pwc-archive/papers-with-abstracts huggingface dataset:
Hoeffding Trees with nmin adaptation
Authors: Eva García-Martín, Niklas Lavesson, Håkan Grahn, Emiliano Casalicchio, Veselka Boeva
Date: 2018-08-03 00:00:00

Machine learning software accounts for a significant amount of energy
consumed in data centers. These algorithms are usually optimized towards
predictive performance, i.e. accuracy, and scalability. This is the case of
data stream mining algorithms. Although these algorithms are adaptive to the
incoming data, they have fixed parameters from the beginning of the execution.
We have observed that having fixed parameters lead to unnecessary computations,
thus making the algorithm energy inefficient. In this paper we present the nmin
adaptation method for Hoeffding trees. This method adapts the value of the nmin
parameter, which significantly affects the energy consumption of the algorithm.
The method reduces unnecessary computations and memory accesses, thus reducing
the energy, while the accuracy is only marginally affected. We experimentally
compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and
CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation).
The results show that VFDT-nmin consumes up to 27% less energy than the
standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent
of accuracy in a few datasets.
When Does Hillclimbing Fail on Monotone Functions: An entropy compression argument
Authors: Johannes Lengler, Anders Martinsson, Angelika Steger
Date: 2018-08-03 00:00:00

Hillclimbing is an essential part of any optimization algorithm. An important
benchmark for hillclimbing algorithms on pseudo-Boolean functions $f: \{0,1\}^n
\to \mathbb{R}$ are (strictly) montone functions, on which a surprising number
of hillclimbers fail to be efficient. For example, the $(1+1)$-Evolutionary
Algorithm is a standard hillclimber which flips each bit independently with
probability $c/n$ in each round. Perhaps surprisingly, this algorithm shows a
phase transition: it optimizes any monotone pseudo-boolean function in
quasilinear time if $c<1$, but there are monotone functions for which the
algorithm needs exponential time if $c>2.2$. But so far it was unclear whether
the threshold is at $c=1$.
  In this paper we show how Moser's entropy compression argument can be adapted
to this situation, that is, we show that a long runtime would allow us to
encode the random steps of the algorithm with less bits than their entropy.
Thus there exists a $c_0 > 1$ such that for all $0<c\le c_0$ the
$(1+1)$-Evolutionary Algorithm with rate $c/n$ finds the optimum in $O(n \log^2
n)$ steps in expectation.
iSPA-Net: Iterative Semantic Pose Alignment Network
Authors: Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V., Aditya Prakash, R. Venkatesh Babu
Date: 2018-08-03 00:00:00
Tasks: Object, Pose Estimation, Semantic correspondence, Viewpoint Estimation

Understanding and extracting 3D information of objects from monocular 2D
images is a fundamental problem in computer vision. In the task of 3D object
pose estimation, recent data driven deep neural network based approaches suffer
from scarcity of real images with 3D keypoint and pose annotations. Drawing
inspiration from human cognition, where the annotators use a 3D CAD model as
structural reference to acquire ground-truth viewpoints for real images; we
propose an iterative Semantic Pose Alignment Network, called iSPA-Net. Our
approach focuses on exploiting semantic 3D structural regularity to solve the
task of fine-grained pose estimation by predicting viewpoint difference between
a given pair of images. Such image comparison based approach also alleviates
the problem of data scarcity and hence enhances scalability of the proposed
approach for novel object categories with minimal annotation. The fine-grained
object pose estimator is also aided by correspondence of learned spatial
descriptor of the input image pair. The proposed pose alignment framework
enjoys the faculty to refine its initial pose estimation in consecutive
iterations by utilizing an online rendering setup along with effectiveness of a
non-uniform bin classification of pose-difference. This enables iSPA-Net to
achieve state-of-the-art performance on various real image viewpoint estimation
datasets. Further, we demonstrate effectiveness of the approach for multiple
applications. First, we show results for active object viewpoint localization
to capture images from similar pose considering only a single image as pose
reference. Second, we demonstrate the ability of the learned semantic
correspondence to perform unsupervised part-segmentation transfer using only a
single part-annotated 3D template model per object class. To encourage
reproducible research, we have released the codes for our proposed algorithm.
Multitask Gaussian Process with Hierarchical Latent Interactions
Authors: Kai Chen, Twan van Laarhoven, Elena Marchiori, Feng Yin, Shuguang Cui
Date: 2018-08-03 00:00:00
Tasks: Gaussian Processes

Multitask Gaussian process (MTGP) is powerful for joint learning of multiple tasks with complicated correlation patterns. However, due to the assembling of additive independent latent functions, all current MTGPs including the salient linear model of coregionalization (LMC) and convolution frameworks cannot effectively represent and learn the hierarchical latent interactions between its latent functions. In this paper, we further investigate the interactions in LMC of MTGP and then propose a novel kernel representation of the hierarchical interactions, which ameliorates both the expressiveness and the interpretability of MTGP. Specifically, we express the interaction as a product of function interaction and coefficient interaction. The function interaction is modeled by using cross convolution of latent functions. The coefficient interaction between the LMCs is described as a cross coregionalization term. We validate that considering the interactions can promote knowledge transferring in MTGP and compare our approach with some state-of-the-art MTGPs on both synthetic- and real-world datasets.
PHI Scrubber: A Deep Learning Approach
Authors: Abhai Kollara Dilip, Kamal Raj K, Malaikannan Sankarasubbu
Date: 2018-08-03 00:00:00
Tasks: Deep Learning

Confidentiality of patient information is an essential part of Electronic
Health Record System. Patient information, if exposed, can cause a serious
damage to the privacy of individuals receiving healthcare. Hence it is
important to remove such details from physician notes. A system is proposed
which consists of a deep learning model where a de-convolutional neural network
and bi-directional LSTM-CNN is used along with regular expressions to recognize
and eliminate the individually identifiable information. This information is
then removed from a medical practitioner's data which further allows the fair
usage of such information among researchers and in clinical trials.
Information-Theoretic Scoring Rules to Learn Additive Bayesian Network Applied to Epidemiology
Authors: Gilles Kratzer, Reinhard Furrer
Date: 2018-08-03 00:00:00
Tasks: Epidemiology

Bayesian network modelling is a well adapted approach to study messy and
highly correlated datasets which are very common in, e.g., systems
epidemiology. A popular approach to learn a Bayesian network from an
observational datasets is to identify the maximum a posteriori network in a
search-and-score approach. Many scores have been proposed both Bayesian or
frequentist based. In an applied perspective, a suitable approach would allow
multiple distributions for the data and is robust enough to run autonomously. A
promising framework to compute scores are generalized linear models. Indeed,
there exists fast algorithms for estimation and many tailored solutions to
common epidemiological issues. The purpose of this paper is to present an R
package abn that has an implementation of multiple frequentist scores and some
realistic simulations that show its usability and performance. It includes
features to deal efficiently with data separation and adjustment which are very
common in systems epidemiology.
Efficient texture retrieval using multiscale local extrema descriptors and covariance embedding
Authors: Minh-Tan Pham
Date: 2018-08-03 00:00:00
Tasks: Retrieval

This paper presents an efficient method for texture retrieval using
multiscale feature extraction and embedding based on the local extrema
keypoints. The idea is to first represent each texture image by its local
maximum and local minimum pixels. The image is then divided into regular
overlapping blocks and each one is characterized by a feature vector
constructed from the radiometric, geometric and structural information of its
local extrema. All feature vectors are finally embedded into a covariance
matrix which will be exploited for dissimilarity measurement within retrieval
task. Thanks to the method's simplicity, multiscale scheme can be easily
implemented to improve its scale-space representation capacity. We argue that
our handcrafted features are easy to implement, fast to run but can provide
very competitive performance compared to handcrafted and CNN-based learned
descriptors from the literature. In particular, the proposed framework provides
highly competitive retrieval rate for several texture databases including
94.95% for MIT Vistex, 79.87% for Stex, 76.15% for Outex TC-00013 and 89.74%
for USPtex.
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
Authors: Yang He, Bernt Schiele, Mario Fritz
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Conditional Image Generation, Diversity, Image Generation, regression

Recent advances in Deep Learning and probabilistic modeling have led to
strong improvements in generative models for images. On the one hand,
Generative Adversarial Networks (GANs) have contributed a highly effective
adversarial learning procedure, but still suffer from stability issues. On the
other hand, Conditional Variational Auto-Encoders (CVAE) models provide a sound
way of conditional modeling but suffer from mode-mixing issues. Therefore,
recent work has turned back to simple and stable regression models that are
effective at generation but give up on the sampling mechanism and the latent
code representation. We propose a novel and efficient stochastic regression
approach with latent drop-out codes that combines the merits of both lines of
research. In addition, a new training objective increases coverage of the
training distribution leading to improvements over the state of the art in
terms of accuracy as well as diversity.
Multi-shot Person Re-identification through Set Distance with Visual Distributional Representation
Authors: Ting-yao Hu, Xiaojun Chang, Alexander G. Hauptmann
Date: 2018-08-03 00:00:00
Tasks: Person Re-Identification

Person re-identification aims to identify a specific person at distinct times
and locations. It is challenging because of occlusion, illumination, and
viewpoint change in camera views. Recently, multi-shot person re-id task
receives more attention since it is closer to real-world application. A key
point of a good algorithm for multi-shot person re-id is the temporal
aggregation of the person appearance features. While most of the current
approaches apply pooling strategies and obtain a fixed-size vector
representation, these may lose the matching evidence between examples. In this
work, we propose the idea of visual distributional representation, which
interprets an image set as samples drawn from an unknown distribution in
appearance feature space. Based on the supervision signals from a downstream
task of interest, the method reshapes the appearance feature space and further
learns the unknown distribution of each image set. In the context of multi-shot
person re-id, we apply this novel concept along with Wasserstein distance and
learn a distributional set distance function between two image sets. In this
way, the proper alignment between two image sets can be discovered naturally in
a non-parametric manner. Our experiment results on two public datasets show the
advantages of our proposed method compared to other state-of-the-art
approaches.
LDSO: Direct Sparse Odometry with Loop Closure
Authors: Xiang Gao, Rui Wang, Nikolaus Demmel, Daniel Cremers
Date: 2018-08-03 00:00:00
Tasks: Loop Closure Detection, Translation

In this paper we present an extension of Direct Sparse Odometry (DSO) to a
monocular visual SLAM system with loop closure detection and pose-graph
optimization (LDSO). As a direct technique, DSO can utilize any image pixel
with sufficient intensity gradient, which makes it robust even in featureless
areas. LDSO retains this robustness, while at the same time ensuring
repeatability of some of these points by favoring corner features in the
tracking frontend. This repeatability allows to reliably detect loop closure
candidates with a conventional feature-based bag-of-words (BoW) approach. Loop
closure candidates are verified geometrically and Sim(3) relative pose
constraints are estimated by jointly minimizing 2D and 3D geometric error
terms. These constraints are fused with a co-visibility graph of relative poses
extracted from DSO's sliding window optimization. Our evaluation on publicly
available datasets demonstrates that the modified point selection strategy
retains the tracking accuracy and robustness, and the integrated pose-graph
optimization significantly reduces the accumulated rotation-, translation- and
scale-drift, resulting in an overall performance comparable to state-of-the-art
feature-based systems, even without global bundle adjustment.
Interaction-aware Spatio-temporal Pyramid Attention Networks for Action Classification
Authors: Yang Du, Chunfeng Yuan, Bing Li, Lili Zhao, Yangxi Li, Weiming Hu
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Classification, General Classification

Local features at neighboring spatial positions in feature maps have high
correlation since their receptive fields are often overlapped. Self-attention
usually uses the weighted sum (or other functions) with internal elements of
each local feature to obtain its weight score, which ignores interactions among
local features. To address this, we propose an effective interaction-aware
self-attention model inspired by PCA to learn attention maps. Furthermore,
since different layers in a deep network capture feature maps of different
scales, we use these feature maps to construct a spatial pyramid and then
utilize multi-scale information to obtain more accurate attention scores, which
are used to weight the local features in all spatial positions of feature maps
to calculate attention maps. Moreover, our spatial pyramid attention is
unrestricted to the number of its input feature maps so it is easily extended
to a spatio-temporal version. Finally, our model is embedded in general CNNs to
form end-to-end attention networks for action classification. Experimental
results show that our method achieves the state-of-the-art results on the
UCF101, HMDB51 and untrimmed Charades.
Improved Deep Spectral Convolution Network For Hyperspectral Unmixing With Multinomial Mixture Kernel and Endmember Uncertainty
Authors: Savas Ozkan, Gozde Bozdagi Akar
Date: 2018-08-03 00:00:00
Tasks: Generative Adversarial Network, Hyperspectral Unmixing

In this study, we propose a novel framework for hyperspectral unmixing by using an improved deep spectral convolution network (DSCN++) combined with endmember uncertainty. DSCN++ is used to compute high-level representations which are further modeled with Multinomial Mixture Model to estimate abundance maps. In the reconstruction step, a new trainable uncertainty term based on a nonlinear neural network model is introduced to provide robustness to endmember uncertainty. For the optimization of the coefficients of the multinomial model and the uncertainty term, Wasserstein Generative Adversarial Network (WGAN) is exploited to improve stability and to capture uncertainty. Experiments are performed on both real and synthetic datasets. The results validate that the proposed method obtains state-of-the-art hyperspectral unmixing performance particularly on the real datasets compared to the baseline techniques.
Hallucinating Agnostic Images to Generalize Across Domains
Authors: Fabio M. Carlucci, Paolo Russo, Tatiana Tommasi, Barbara Caputo
Date: 2018-08-03 00:00:00
Tasks: Domain Adaptation, Domain Generalization, Unsupervised Domain Adaptation

The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, the access to even unlabeled target samples cannot be taken for granted, which makes standard unsupervised domain adaptation methods inapplicable in the wild. In this work we investigate how to exploit multiple sources by hallucinating a deep visual domain composed of images, possibly unrealistic, able to maintain categorical knowledge while discarding specific source styles. The produced agnostic images are the result of a deep architecture that applies pixel adaptation on the original source data guided by two adversarial domain classifier branches at image and feature level. Our approach is conceived to learn only from source data, but it seamlessly extends to the use of unlabeled target samples. Remarkable results for both multi-source domain adaptation and domain generalization support the power of hallucinating agnostic images in this framework.
Exploiting Local Indexing and Deep Feature Confidence Scores for Fast Image-to-Video Search
Authors: Savas Ozkan, Gozde Bozdagi Akar
Date: 2018-08-03 00:00:00
Tasks: Retrieval

The cost-effective visual representation and fast query-by-example search are two challenging goals that should be maintained for web-scale visual retrieval tasks on moderate hardware. This paper introduces a fast and robust method that ensures both of these goals by obtaining state-of-the-art performance for an image-to-video search scenario. Hence, we present critical enhancements to well-known indexing and visual representation techniques by promoting faster, better and moderate retrieval performance. We also boost the superiority of our method for some visual challenges by exploiting individual decisions of local and global descriptors at query time. For instance, local content descriptors represent copied/duplicated scenes with large geometric deformations such as scale, orientation and affine transformation. In contrast, the use of global content descriptors is more practical for near-duplicate and semantic searches. Experiments are conducted on a large-scale Stanford I2V dataset. The experimental results show that our method is useful in terms of complexity and query processing time for large-scale visual retrieval scenarios, even if local and global representations are used together. The proposed method is superior and achieves state-of-the-art performance based on the mean average precision (MAP) score of this dataset. Lastly, we report additional MAP scores after updating the ground annotations unveiled by retrieval results of the proposed method, and it shows that the actual performance.
Real-Time Object Pose Estimation with Pose Interpreter Networks
Authors: Jimmy Wu, Bolei Zhou, Rebecca Russell, Vincent Kee, Syler Wagner, Mitchell Hebert, Antonio Torralba, David M. S. Johnson
Date: 2018-08-03 00:00:00
Tasks: Object, Pose Estimation

In this work, we introduce pose interpreter networks for 6-DoF object pose
estimation. In contrast to other CNN-based approaches to pose estimation that
require expensively annotated object pose data, our pose interpreter network is
trained entirely on synthetic pose data. We use object masks as an intermediate
representation to bridge real and synthetic. We show that when combined with a
segmentation model trained on RGB images, our synthetically trained pose
interpreter network is able to generalize to real data. Our end-to-end system
for object pose estimation runs in real-time (20 Hz) on live RGB data, without
using depth information or ICP refinement.
CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images
Authors: Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R. Scott, Dinglong Huang
Date: 2018-08-03 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Classification, Weakly-supervised Learning

We present a simple yet efficient approach capable of training deep neural
networks on large-scale weakly-supervised web images, which are crawled raw
from the Internet by using text queries, without any human annotation. We
develop a principled learning strategy by leveraging curriculum learning, with
the goal of handling a massive amount of noisy labels and data imbalance
effectively. We design a new learning curriculum by measuring the complexity of
data using its distribution density in a feature space, and rank the complexity
in an unsupervised manner. This allows for an efficient implementation of
curriculum learning on large-scale web images, resulting in a high-performance
CNN model, where the negative impact of noisy labels is reduced substantially.
Importantly, we show by experiments that those images with highly noisy labels
can surprisingly improve the generalization capability of the model, by serving
as a manner of regularization. Our approaches obtain state-of-the-art
performance on four benchmarks: WebVision, ImageNet, Clothing-1M and Food-101.
With an ensemble of multiple models, we achieved a top-5 error rate of 5.2% on
the WebVision challenge for 1000-category classification. This result was the
top performance by a wide margin, outperforming second place by a nearly 50%
relative error rate. Code and models are available at:
https://github.com/MalongTech/CurriculumNet .
Helix: Accelerating Human-in-the-loop Machine Learning
Authors: Doris Xin, Litian Ma, Jialin Liu, Stephen Macke, Shuchen Song, Aditya Parameswaran
Date: 2018-08-03 00:00:00
Tasks: BIG-bench Machine Learning, Structured Prediction

Data application developers and data scientists spend an inordinate amount of
time iterating on machine learning (ML) workflows -- by modifying the data
pre-processing, model training, and post-processing steps -- via
trial-and-error to achieve the desired model performance. Existing work on
accelerating machine learning focuses on speeding up one-shot execution of
workflows, failing to address the incremental and dynamic nature of typical ML
development. We propose Helix, a declarative machine learning system that
accelerates iterative development by optimizing workflow execution end-to-end
and across iterations. Helix minimizes the runtime per iteration via program
analysis and intelligent reuse of previous results, which are selectively
materialized -- trading off the cost of materialization for potential future
benefits -- to speed up future iterations. Additionally, Helix offers a
graphical interface to visualize workflow DAGs and compare versions to
facilitate iterative development. Through two ML applications, in
classification and in structured prediction, attendees will experience the
succinctness of Helix programming interface and the speed and ease of iterative
development using Helix. In our evaluations, Helix achieved up to an order of
magnitude reduction in cumulative run time compared to state-of-the-art machine
learning tools.