pwc-archive/papers-with-abstracts huggingface dataset:
On the Effect of Low-Frequency Terms on Neural-IR Models
Authors: Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury
Date: 2019-04-29 00:00:00
Tasks: GPU, Information Retrieval, Passage Retrieval, Retrieval, Word Embeddings

Low-frequency terms are a recurring challenge for information retrieval
models, especially neural IR frameworks struggle with adequately capturing
infrequently observed words. While these terms are often removed from neural
models - mainly as a concession to efficiency demands - they traditionally play
an important role in the performance of IR models. In this paper, we analyze
the effects of low-frequency terms on the performance and robustness of neural
IR models. We conduct controlled experiments on three recent neural IR models,
trained on a large-scale passage retrieval collection. We evaluate the neural
IR models with various vocabulary sizes for their respective word embeddings,
considering different levels of constraints on the available GPU memory. We
observe that despite the significant benefits of using larger vocabularies, the
performance gap between the vocabularies can be, to a great extent, mitigated
by extensive tuning of a related parameter: the number of documents to re-rank.
We further investigate the use of subword-token embedding models, and in
particular FastText, for neural IR models. Our experiments show that using
FastText brings slight improvements to the overall performance of the neural IR
models in comparison to models trained on the full vocabulary, while the
improvement becomes much more pronounced for queries containing low-frequency
terms.
DeLiO: Decoupled LiDAR Odometry
Authors: Queens Maria Thomas, Oliver Wasenmüller, Didier Stricker
Date: 2019-04-29 00:00:00
Tasks: Translation

Most LiDAR odometry algorithms estimate the transformation between two
consecutive frames by estimating the rotation and translation in an intervening
fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which
-- for the first time -- decouples the rotation estimation completely from the
translation estimation. In particular, the rotation is estimated by extracting
the surface normals from the input point clouds and tracking their
characteristic pattern on a unit sphere. Using this rotation the point clouds
are unrotated so that the underlying transformation is pure translation, which
can be easily estimated using a line cloud approach. An evaluation is performed
on the KITTI dataset and the results are compared against state-of-the-art
algorithms.
Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage
Authors: Taylan Şahin, Ramin Khalili, Mate Boban, Adam Wolisz
Date: 2019-04-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled
either by a centralized scheduler residing in the network (e.g., a base station
in case of cellular systems) or a distributed scheduler, where the resources
are autonomously selected by the vehicles. The former approach yields a
considerably higher resource utilization in case the network coverage is
uninterrupted. However, in case of intermittent or out-of-coverage, due to not
having input from centralized scheduler, vehicles need to revert to distributed
scheduling. Motivated by recent advances in reinforcement learning (RL), we
investigate whether a centralized learning scheduler can be taught to
efficiently pre-assign the resources to vehicles for out-of-coverage V2V
communication. Specifically, we use the actor-critic RL algorithm to train the
centralized scheduler to provide non-interfering resources to vehicles before
they enter the out-of-coverage area. Our initial results show that a RL-based
scheduler can achieve performance as good as or better than the state-of-art
distributed scheduler, often outperforming it. Furthermore, the learning
process completes within a reasonable time (ranging from a few hundred to a few
thousand epochs), thus making the RL-based scheduler a promising solution for
V2V communications with intermittent network coverage.
Weakly and Semi Supervised Detection in Medical Imaging via Deep Dual Branch Net
Authors: Ran Bakalo, Jacob Goldberger, Rami Ben-Ari
Date: 2019-04-29 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, Weakly-supervised Learning

This study presents a novel deep learning architecture for multi-class classification and localization of abnormalities in medical imaging illustrated through experiments on mammograms. The proposed network combines two learning branches. One branch is for region classification with a newly added normal-region class. Second branch is region detection branch for ranking regions relative to one another. Our method enables detection of abnormalities at full mammogram resolution for both weakly and semi-supervised settings. A novel objective function allows for the incorporation of local annotations into the model. We present the impact of our schemes on several performance measures for classification and localization, to evaluate the cost effectiveness of the lesion annotation effort. Our evaluation was primarily conducted over a large multi-center mammography dataset of $\sim$3,000 mammograms with various findings. The results for weakly supervised learning showed significant improvement compared to previous approaches. We show that the time consuming local annotations involved in supervised learning can be addressed by a weakly supervised method that can leverage a subset of locally annotated data. Weakly and semi-supervised methods coupled with detection can produce a cost effective and explainable model to be adopted by radiologists in the field.
Venue Analytics: A Simple Alternative to Citation-Based Metrics
Authors: Leonid Keselman
Date: 2019-04-29 00:00:00

We present a method for automatically organizing and evaluating the quality of different publishing venues in Computer Science. Since this method only requires paper publication data as its input, we can demonstrate our method on a large portion of the DBLP dataset, spanning 50 years, with millions of authors and thousands of publishing venues. By formulating venue authorship as a regression problem and targeting metrics of interest, we obtain venue scores for every conference and journal in our dataset. The obtained scores can also provide a per-year model of conference quality, showing how fields develop and change over time. Additionally, these venue scores can be used to evaluate individual academic authors and academic institutions. We show that using venue scores to evaluate both authors and institutions produces quantitative measures that are comparable to approaches using citations or peer assessment. In contrast to many other existing evaluation metrics, our use of large-scale, openly available data enables this approach to be repeatable and transparent. To help others build upon this work, all of our code and data is available at https://github.com/leonidk/venue_scores
Soft edit distance for differentiable comparison of symbolic sequences
Authors: Evgenii Ofitserov, Vasily Tsvetkov, Vadim Nazarov
Date: 2019-04-29 00:00:00
Tasks: Clustering

Edit distance, also known as Levenshtein distance, is an essential way to
compare two strings that proved to be particularly useful in the analysis of
genetic sequences and natural language processing. However, edit distance is a
discrete function that is known to be hard to optimize. This fact hampers the
use of this metric in Machine Learning. Even as simple algorithm as K-means
fails to cluster a set of sequences using edit distance if they are of variable
length and abundance. In this paper we propose a novel metric - soft edit
distance (SED), which is a smooth approximation of edit distance. It is
differentiable and therefore it is possible to optimize it with gradient
methods. Similar to original edit distance, SED as well as its derivatives can
be calculated with recurrent formulas at polynomial time. We prove usefulness
of the proposed metric on synthetic datasets and clustering of biological
sequences.
Semantic Matching of Documents from Heterogeneous Collections: A Simple and Transparent Method for Practical Applications
Authors: Mark-Christoph Müller
Date: 2019-04-29 00:00:00
Tasks: Binary Classification, General Classification

We present a very simple, unsupervised method for the pairwise matching of
documents from heterogeneous collections. We demonstrate our method with the
Concept-Project matching task, which is a binary classification task involving
pairs of documents from heterogeneous collections. Although our method only
employs standard resources without any domain- or task-specific modifications,
it clearly outperforms the more complex system of the original authors. In
addition, our method is transparent, because it provides explicit information
about how a similarity score was computed, and efficient, because it is based
on the aggregation of (pre-computable) word-level similarities.
ConvTimeNet: A Pre-trained Deep Convolutional Neural Network for Time Series Classification
Authors: Kathan Kashiparekh, Jyoti Narwariya, Pankaj Malhotra, Lovekesh Vig, Gautam Shroff
Date: 2019-04-29 00:00:00
Tasks: Computational Efficiency, General Classification, Time Series, Time Series Analysis, Time Series Classification

Training deep neural networks often requires careful hyper-parameter tuning
and significant computational resources. In this paper, we propose ConvTimeNet
(CTN): an off-the-shelf deep convolutional neural network (CNN) trained on
diverse univariate time series classification (TSC) source tasks. Once trained,
CTN can be easily adapted to new TSC target tasks via a small amount of
fine-tuning using labeled instances from the target tasks. We note that the
length of convolutional filters is a key aspect when building a pre-trained
model that can generalize to time series of different lengths across datasets.
To achieve this, we incorporate filters of multiple lengths in all
convolutional layers of CTN to capture temporal features at multiple time
scales. We consider all 65 datasets with time series of lengths up to 512
points from the UCR TSC Benchmark for training and testing transferability of
CTN: We train CTN on a randomly chosen subset of 24 datasets using a multi-head
approach with a different softmax layer for each training dataset, and study
generalizability and transferability of the learned filters on the remaining 41
TSC datasets. We observe significant gains in classification accuracy as well
as computational efficiency when using pre-trained CTN as a starting point for
subsequent task-specific fine-tuning compared to existing state-of-the-art TSC
approaches. We also provide qualitative insights into the working of CTN by: i)
analyzing the activations and filters of first convolution layer suggesting the
filters in CTN are generically useful, ii) analyzing the impact of the design
decision to incorporate multiple length decisions, and iii) finding regions of
time series that affect the final classification decision via occlusion
sensitivity analysis.
Adversarial Invariant Feature Learning with Accuracy Constraint for Domain Generalization
Authors: Kei Akuzawa, Yusuke Iwasawa, Yutaka Matsuo
Date: 2019-04-29 00:00:00
Tasks: Domain Generalization

Learning domain-invariant representation is a dominant approach for domain generalization (DG), where we need to build a classifier that is robust toward domain shifts. However, previous domain-invariance-based methods overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and domain invariance. Because the primary purpose of DG is to classify unseen domains rather than the invariance itself, the improvement of the invariance can negatively affect DG performance under this trade-off. To overcome the problem, this study first expands the analysis of the trade-off by Xie et. al., and provides the notion of accuracy-constrained domain invariance, which means the maximum domain invariance within a range that does not interfere with accuracy. We then propose a novel method adversarial feature learning with accuracy constraint (AFLAC), which explicitly leads to that invariance on adversarial training. Empirical validations show that the performance of AFLAC is superior to that of domain-invariance-based methods on both synthetic and three real-world datasets, supporting the importance of considering the dependency and the efficacy of the proposed method.
Logician: A Unified End-to-End Neural Approach for Open-Domain Information Extraction
Authors: Mingming Sun, Xu Li, Xin Wang, Miao Fan, Yue Feng, Ping Li
Date: 2019-04-29 00:00:00
Tasks: Attribute, global-optimization, Open Information Extraction, Relation, Relation Extraction, Sentence

In this paper, we consider the problem of open information extraction (OIE)
for extracting entity and relation level intermediate structures from sentences
in open-domain. We focus on four types of valuable intermediate structures
(Relation, Attribute, Description, and Concept), and propose a unified
knowledge expression form, SAOKE, to express them. We publicly release a data
set which contains more than forty thousand sentences and the corresponding
facts in the SAOKE format labeled by crowd-sourcing. To our knowledge, this is
the largest publicly available human labeled data set for open information
extraction tasks. Using this labeled SAOKE data set, we train an end-to-end
neural model using the sequenceto-sequence paradigm, called Logician, to
transform sentences into facts. For each sentence, different to existing
algorithms which generally focus on extracting each single fact without
concerning other possible facts, Logician performs a global optimization over
all possible involved facts, in which facts not only compete with each other to
attract the attention of words, but also cooperate to share words. An
experimental study on various types of open domain relation extraction tasks
reveals the consistent superiority of Logician to other states-of-the-art
algorithms. The experiments verify the reasonableness of SAOKE format, the
valuableness of SAOKE data set, the effectiveness of the proposed Logician
model, and the feasibility of the methodology to apply end-to-end learning
paradigm on supervised data sets for the challenging tasks of open information
extraction.
Casting Geometric Constraints in Semantic Segmentation as Semi-Supervised Learning
Authors: Sinisa Stekovic, Friedrich Fraundorfer, Vincent Lepetit
Date: 2019-04-29 00:00:00
Tasks: Semantic Segmentation

We propose a simple yet effective method to learn to segment new indoor scenes from video frames: State-of-the-art methods trained on one dataset, even as large as the SUNRGB-D dataset, can perform poorly when applied to images that are not part of the dataset, because of the dataset bias, a common phenomenon in computer vision. To make semantic segmentation more useful in practice, one can exploit geometric constraints. Our main contribution is to show that these constraints can be cast conveniently as semi-supervised terms, which enforce the fact that the same class should be predicted for the projections of the same 3D location in different images. This is interesting as we can exploit general existing techniques developed for semi-supervised learning to efficiently incorporate the constraints. We show that this approach can efficiently and accurately learn to segment target sequences of ScanNet and our own target sequences using only annotations from SUNRGB-D, and geometric relations between the video frames of target sequences.
A Complete Classification of the Complexity and Rewritability of Ontology-Mediated Queries based on the Description Logic EL
Authors: Carsten Lutz, Leif Sabellek
Date: 2019-04-29 00:00:00
Tasks: General Classification

We provide an ultimately fine-grained analysis of the data complexity and
rewritability of ontology-mediated queries (OMQs) based on an EL ontology and a
conjunctive query (CQ). Our main results are that every such OMQ is in AC0,
NL-complete, or PTime-complete and that containment in NL coincides with
rewritability into linear Datalog (whereas containment in AC0 coincides with
rewritability into first-order logic). We establish natural characterizations
of the three cases in terms of bounded depth and (un)bounded pathwidth, and
show that every of the associated meta problems such as deciding wether a given
OMQ is rewritable into linear Datalog is ExpTime-complete. We also give a way
to construct linear Datalog rewritings when they exist and prove that there is
no constant Datalog rewritings.
Open Research Knowledge Graph: Next Generation Infrastructure for Semantic Scholarly Knowledge
Authors: Mohamad Yaser Jaradeh, Allard Oelen, Kheir Eddine Farfar, Manuel Prinz, Jennifer D'Souza, Gábor Kismihók, Markus Stocker, Sören Auer
Date: 2019-01-30 00:00:00
Tasks: Form, Knowledge Graphs

Despite improved digital access to scholarly knowledge in recent decades, scholarly communication remains exclusively document-based. In this form, scholarly knowledge is hard to process automatically. In this paper, we present the first steps towards a knowledge graph based infrastructure that acquires scholarly knowledge in machine actionable form thus enabling new possibilities for scholarly knowledge curation, publication and processing. The primary contribution is to present, evaluate and discuss multi-modal scholarly knowledge acquisition, combining crowdsourced and automated techniques. We present the results of the first user evaluation of the infrastructure with the participants of a recent international conference. Results suggest that users were intrigued by the novelty of the proposed infrastructure and by the possibilities for innovative scholarly knowledge processing it could enable.
Learning Meta Model for Zero- and Few-shot Face Anti-spoofing
Authors: Yunxiao Qin, Chenxu Zhao, Xiangyu Zhu, Zezheng Wang, Zitong Yu, Tianyu Fu, Feng Zhou, Jingping Shi, Zhen Lei
Date: 2019-04-29 00:00:00
Tasks: Face Anti-Spoofing, Face Recognition, Few-Shot Learning, Meta-Learning

Face anti-spoofing is crucial to the security of face recognition systems. Most previous methods formulate face anti-spoofing as a supervised learning problem to detect various predefined presentation attacks, which need large scale training data to cover as many attacks as possible. However, the trained model is easy to overfit several common attacks and is still vulnerable to unseen attacks. To overcome this challenge, the detector should: 1) learn discriminative features that can generalize to unseen spoofing types from predefined presentation attacks; 2) quickly adapt to new spoofing types by learning from both the predefined attacks and a few examples of the new spoofing types. Therefore, we define face anti-spoofing as a zero- and few-shot learning problem. In this paper, we propose a novel Adaptive Inner-update Meta Face Anti-Spoofing (AIM-FAS) method to tackle this problem through meta-learning. Specifically, AIM-FAS trains a meta-learner focusing on the task of detecting unseen spoofing types by learning from predefined living and spoofing faces and a few examples of new attacks. To assess the proposed approach, we propose several benchmarks for zero- and few-shot FAS. Experiments show its superior performances on the presented benchmarks to existing methods in existing zero-shot FAS protocols.
Teaching AI, Ethics, Law and Policy
Authors: Asher Wilk
Date: 2019-04-29 00:00:00
Tasks: Ethics, Self-Driving Cars

The cyberspace and development of intelligent systems using Artificial Intelligence (AI) creates new challenges to computer professionals, data scientists, regulators and policy makers. For example, self-driving cars raise new technical, ethical, legal and public policy issues. This paper proposes a course named Computers, Ethics, Law, and Public Policy, and suggests a curriculum for such a course. This paper presents ethical, legal, and public policy issues relevant to building and using intelligent systems.
Asymmetric Impurity Functions, Class Weighting, and Optimal Splits for Binary Classification Trees
Authors: David Zimmermann
Date: 2019-04-29 00:00:00
Tasks: Binary Classification, General Classification

We investigate how asymmetrizing an impurity function affects the choice of
optimal node splits when growing a decision tree for binary classification. In
particular, we relax the usual axioms of an impurity function and show how
skewing an impurity function biases the optimal splits to isolate points of a
particular class when splitting a node. We give a rigorous definition of this
notion, then give a necessary and sufficient condition for such a bias to hold.
We also show that the technique of class weighting is equivalent to applying a
specific transformation to the impurity function, and tie all these notions
together for a class of impurity functions that includes the entropy and Gini
impurity. We also briefly discuss cost-insensitive impurity functions and give
a characterization of such functions.
Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem
Authors: Junyu Cao, Wei Sun
Date: 2019-04-29 00:00:00
Tasks: Product Recommendation

Motivated by the phenomenon that companies introduce new products to keep
abreast with customers' rapidly changing tastes, we consider a novel online
learning setting where a profit-maximizing seller needs to learn customers'
preferences through offering recommendations, which may contain existing
products and new products that are launched in the middle of a selling period.
We propose a sequential multinomial logit (SMNL) model to characterize
customers' behavior when product recommendations are presented in tiers. For
the offline version with known customers' preferences, we propose a
polynomial-time algorithm and characterize the properties of the optimal tiered
product recommendation. For the online problem, we propose a learning algorithm
and quantify its regret bound. Moreover, we extend the setting to incorporate a
constraint which ensures every new product is learned to a given accuracy. Our
results demonstrate the tier structure can be used to mitigate the risks
associated with learning new products.
Making the Last Iterate of SGD Information Theoretically Optimal
Authors: Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Date: 2019-04-29 00:00:00

Stochastic gradient descent (SGD) is one of the most widely used algorithms for large scale optimization problems. While classical theoretical analysis of SGD for convex problems studies (suffix) \emph{averages} of iterates and obtains information theoretically optimal bounds on suboptimality, the \emph{last point} of SGD is, by far, the most preferred choice in practice. The best known results for last point of SGD \cite{shamir2013stochastic} however, are suboptimal compared to information theoretic lower bounds by a $\log T$ factor, where $T$ is the number of iterations. \cite{harvey2018tight} shows that in fact, this additional $\log T$ factor is tight for standard step size sequences of $\OTheta{\frac{1}{\sqrt{t}}}$ and $\OTheta{\frac{1}{t}}$ for non-strongly convex and strongly convex settings, respectively. Similarly, even for subgradient descent (GD) when applied to non-smooth, convex functions, the best known step-size sequences still lead to $O(\log T)$-suboptimal convergence rates (on the final iterate). The main contribution of this work is to design new step size sequences that enjoy information theoretically optimal bounds on the suboptimality of \emph{last point} of SGD as well as GD. We achieve this by designing a modification scheme, that converts one sequence of step sizes to another so that the last point of SGD/GD with modified sequence has the same suboptimality guarantees as the average of SGD/GD with original sequence. We also show that our result holds with high-probability. We validate our results through simulations which demonstrate that the new step size sequence indeed improves the final iterate significantly compared to the standard step size sequences.