pwc-archive/papers-with-abstracts huggingface dataset:
Sequential Learning over Implicit Feedback for Robust Large-Scale Recommender Systems
Authors: Alexandra Burashnikova, Yury Maximov, Massih-Reza Amini
Date: 2019-02-21 00:00:00
Tasks: Recommendation Systems

In this paper, we propose a robust sequential learning strategy for training
large-scale Recommender Systems (RS) over implicit feedback mainly in the form
of clicks. Our approach relies on the minimization of a pairwise ranking loss
over blocks of consecutive items constituted by a sequence of non-clicked items
followed by a clicked one for each user. Parameter updates are discarded if for
a given user the number of sequential blocks is below or above some given
thresholds estimated over the distribution of the number of blocks in the
training set. This is to prevent from an abnormal number of clicks over some
targeted items, mainly due to bots; or very few user interactions. Both
scenarios affect the decision of RS and imply a shift over the distribution of
items that are shown to the users. We provide a theoretical analysis showing
that in the case where the ranking loss is convex, the deviation between the
loss with respect to the sequence of weights found by the proposed algorithm
and its minimum is bounded. Furthermore, experimental results on five
large-scale collections demonstrate the efficiency of the proposed algorithm
with respect to the state-of-the-art approaches, both regarding different
ranking measures and computation time.
Jointly Sparse Convolutional Neural Networks in Dual Spatial-Winograd Domains
Authors: Yoojin Choi, Mostafa El-Khamy, Jungwon Lee
Date: 2019-02-21 00:00:00

We consider the optimization of deep convolutional neural networks (CNNs)
such that they provide good performance while having reduced complexity if
deployed on either conventional systems with spatial-domain convolution or
lower-complexity systems designed for Winograd convolution. The proposed
framework produces one compressed model whose convolutional filters can be made
sparse either in the spatial domain or in the Winograd domain. Hence, the
compressed model can be deployed universally on any platform, without need for
re-training on the deployed platform. To get a better compression ratio, the
sparse model is compressed in the spatial domain that has a fewer number of
parameters. From our experiments, we obtain $24.2\times$ and $47.7\times$
compressed models for ResNet-18 and AlexNet trained on the ImageNet dataset,
while their computational cost is also reduced by $4.5\times$ and $5.1\times$,
respectively.
Regression-based Inverter Control for Decentralized Optimal Power Flow and Voltage Regulation
Authors: Oscar Sondermeijer, Roel Dobbe, Daniel Arnold, Claire Tomlin, Tamás Keviczky
Date: 2019-02-20 00:00:00
Tasks: regression

Electronic power inverters are capable of quickly delivering reactive power
to maintain customer voltages within operating tolerances and to reduce system
losses in distribution grids. This paper proposes a systematic and data-driven
approach to determine reactive power inverter output as a function of local
measurements in a manner that obtains near optimal results. First, we use a
network model and historic load and generation data and do optimal power flow
to compute globally optimal reactive power injections for all controllable
inverters in the network. Subsequently, we use regression to find a function
for each inverter that maps its local historical data to an approximation of
its optimal reactive power injection. The resulting functions then serve as
decentralized controllers in the participating inverters to predict the optimal
injection based on a new local measurements. The method achieves near-optimal
results when performing voltage- and capacity-constrained loss minimization and
voltage flattening, and allows for an efficient volt-VAR optimization (VVO)
scheme in which legacy control equipment collaborates with existing inverters
to facilitate safe operation of distribution networks with higher levels of
distributed generation.
Fast and Exact Nearest Neighbor Search in Hamming Space on Full-Text Search Engines
Authors: Cun Mu, Jun Zhao, Guang Yang, Binwei Yang, Zheng Yan
Date: 2019-02-20 00:00:00
Tasks: Information Retrieval, Representation Learning, Retrieval

A growing interest has been witnessed recently from both academia and industry in building nearest neighbor search (NNS) solutions on top of full-text search engines. Compared with other NNS systems, such solutions are capable of effectively reducing main memory consumption, coherently supporting multi-model search and being immediately ready for production deployment. In this paper, we continue the journey to explore specifically how to empower full-text search engines with fast and exact NNS in Hamming space (i.e., the set of binary codes). By revisiting three techniques (bit operation, subs-code filtering and data preprocessing with permutation) in information retrieval literature, we develop a novel engineering solution for full-text search engines to efficiently accomplish this special but important NNS task. In the experiment, we show that our proposed approach enables full-text search engines to achieve significant speed-ups over its state-of-the-art term match approach for NNS within binary codes.
Graph Adversarial Training: Dynamically Regularizing Based on Graph Structure
Authors: Fuli Feng, Xiangnan He, Jie Tang, Tat-Seng Chua
Date: 2019-02-20 00:00:00
Tasks: Articles, General Classification, Graph Neural Network, Node Classification

Recent efforts show that neural networks are vulnerable to small but intentional perturbations on input features in visual classification tasks. Due to the additional consideration of connections between examples (\eg articles with citation link tend to be in the same class), graph neural networks could be more sensitive to the perturbations, since the perturbations from connected examples exacerbate the impact on a target example. Adversarial Training (AT), a dynamic regularization technique, can resist the worst-case perturbations on input features and is a promising choice to improve model robustness and generalization. However, existing AT methods focus on standard classification, being less effective when training models on graph since it does not model the impact from connected examples. In this work, we explore adversarial training on graph, aiming to improve the robustness and generalization of models learned on graph. We propose Graph Adversarial Training (GraphAT), which takes the impact from connected examples into account when learning to construct and resist perturbations. We give a general formulation of GraphAT, which can be seen as a dynamic regularization scheme based on the graph structure. To demonstrate the utility of GraphAT, we employ it on a state-of-the-art graph neural network model --- Graph Convolutional Network (GCN). We conduct experiments on two citation graphs (Citeseer and Cora) and a knowledge graph (NELL), verifying the effectiveness of GraphAT which outperforms normal training on GCN by 4.51% in node classification accuracy. Codes are available via: https://github.com/fulifeng/GraphAT.
Necessary and Sufficient Conditions for Existence and Uniqueness of Recursive Utilities
Authors: Jaroslav Borovicka, John Stachurski
Date: 2017-10-17 00:00:00

We study existence, uniqueness and computability of solutions for a class of
discrete time recursive utilities models. By combining two streams of the
recent literature on recursive preferences---one that analyzes principal
eigenvalues of valuation operators and another that exploits the theory of
monotone concave operators---we obtain conditions that are both necessary and
sufficient for existence and uniqueness of solutions. We also show that the
natural iterative algorithm is convergent if and only if a solution exists.
Consumption processes are allowed to be nonstationary.
Condition-Invariant Multi-View Place Recognition
Authors: Jose M. Facil, Daniel Olid, Luis Montesano, Javier Civera
Date: 2019-02-25 00:00:00
Tasks: Visual Place Recognition

Visual place recognition is particularly challenging when places suffer
changes in its appearance. Such changes are indeed common, e.g., due to
weather, night/day or seasons. In this paper we leverage on recent research
using deep networks, and explore how they can be improved by exploiting the
temporal sequence information. Specifically, we propose 3 different
alternatives (Descriptor Grouping, Fusion and Recurrent Descriptors) for deep
networks to use several frames of a sequence. We show that our approaches
produce more compact and best performing descriptors than single- and
multi-view baselines in the literature in two public databases.
Lost in Machine Translation: A Method to Reduce Meaning Loss
Authors: Reuben Cohn-Gordon, Noah Goodman
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Sentence, Translation

A desideratum of high-quality translation systems is that they preserve
meaning, in the sense that two sentences with different meanings should not
translate to one and the same sentence in another language. However,
state-of-the-art systems often fail in this regard, particularly in cases where
the source and target languages partition the "meaning space" in different
ways. For instance, "I cut my finger." and "I cut my finger off." describe
different states of the world but are translated to French (by both Fairseq and
Google Translate) as "Je me suis coupe le doigt.", which is ambiguous as to
whether the finger is detached. More generally, translation systems are
typically many-to-one (non-injective) functions from source to target language,
which in many cases results in important distinctions in meaning being lost in
translation. Building on Bayesian models of informative utterance production,
we present a method to define a less ambiguous translation system in terms of
an underlying pre-trained neural sequence-to-sequence model. This method
increases injectivity, resulting in greater preservation of meaning as measured
by improvement in cycle-consistency, without impeding translation quality
(measured by BLEU score).
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
Authors: Paul Voigtlaender, Yuning Chai, Florian Schroff, Hartwig Adam, Bastian Leibe, Liang-Chieh Chen
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object, Segmentation, Semantic Segmentation, Semi-Supervised Video Object Segmentation, Video Object Segmentation, Video Semantic Segmentation

Many of the recent successful methods for video object segmentation (VOS) are
overly complicated, heavily rely on fine-tuning on the first frame, and/or are
slow, and are hence of limited practical use. In this work, we propose FEELVOS
as a simple and fast method which does not rely on fine-tuning. In order to
segment a video, for each frame FEELVOS uses a semantic pixel-wise embedding
together with a global and a local matching mechanism to transfer information
from the first frame and from the previous frame of the video to the current
frame. In contrast to previous work, our embedding is only used as an internal
guidance of a convolutional network. Our novel dynamic segmentation head allows
us to train the network, including the embedding, end-to-end for the multiple
object segmentation task with a cross entropy loss. We achieve a new state of
the art in video object segmentation without fine-tuning with a J&F measure of
71.5% on the DAVIS 2017 validation set. We make our code and models available
at https://github.com/tensorflow/models/tree/master/research/feelvos.
Improving Robustness of Machine Translation with Synthetic Noise
Authors: Vaibhav  Vaibhav, Sumeet Singh, Craig Stewart, Graham Neubig
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Translation

Modern Machine Translation (MT) systems perform consistently well on clean,
in-domain text. However most human generated text, particularly in the realm of
social media, is full of typos, slang, dialect, idiolect and other noise which
can have a disastrous impact on the accuracy of output translation. In this
paper we leverage the Machine Translation of Noisy Text (MTNT) dataset to
enhance the robustness of MT systems by emulating naturally occurring noise in
otherwise clean data. Synthesizing noise in this manner we are ultimately able
to make a vanilla MT system resilient to naturally occurring noise and
partially mitigate loss in accuracy resulting therefrom.
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Authors: Drew A. Hudson, Christopher D. Manning
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Visual Question Answering (VQA), Visual Reasoning

We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate question biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.
Forecasting intracranial hypertension using multi-scale waveform metrics
Authors: Matthias Hüser, Adrian Kündig, Walter Karlen, Valeria De Luca, Martin Jaggi
Date: 2019-02-25 00:00:00
Tasks: Time Series Analysis

Objective: Acute intracranial hypertension is an important risk factor of secondary brain damage after traumatic brain injury. Hypertensive episodes are often diagnosed reactively, leading to late detection and lost time for intervention planning. A pro-active approach that predicts critical events several hours ahead of time could assist in directing attention to patients at risk. Approach: We developed a prediction framework that forecasts onsets of acute intracranial hypertension in the next 8 hours. It jointly uses cerebral auto-regulation indices, spectral energies and morphological pulse metrics to describe the neurological state of the patient. One-minute base windows were compressed by computing signal metrics, and then stored in a multi-scale history, from which physiological features were derived. Main results: Our model predicted events up to 8 hours in advance with alarm recall rates of 90% at a precision of 30.3% in the MIMIC-III waveform database, improving upon two baselines from the literature. We found that features derived from high-frequency waveforms substantially improved the prediction performance over simple statistical summaries of low-frequency time series, and each of the three feature classes contributed to the performance gain. The inclusion of long-term history up to 8 hours was especially important. Significance: Our results highlight the importance of information contained in high-frequency waveforms in the neurological intensive care unit. They could motivate future studies on pre-hypertensive patterns and the design of new alarm algorithms for critical events in the injured brain.
Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing
Authors: Tal Schuster, Ori Ram, Regina Barzilay, Amir Globerson
Date: 2019-02-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Cross-lingual zero-shot dependency parsing, Dependency Parsing, Few-Shot Learning, Word Embeddings

We introduce a novel method for multilingual transfer that utilizes deep
contextual embeddings, pretrained in an unsupervised fashion. While contextual
embeddings have been shown to yield richer representations of meaning compared
to their static counterparts, aligning them poses a challenge due to their
dynamic nature. To this end, we construct context-independent variants of the
original monolingual spaces and utilize their mapping to derive an alignment
for the context-dependent spaces. This mapping readily supports processing of a
target language, improving transfer by context-aware embeddings. Our
experimental results demonstrate the effectiveness of this approach for
zero-shot and few-shot learning of dependency parsing. Specifically, our method
consistently outperforms the previous state-of-the-art on 6 tested languages,
yielding an improvement of 6.8 LAS points on average.
MUREL: Multimodal Relational Reasoning for Visual Question Answering
Authors: Remi Cadene, Hedi Ben-Younes, Matthieu Cord, Nicolas Thome
Date: 2019-02-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Relational Reasoning, Visual Question Answering, Visual Question Answering (VQA)

Multimodal attentional networks are currently state-of-the-art models for
Visual Question Answering (VQA) tasks involving real images. Although attention
allows to focus on the visual content relevant to the question, this simple
mechanism is arguably insufficient to model complex reasoning features required
for VQA or other high-level tasks.
  In this paper, we propose MuRel, a multimodal relational network which is
learned end-to-end to reason over real images. Our first contribution is the
introduction of the MuRel cell, an atomic reasoning primitive representing
interactions between question and image regions by a rich vectorial
representation, and modeling region relations with pairwise combinations.
Secondly, we incorporate the cell into a full MuRel network, which
progressively refines visual and question interactions, and can be leveraged to
define visualization schemes finer than mere attention maps.
  We validate the relevance of our approach with various ablation studies, and
show its superiority to attention-based methods on three datasets: VQA 2.0,
VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms
state-of-the-art results in this challenging context.
  Our code is available: https://github.com/Cadene/murel.bootstrap.pytorch
Logistic principal component analysis via non-convex singular value thresholding
Authors: Yipeng Song, Johan A. Westerhuis, Age K. Smilde
Date: 2019-02-25 00:00:00
Tasks: Model Selection

Multivariate binary data is becoming abundant in current biological research.
Logistic principal component analysis (PCA) is one of the commonly used tools
to explore the relationships inside a multivariate binary data set by
exploiting the underlying low rank structure. We re-expressed the logistic PCA
model based on the latent variable interpretation of the generalized linear
model on binary data. The multivariate binary data set is assumed to be the
sign observation of an unobserved quantitative data set, on which a low rank
structure is assumed to exist. However, the standard logistic PCA model (using
exact low rank constraint) is prone to overfitting, which could lead to
divergence of some estimated parameters towards infinity. We propose to fit a
logistic PCA model through non-convex singular value thresholding to alleviate
the overfitting issue. An efficient Majorization-Minimization algorithm is
implemented to fit the model and a missing value based cross validation (CV)
procedure is introduced for the model selection. Our experiments on realistic
simulations of imbalanced binary data and low signal to noise ratio show that
the CV error based model selection procedure is successful in selecting the
proposed model. Furthermore, the selected model demonstrates superior
performance in recovering the underlying low rank structure compared to models
with convex nuclear norm penalty and exact low rank constraint. A binary copy
number aberration data set is used to illustrate the proposed methodology in
practice.
MedMentions: A Large Biomedical Corpus Annotated with UMLS Concepts
Authors: Sunil Mohan, Donghui Li
Date: 2019-02-25 00:00:00
Proceeding: AKBC 2019
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Retrieval

This paper presents the formal release of MedMentions, a new manually
annotated resource for the recognition of biomedical concepts. What
distinguishes MedMentions from other annotated biomedical corpora is its size
(over 4,000 abstracts and over 350,000 linked mentions), as well as the size of
the concept ontology (over 3 million concepts from UMLS 2017) and its broad
coverage of biomedical disciplines. In addition to the full corpus, a
sub-corpus of MedMentions is also presented, comprising annotations for a
subset of UMLS 2017 targeted towards document retrieval. To encourage research
in Biomedical Named Entity Recognition and Linking, data splits for training
and testing are included in the release, and a baseline model and its metrics
for entity linking are also described.
Matrix denoising for weighted loss functions and heterogeneous signals
Authors: William Leeb
Date: 2019-02-25 00:00:00
Tasks: Denoising

We consider the problem of estimating a low-rank matrix from a noisy observed matrix. Previous work has shown that the optimal method depends crucially on the choice of loss function. In this paper, we use a family of weighted loss functions, which arise naturally for problems such as submatrix denoising, denoising with heteroscedastic noise, and denoising with missing data. However, weighted loss functions are challenging to analyze because they are not orthogonally-invariant. We derive optimal spectral denoisers for these weighted loss functions. By combining different weights, we then use these optimal denoisers to construct a new denoiser that exploits heterogeneity in the signal matrix to boost estimation with unweighted loss.
Embedded Agency
Authors: Abram Demski, Scott Garrabrant
Date: 2019-02-25 00:00:00

Traditional models of rational action treat the agent as though it is cleanly separated from its environment, and can act on that environment from the outside. Such agents have a known functional relationship with their environment, can model their environment in every detail, and do not need to reason about themselves or their internal parts. We provide an informal survey of obstacles to formalizing good reasoning for agents embedded in their environment. Such agents must optimize an environment that is not of type "function"; they must rely on models that fit within the modeled environment; and they must reason about themselves as just another physical system, made of parts that can be modified and that can work at cross purposes.
Adaptive Estimation for Approximate k-Nearest-Neighbor Computations
Authors: Daniel LeJeune, Richard G. Baraniuk, Reinhard Heckel
Date: 2019-02-25 00:00:00

Algorithms often carry out equally many computations for "easy" and "hard"
problem instances. In particular, algorithms for finding nearest neighbors
typically have the same running time regardless of the particular problem
instance. In this paper, we consider the approximate k-nearest-neighbor
problem, which is the problem of finding a subset of O(k) points in a given set
of points that contains the set of k nearest neighbors of a given query point.
We propose an algorithm based on adaptively estimating the distances, and show
that it is essentially optimal out of algorithms that are only allowed to
adaptively estimate distances. We then demonstrate both theoretically and
experimentally that the algorithm can achieve significant speedups relative to
the naive method.