pwc-archive/papers-with-abstracts huggingface dataset:
Scalable Importance Tempering and Bayesian Variable Selection
Authors: Giacomo Zanella, Gareth Roberts
Date: 2018-05-01 00:00:00
Tasks: Variable Selection

We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful theoretical analysis, including guarantees on robustness to high dimensionality, explicit comparison with standard Markov chain Monte Carlo methods and illustrations of the potential improvements in efficiency. Simple and concrete intuition is provided for when the novel scheme is expected to outperform standard schemes. When applied to Bayesian variable-selection problems, the novel algorithm is orders of magnitude more efficient than available alternative sampling schemes and enables fast and reliable fully Bayesian inferences with tens of thousand regressors.
Probabilistic Matrix Factorization for Automated Machine Learning
Authors: Nicolo Fusi, Rishit Sheth, Huseyn Melih Elibol
Date: 2017-05-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Collaborative Filtering, Meta-Learning, Model Selection

In order to achieve state-of-the-art performance, modern machine learning
techniques require careful data pre-processing and hyperparameter tuning.
Moreover, given the ever increasing number of machine learning models being
developed, model selection is becoming increasingly important. Automating the
selection and tuning of machine learning pipelines consisting of data
pre-processing methods and machine learning models, has long been one of the
goals of the machine learning community. In this paper, we tackle this
meta-learning task by combining ideas from collaborative filtering and Bayesian
optimization. Using probabilistic matrix factorization techniques and
acquisition functions from Bayesian optimization, we exploit experiments
performed in hundreds of different datasets to guide the exploration of the
space of possible pipelines. In our experiments, we show that our approach
quickly identifies high-performing pipelines across a wide range of datasets,
significantly outperforming the current state-of-the-art.
Spiking Neural Algorithms for Markov Process Random Walk
Authors: William Severa, Rich Lehoucq, Ojas Parekh, James B. Aimone
Date: 2018-05-01 00:00:00

The random walk is a fundamental stochastic process that underlies many
numerical tasks in scientific computing applications. We consider here two
neural algorithms that can be used to efficiently implement random walks on
spiking neuromorphic hardware. The first method tracks the positions of
individual walkers independently by using a modular code inspired by the grid
cell spatial representation in the brain. The second method tracks the
densities of random walkers at each spatial location directly. We analyze the
scaling complexity of each of these methods and illustrate their ability to
model random walkers under different probabilistic conditions.
Learning under $p$-Tampering Attacks
Authors: Saeed Mahloujifar, Dimitrios I. Diochnos, Mohammad Mahmoody
Date: 2017-11-10 00:00:00
Tasks: PAC learning

Recently, Mahloujifar and Mahmoody (TCC'17) studied attacks against learning
algorithms using a special case of Valiant's malicious noise, called
$p$-tampering, in which the adversary gets to change any training example with
independent probability $p$ but is limited to only choose malicious examples
with correct labels. They obtained $p$-tampering attacks that increase the
error probability in the so called targeted poisoning model in which the
adversary's goal is to increase the loss of the trained hypothesis over a
particular test example. At the heart of their attack was an efficient
algorithm to bias the expected value of any bounded real-output function
through $p$-tampering.
  In this work, we present new biasing attacks for increasing the expected
value of bounded real-valued functions. Our improved biasing attacks, directly
imply improved $p$-tampering attacks against learners in the targeted poisoning
model. As a bonus, our attacks come with considerably simpler analysis. We also
study the possibility of PAC learning under $p$-tampering attacks in the
non-targeted (aka indiscriminate) setting where the adversary's goal is to
increase the risk of the generated hypothesis (for a random test example). We
show that PAC learning is possible under $p$-tampering poisoning attacks
essentially whenever it is possible in the realizable setting without the
attacks. We further show that PAC learning under "correct-label" adversarial
noise is not possible in general, if the adversary could choose the (still
limited to only $p$ fraction of) tampered examples that she substitutes with
adversarially chosen ones. Our formal model for such "bounded-budget" tampering
attackers is inspired by the notions of (strong) adaptive corruption in secure
multi-party computation.
Adaptive View Planning for Aerial 3D Reconstruction
Authors: Cheng Peng, Volkan Isler
Date: 2018-05-01 00:00:00
Tasks: 3D Reconstruction

With the proliferation of small aerial vehicles, acquiring close up aerial imagery for high quality reconstruction of complex scenes is gaining importance. We present an adaptive view planning method to collect such images in an automated fashion. We start by sampling a small set of views to build a coarse proxy to the scene. We then present (i)~a method that builds a view manifold for view selection, and (ii) an algorithm to select a sparse set of views. The vehicle then visits these viewpoints to cover the scene, and the procedure is repeated until reconstruction quality converges or a desired level of quality is achieved. The view manifold provides an effective efficiency/quality compromise between using the entire 6 degree of freedom pose space and using a single view hemisphere to select the views. Our results show that, in contrast to existing "explore and exploit" methods which collect only two sets of views, reconstruction quality can be drastically improved by adding a third set. They also indicate that three rounds of data collection is sufficient even for very complex scenes. We compare our algorithm to existing methods in three challenging scenes. We require each algorithm to select the same number of views. Our algorithm generates views which produce the least reconstruction error.
Learning Two-Branch Neural Networks for Image-Text Matching Tasks
Authors: Liwei Wang, Yin Li, Jing Huang, Svetlana Lazebnik
Date: 2017-04-11 00:00:00
Tasks: Image-text matching, Retrieval, Sentence, Sentence Retrieval, Text Matching, Triplet, Visual Grounding, Vocal Bursts Valence Prediction

Image-language matching tasks have recently attracted a lot of attention in
the computer vision field. These tasks include image-sentence matching, i.e.,
given an image query, retrieving relevant sentences and vice versa, and
region-phrase matching or visual grounding, i.e., matching a phrase to relevant
regions. This paper investigates two-branch neural networks for learning the
similarity between these two data modalities. We propose two network structures
that produce different output representations. The first one, referred to as an
embedding network, learns an explicit shared latent embedding space with a
maximum-margin ranking loss and novel neighborhood constraints. Compared to
standard triplet sampling, we perform improved neighborhood sampling that takes
neighborhood information into consideration while constructing mini-batches.
The second network structure, referred to as a similarity network, fuses the
two branches via element-wise product and is trained with regression loss to
directly predict a similarity score. Extensive experiments show that our
networks achieve high accuracies for phrase localization on the Flickr30K
Entities dataset and for bi-directional image-sentence retrieval on Flickr30K
and MSCOCO datasets.
Adapting Mask-RCNN for Automatic Nucleus Segmentation
Authors: Jeremiah W. Johnson
Date: 2018-05-01 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Object Localization, Segmentation, Semantic Segmentation

Automatic segmentation of microscopy images is an important task in medical
image processing and analysis. Nucleus detection is an important example of
this task. Mask-RCNN is a recently proposed state-of-the-art algorithm for
object detection, object localization, and object instance segmentation of
natural images. In this paper we demonstrate that Mask-RCNN can be used to
perform highly effective and efficient automatic segmentations of a wide range
of microscopy images of cell nuclei, for a variety of cells acquired under a
variety of conditions.
Inherent Biases in Reference based Evaluation for Grammatical Error Correction and Text Simplification
Authors: Leshem Choshen, Omri Abend
Date: 2018-04-30 00:00:00
Tasks: Grammatical Error Correction, Sentence, Text Generation, Text Simplification, valid

The prevalent use of too few references for evaluating text-to-text generation is known to bias estimates of their quality ({\it low coverage bias} or LCB). This paper shows that overcoming LCB in Grammatical Error Correction (GEC) evaluation cannot be attained by re-scaling or by increasing the number of references in any feasible range, contrary to previous suggestions. This is due to the long-tailed distribution of valid corrections for a sentence. Concretely, we show that LCB incentivizes GEC systems to avoid correcting even when they can generate a valid correction. Consequently, existing systems obtain comparable or superior performance compared to humans, by making few but targeted changes to the input. Similar effects on Text Simplification further support our claims.
Coupling and Convergence for Hamiltonian Monte Carlo
Authors: Nawaf Bou-Rabee, Andreas Eberle, Raphael Zimmer
Date: 2018-05-01 00:00:00

Based on a new coupling approach, we prove that the transition step of the Hamiltonian Monte Carlo algorithm is contractive w.r.t. a carefully designed Kantorovich (L1 Wasserstein) distance. The lower bound for the contraction rate is explicit. Global convexity of the potential is not required, and thus multimodal target distributions are included. Explicit quantitative bounds for the number of steps required to approximate the stationary distribution up to a given error are a direct consequence of contractivity. These bounds show that HMC can overcome diffusive behaviour if the duration of the Hamiltonian dynamics is adjusted appropriately.
Extracting textual overlays from social media videos using neural networks
Authors: Adam Słucki, Tomasz Trzcinski, Adam Bielski, Paweł Cyrta
Date: 2018-04-27 00:00:00
Tasks: Retrieval, Text Detection

Textual overlays are often used in social media videos as people who watch
them without the sound would otherwise miss essential information conveyed in
the audio stream. This is why extraction of those overlays can serve as an
important meta-data source, e.g. for content classification or retrieval tasks.
In this work, we present a robust method for extracting textual overlays from
videos that builds up on multiple neural network architectures. The proposed
solution relies on several processing steps: keyframe extraction, text
detection and text recognition. The main component of our system, i.e. the text
recognition module, is inspired by a convolutional recurrent neural network
architecture and we improve its performance using synthetically generated
dataset of over 600,000 images with text prepared by authors specifically for
this task. We also develop a filtering method that reduces the amount of
overlapping text phrases using Levenshtein distance and further boosts system's
performance. The final accuracy of our solution reaches over 80A% and is au
pair with state-of-the-art methods.
Optimal Transport on Discrete Domains
Authors: Justin Solomon
Date: 2018-01-23 00:00:00

Inspired by the matching of supply to demand in logistical problems, the
optimal transport (or Monge--Kantorovich) problem involves the matching of
probability distributions defined over a geometric domain such as a surface or
manifold. In its most obvious discretization, optimal transport becomes a
large-scale linear program, which typically is infeasible to solve efficiently
on triangle meshes, graphs, point clouds, and other domains encountered in
graphics and machine learning. Recent breakthroughs in numerical optimal
transport, however, enable scalability to orders-of-magnitude larger problems,
solvable in a fraction of a second. Here, we discuss advances in numerical
optimal transport that leverage understanding of both discrete and smooth
aspects of the problem. State-of-the-art techniques in discrete optimal
transport combine insight from partial differential equations (PDE) with convex
analysis to reformulate, discretize, and optimize transportation problems. The
end result is a set of theoretically-justified models suitable for domains with
thousands or millions of vertices. Since numerical optimal transport is a
relatively new discipline, special emphasis is placed on identifying and
explaining open problems in need of mathematical insight and additional
research.
Image Denoising via Collaborative Dual-Domain Patch Filtering
Authors: Muzammil Behzad
Date: 2018-05-01 00:00:00
Tasks: Denoising, Image Denoising, SSIM

In this paper, we propose a novel image denoising algorithm exploiting
features from both spatial as well as transformed domain. We implement
intensity-invariance based improved grouping for collaborative support-agnostic
sparse reconstruction. For collaboration firstly, we stack similar-structured
patches via intensity-invariant correlation measure. The grouped patches
collaborate to yield desirable sparse estimates for noise filtering. This is
because similar patches share the same support in the transformed domain, such
similar supports can be used as probabilities of active taps to refine the
sparse estimates. This ultimately produces a very useful patch estimate thus
increasing the quality of recovered image by discarding the noise-causing
components. A region growing based spatially developed post-processor is then
applied to further enhance the smooth regions by extracting the spatial domain
features. We also extend our proposed method for denoising of color images.
Comparison results with the state-of-the-art algorithms in terms of peak
signal-to-noise ratio (PNSR) and structural similarity (SSIM) index from
extensive experimentations via a broad range of scenarios demonstrate the
superiority of our proposed algorithm.
Robust Face Recognition with Deeply Normalized Depth Images
Authors: Ziqing Feng, Qijun Zhao
Date: 2018-05-01 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Depth information has been proven useful for face recognition. However,
existing depth-image-based face recognition methods still suffer from noisy
depth values and varying poses and expressions. In this paper, we propose a
novel method for normalizing facial depth images to frontal pose and neutral
expression and extracting robust features from the normalized depth images. The
method is implemented via two deep convolutional neural networks (DCNN),
normalization network ($Net_{N}$) and feature extraction network ($Net_{F}$).
Given a facial depth image, $Net_{N}$ first converts it to an HHA image, from
which the 3D face is reconstructed via a DCNN. $Net_{N}$ then generates a
pose-and-expression normalized (PEN) depth image from the reconstructed 3D
face. The PEN depth image is finally passed to $Net_{F}$, which extracts a
robust feature representation via another DCNN for face recognition. Our
preliminary evaluation results demonstrate the superiority of the proposed
method in recognizing faces of arbitrary poses and expressions with depth
images.
Boosting Self-Supervised Learning via Knowledge Transfer
Authors: Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro, Hamed Pirsiavash
Date: 2018-05-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: object-detection, Object Detection, Self-Supervised Learning, Transfer Learning

In self-supervised learning, one trains a model to solve a so-called pretext
task on a dataset without the need for human annotation. The main objective,
however, is to transfer this model to a target domain and task. Currently, the
most effective transfer strategy is fine-tuning, which restricts one to use the
same model or parts thereof for both pretext and target tasks. In this paper,
we present a novel framework for self-supervised learning that overcomes
limitations in designing and comparing different tasks, models, and data
domains. In particular, our framework decouples the structure of the
self-supervised model from the final task-specific fine-tuned model. This
allows us to: 1) quantitatively assess previously incompatible models including
handcrafted features; 2) show that deeper neural network models can learn
better representations from the same pretext task; 3) transfer knowledge
learned with a deep model to a shallower one and thus boost its learning. We
use this framework to design a novel self-supervised task, which achieves
state-of-the-art performance on the common benchmarks in PASCAL VOC 2007,
ILSVRC12 and Places by a significant margin. Our learned features shrink the
mAP gap between models trained via self-supervised learning and supervised
learning from 5.9% to 2.6% in object detection on PASCAL VOC 2007.
Which Facial Expressions Can Reveal Your Gender? A Study With 3D Faces
Authors: Baiqiang Xia
Date: 2018-05-01 00:00:00
Tasks: Gender Classification

Human exhibit rich gender cues in both appearance and behavior. In computer
vision domain, gender recognition from facial appearance have been extensively
studied, while facial behavior based gender recognition studies remain rare. In
this work, we first demonstrate that facial expressions influence the gender
patterns presented in 3D face, and gender recognition performance increases
when training and testing within the same expression. In further, we design
experiments which directly extract the morphological changes resulted from
facial expressions as features, for expression-based gender recognition.
Experimental results demonstrate that gender can be recognized with
considerable accuracy in Happy and Disgust expressions, while Surprise and Sad
expressions do not convey much gender related information. This is the first
work in the literature which investigates expression-based gender
classification with 3D faces, and reveals the strength of gender patterns
incorporated in different types of expressions, namely the Happy, the Disgust,
the Surprise and the Sad expressions.
Incremental and Iterative Learning of Answer Set Programs from Mutually Distinct Examples
Authors: Arindam Mitra, Chitta Baral
Date: 2018-02-22 00:00:00
Tasks: BIG-bench Machine Learning, Handwritten Digit Recognition, Inductive logic programming, Question Answering

Over the years the Artificial Intelligence (AI) community has produced
several datasets which have given the machine learning algorithms the
opportunity to learn various skills across various domains. However, a subclass
of these machine learning algorithms that aimed at learning logic programs,
namely the Inductive Logic Programming algorithms, have often failed at the
task due to the vastness of these datasets. This has impacted the usability of
knowledge representation and reasoning techniques in the development of AI
systems. In this research, we try to address this scalability issue for the
algorithms that learn answer set programs. We present a sound and complete
algorithm which takes the input in a slightly different manner and performs an
efficient and more user controlled search for a solution. We show via
experiments that our algorithm can learn from two popular datasets from machine
learning community, namely bAbl (a question answering dataset) and MNIST (a
dataset for handwritten digit recognition), which to the best of our knowledge
was not previously possible. The system is publicly available at
https://goo.gl/KdWAcV. This paper is under consideration for acceptance in
TPLP.
Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity
Authors: Christian Koch, Moritz Lode, Denny Stohr, Amr Rizk, Ralf Steinmetz
Date: 2018-05-01 00:00:00

YouTube is one of the most popular platforms for streaming of user-generated
video. Nowadays, professional YouTubers are organized in so called
multi-channel networks (MCNs). These networks offer services such as brand
deals, equipment, and strategic advice in exchange for a share of the
YouTubers' revenue. A major strategy to gain more subscribers and, hence,
revenue is collaborating with other YouTubers. Yet, collaborations on YouTube
have not been studied in a detailed quantitative manner. This paper aims to
close this gap with the following contributions. First, we collect a YouTube
dataset covering video statistics over three months for 7,942 channels. Second,
we design a framework for collaboration detection given a previously unknown
number of persons featuring in YouTube videos. We denote this framework for the
analysis of collaborations in YouTube videos using a Deep Neural Network (DNN)
based approach as CATANA. Third, we analyze about 2.4 years of video content
and use CATANA to answer research questions providing guidance for YouTubers
and MCNs for efficient collaboration strategies. Thereby, we focus on (i)
collaboration frequency and partner selectivity, (ii) the influence of MCNs on
channel collaborations, (iii) collaborating channel types, and (iv) the impact
of collaborations on video and channel popularity. Our results show that
collaborations are in many cases significantly beneficial in terms of viewers
and newly attracted subscribers for both collaborating channels, showing often
more than 100% popularity growth compared with non-collaboration videos.
Deep Factorization Machines for Knowledge Tracing
Authors: Jill-Jênn Vie
Date: 2018-05-01 00:00:00
Tasks: Knowledge Tracing, Language Acquisition, regression

This paper introduces our solution to the 2018 Duolingo Shared Task on Second
Language Acquisition Modeling (SLAM). We used deep factorization machines, a
wide and deep learning model of pairwise relationships between users, items,
skills, and other entities considered. Our solution (AUC 0.815) hopefully
managed to beat the logistic regression baseline (AUC 0.774) but not the top
performing model (AUC 0.861) and reveals interesting strategies to build upon
item response theory models.
Sample-to-Sample Correspondence for Unsupervised Domain Adaptation
Authors: Debasmit Das, C. S. George Lee
Date: 2018-05-01 00:00:00
Tasks: Domain Adaptation, General Classification, image-classification, Image Classification, Sentiment Analysis, Sentiment Classification, Unsupervised Domain Adaptation

The assumption that training and testing samples are generated from the same
distribution does not always hold for real-world machine-learning applications.
The procedure of tackling this discrepancy between the training (source) and
testing (target) domains is known as domain adaptation. We propose an
unsupervised version of domain adaptation that considers the presence of only
unlabelled data in the target domain. Our approach centers on finding
correspondences between samples of each domain. The correspondences are
obtained by treating the source and target samples as graphs and using a convex
criterion to match them. The criteria used are first-order and second-order
similarities between the graphs as well as a class-based regularization. We
have also developed a computationally efficient routine for the convex
optimization, thus allowing the proposed method to be used widely. To verify
the effectiveness of the proposed method, computer simulations were conducted
on synthetic, image classification and sentiment classification datasets.
Results validated that the proposed local sample-to-sample matching method
out-performs traditional moment-matching methods and is competitive with
respect to current local domain-adaptation methods.