pwc-archive/papers-with-abstracts huggingface dataset:
Perceptual Generative Adversarial Networks for Small Object Detection
Authors: Jianan Li, Xiaodan Liang, Yunchao Wei, Tingfa Xu, Jiashi Feng, Shuicheng Yan
Date: 2017-06-16 00:00:00
Proceeding: CVPR 2017 7
Tasks: Generative Adversarial Network, Object, object-detection, Object Detection, Small Object Detection

Detecting small objects is notoriously challenging due to their low
resolution and noisy representation. Existing object detection pipelines
usually detect small objects through learning representations of all the
objects at multiple scales. However, the performance gain of such ad hoc
architectures is usually limited to pay off the computational cost. In this
work, we address the small object detection problem by developing a single
architecture that internally lifts representations of small objects to
"super-resolved" ones, achieving similar characteristics as large objects and
thus more discriminative for detection. For this purpose, we propose a new
Perceptual Generative Adversarial Network (Perceptual GAN) model that improves
small object detection through narrowing representation difference of small
objects from the large ones. Specifically, its generator learns to transfer
perceived poor representations of the small objects to super-resolved ones that
are similar enough to real large objects to fool a competing discriminator.
Meanwhile its discriminator competes with the generator to identify the
generated representation and imposes an additional perceptual requirement -
generated representations of small objects must be beneficial for detection
purpose - on the generator. Extensive evaluations on the challenging
Tsinghua-Tencent 100K and the Caltech benchmark well demonstrate the
superiority of Perceptual GAN in detecting small objects, including traffic
signs and pedestrians, over well-established state-of-the-arts.
Towards Proof Synthesis Guided by Neural Machine Translation for Intuitionistic Propositional Logic
Authors: Taro Sekiyama, Akifumi Imanishi, Kohei Suenaga
Date: 2017-06-20 00:00:00
Tasks: Machine Translation, Negation, Translation

Inspired by the recent evolution of deep neural networks (DNNs) in machine
learning, we explore their application to PL-related topics. This paper is the
first step towards this goal; we propose a proof-synthesis method for the
negation-free propositional logic in which we use a DNN to obtain a guide of
proof search. The idea is to view the proof-synthesis problem as a translation
from a proposition to its proof. We train seq2seq, which is a popular network
in neural machine translation, so that it generates a proof encoded as a
$\lambda$-term of a given proposition. We implement the whole framework and
empirically observe that a generated proof term is close to a correct proof in
terms of the tree edit distance of AST. This observation justifies using the
output from a trained seq2seq model as a guide for proof search.
Stochastic modified equations and adaptive stochastic gradient algorithms
Authors: Qianxiao Li, Cheng Tai, Weinan E
Date: 2015-11-19 00:00:00
Proceeding: ICML 2017 8

We develop the method of stochastic modified equations (SME), in which
stochastic gradient algorithms are approximated in the weak sense by
continuous-time stochastic differential equations. We exploit the continuous
formulation together with optimal control theory to derive novel adaptive
hyper-parameter adjustment policies. Our algorithms have competitive
performance with the added benefit of being robust to varying models and
datasets. This provides a general methodology for the analysis and design of
stochastic gradient algorithms.
THUMT: An Open Source Toolkit for Neural Machine Translation
Authors: Jiacheng Zhang, Yanzhuo Ding, Shiqi Shen, Yong Cheng, Maosong Sun, Huanbo Luan, Yang Liu
Date: 2017-06-20 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

This paper introduces THUMT, an open-source toolkit for neural machine
translation (NMT) developed by the Natural Language Processing Group at
Tsinghua University. THUMT implements the standard attention-based
encoder-decoder framework on top of Theano and supports three training
criteria: maximum likelihood estimation, minimum risk training, and
semi-supervised training. It features a visualization tool for displaying the
relevance between hidden states in neural networks and contextual words, which
helps to analyze the internal workings of NMT. Experiments on Chinese-English
datasets show that THUMT using minimum risk training significantly outperforms
GroundHog, a state-of-the-art toolkit for NMT.
A Framework for Accurate Drought Forecasting System Using Semantics-Based Data Integration Middleware
Authors: A. K. Akanbi, M. Masinde
Date: 2017-06-20 00:00:00
Tasks: Data Integration

Technological advancement in Wireless Sensor Networks (WSN) has made it
become an invaluable component of a reliable environmental monitoring system;
they form the digital skin' through which to 'sense' and collect the context of
the surroundings and provides information on the process leading to complex
events such as drought. However, these environmental properties are measured by
various heterogeneous sensors of different modalities in distributed locations
making up the WSN, using different abstruse terms and vocabulary in most cases
to denote the same observed property, causing data heterogeneity. Adding
semantics and understanding the relationships that exist between the observed
properties, and augmenting it with local indigenous knowledge is necessary for
an accurate drought forecasting system. In this paper, we propose the framework
for the semantic representation of sensor data and integration with indigenous
knowledge on drought using a middleware for an efficient drought forecasting
system.
Revisiting L21-norm Robustness with Vector Outlier Regularization
Authors: Bo Jiang, Chris Ding
Date: 2017-06-20 00:00:00

In many real-world applications, data usually contain outliers. One popular approach is to use L2,1 norm function as a robust error/loss function. However, the robustness of L2,1 norm function is not well understood so far. In this paper, we propose a new Vector Outlier Regularization (VOR) framework to understand and analyze the robustness of L2,1 norm function. Our VOR function defines a data point to be outlier if it is outside a threshold with respect to a theoretical prediction, and regularize it-pull it back to the threshold line. We then prove that L2,1 function is the limiting case of this VOR with the usual least square/L2 error function as the threshold shrinks to zero. One interesting property of VOR is that how far an outlier lies away from its theoretically predicted value does not affect the final regularization and analysis results. This VOR property unmasks one of the most peculiar property of L2,1 norm function: The effects of outliers seem to be independent of how outlying they are-if an outlier is moved further away from the intrinsic manifold/subspace, the final analysis results do not change. VOR provides a new way to understand and analyze the robustness of L2,1 norm function. Applying VOR to matrix factorization leads to a new VORPCA model. We give a comprehensive comparison with trace-norm based L21-norm PCA to demonstrate the advantages of VORPCA.
Programmable Agents
Authors: Misha Denil, Sergio Gómez Colmenarejo, Serkan Cabi, David Saxton, Nando de Freitas
Date: 2017-06-20 00:00:00

We build deep RL agents that execute declarative programs expressed in formal
language. The agents learn to ground the terms in this language in their
environment, and can generalize their behavior at test time to execute new
programs that refer to objects that were not referenced during training. The
agents develop disentangled interpretable representations that allow them to
generalize to a wide variety of zero-shot semantic tasks.
Logic Programming for an Introductory Computer Science Course for High School Students
Authors: Timothy Yuen, Maritz Reyes, Yuanlin Zhang
Date: 2017-06-20 00:00:00

This paper investigates how high school students approach computing through
an introductory computer science course situated in the Logic Programming (LP)
paradigm. This study shows how novice students operate within the LP paradigm
while engaging in foundational computing concepts and skills, and presents a
case for LP as a viable paradigm choice for introductory CS courses.
Distributed Multi-Task Relationship Learning
Authors: Sulin Liu, Sinno Jialin Pan, Qirong Ho
Date: 2016-12-13 00:00:00
Tasks: Distributed Optimization, Multi-Task Learning

Multi-task learning aims to learn multiple tasks jointly by exploiting their
relatedness to improve the generalization performance for each task.
Traditionally, to perform multi-task learning, one needs to centralize data
from all the tasks to a single machine. However, in many real-world
applications, data of different tasks may be geo-distributed over different
local machines. Due to heavy communication caused by transmitting the data and
the issue of data privacy and security, it is impossible to send data of
different task to a master machine to perform multi-task learning. Therefore,
in this paper, we propose a distributed multi-task learning framework that
simultaneously learns predictive models for each task as well as task
relationships between tasks alternatingly in the parameter server paradigm. In
our framework, we first offer a general dual form for a family of regularized
multi-task relationship learning methods. Subsequently, we propose a
communication-efficient primal-dual distributed optimization algorithm to solve
the dual problem by carefully designing local subproblems to make the dual
problem decomposable. Moreover, we provide a theoretical convergence analysis
for the proposed algorithm, which is specific for distributed multi-task
relationship learning. We conduct extensive experiments on both synthetic and
real-world datasets to evaluate our proposed framework in terms of
effectiveness and convergence.
Toward Optimal Run Racing: Application to Deep Learning Calibration
Authors: Olivier Bousquet, Sylvain Gelly, Karol Kurach, Marc Schoenauer, Michele Sebag, Olivier Teytaud, Damien Vincent
Date: 2017-06-10 00:00:00
Tasks: Deep Learning, One-Shot Learning, Two-sample testing

This paper aims at one-shot learning of deep neural nets, where a highly
parallel setting is considered to address the algorithm calibration problem -
selecting the best neural architecture and learning hyper-parameter values
depending on the dataset at hand. The notoriously expensive calibration problem
is optimally reduced by detecting and early stopping non-optimal runs. The
theoretical contribution regards the optimality guarantees within the multiple
hypothesis testing framework. Experimentations on the Cifar10, PTB and Wiki
benchmarks demonstrate the relevance of the approach with a principled and
consistent improvement on the state of the art with no extra hyper-parameter.
Improving text classification with vectors of reduced precision
Authors: Krzysztof Wróbel, Maciej Wielgosz, Marcin Pietroń, Michał Karwatowski, Aleksander Smywiński-Pohl
Date: 2017-06-20 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification, text-classification, Text Classification

This paper presents the analysis of the impact of a floating-point number
precision reduction on the quality of text classification. The precision
reduction of the vectors representing the data (e.g. TF-IDF representation in
our case) allows for a decrease of computing time and memory footprint on
dedicated hardware platforms. The impact of precision reduction on the
classification quality was performed on 5 corpora, using 4 different
classifiers. Also, dimensionality reduction was taken into account. Results
indicate that the precision reduction improves classification accuracy for most
cases (up to 25% of error reduction). In general, the reduction from 64 to 4
bits gives the best scores and ensures that the results will not be worse than
with the full floating-point representation.
Fast Back-Projection for Non-Line of Sight Reconstruction
Authors: Victor Arellano, Diego Gutierrez, Adrian Jarabo
Date: 2017-03-06 00:00:00
Tasks: GPU

Recent works have demonstrated non-line of sight (NLOS) reconstruction by
using the time-resolved signal frommultiply scattered light. These works
combine ultrafast imaging systems with computation, which back-projects the
recorded space-time signal to build a probabilistic map of the hidden geometry.
Unfortunately, this computation is slow, becoming a bottleneck as the imaging
technology improves. In this work, we propose a new back-projection technique
for NLOS reconstruction, which is up to a thousand times faster than previous
work, with almost no quality loss. We base on the observation that the hidden
geometry probability map can be built as the intersection of the three-bounce
space-time manifolds defined by the light illuminating the hidden geometry and
the visible point receiving the scattered light from such hidden geometry. This
allows us to pose the reconstruction of the hidden geometry as the voxelization
of these space-time manifolds, which has lower theoretic complexity and is
easily implementable in the GPU. We demonstrate the efficiency and quality of
our technique compared against previous methods in both captured and synthetic
data
Web-STAR: Towards a Visual Web-Based IDE for a Story Comprehension System
Authors: Christos Rodosthenous, Loizos Michael
Date: 2017-06-20 00:00:00

In this work, we present Web-STAR, an online platform for story understanding
built on top of the STAR (STory comprehension through ARgumentation) reasoning
engine. This platform includes a web-based IDE, integration with the STAR
system and a web service infrastructure to support integration with other
systems that rely on story understanding functionality to complete their tasks.
The platform also delivers a number of "social" features like public story
sharing with a built-in commenting system, a public repository for sharing
stories with the community and collaboration tools that can be used from both
project team members for development and educators for teaching. Moreover, we
discuss the ongoing work on adding new features and functionality to this
platform.
Clustering-Based Quantisation for PDE-Based Image Compression
Authors: Laurent Hoeltgen, Pascal Peter, Michael Breuß
Date: 2017-06-20 00:00:00
Tasks: Clustering, Image Compression

Finding optimal data for inpainting is a key problem in the context of
partial differential equation based image compression. The data that yields the
most accurate reconstruction is real-valued. Thus, quantisation models are
mandatory to allow an efficient encoding. These can also be understood as
challenging data clustering problems. Although clustering approaches are well
suited for this kind of compression codecs, very few works actually consider
them. Each pixel has a global impact on the reconstruction and optimal data
locations are strongly correlated with their corresponding colour values. These
facts make it hard to predict which feature works best.
  In this paper we discuss quantisation strategies based on popular methods
such as k-means. We are lead to the central question which kind of feature
vectors are best suited for image compression. To this end we consider choices
such as the pixel values, the histogram or the colour map.
  Our findings show that the number of colours can be reduced significantly
without impacting the reconstruction quality. Surprisingly, these benefits do
not directly translate to a good image compression performance. The gains in
the compression ratio are lost due to increased storage costs. This suggests
that it is integral to evaluate the clustering on both, the reconstruction
error and the final file size.
An Online Development Environment for Answer Set Programming
Authors: Elias Marcopoulos, Christian Reotutar, Yuanlin Zhang
Date: 2017-06-20 00:00:00

Recent progress in logic programming (e.g., the development of the Answer Set
Programming paradigm) has made it possible to teach it to general undergraduate
and even high school students. Given the limited exposure of these students to
computer science, the complexity of downloading, installing and using tools for
writing logic programs could be a major barrier for logic programming to reach
a much wider audience. We developed an online answer set programming
environment with a self contained file system and a simple interface, allowing
users to write logic programs and perform several tasks over the programs.
Effective Spoken Language Labeling with Deep Recurrent Neural Networks
Authors: Marco Dinarelli, Yoann Dupont, Isabelle Tellier
Date: 2017-06-20 00:00:00
Tasks: Spoken Language Understanding

Understanding spoken language is a highly complex problem, which can be
decomposed into several simpler tasks. In this paper, we focus on Spoken
Language Understanding (SLU), the module of spoken dialog systems responsible
for extracting a semantic interpretation from the user utterance. The task is
treated as a labeling problem. In the past, SLU has been performed with a wide
variety of probabilistic models. The rise of neural networks, in the last
couple of years, has opened new interesting research directions in this domain.
Recurrent Neural Networks (RNNs) in particular are able not only to represent
several pieces of information as embeddings but also, thanks to their recurrent
architecture, to encode as embeddings relatively long contexts. Such long
contexts are in general out of reach for models previously used for SLU. In
this paper we propose novel RNNs architectures for SLU which outperform
previous ones. Starting from a published idea as base block, we design new deep
RNNs achieving state-of-the-art results on two widely used corpora for SLU:
ATIS (Air Traveling Information System), in English, and MEDIA (Hotel
information and reservation in France), in French.
Session Analysis using Plan Recognition
Authors: Reuth Mirsky, Ya'akov Gal, David Tolpin
Date: 2017-06-20 00:00:00

This paper presents preliminary results of our work with a major financial
company, where we try to use methods of plan recognition in order to
investigate the interactions of a costumer with the company's online interface.
In this paper, we present the first steps of integrating a plan recognition
algorithm in a real-world application for detecting and analyzing the
interactions of a costumer. It uses a novel approach for plan recognition from
bare-bone UI data, which reasons about the plan library at the lowest
recognition level in order to define the relevancy of actions in our domain,
and then uses it to perform plan recognition.
  We present preliminary results of inference on three different use-cases
modeled by domain experts from the company, and show that this approach manages
to decrease the overload of information required from an analyst to evaluate a
costumer's session - whether this is a malicious or benign session, whether the
intended tasks were completed, and if not - what actions are expected next.
A new kernel-based approach to system identification with quantized output data
Authors: Giulio Bottegal, Håkan Hjalmarsson, Gianluigi Pillonetto
Date: 2016-10-03 00:00:00

In this paper we introduce a novel method for linear system identification
with quantized output data. We model the impulse response as a zero-mean
Gaussian process whose covariance (kernel) is given by the recently proposed
stable spline kernel, which encodes information on regularity and exponential
stability. This serves as a starting point to cast our system identification
problem into a Bayesian framework. We employ Markov Chain Monte Carlo methods
to provide an estimate of the system. In particular, we design two methods
based on the so-called Gibbs sampler that allow also to estimate the kernel
hyperparameters by marginal likelihood maximization via the
expectation-maximization method. Numerical simulations show the effectiveness
of the proposed scheme, as compared to the state-of-the-art kernel-based
methods when these are employed in system identification with quantized data.
Reviving Threshold-Moving: a Simple Plug-in Bagging Ensemble for Binary and Multiclass Imbalanced Data
Authors: Guillem Collell, Drazen Prelec, Kaustubh Patil
Date: 2016-06-28 00:00:00

Class imbalance presents a major hurdle in the application of data mining
methods. A common practice to deal with it is to create ensembles of
classifiers that learn from resampled balanced data. For example, bagged
decision trees combined with random undersampling (RUS) or the synthetic
minority oversampling technique (SMOTE). However, most of the resampling
methods entail asymmetric changes to the examples of different classes, which
in turn can introduce its own biases in the model. Furthermore, those methods
require a performance measure to be specified a priori before learning. An
alternative is to use a so-called threshold-moving method that a posteriori
changes the decision threshold of a model to counteract the imbalance, thus has
a potential to adapt to the performance measure of interest. Surprisingly,
little attention has been paid to the potential of combining bagging ensemble
with threshold-moving. In this paper, we present probability thresholding
bagging (PT-bagging), a versatile plug-in method that fills this gap. Contrary
to usual rebalancing practice, our method preserves the natural class
distribution of the data resulting in well calibrated posterior probabilities.
We also extend the proposed method to handle multiclass data. The method is
validated on binary and multiclass benchmark data sets. We perform analyses
that provide insights into the proposed method.
Pedestrian Prediction by Planning using Deep Neural Networks
Authors: Eike Rehder, Florian Wirth, Martin Lauer, Christoph Stiller
Date: 2017-06-19 00:00:00
Tasks: Autonomous Vehicles, Collision Avoidance, Motion Planning, motion prediction, Prediction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Accurate traffic participant prediction is the prerequisite for collision
avoidance of autonomous vehicles. In this work, we predict pedestrians by
emulating their own motion planning. From online observations, we infer a
mixture density function for possible destinations. We use this result as the
goal states of a planning stage that performs motion prediction based on common
behavior patterns. The entire system is modeled as one monolithic neural
network and trained via inverse reinforcement learning. Experimental validation
on real world data shows the system's ability to predict both, destinations and
trajectories accurately.
Online Learning to Rank in Stochastic Click Models
Authors: Masrour Zoghi, Tomas Tunys, Mohammad Ghavamzadeh, Branislav Kveton, Csaba Szepesvari, Zheng Wen
Date: 2017-03-07 00:00:00
Proceeding: ICML 2017 8
Tasks: Information Retrieval, Learning-To-Rank, Retrieval

Online learning to rank is a core problem in information retrieval and
machine learning. Many provably efficient algorithms have been recently
proposed for this problem in specific click models. The click model is a model
of how the user interacts with a list of documents. Though these results are
significant, their impact on practice is limited, because all proposed
algorithms are designed for specific click models and lack convergence
guarantees in other models. In this work, we propose BatchRank, the first
online learning to rank algorithm for a broad class of click models. The class
encompasses two most fundamental click models, the cascade and position-based
models. We derive a gap-dependent upper bound on the $T$-step regret of
BatchRank and evaluate it on a range of web search queries. We observe that
BatchRank outperforms ranked bandits and is more robust than CascadeKL-UCB, an
existing algorithm for the cascade model.