pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Temporal Pyramid Network: A Closer Look at Multi-Scale Modeling for Activity Detection
Authors: Da Zhang, Xiyang Dai, Yuan-Fang Wang
Date: 2018-08-07 00:00:00
Tasks: Action Detection, Activity Detection, object-detection, Object Detection

Recognizing instances at different scales simultaneously is a fundamental
challenge in visual detection problems. While spatial multi-scale modeling has
been well studied in object detection, how to effectively apply a multi-scale
architecture to temporal models for activity detection is still under-explored.
In this paper, we identify three unique challenges that need to be specifically
handled for temporal activity detection compared to its spatial counterpart. To
address all these issues, we propose Dynamic Temporal Pyramid Network (DTPN), a
new activity detection framework with a multi-scale pyramidal architecture
featuring three novel designs: (1) We sample input video frames dynamically
with varying frame per seconds (FPS) to construct a natural pyramidal input for
video of an arbitrary length. (2) We design a two-branch multi-scale temporal
feature hierarchy to deal with the inherent temporal scale variation of
activity instances. (3) We further exploit the temporal context of activities
by appropriately fusing multi-scale feature maps, and demonstrate that both
local and global temporal contexts are important. By combining all these
components into a uniform network, we end up with a single-shot activity
detector involving single-pass inferencing and end-to-end training. Extensive
experiments show that the proposed DTPN achieves state-of-the-art performance
on the challenging ActvityNet dataset.
SchiNet: Automatic Estimation of Symptoms of Schizophrenia from Facial Behaviour Analysis
Authors: Mina Bishay, Petar Palasek, Stefan Priebe, Ioannis Patras
Date: 2018-08-07 00:00:00

Patients with schizophrenia often display impairments in the expression of
emotion and speech and those are observed in their facial behaviour. Automatic
analysis of patients' facial expressions that is aimed at estimating symptoms
of schizophrenia has received attention recently. However, the datasets that
are typically used for training and evaluating the developed methods, contain
only a small number of patients (4-34) and are recorded while the subjects were
performing controlled tasks such as listening to life vignettes, or answering
emotional questions. In this paper, we use videos of professional-patient
interviews, in which symptoms were assessed in a standardised way as they
should/may be assessed in practice, and which were recorded in realistic
conditions (i.e. varying illumination levels and camera viewpoints) at the
patients' homes or at mental health services. We automatically analyse the
facial behaviour of 91 out-patients - this is almost 3 times the number of
patients in other studies - and propose SchiNet, a novel neural network
architecture that estimates expression-related symptoms in two different
assessment interviews. We evaluate the proposed SchiNet for patient-independent
prediction of symptoms of schizophrenia. Experimental results show that some
automatically detected facial expressions are significantly correlated to
symptoms of schizophrenia, and that the proposed network for estimating symptom
severity delivers promising results.
Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning
Authors: Max Ferguson, Ronay Ak, Yung-Tsun Tina Lee, Kincho H. Law
Date: 2018-08-07 00:00:00
Tasks: Defect Detection, image-classification, Image Classification, Instance Segmentation, Multi-Task Learning, Semantic Segmentation, Transfer Learning

Quality control is a fundamental component of many manufacturing processes,
especially those involving casting or welding. However, manual quality control
procedures are often time-consuming and error-prone. In order to meet the
growing demand for high-quality products, the use of intelligent visual
inspection systems is becoming essential in production lines. Recently,
Convolutional Neural Networks (CNNs) have shown outstanding performance in both
image classification and localization tasks. In this article, a system is
proposed for the identification of casting defects in X-ray images, based on
the Mask Region-based CNN architecture. The proposed defect detection system
simultaneously performs defect detection and segmentation on input images,
making it suitable for a range of defect detection tasks. It is shown that
training the network to simultaneously perform defect detection and defect
instance segmentation, results in a higher defect detection accuracy than
training on defect detection alone. Transfer learning is leveraged to reduce
the training data demands and increase the prediction accuracy of the trained
model. More specifically, the model is first trained with two large
openly-available image datasets before finetuning on a relatively small metal
casting X-ray dataset. The accuracy of the trained model exceeds state-of-the
art performance on the GRIMA database of X-ray images (GDXray) Castings dataset
and is fast enough to be used in a production setting. The system also performs
well on the GDXray Welds dataset. A number of in-depth studies are conducted to
explore how transfer learning, multi-task learning, and multi-class learning
influence the performance of the trained system.
Rethinking Numerical Representations for Deep Neural Networks
Authors: Parker Hill, Babak Zamirai, Shengshuo Lu, Yu-Wei Chao, Michael Laurenzano, Mehrzad Samadi, Marios Papaefthymiou, Scott Mahlke, Thomas Wenisch, Jia Deng, Lingjia Tang, Jason Mars
Date: 2018-08-07 00:00:00
Tasks: Computational Efficiency

With ever-increasing computational demand for deep learning, it is critical
to investigate the implications of the numeric representation and precision of
DNN model weights and activations on computational efficiency. In this work, we
explore unconventional narrow-precision floating-point representations as it
relates to inference accuracy and efficiency to steer the improved design of
future DNN platforms. We show that inference using these custom numeric
representations on production-grade DNNs, including GoogLeNet and VGG, achieves
an average speedup of 7.6x with less than 1% degradation in inference accuracy
relative to a state-of-the-art baseline platform representing the most
sophisticated hardware using single-precision floating point. To facilitate the
use of such customized precision, we also present a novel technique that
drastically reduces the time required to derive the optimal precision
configuration.
Message Passing Graph Kernels
Authors: Giannis Nikolentzos, Michalis Vazirgiannis
Date: 2018-08-07 00:00:00
Tasks: Graph Similarity

Graph kernels have recently emerged as a promising approach for tackling the
graph similarity and learning tasks at the same time. In this paper, we propose
a general framework for designing graph kernels. The proposed framework
capitalizes on the well-known message passing scheme on graphs. The kernels
derived from the framework consist of two components. The first component is a
kernel between vertices, while the second component is a kernel between graphs.
The main idea behind the proposed framework is that the representations of the
vertices are implicitly updated using an iterative procedure. Then, these
representations serve as the building blocks of a kernel that compares pairs of
graphs. We derive four instances of the proposed framework, and show through
extensive experiments that these instances are competitive with
state-of-the-art methods in various tasks.
Device-directed Utterance Detection
Authors: Sri Harish Mallidi, Roland Maas, Kyle Goehner, Ariya Rastrow, Spyros Matsoukas, Björn Hoffmeister
Date: 2018-08-07 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, speech-recognition, Speech Recognition

In this work, we propose a classifier for distinguishing device-directed
queries from background speech in the context of interactions with voice
assistants. Applications include rejection of false wake-ups or unintended
interactions as well as enabling wake-word free follow-up queries. Consider the
example interaction: $"Computer,~play~music", "Computer,~reduce~the~volume"$.
In this interaction, the user needs to repeat the wake-word ($Computer$) for
the second query. To allow for more natural interactions, the device could
immediately re-enter listening state after the first query (without wake-word
repetition) and accept or reject a potential follow-up as device-directed or
background speech. The proposed model consists of two long short-term memory
(LSTM) neural networks trained on acoustic features and automatic speech
recognition (ASR) 1-best hypotheses, respectively. A feed-forward deep neural
network (DNN) is then trained to combine the acoustic and 1-best embeddings,
derived from the LSTMs, with features from the ASR decoder. Experimental
results show that ASR decoder, acoustic embeddings, and 1-best embeddings yield
an equal-error-rate (EER) of $9.3~\%$, $10.9~\%$ and $20.1~\%$, respectively.
Combination of the features resulted in a $44~\%$ relative improvement and a
final EER of $5.2~\%$.
Effective Character-augmented Word Embedding for Machine Reading Comprehension
Authors: Zhuosheng Zhang, Yafang Huang, Pengfei Zhu, Hai Zhao
Date: 2018-08-07 00:00:00
Tasks: Machine Reading Comprehension, Reading Comprehension

Machine reading comprehension is a task to model relationship between passage and query. In terms of deep learning framework, most of state-of-the-art models simply concatenate word and character level representations, which has been shown suboptimal for the concerned task. In this paper, we empirically explore different integration strategies of word and character embeddings and propose a character-augmented reader which attends character-level representation to augment word embedding with a short list to improve word representations, especially for rare words. Experimental results show that the proposed approach helps the baseline model significantly outperform state-of-the-art baselines on various public benchmarks.
Improved survival of cancer patients admitted to the ICU between 2002 and 2011 at a U.S. teaching hospital
Authors: Chris Sauer, Jinghui Dong, Leo Celi, Daniele Ramazzotti
Date: 2018-08-06 00:00:00

Over the past decades, both critical care and cancer care have improved
substantially. Due to increased cancer-specific survival, we hypothesized that
both the number of cancer patients admitted to the ICU and overall survival
have increased since the millennium change. MIMIC-III, a freely accessible
critical care database of Beth Israel Deaconess Medical Center, Boston, USA was
used to retrospectively study trends and outcomes of cancer patients admitted
to the ICU between 2002 and 2011. Multiple logistic regression analysis was
performed to adjust for confounders of 28-day and 1-year mortality.
  Out of 41,468 unique ICU admissions, 1,100 hemato-oncologic, 3,953 oncologic
and 49 patients with both a hematological and solid malignancy were analyzed.
Hematological patients had higher critical illness scores than non-cancer
patients, while oncologic patients had similar APACHE-III and SOFA-scores
compared to non-cancer patients. In the univariate analysis, cancer was
strongly associated with mortality (OR= 2.74, 95%CI: 2.56, 2.94). Over the
10-year study period, 28-day mortality of cancer patients decreased by 30%.
This trend persisted after adjustment for covariates, with cancer patients
having significantly higher mortality (OR=2.63, 95%CI: 2.38, 2.88). Between
2002 and 2011, both the adjusted odds of 28-day mortality and the adjusted odds
of 1-year mortality for cancer patients decreased by 6% (95%CI: 4%, 9%). Having
cancer was the strongest single predictor of 1-year mortality in the
multivariate model (OR=4.47, 95%CI: 4.11, 4.84).
Fast pseudolikelihood maximization for direct-coupling analysis of protein structure from many homologous amino-acid sequences
Authors: Magnus Ekeberg, Tuomo Hartonen, Erik Aurell
Date: 2014-01-20 00:00:00

Direct-Coupling Analysis is a group of methods to harvest information about
coevolving residues in a protein family by learning a generative model in an
exponential family from data. In protein families of realistic size, this
learning can only be done approximately, and there is a trade-off between
inference precision and computational speed. We here show that an earlier
introduced $l_2$-regularized pseudolikelihood maximization method called plmDCA
can be modified as to be easily parallelizable, as well as inherently faster on
a single processor, at negligible difference in accuracy. We test the new
incarnation of the method on 148 protein families from the Protein Families
database (PFAM), one of the largest tests of this class of algorithms to date.
A New Optimization Layer for Real-Time Bidding Advertising Campaigns
Authors: Gianluca Micchi, Saeid Soheily-Khah, Jacob Turner
Date: 2018-08-07 00:00:00

While it is relatively easy to start an online advertising campaign,
obtaining a high Key Performance Indicator (KPI) can be challenging. A large
body of work on this subject has already been performed and platforms known as
DSPs are available on the market that deal with such an optimization. From the
advertiser's point of view, each DSP is a different black box, with its pros
and cons, that needs to be configured. In order to take advantage of the pros
of every DSP, advertisers are well-advised to use a combination of them when
setting up their campaigns. In this paper, we propose an algorithm for
advertisers to add an optimization layer on top of DSPs. The algorithm we
introduce, called SKOTT, maximizes the chosen KPI by optimally configuring the
DSPs and putting them in competition with each other. SKOTT is a highly
specialized iterative algorithm loosely based on gradient descent that is made
up of three independent sub-routines, each dealing with a different problem:
partitioning the budget, setting the desired average bid, and preventing
under-delivery. In particular, one of the novelties of our approach lies in our
taking the perspective of the advertisers rather than the DSPs. Synthetic
market data is used to evaluate the efficiency of SKOTT against other
state-of-the-art approaches adapted from similar problems. The results
illustrate the benefits of our proposals, which greatly outperforms the other
methods.
Counterfactual Normalization: Proactively Addressing Dataset Shift and Improving Reliability Using Causal Mechanisms
Authors: Adarsh Subbaswamy, Suchi Saria
Date: 2018-08-09 00:00:00
Tasks: counterfactual

Predictive models can fail to generalize from training to deployment
environments because of dataset shift, posing a threat to model reliability and
the safety of downstream decisions made in practice. Instead of using samples
from the target distribution to reactively correct dataset shift, we use
graphical knowledge of the causal mechanisms relating variables in a prediction
problem to proactively remove relationships that do not generalize across
environments, even when these relationships may depend on unobserved variables
(violations of the "no unobserved confounders" assumption). To accomplish this,
we identify variables with unstable paths of statistical influence and remove
them from the model. We also augment the causal graph with latent
counterfactual variables that isolate unstable paths of statistical influence,
allowing us to retain stable paths that would otherwise be removed. Our
experiments demonstrate that models that remove vulnerable variables and use
estimates of the latent variables transfer better, often outperforming in the
target domain despite some accuracy loss in the training domain.
3D Shape Perception from Monocular Vision, Touch, and Shape Priors
Authors: Shaoxiong Wang, Jiajun Wu, Xingyuan Sun, Wenzhen Yuan, William T. Freeman, Joshua B. Tenenbaum, Edward H. Adelson
Date: 2018-08-09 00:00:00
Tasks: Object

Perceiving accurate 3D object shape is important for robots to interact with
the physical world. Current research along this direction has been primarily
relying on visual observations. Vision, however useful, has inherent
limitations due to occlusions and the 2D-3D ambiguities, especially for
perception with a monocular camera. In contrast, touch gets precise local shape
information, though its efficiency for reconstructing the entire shape could be
low. In this paper, we propose a novel paradigm that efficiently perceives
accurate 3D object shape by incorporating visual and tactile observations, as
well as prior knowledge of common object shapes learned from large-scale shape
repositories. We use vision first, applying neural networks with learned shape
priors to predict an object's 3D shape from a single-view color image. We then
use tactile sensing to refine the shape; the robot actively touches the object
regions where the visual prediction has high uncertainty. Our method
efficiently builds the 3D shape of common objects from a color image and a
small number of tactile explorations (around 10). Our setup is easy to apply
and has potentials to help robots better perform grasping or manipulation tasks
on real-world objects.
Augmenting Physical Simulators with Stochastic Neural Networks: Case Study of Planar Pushing and Bouncing
Authors: Anurag Ajay, Jiajun Wu, Nima Fazeli, Maria Bauza, Leslie P. Kaelbling, Joshua B. Tenenbaum, Alberto Rodriguez
Date: 2018-08-09 00:00:00
Tasks: Gaussian Processes, Object, State Estimation

An efficient, generalizable physical simulator with universal uncertainty
estimates has wide applications in robot state estimation, planning, and
control. In this paper, we build such a simulator for two scenarios, planar
pushing and ball bouncing, by augmenting an analytical rigid-body simulator
with a neural network that learns to model uncertainty as residuals. Combining
symbolic, deterministic simulators with learnable, stochastic neural nets
provides us with expressiveness, efficiency, and generalizability
simultaneously. Our model outperforms both purely analytical and purely learned
simulators consistently on real, standard benchmarks. Compared with methods
that model uncertainty using Gaussian processes, our model runs much faster,
generalizes better to new object shapes, and is able to characterize the
complex distribution of object trajectories.
User-Guided Deep Anime Line Art Colorization with Conditional Adversarial Networks
Authors: Yuanzheng Ci, Xinzhu Ma, Zhihui Wang, Haojie Li, Zhongxuan Luo
Date: 2018-08-09 00:00:00
Tasks: Benchmarking, Colorization, Line Art Colorization

Scribble colors based line art colorization is a challenging computer vision
problem since neither greyscale values nor semantic information is presented in
line arts, and the lack of authentic illustration-line art training pairs also
increases difficulty of model generalization. Recently, several Generative
Adversarial Nets (GANs) based methods have achieved great success. They can
generate colorized illustrations conditioned on given line art and color hints.
However, these methods fail to capture the authentic illustration distributions
and are hence perceptually unsatisfying in the sense that they often lack
accurate shading. To address these challenges, we propose a novel deep
conditional adversarial architecture for scribble based anime line art
colorization. Specifically, we integrate the conditional framework with WGAN-GP
criteria as well as the perceptual loss to enable us to robustly train a deep
network that makes the synthesized images more natural and real. We also
introduce a local features network that is independent of synthetic data. With
GANs conditioned on features from such network, we notably increase the
generalization capability over "in the wild" line arts. Furthermore, we collect
two datasets that provide high-quality colorful illustrations and authentic
line arts for training and benchmarking. With the proposed model trained on our
illustration dataset, we demonstrate that images synthesized by the presented
approach are considerably more realistic and precise than alternative
approaches.
OBOE: Collaborative Filtering for AutoML Model Selection
Authors: Chengrun Yang, Yuji Akimoto, Dae Won Kim, Madeleine Udell
Date: 2018-08-09 00:00:00
Tasks: Active Learning, AutoML, BIG-bench Machine Learning, Collaborative Filtering, Matrix Completion, model, Model Selection

Algorithm selection and hyperparameter tuning remain two of the most challenging tasks in machine learning. Automated machine learning (AutoML) seeks to automate these tasks to enable widespread use of machine learning by non-experts. This paper introduces OBOE, a collaborative filtering method for time-constrained model selection and hyperparameter tuning. OBOE forms a matrix of the cross-validated errors of a large number of supervised learning models (algorithms together with hyperparameters) on a large number of datasets, and fits a low rank model to learn the low-dimensional feature vectors for the models and datasets that best predict the cross-validated errors. To find promising models for a new dataset, OBOE runs a set of fast but informative algorithms on the new dataset and uses their cross-validated errors to infer the feature vector for the new dataset. OBOE can find good models under constraints on the number of models fit or the total time budget. To this end, this paper develops a new heuristic for active learning in time-constrained matrix completion based on optimal experiment design. Our experiments demonstrate that OBOE delivers state-of-the-art performance faster than competing approaches on a test bed of supervised learning problems. Moreover, the success of the bilinear model used by OBOE suggests that AutoML may be simpler than was previously understood.
Deep Video Color Propagation
Authors: Simone Meyer, Victor Cornillère, Abdelaziz Djelouah, Christopher Schroers, Markus Gross
Date: 2018-08-09 00:00:00
Tasks: Style Transfer

Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.