pwc-archive/papers-with-abstracts huggingface dataset:
End-to-End Musical Key Estimation Using a Convolutional Neural Network
Authors: Filip Korzeniowski, Gerhard Widmer
Date: 2017-06-09 00:00:00

We present an end-to-end system for musical key estimation, based on a
convolutional neural network. The proposed system not only out-performs
existing key estimation methods proposed in the academic literature; it is also
capable of learning a unified model for diverse musical genres that performs
comparably to existing systems specialised for specific genres. Our experiments
confirm that different genres do differ in their interpretation of tonality,
and thus a system tuned e.g. for pop music performs subpar on pieces of
electronic music. They also reveal that such cross-genre setups evoke specific
types of error (predicting the relative or parallel minor). However, using the
data-driven approach proposed in this paper, we can train models that deal with
multiple musical styles adequately, and without major losses in accuracy.
Multi-Modal Obstacle Detection in Unstructured Environments with Conditional Random Fields
Authors: Mikkel Kragh, James Underwood
Date: 2017-06-09 00:00:00
Tasks: General Classification, Semantic Segmentation

Reliable obstacle detection and classification in rough and unstructured
terrain such as agricultural fields or orchards remains a challenging problem.
These environments involve large variations in both geometry and appearance,
challenging perception systems that rely on only a single sensor modality.
Geometrically, tall grass, fallen leaves, or terrain roughness can mistakenly
be perceived as nontraversable or might even obscure actual obstacles.
Likewise, traversable grass or dirt roads and obstacles such as trees and
bushes might be visually ambiguous. In this paper, we combine appearance- and
geometry-based detection methods by probabilistically fusing lidar and camera
sensing with semantic segmentation using a conditional random field. We apply a
state-of-the-art multimodal fusion algorithm from the scene analysis domain and
adjust it for obstacle detection in agriculture with moving ground vehicles.
This involves explicitly handling sparse point cloud data and exploiting both
spatial, temporal, and multimodal links between corresponding 2D and 3D
regions. The proposed method was evaluated on a diverse data set, comprising a
dairy paddock and different orchards gathered with a perception research robot
in Australia. Results showed that for a two-class classification problem
(ground and nonground), only the camera leveraged from information provided by
the other modality with an increase in the mean classification score of 0.5%.
However, as more classes were introduced (ground, sky, vegetation, and object),
both modalities complemented each other with improvements of 1.4% in 2D and
7.9% in 3D. Finally, introducing temporal links between successive frames
resulted in improvements of 0.2% in 2D and 1.5% in 3D.
SEGAN: Speech Enhancement Generative Adversarial Network
Authors: Santiago Pascual, Antonio Bonafonte, Joan Serrà
Date: 2017-03-28 00:00:00
Tasks: Generative Adversarial Network, Speech Enhancement

Current speech enhancement techniques operate on the spectral domain and/or
exploit some higher-level feature. The majority of them tackle a limited number
of noise conditions and rely on first-order statistics. To circumvent these
issues, deep networks are being increasingly used, thanks to their ability to
learn complex functions from large example sets. In this work, we propose the
use of generative adversarial networks for speech enhancement. In contrast to
current techniques, we operate at the waveform level, training the model
end-to-end, and incorporate 28 speakers and 40 different noise conditions into
the same model, such that model parameters are shared across them. We evaluate
the proposed model using an independent, unseen test set with two speakers and
20 alternative noise conditions. The enhanced samples confirm the viability of
the proposed model, and both objective and subjective evaluations confirm the
effectiveness of it. With that, we open the exploration of generative
architectures for speech enhancement, which may progressively incorporate
further speech-centric design choices to improve their performance.
Assessing the Performance of Deep Learning Algorithms for Newsvendor Problem
Authors: Yanfei Zhang, Junbin Gao
Date: 2017-06-09 00:00:00
Tasks: Deep Learning, Management

In retailer management, the Newsvendor problem has widely attracted attention
as one of basic inventory models. In the traditional approach to solving this
problem, it relies on the probability distribution of the demand. In theory, if
the probability distribution is known, the problem can be considered as fully
solved. However, in any real world scenario, it is almost impossible to even
approximate or estimate a better probability distribution for the demand. In
recent years, researchers start adopting machine learning approach to learn a
demand prediction model by using other feature information. In this paper, we
propose a supervised learning that optimizes the demand quantities for products
based on feature information. We demonstrate that the original Newsvendor loss
function as the training objective outperforms the recently suggested quadratic
loss function. The new algorithm has been assessed on both the synthetic data
and real-world data, demonstrating better performance.
Every Moment Counts: Dense Detailed Labeling of Actions in Complex Videos
Authors: Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori, Li Fei-Fei
Date: 2015-07-21 00:00:00
Tasks: Action Recognition, Retrieval, Temporal Action Localization

Every moment counts in action recognition. A comprehensive understanding of
human activity in video requires labeling every frame according to the actions
occurring, placing multiple labels densely over a video sequence. To study this
problem we extend the existing THUMOS dataset and introduce MultiTHUMOS, a new
dataset of dense labels over unconstrained internet videos. Modeling multiple,
dense labels benefits from temporal relations within and across classes. We
define a novel variant of long short-term memory (LSTM) deep networks for
modeling these temporal relations via multiple input and output connections. We
show that this model improves action labeling accuracy and further enables
deeper understanding tasks ranging from structured retrieval to action
prediction.
DCCO: Towards Deformable Continuous Convolution Operators
Authors: Joakim Johnander, Martin Danelljan, Fahad Shahbaz Khan, Michael Felsberg
Date: 2017-06-09 00:00:00

Discriminative Correlation Filter (DCF) based methods have shown competitive
performance on tracking benchmarks in recent years. Generally, DCF based
trackers learn a rigid appearance model of the target. However, this reliance
on a single rigid appearance model is insufficient in situations where the
target undergoes non-rigid transformations. In this paper, we propose a unified
formulation for learning a deformable convolution filter. In our framework, the
deformable filter is represented as a linear combination of sub-filters. Both
the sub-filter coefficients and their relative locations are inferred jointly
in our formulation. Experiments are performed on three challenging tracking
benchmarks: OTB- 2015, TempleColor and VOT2016. Our approach improves the
baseline method, leading to performance comparable to state-of-the-art.
Learning to Learn from Noisy Web Videos
Authors: Serena Yeung, Vignesh Ramanathan, Olga Russakovsky, Liyue Shen, Greg Mori, Li Fei-Fei
Date: 2017-06-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Q-Learning, Reinforcement Learning, Temporal Action Localization

Understanding the simultaneously very diverse and intricately fine-grained
set of possible human actions is a critical open problem in computer vision.
Manually labeling training videos is feasible for some action classes but
doesn't scale to the full long-tailed distribution of actions. A promising way
to address this is to leverage noisy data from web queries to learn new
actions, using semi-supervised or "webly-supervised" approaches. However, these
methods typically do not learn domain-specific knowledge, or rely on iterative
hand-tuned data labeling policies. In this work, we instead propose a
reinforcement learning-based formulation for selecting the right examples for
training a classifier from noisy web search results. Our method uses Q-learning
to learn a data labeling policy on a small labeled training dataset, and then
uses this to automatically label noisy web data for new visual concepts.
Experiments on the challenging Sports-1M action recognition benchmark as well
as on additional fine-grained and newly emerging action classes demonstrate
that our method is able to learn good labeling policies for noisy data and use
this to learn accurate visual concept classifiers.
Overview of the NLPCC 2017 Shared Task: Chinese News Headline Categorization
Authors: Xipeng Qiu, Jingjing Gong, Xuanjing Huang
Date: 2017-06-09 00:00:00

In this paper, we give an overview for the shared task at the CCF Conference
on Natural Language Processing \& Chinese Computing (NLPCC 2017): Chinese News
Headline Categorization. The dataset of this shared task consists 18 classes,
12,000 short texts along with corresponded labels for each class. The dataset
and example code can be accessed at
https://github.com/FudanNLP/nlpcc2017_news_headline_categorization.
Class-specific Poisson denoising by patch-based importance sampling
Authors: Milad Niknejad, Jose M. Bioucas-Dias, Mario A. T. Figueiredo
Date: 2017-06-09 00:00:00
Tasks: Denoising

In this paper, we address the problem of recovering images degraded by
Poisson noise, where the image is known to belong to a specific class. In the
proposed method, a dataset of clean patches from images of the class of
interest is clustered using multivariate Gaussian distributions. In order to
recover the noisy image, each noisy patch is assigned to one of these
distributions, and the corresponding minimum mean squared error (MMSE) estimate
is obtained. We propose to use a self-normalized importance sampling approach,
which is a method of the Monte-Carlo family, for the both determining the most
likely distribution and approximating the MMSE estimate of the clean patch.
Experimental results shows that our proposed method outperforms other methods
for Poisson denoising at a low SNR regime.
Face Detection through Scale-Friendly Deep Convolutional Networks
Authors: Shuo Yang, Yuanjun Xiong, Chen Change Loy, Xiaoou Tang
Date: 2017-06-09 00:00:00
Tasks: Face Detection

In this paper, we share our experience in designing a convolutional
network-based face detector that could handle faces of an extremely wide range
of scales. We show that faces with different scales can be modeled through a
specialized set of deep convolutional networks with different structures. These
detectors can be seamlessly integrated into a single unified network that can
be trained end-to-end. In contrast to existing deep models that are designed
for wide scale range, our network does not require an image pyramid input and
the model is of modest complexity. Our network, dubbed ScaleFace, achieves
promising performance on WIDER FACE and FDDB datasets with practical runtime
speed. Specifically, our method achieves 76.4 average precision on the
challenging WIDER FACE dataset and 96% recall rate on the FDDB dataset with 7
frames per second (fps) for 900 * 1300 input image.
A Maximum Matching Algorithm for Basis Selection in Spectral Learning
Authors: Ariadna Quattoni, Xavier Carreras, Matthias Gallé
Date: 2017-06-09 00:00:00

We present a solution to scale spectral algorithms for learning sequence
functions. We are interested in the case where these functions are sparse (that
is, for most sequences they return 0). Spectral algorithms reduce the learning
problem to the task of computing an SVD decomposition over a special type of
matrix called the Hankel matrix. This matrix is designed to capture the
relevant statistics of the training sequences. What is crucial is that to
capture long range dependencies we must consider very large Hankel matrices.
Thus the computation of the SVD becomes a critical bottleneck. Our solution
finds a subset of rows and columns of the Hankel that realizes a compact and
informative Hankel submatrix. The novelty lies in the way that this subset is
selected: we exploit a maximal bipartite matching combinatorial algorithm to
look for a sub-block with full structural rank, and show how computation of
this sub-block can be further improved by exploiting the specific structure of
Hankel matrices.
Discovering Latent Covariance Structures for Multiple Time Series
Authors: Anh Tong, Jaesik Choi
Date: 2017-03-28 00:00:00
Tasks: Time Series, Time Series Analysis

Analyzing multivariate time series data is important to predict future events and changes of complex systems in finance, manufacturing, and administrative decisions. The expressiveness power of Gaussian Process (GP) regression methods has been significantly improved by compositional covariance structures. In this paper, we present a new GP model which naturally handles multiple time series by placing an Indian Buffet Process (IBP) prior on the presence of shared kernels. Our selective covariance structure decomposition allows exploiting shared parameters over a set of multiple, selected time series. We also investigate the well-definedness of the models when infinite latent components are introduced. We present a pragmatic search algorithm which explores a larger structure space efficiently. Experiments conducted on five real-world data sets demonstrate that our new model outperforms existing methods in term of structure discoveries and predictive performances.
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
Authors: Alessandro Corbetta, Vlado Menkovski, Federico Toschi
Date: 2017-06-09 00:00:00
Tasks: Data Augmentation, Object Localization

Overhead depth map measurements capture sufficient amount of information to
enable human experts to track pedestrians accurately. However, fully automating
this process using image analysis algorithms can be challenging. Even though
hand-crafted image analysis algorithms are successful in many common cases,
they fail frequently when there are complex interactions of multiple objects in
the image. Many of the assumptions underpinning the hand-crafted solutions do
not hold in these cases and the multitude of exceptions are hard to model
precisely. Deep Learning (DL) algorithms, on the other hand, do not require
hand crafted solutions and are the current state-of-the-art in object
localization in images. However, they require exceeding amount of annotations
to produce successful models. In the case of object localization these
annotations are difficult and time consuming to produce. In this work we
present an approach for developing pedestrian localization models using DL
algorithms with efficient weak supervision from an expert. We circumvent the
need for annotation of large corpus of data by annotating only small amount of
patches and relying on synthetic data augmentation as a vehicle for injecting
expert knowledge in the model training. This approach of weak supervision
through expert selection of representative patches, suitable transformations
and synthetic data augmentations enables us to successfully develop DL models
for pedestrian localization efficiently.
Active Learning for Structured Prediction from Partially Labeled Data
Authors: Mehran Khodabandeh, Zhiwei Deng, Mostafa S. Ibrahim, ShinIchi Satoh, Greg Mori
Date: 2017-06-07 00:00:00
Tasks: Active Learning, Prediction, Structured Prediction

We propose a general purpose active learning algorithm for structured
prediction, gathering labeled data for training a model that outputs a set of
related labels for an image or video. Active learning starts with a limited
initial training set, then iterates querying a user for labels on unlabeled
data and retraining the model. We propose a novel algorithm for selecting data
for labeling, choosing examples to maximize expected information gain based on
belief propagation inference. This is a general purpose method and can be
applied to a variety of tasks or models. As a specific example we demonstrate
this framework for learning to recognize human actions and group activities in
video sequences. Experiments show that our proposed algorithm outperforms
previous active learning methods and can achieve accuracy comparable to fully
supervised methods while utilizing significantly less labeled data.
Stochastic Variance Reduction Methods for Policy Evaluation
Authors: Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou
Date: 2017-02-25 00:00:00
Proceeding: ICML 2017 8
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Policy evaluation is a crucial step in many reinforcement-learning
procedures, which estimates a value function that predicts states' long-term
value under a given policy. In this paper, we focus on policy evaluation with
linear function approximation over a fixed dataset. We first transform the
empirical policy evaluation problem into a (quadratic) convex-concave saddle
point problem, and then present a primal-dual batch gradient method, as well as
two stochastic variance reduction methods for solving the problem. These
algorithms scale linearly in both sample size and feature dimension. Moreover,
they achieve linear convergence even when the saddle-point problem has only
strong concavity in the dual variables but no strong convexity in the primal
variables. Numerical experiments on benchmark problems demonstrate the
effectiveness of our methods.
A Tutor Agent for MOBA Games
Authors: Victor do Nascimento Silva, Luiz Chaimowicz
Date: 2017-06-09 00:00:00

Digital games have become a key player in the entertainment industry,
attracting millions of new players each year. In spite of that, novice players
may have a hard time when playing certain types of games, such as MOBAs and
MMORPGs, due to their steep learning curves and not so friendly online
communities. In this paper, we present an approach to help novice players in
MOBA games overcome these problems. An artificial intelligence agent plays
alongside the player analyzing his/her performance and giving tips about the
game. Experiments performed with the game {\em League of Legends} show the
potential of this approach.
Learning Deep Representations for Scene Labeling with Semantic Context Guided Supervision
Authors: Zhe Wang, Hongsheng Li, Wanli Ouyang, Xiaogang Wang
Date: 2017-06-08 00:00:00
Tasks: Scene Labeling

Scene labeling is a challenging classification problem where each input image
requires a pixel-level prediction map. Recently, deep-learning-based methods
have shown their effectiveness on solving this problem. However, we argue that
the large intra-class variation provides ambiguous training information and
hinders the deep models' ability to learn more discriminative deep feature
representations. Unlike existing methods that mainly utilize semantic context
for regularizing or smoothing the prediction map, we design novel supervisions
from semantic context for learning better deep feature representations. Two
types of semantic context, scene names of images and label map statistics of
image patches, are exploited to create label hierarchies between the original
classes and newly created subclasses as the learning supervisions. Such
subclasses show lower intra-class variation, and help CNN detect more
meaningful visual patterns and learn more effective deep features. Novel
training strategies and network structure that take advantages of such label
hierarchies are introduced. Our proposed method is evaluated extensively on
four popular datasets, Stanford Background (8 classes), SIFTFlow (33 classes),
Barcelona (170 classes) and LM+Sun datasets (232 classes) with 3 different
networks structures, and show state-of-the-art performance. The experiments
show that our proposed method makes deep models learn more discriminative
feature representations without increasing model size or complexity.
Infrastructure for Usable Machine Learning: The Stanford DAWN Project
Authors: Peter Bailis, Kunle Olukotun, Christopher Re, Matei Zaharia
Date: 2017-05-22 00:00:00
Tasks: BIG-bench Machine Learning

Despite incredible recent advances in machine learning, building machine
learning applications remains prohibitively time-consuming and expensive for
all but the best-trained, best-funded engineering organizations. This expense
comes not from a need for new and improved statistical models but instead from
a lack of systems and tools for supporting end-to-end machine learning
application development, from data preparation and labeling to
productionization and monitoring. In this document, we outline opportunities
for infrastructure supporting usable, end-to-end machine learning applications
in the context of the nascent DAWN (Data Analytics for What's Next) project at
Stanford.
Synergistic Union of Word2Vec and Lexicon for Domain Specific Semantic Similarity
Authors: Keet Sugathadasa, Buddhi Ayesha, Nisansa de Silva, Amal Shehan Perera, Vindula Jayawardana, Dimuthu Lakmal, Madhavi Perera
Date: 2017-06-06 00:00:00
Tasks: Lemmatization, Semantic Similarity, Semantic Textual Similarity

Semantic similarity measures are an important part in Natural Language
Processing tasks. However Semantic similarity measures built for general use do
not perform well within specific domains. Therefore in this study we introduce
a domain specific semantic similarity measure that was created by the
synergistic union of word2vec, a word embedding method that is used for
semantic similarity calculation and lexicon based (lexical) semantic similarity
methods. We prove that this proposed methodology out performs word embedding
methods trained on generic corpus and methods trained on domain specific corpus
but do not use lexical semantic similarity methods to augment the results.
Further, we prove that text lemmatization can improve the performance of word
embedding methods.
Diameter-Based Active Learning
Authors: Christopher Tosh, Sanjoy Dasgupta
Date: 2017-02-27 00:00:00
Proceeding: ICML 2017 8
Tasks: Active Learning

To date, the tightest upper and lower-bounds for the active learning of
general concept classes have been in terms of a parameter of the learning
problem called the splitting index. We provide, for the first time, an
efficient algorithm that is able to realize this upper bound, and we
empirically demonstrate its good performance.
McGan: Mean and Covariance Feature Matching GAN
Authors: Youssef Mroueh, Tom Sercu, Vaibhava Goel
Date: 2017-02-27 00:00:00
Proceeding: ICML 2017 8

We introduce new families of Integral Probability Metrics (IPM) for training
Generative Adversarial Networks (GAN). Our IPMs are based on matching
statistics of distributions embedded in a finite dimensional feature space.
Mean and covariance feature matching IPMs allow for stable training of GANs,
which we will call McGan. McGan minimizes a meaningful loss between
distributions.