pwc-archive/papers-with-abstracts huggingface dataset:
Adversarial Complementary Learning for Weakly Supervised Object Localization
Authors: Xiaolin Zhang, Yunchao Wei, Jiashi Feng, Yi Yang, Thomas Huang
Date: 2018-04-19 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification, Object, Object Localization, Weakly-Supervised Object Localization

In this work, we propose Adversarial Complementary Learning (ACoL) to
automatically localize integral objects of semantic interest with weak
supervision. We first mathematically prove that class localization maps can be
obtained by directly selecting the class-specific feature maps of the last
convolutional layer, which paves a simple way to identify object regions. We
then present a simple network architecture including two parallel-classifiers
for object localization. Specifically, we leverage one classification branch to
dynamically localize some discriminative object regions during the forward
pass. Although it is usually responsive to sparse parts of the target objects,
this classifier can drive the counterpart classifier to discover new and
complementary object regions by erasing its discovered regions from the feature
maps. With such an adversarial learning, the two parallel-classifiers are
forced to leverage complementary object regions for classification and can
finally generate integral object localization together. The merits of ACoL are
mainly two-fold: 1) it can be trained in an end-to-end manner; 2) dynamically
erasing enables the counterpart classifier to discover complementary object
regions more effectively. We demonstrate the superiority of our ACoL approach
in a variety of experiments. In particular, the Top-1 localization error rate
on the ILSVRC dataset is 45.14%, which is the new state-of-the-art.
Distributed Simulation and Distributed Inference
Authors: Jayadev Acharya, Clément L. Canonne, Himanshu Tyagi
Date: 2018-04-19 00:00:00

Independent samples from an unknown probability distribution $\bf p$ on a domain of size $k$ are distributed across $n$ players, with each player holding one sample. Each player can communicate $\ell$ bits to a central referee in a simultaneous message passing model of communication to help the referee infer a property of the unknown $\bf p$. What is the least number of players for inference required in the communication-starved setting of $\ell<\log k$? We begin by exploring a general "simulate-and-infer" strategy for such inference problems where the center simulates the desired number of samples from the unknown distribution and applies standard inference algorithms for the collocated setting. Our first result shows that for $\ell<\log k$ perfect simulation of even a single sample is not possible. Nonetheless, we present a Las Vegas algorithm that simulates a single sample from the unknown distribution using $O(k/2^\ell)$ samples in expectation. As an immediate corollary, we get that simulate-and-infer attains the optimal sample complexity of $\Theta(k^2/2^\ell\epsilon^2)$ for learning the unknown distribution to total variation distance $\epsilon$. For the prototypical testing problem of identity testing, simulate-and-infer works with $O(k^{3/2}/2^\ell\epsilon^2)$ samples, a requirement that seems to be inherent for all communication protocols not using any additional resources. Interestingly, we can break this barrier using public coins. Specifically, we exhibit a public-coin communication protocol that performs identity testing using $O(k/\sqrt{2^\ell}\epsilon^2)$ samples. Furthermore, we show that this is optimal up to constant factors. Our theoretically sample-optimal protocol is easy to implement in practice. Our proof of lower bound entails showing a contraction in $\chi^2$ distance of product distributions due to communication constraints and may be of independent interest.
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
Authors: Dayvid V. R. Oliveira, George D. C. Cavalcanti, Thyago N. Porpino, Rafael M. O. Cruz, Robert Sabourin
Date: 2018-04-18 00:00:00

Dynamic Ensemble Selection (DES) techniques aim to select locally competent
classifiers for the classification of each new test sample. Most DES techniques
estimate the competence of classifiers using a given criterion over the region
of competence of the test sample (its the nearest neighbors in the validation
set). The K-Nearest Oracles Eliminate (KNORA-E) DES selects all classifiers
that correctly classify all samples in the region of competence of the test
sample, if such classifier exists, otherwise, it removes from the region of
competence the sample that is furthest from the test sample, and the process
repeats. When the region of competence has samples of different classes,
KNORA-E can reduce the region of competence in such a way that only samples of
a single class remain in the region of competence, leading to the selection of
locally incompetent classifiers that classify all samples in the region of
competence as being from the same class. In this paper, we propose two DES
techniques: K-Nearest Oracles Borderline (KNORA-B) and K-Nearest Oracles
Borderline Imbalanced (KNORA-BI). KNORA-B is a DES technique based on KNORA-E
that reduces the region of competence but maintains at least one sample from
each class that is in the original region of competence. KNORA-BI is a
variation of KNORA-B for imbalance datasets that reduces the region of
competence but maintains at least one minority class sample if there is any in
the original region of competence. Experiments are conducted comparing the
proposed techniques with 19 DES techniques from the literature using 40
datasets. The results show that the proposed techniques achieved interesting
results, with KNORA-BI outperforming state-of-art techniques.
Towards Neural Phrase-based Machine Translation
Authors: Po-Sen Huang, Chong Wang, Sitao Huang, Dengyong Zhou, Li Deng
Date: 2017-06-17 00:00:00
Proceeding: ICLR 2018 1
Tasks: Machine Translation, NMT, Translation

In this paper, we present Neural Phrase-based Machine Translation (NPMT). Our
method explicitly models the phrase structures in output sequences using
Sleep-WAke Networks (SWAN), a recently proposed segmentation-based sequence
modeling method. To mitigate the monotonic alignment requirement of SWAN, we
introduce a new layer to perform (soft) local reordering of input sequences.
Different from existing neural machine translation (NMT) approaches, NPMT does
not use attention-based decoding mechanisms. Instead, it directly outputs
phrases in a sequential order and can decode in linear time. Our experiments
show that NPMT achieves superior performances on IWSLT 2014
German-English/English-German and IWSLT 2015 English-Vietnamese machine
translation tasks compared with strong NMT baselines. We also observe that our
method produces meaningful phrases in output languages.
One-Shot Learning using Mixture of Variational Autoencoders: a Generalization Learning approach
Authors: Decebal Constantin Mocanu, Elena Mocanu
Date: 2018-04-18 00:00:00
Tasks: Classification, General Classification, One-Shot Learning

Deep learning, even if it is very successful nowadays, traditionally needs
very large amounts of labeled data to perform excellent on the classification
task. In an attempt to solve this problem, the one-shot learning paradigm,
which makes use of just one labeled sample per class and prior knowledge,
becomes increasingly important. In this paper, we propose a new one-shot
learning method, dubbed MoVAE (Mixture of Variational AutoEncoders), to perform
classification. Complementary to prior studies, MoVAE represents a shift of
paradigm in comparison with the usual one-shot learning methods, as it does not
use any prior knowledge. Instead, it starts from zero knowledge and one labeled
sample per class. Afterward, by using unlabeled data and the generalization
learning concept (in a way, more as humans do), it is capable to gradually
improve by itself its performance. Even more, if there are no unlabeled data
available MoVAE can still perform well in one-shot learning classification. We
demonstrate empirically the efficiency of our proposed approach on three
datasets, i.e. the handwritten digits (MNIST), fashion products
(Fashion-MNIST), and handwritten characters (Omniglot), showing that MoVAE
outperforms state-of-the-art one-shot learning algorithms.
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Authors: Guokun Lai, Wei-Cheng Chang, Yiming Yang, Hanxiao Liu
Date: 2017-03-21 00:00:00
Tasks: Multivariate Time Series Forecasting, Time Series, Time Series Analysis, Time Series Forecasting, Univariate Time Series Forecasting

Multivariate time series forecasting is an important machine learning problem
across many domains, including predictions of solar plant energy output,
electricity consumption, and traffic jam situation. Temporal data arise in
these real-world applications often involves a mixture of long-term and
short-term patterns, for which traditional approaches such as Autoregressive
models and Gaussian Process may fail. In this paper, we proposed a novel deep
learning framework, namely Long- and Short-term Time-series network (LSTNet),
to address this open challenge. LSTNet uses the Convolution Neural Network
(CNN) and the Recurrent Neural Network (RNN) to extract short-term local
dependency patterns among variables and to discover long-term patterns for time
series trends. Furthermore, we leverage traditional autoregressive model to
tackle the scale insensitive problem of the neural network model. In our
evaluation on real-world data with complex mixtures of repetitive patterns,
LSTNet achieved significant performance improvements over that of several
state-of-the-art baseline methods. All the data and experiment codes are
available online.
Sentences with Gapping: Parsing and Reconstructing Elided Predicates
Authors: Sebastian Schuster, Joakim Nivre, Christopher D. Manning
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: Natural Language Understanding, Relation, Relation Extraction

Sentences with gapping, such as Paul likes coffee and Mary tea, lack an overt
predicate to indicate the relation between two or more arguments. Surface
syntax representations of such sentences are often produced poorly by parsers,
and even if correct, not well suited to downstream natural language
understanding tasks such as relation extraction that are typically designed to
extract information from sentences with canonical clause structure. In this
paper, we present two methods for parsing to a Universal Dependencies graph
representation that explicitly encodes the elided material with additional
nodes and edges. We find that both methods can reconstruct elided material from
dependency trees with high accuracy when the parser correctly predicts the
existence of a gap. We further demonstrate that one of our methods can be
applied to other languages based on a case study on Swedish.
Video Compression through Image Interpolation
Authors: Chao-yuan Wu, Nayan Singhal, Philipp Krähenbühl
Date: 2018-04-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Video Compression

An ever increasing amount of our digital communication, media consumption,
and content creation revolves around videos. We share, watch, and archive many
aspects of our lives through them, all of which are powered by strong video
compression. Traditional video compression is laboriously hand designed and
hand optimized. This paper presents an alternative in an end-to-end deep
learning codec. Our codec builds on one simple idea: Video compression is
repeated image interpolation. It thus benefits from recent advances in deep
image interpolation and generation. Our deep video codec outperforms today's
prevailing codecs, such as H.261, MPEG-4 Part 2, and performs on par with
H.264.
Generating Music using an LSTM Network
Authors: Nikhil Kotecha, Paul Young
Date: 2018-04-18 00:00:00

A model of music needs to have the ability to recall past details and have a
clear, coherent understanding of musical structure. Detailed in the paper is a
neural network architecture that predicts and generates polyphonic music
aligned with musical rules. The probabilistic model presented is a Bi-axial
LSTM trained with a kernel reminiscent of a convolutional kernel. When analyzed
quantitatively and qualitatively, this approach performs well in composing
polyphonic music. Link to the code is provided.
Modeling and Simultaneously Removing Bias via Adversarial Neural Networks
Authors: John Moore, Joel Pfeiffer, Kai Wei, Rishabh Iyer, Denis Charles, Ran Gilad-Bachrach, Levi Boyles, Eren Manavoglu
Date: 2018-04-18 00:00:00
Tasks: Position, Reinforcement Learning

In real world systems, the predictions of deployed Machine Learned models
affect the training data available to build subsequent models. This introduces
a bias in the training data that needs to be addressed. Existing solutions to
this problem attempt to resolve the problem by either casting this in the
reinforcement learning framework or by quantifying the bias and re-weighting
the loss functions. In this work, we develop a novel Adversarial Neural Network
(ANN) model, an alternative approach which creates a representation of the data
that is invariant to the bias. We take the Paid Search auction as our working
example and ad display position features as the confounding features for this
setting. We show the success of this approach empirically on both synthetic
data as well as real world paid search auction data from a major search engine.
Computing FO-Rewritings in EL in Practice: from Atomic to Conjunctive Queries
Authors: Peter Hansen, Carsten Lutz
Date: 2018-04-18 00:00:00

A prominent approach to implementing ontology-mediated queries (OMQs) is to
rewrite into a first-order query, which is then executed using a conventional
SQL database system. We consider the case where the ontology is formulated in
the description logic EL and the actual query is a conjunctive query and show
that rewritings of such OMQs can be efficiently computed in practice, in a
sound and complete way. Our approach combines a reduction with a decomposed
backwards chaining algorithm for OMQs that are based on the simpler atomic
queries, also illuminating the relationship between first-order rewritings of
OMQs based on conjunctive and on atomic queries. Experiments with real-world
ontologies show promising results.
Dichotomies in Ontology-Mediated Querying with the Guarded Fragment
Authors: Andre Hernich, Carsten Lutz, Fabio Papacchini, Frank Wolter
Date: 2018-04-18 00:00:00

We study the complexity of ontology-mediated querying when ontologies are
formulated in the guarded fragment of first-order logic (GF). Our general aim
is to classify the data complexity on the level of ontologies where query
evaluation w.r.t. an ontology O is considered to be in PTime if all (unions of
conjunctive) queries can be evaluated in PTime w.r.t. O and coNP-hard if at
least one query is coNP-hard w.r.t. O. We identify several large and relevant
fragments of GF that enjoy a dichotomy between PTime and coNP, some of them
additionally admitting a form of counting. In fact, almost all ontologies in
the BioPortal repository fall into these fragments or can easily be rewritten
to do so. We then establish a variation of Ladner's Theorem on the existence of
NP-intermediate problems and use this result to show that for other fragments,
there is provably no such dichotomy. Again for other fragments (such as full
GF), establishing a dichotomy implies the Feder-Vardi conjecture on the
complexity of constraint satisfaction problems. We also link these results to
Datalog-rewritability and study the decidability of whether a given ontology
enjoys PTime query evaluation, presenting both positive and negative results.
Pelee: A Real-Time Object Detection System on Mobile Devices
Authors: Robert J. Wang, Xiang Li, Charles X. Ling
Date: 2018-04-18 00:00:00
Proceeding: NeurIPS 2018
Tasks: object-detection, Object Detection, Real-Time Object Detection

An increasing need of running Convolutional Neural Network (CNN) models on
mobile devices with limited computing power and memory resource encourages
studies on efficient model design. A number of efficient architectures have
been proposed in recent years, for example, MobileNet, ShuffleNet, and
MobileNetV2. However, all these models are heavily dependent on depthwise
separable convolution which lacks efficient implementation in most deep
learning frameworks. In this study, we propose an efficient architecture named
PeleeNet, which is built with conventional convolution instead. On ImageNet
ILSVRC 2012 dataset, our proposed PeleeNet achieves a higher accuracy and over
1.8 times faster speed than MobileNet and MobileNetV2 on NVIDIA TX2. Meanwhile,
PeleeNet is only 66% of the model size of MobileNet. We then propose a
real-time object detection system by combining PeleeNet with Single Shot
MultiBox Detector (SSD) method and optimizing the architecture for fast speed.
Our proposed detection system2, named Pelee, achieves 76.4% mAP (mean average
precision) on PASCAL VOC2007 and 22.4 mAP on MS COCO dataset at the speed of
23.6 FPS on iPhone 8 and 125 FPS on NVIDIA TX2. The result on COCO outperforms
YOLOv2 in consideration of a higher precision, 13.6 times lower computational
cost and 11.3 times smaller model size.
Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods
Authors: Jieyu Zhao, Tianlu Wang, Mark Yatskar, Vicente Ordonez, Kai-Wei Chang
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: coreference-resolution, Coreference Resolution, Data Augmentation

We introduce a new benchmark, WinoBias, for coreference resolution focused on
gender bias. Our corpus contains Winograd-schema style sentences with entities
corresponding to people referred by their occupation (e.g. the nurse, the
doctor, the carpenter). We demonstrate that a rule-based, a feature-rich, and a
neural coreference system all link gendered pronouns to pro-stereotypical
entities with higher accuracy than anti-stereotypical entities, by an average
difference of 21.1 in F1 score. Finally, we demonstrate a data-augmentation
approach that, in combination with existing word-embedding debiasing
techniques, removes the bias demonstrated by these systems in WinoBias without
significantly affecting their performance on existing coreference benchmark
datasets. Our dataset and code are available at http://winobias.org.
Object Ordering with Bidirectional Matchings for Visual Reasoning
Authors: Hao Tan, Mohit Bansal
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: Object, Visual Reasoning

Visual reasoning with compositional natural language instructions, e.g.,
based on the newly-released Cornell Natural Language Visual Reasoning (NLVR)
dataset, is a challenging task, where the model needs to have the ability to
create an accurate mapping between the diverse phrases and the several objects
placed in complex arrangements in the image. Further, this mapping needs to be
processed to answer the question in the statement given the ordering and
relationship of the objects across three similar images. In this paper, we
propose a novel end-to-end neural model for the NLVR task, where we first use
joint bidirectional attention to build a two-way conditioning between the
visual information and the language phrases. Next, we use an RL-based pointer
network to sort and process the varying number of unordered objects (so as to
match the order of the statement phrases) in each of the three images and then
pool over the three decisions. Our model achieves strong improvements (of 4-6%
absolute) over the state-of-the-art on both the structured representation and
raw image versions of the dataset.
M-PACT: An Open Source Platform for Repeatable Activity Classification Research
Authors: Eric Hofesmann, Madan Ravi Ganesh, Jason J. Corso
Date: 2018-04-16 00:00:00
Tasks: Action Classification, Activity Recognition, Classification, General Classification

There are many hurdles that prevent the replication of existing work which
hinders the development of new activity classification models. These hurdles
include switching between multiple deep learning libraries and the development
of boilerplate experimental pipelines. We present M-PACT to overcome existing
issues by removing the need to develop boilerplate code which allows users to
quickly prototype action classification models while leveraging existing
state-of-the-art (SOTA) models available in the platform. M-PACT is the first
to offer four SOTA activity classification models, I3D, C3D, ResNet50+LSTM, and
TSN, under a single platform with reproducible competitive results. This
platform allows for the generation of models and results over activity
recognition datasets through the use of modular code, various preprocessing and
neural network layers, and seamless data flow. In this paper, we present the
system architecture, detail the functions of various modules, and describe the
basic tools to develop a new model in M-PACT.
A Popperian Falsification of Artificial Intelligence -- Lighthill Defended
Authors: Steven Meyer
Date: 2017-04-23 00:00:00
Tasks: Evolutionary Algorithms, Game of Chess, Game of Go

The area of computation called artificial intelligence (AI) is falsified by describing a previous 1972 falsification of AI by British mathematical physicist James Lighthill. How Lighthill's arguments continue to apply to current AI is explained. It is argued that AI should use the Popperian scientific method in which it is the duty of scientists to attempt to falsify theories and if theories are falsified to replace or modify them. The paper describes the Popperian method and discusses Paul Nurse's application of the method to cell biology that also involves questions of mechanism and behavior. It is shown how Lighthill's falsifying arguments especially combinatorial explosion continue to apply to modern AI. Various skeptical arguments against the assumptions of AI mostly by physicists especially against Hilbert's philosophical programme that defined knowledge and truth as provable formal sentences. John von Neumann's arguments from natural complexity against neural networks and evolutionary algorithms are discussed. Next the game of chess is discussed to show how modern chess experts have reacted to computer chess programs. It is shown that currently chess masters can defeat any chess program using Kasperov's arguments from his 1997 Deep Blue match and aftermath. The game of 'go' and climate models are discussed to show computer applications where combinatorial explosion may not apply. The paper concludes by advocating studying computation as Peter Naur's Dataology.
Unveiling the Power of Deep Tracking
Authors: Goutam Bhat, Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
Date: 2018-04-18 00:00:00
Proceeding: ECCV 2018 9
Tasks: Object Tracking

In the field of generic object tracking numerous attempts have been made to
exploit deep features. Despite all expectations, deep trackers are yet to reach
an outstanding level of performance compared to methods solely based on
handcrafted features. In this paper, we investigate this key issue and propose
an approach to unlock the true potential of deep features for tracking. We
systematically study the characteristics of both deep and shallow features, and
their relation to tracking accuracy and robustness. We identify the limited
data and low spatial resolution as the main challenges, and propose strategies
to counter these issues when integrating deep features for tracking.
Furthermore, we propose a novel adaptive fusion approach that leverages the
complementary properties of deep and shallow features to improve both
robustness and accuracy. Extensive experiments are performed on four
challenging datasets. On VOT2017, our approach significantly outperforms the
top performing tracker from the challenge with a relative gain of 17% in EAO.