action understanding, robotics, and healthcare.
Comparison of multi-task convolutional neural network (MT-CNN) and a few other methods for toxicity prediction
Authors: Kedi Wu, Guo-Wei Wei
Date: 2017-03-31 00:00:00

Toxicity analysis and prediction are of paramount importance to human health
and environmental protection. Existing computational methods are built from a
wide variety of descriptors and regressors, which makes their performance
analysis difficult. For example, deep neural network (DNN), a successful
approach in many occasions, acts like a black box and offers little conceptual
elegance or physical understanding. The present work constructs a common set of
microscopic descriptors based on established physical models for charges,
surface areas and free energies to assess the performance of multi-task
convolutional neural network (MT-CNN) architectures and a few other approaches,
including random forest (RF) and gradient boosting decision tree (GBDT), on an
equal footing. Comparison is also given to convolutional neural network (CNN)
and non-convolutional deep neural network (DNN) algorithms. Four benchmark
toxicity data sets (i.e., endpoints) are used to evaluate various approaches.
Extensive numerical studies indicate that the present MT-CNN architecture is
able to outperform the state-of-the-art methods.
Transition Forests: Learning Discriminative Temporal Transitions for Action Recognition and Detection
Authors: Guillermo Garcia-Hernando, Tae-Kyun Kim
Date: 2016-07-10 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Detection, Action Recognition, Spatio-Temporal Action Localization, Temporal Action Localization

A human action can be seen as transitions between one's body poses over time,
where the transition depicts a temporal relation between two poses. Recognizing
actions thus involves learning a classifier sensitive to these pose transitions
as well as to static poses. In this paper, we introduce a novel method called
transitions forests, an ensemble of decision trees that both learn to
discriminate static poses and transitions between pairs of two independent
frames. During training, node splitting is driven by alternating two criteria:
the standard classification objective that maximizes the discrimination power
in individual frames, and the proposed one in pairwise frame transitions.
Growing the trees tends to group frames that have similar associated
transitions and share same action label incorporating temporal information that
was not available otherwise. Unlike conventional decision trees where the best
split in a node is determined independently of other nodes, the transition
forests try to find the best split of nodes jointly (within a layer) for
incorporating distant node transitions. When inferring the class label of a new
frame, it is passed down the trees and the prediction is made based on previous
frame predictions and the current one in an efficient and online manner. We
apply our method on varied skeleton action recognition and online detection
datasets showing its suitability over several baselines and state-of-the-art
approaches.
Prediction of infectious disease epidemics via weighted density ensembles
Authors: Evan L. Ray, Nicholas G. Reich
Date: 2017-03-31 00:00:00

Accurate and reliable predictions of infectious disease dynamics can be
valuable to public health organizations that plan interventions to decrease or
prevent disease transmission. A great variety of models have been developed for
this task, using different model structures, covariates, and targets for
prediction. Experience has shown that the performance of these models varies;
some tend to do better or worse in different seasons or at different points
within a season. Ensemble methods combine multiple models to obtain a single
prediction that leverages the strengths of each model. We considered a range of
ensemble methods that each form a predictive density for a target of interest
as a weighted sum of the predictive densities from component models. In the
simplest case, equal weight is assigned to each component model; in the most
complex case, the weights vary with the region, prediction target, week of the
season when the predictions are made, a measure of component model uncertainty,
and recent observations of disease incidence. We applied these methods to
predict measures of influenza season timing and severity in the United States,
both at the national and regional levels, using three component models. We
trained the models on retrospective predictions from 14 seasons (1997/1998 -
2010/2011) and evaluated each model's prospective, out-of-sample performance in
the five subsequent influenza seasons. In this test phase, the ensemble methods
showed overall performance that was similar to the best of the component
models, but offered more consistent performance across seasons than the
component models. Ensemble methods offer the potential to deliver more reliable
predictions to public health decision makers.