pwc-archive/papers-with-abstracts huggingface dataset:
Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation
Authors: Yi Luo, Nima Mesgarani
Date: 2018-09-20 00:00:00
Tasks: Multi-task Audio Source Seperation, Music Source Separation, Speaker Separation, Speech Enhancement, Speech Separation

Single-channel, speaker-independent speech separation methods have recently seen great progress. However, the accuracy, latency, and computational cost of such methods remain insufficient. The majority of the previous methods have formulated the separation problem through the time-frequency representation of the mixed signal, which has several drawbacks, including the decoupling of the phase and magnitude of the signal, the suboptimality of time-frequency representation for speech separation, and the long latency in calculating the spectrograms. To address these shortcomings, we propose a fully-convolutional time-domain audio separation network (Conv-TasNet), a deep learning framework for end-to-end time-domain speech separation. Conv-TasNet uses a linear encoder to generate a representation of the speech waveform optimized for separating individual speakers. Speaker separation is achieved by applying a set of weighting functions (masks) to the encoder output. The modified encoder representations are then inverted back to the waveforms using a linear decoder. The masks are found using a temporal convolutional network (TCN) consisting of stacked 1-D dilated convolutional blocks, which allows the network to model the long-term dependencies of the speech signal while maintaining a small model size. The proposed Conv-TasNet system significantly outperforms previous time-frequency masking methods in separating two- and three-speaker mixtures. Additionally, Conv-TasNet surpasses several ideal time-frequency magnitude masks in two-speaker speech separation as evaluated by both objective distortion measures and subjective quality assessment by human listeners. Finally, Conv-TasNet has a significantly smaller model size and a shorter minimum latency, making it a suitable solution for both offline and real-time speech separation applications.
A Coupled Evolutionary Network for Age Estimation
Authors: Pei-Pei Li, Yibo Hu, Ran He, Zhenan Sun
Date: 2018-09-20 00:00:00
Tasks: Age Estimation, MORPH, regression

Age estimation of unknown persons is a challenging pattern analysis task due
to the lacking of training data and various aging mechanisms for different
people. Label distribution learning-based methods usually make distribution
assumptions to simplify age estimation. However, age label distributions are
often complex and difficult to be modeled in a parameter way. Inspired by the
biological evolutionary mechanism, we propose a Coupled Evolutionary Network
(CEN) with two concurrent evolutionary processes: evolutionary label
distribution learning and evolutionary slack regression. Evolutionary network
learns and refines age label distributions in an iteratively learning way.
Evolutionary label distribution learning adaptively learns and constantly
refines the age label distributions without making strong assumptions on the
distribution patterns. To further utilize the ordered and continuous
information of age labels, we accordingly propose an evolutionary slack
regression to convert the discrete age label regression into the continuous age
interval regression. Experimental results on Morph, ChaLearn15 and
MegaAge-Asian datasets show the superiority of our method.
Deep Generative Classifiers for Thoracic Disease Diagnosis with Chest X-ray Images
Authors: Chengsheng Mao, Yiheng Pan, Zexian Zeng, Liang Yao, Yuan Luo
Date: 2018-09-20 00:00:00
Tasks: General Classification, image-classification, Image Classification

Thoracic diseases are very serious health problems that plague a large number
of people. Chest X-ray is currently one of the most popular methods to diagnose
thoracic diseases, playing an important role in the healthcare workflow.
However, reading the chest X-ray images and giving an accurate diagnosis remain
challenging tasks for expert radiologists. With the success of deep learning in
computer vision, a growing number of deep neural network architectures were
applied to chest X-ray image classification. However, most of the previous deep
neural network classifiers were based on deterministic architectures which are
usually very noise-sensitive and are likely to aggravate the overfitting issue.
In this paper, to make a deep architecture more robust to noise and to reduce
overfitting, we propose using deep generative classifiers to automatically
diagnose thorax diseases from the chest X-ray images. Unlike the traditional
deterministic classifier, a deep generative classifier has a distribution
middle layer in the deep neural network. A sampling layer then draws a random
sample from the distribution layer and input it to the following layer for
classification. The classifier is generative because the class label is
generated from samples of a related distribution. Through training the model
with a certain amount of randomness, the deep generative classifiers are
expected to be robust to noise and can reduce overfitting and then achieve good
performances. We implemented our deep generative classifiers based on a number
of well-known deterministic neural network architectures, and tested our models
on the chest X-ray14 dataset. The results demonstrated the superiority of deep
generative classifiers compared with the corresponding deep deterministic
classifiers.
Predicting Periodicity with Temporal Difference Learning
Authors: Kristopher De Asis, Brendan Bennett, Richard S. Sutton
Date: 2018-09-20 00:00:00
Tasks: Decision Making, Reinforcement Learning, Sequential Decision Making

Temporal difference (TD) learning is an important approach in reinforcement
learning, as it combines ideas from dynamic programming and Monte Carlo methods
in a way that allows for online and incremental model-free learning. A key idea
of TD learning is that it is learning predictive knowledge about the
environment in the form of value functions, from which it can derive its
behavior to address long-term sequential decision making problems. The agent's
horizon of interest, that is, how immediate or long-term a TD learning agent
predicts into the future, is adjusted through a discount rate parameter. In
this paper, we introduce an alternative view on the discount rate, with insight
from digital signal processing, to include complex-valued discounting. Our
results show that setting the discount rate to appropriately chosen complex
numbers allows for online and incremental estimation of the Discrete Fourier
Transform (DFT) of a signal of interest with TD learning. We thereby extend the
types of knowledge representable by value functions, which we show are
particularly useful for identifying periodic effects in the reward sequence.
Ranking Distillation: Learning Compact Ranking Models With High Performance for Recommender System
Authors: Jiaxi Tang, Ke Wang
Date: 2018-09-19 00:00:00
Tasks: Knowledge Distillation, Learning-To-Rank, Recommendation Systems, Vocal Bursts Intensity Prediction

We propose a novel way to train ranking models, such as recommender systems,
that are both effective and efficient. Knowledge distillation (KD) was shown to
be successful in image recognition to achieve both effectiveness and
efficiency. We propose a KD technique for learning to rank problems, called
\emph{ranking distillation (RD)}. Specifically, we train a smaller student
model to learn to rank documents/items from both the training data and the
supervision of a larger teacher model. The student model achieves a similar
ranking performance to that of the large teacher model, but its smaller model
size makes the online inference more efficient. RD is flexible because it is
orthogonal to the choices of ranking models for the teacher and student. We
address the challenges of RD for ranking problems. The experiments on public
data sets and state-of-the-art recommendation models showed that RD achieves
its design purposes: the student model learnt with RD has a model size less
than half of the teacher model while achieving a ranking performance similar to
the teacher model and much better than the student model learnt without RD.
Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding
Authors: Jiaxi Tang, Ke Wang
Date: 2018-09-19 00:00:00
Tasks: Recommendation Systems, Sequential Recommendation

Top-$N$ sequential recommendation models each user as a sequence of items
interacted in the past and aims to predict top-$N$ ranked items that a user
will likely interact in a `near future'. The order of interaction implies that
sequential patterns play an important role where more recent items in a
sequence have a larger impact on the next item. In this paper, we propose a
Convolutional Sequence Embedding Recommendation Model (\emph{Caser}) as a
solution to address this requirement. The idea is to embed a sequence of recent
items into an `image' in the time and latent spaces and learn sequential
patterns as local features of the image using convolutional filters. This
approach provides a unified and flexible network structure for capturing both
general preferences and sequential patterns. The experiments on public datasets
demonstrated that Caser consistently outperforms state-of-the-art sequential
recommendation methods on a variety of common evaluation metrics.
Towards Accountable AI: Hybrid Human-Machine Analyses for Characterizing System Failure
Authors: Besmira Nushi, Ece Kamar, Eric Horvitz
Date: 2018-09-19 00:00:00
Tasks: BIG-bench Machine Learning, Image Captioning

As machine learning systems move from computer-science laboratories into the
open world, their accountability becomes a high priority problem.
Accountability requires deep understanding of system behavior and its failures.
Current evaluation methods such as single-score error metrics and confusion
matrices provide aggregate views of system performance that hide important
shortcomings. Understanding details about failures is important for identifying
pathways for refinement, communicating the reliability of systems in different
settings, and for specifying appropriate human oversight and engagement.
Characterization of failures and shortcomings is particularly complex for
systems composed of multiple machine learned components. For such systems,
existing evaluation methods have limited expressiveness in describing and
explaining the relationship among input content, the internal states of system
components, and final output quality. We present Pandora, a set of hybrid
human-machine methods and tools for describing and explaining system failures.
Pandora leverages both human and system-generated observations to summarize
conditions of system malfunction with respect to the input content and system
architecture. We share results of a case study with a machine learning pipeline
for image captioning that show how detailed performance views can be beneficial
for analysis and debugging.
Deep Part Induction from Articulated Object Pairs
Authors: Li Yi, Haibin Huang, Difan Liu, Evangelos Kalogerakis, Hao Su, Leonidas Guibas
Date: 2018-09-19 00:00:00
Tasks: Object

Object functionality is often expressed through part articulation -- as when
the two rigid parts of a scissor pivot against each other to perform the
cutting function. Such articulations are often similar across objects within
the same functional category. In this paper, we explore how the observation of
different articulation states provides evidence for part structure and motion
of 3D objects. Our method takes as input a pair of unsegmented shapes
representing two different articulation states of two functionally related
objects, and induces their common parts along with their underlying rigid
motion. This is a challenging setting, as we assume no prior shape structure,
no prior shape category information, no consistent shape orientation, the
articulation states may belong to objects of different geometry, plus we allow
inputs to be noisy and partial scans, or point clouds lifted from RGB images.
Our method learns a neural network architecture with three modules that
respectively propose correspondences, estimate 3D deformation flows, and
perform segmentation. To achieve optimal performance, our architecture
alternates between correspondence, deformation flow, and segmentation
prediction iteratively in an ICP-like fashion. Our results demonstrate that our
method significantly outperforms state-of-the-art techniques in the task of
discovering articulated parts of objects. In addition, our part induction is
object-class agnostic and successfully generalizes to new and unseen objects.
Learning, Planning, and Control in a Monolithic Neural Event Inference Architecture
Authors: Martin V. Butz, David Bilkey, Dania Humaidan, Alistair Knott, Sebastian Otte
Date: 2018-09-19 00:00:00
Tasks: Model Predictive Control

We introduce REPRISE, a REtrospective and PRospective Inference SchEme, which learns temporal event-predictive models of dynamical systems. REPRISE infers the unobservable contextual event state and accompanying temporal predictive models that best explain the recently encountered sensorimotor experiences retrospectively. Meanwhile, it optimizes upcoming motor activities prospectively in a goal-directed manner. Here, REPRISE is implemented by a recurrent neural network (RNN), which learns temporal forward models of the sensorimotor contingencies generated by different simulated dynamic vehicles. The RNN is augmented with contextual neurons, which enable the encoding of distinct, but related, sensorimotor dynamics as compact event codes. We show that REPRISE concurrently learns to separate and approximate the encountered sensorimotor dynamics: it analyzes sensorimotor error signals adapting both internal contextual neural activities and connection weight values. Moreover, we show that REPRISE can exploit the learned model to induce goal-directed, model-predictive control, that is, approximate active inference: Given a goal state, the system imagines a motor command sequence optimizing it with the prospective objective to minimize the distance to the goal. The RNN activities thus continuously imagine the upcoming future and reflect on the recent past, optimizing the predictive model, the hidden neural state activities, and the upcoming motor activities. As a result, event-predictive neural encodings develop, which allow the invocation of highly effective and adaptive goal-directed sensorimotor control.
Egocentric Vision-based Future Vehicle Localization for Intelligent Driving Assistance Systems
Authors: Yu Yao, Mingze Xu, Chiho Choi, David J. Crandall, Ella M. Atkins, Behzad Dariush
Date: 2018-09-19 00:00:00
Tasks: Autonomous Driving, Decoder, Motion Planning, Optical Flow Estimation, Prediction

Predicting the future location of vehicles is essential for safety-critical
applications such as advanced driver assistance systems (ADAS) and autonomous
driving. This paper introduces a novel approach to simultaneously predict both
the location and scale of target vehicles in the first-person (egocentric) view
of an ego-vehicle. We present a multi-stream recurrent neural network (RNN)
encoder-decoder model that separately captures both object location and scale
and pixel-level observations for future vehicle localization. We show that
incorporating dense optical flow improves prediction results significantly
since it captures information about motion as well as appearance change. We
also find that explicitly modeling future motion of the ego-vehicle improves
the prediction accuracy, which could be especially beneficial in intelligent
and automated vehicles that have motion planning capability. To evaluate the
performance of our approach, we present a new dataset of first-person videos
collected from a variety of scenarios at road intersections, which are
particularly challenging moments for prediction because vehicle trajectories
are diverse and dynamic.
Exploiting Tournament Selection for Efficient Parallel Genetic Programming
Authors: Darren M. Chitty
Date: 2018-09-19 00:00:00
Tasks: CPU, General Classification

Genetic Programming (GP) is a computationally intensive technique which is
naturally parallel in nature. Consequently, many attempts have been made to
improve its run-time from exploiting highly parallel hardware such as GPUs.
However, a second methodology of improving the speed of GP is through
efficiency techniques such as subtree caching. However achieving parallel
performance and efficiency is a difficult task. This paper will demonstrate an
efficiency saving for GP compatible with the harnessing of parallel CPU
hardware by exploiting tournament selection. Significant efficiency savings are
demonstrated whilst retaining the capability of a high performance parallel
implementation of GP. Indeed, a 74% improvement in the speed of GP is achieved
with a peak rate of 96 billion GPop/s for classification type problems.
Distances for WiFi Based Topological Indoor Mapping
Authors: Bastian Schäfermeier, Tom Hanika, Gerd Stumme
Date: 2018-09-19 00:00:00
Tasks: Density Estimation

For localization and mapping of indoor environments through WiFi signals,
locations are often represented as likelihoods of the received signal strength
indicator. In this work we compare various measures of distance between such
likelihoods in combination with different methods for estimation and
representation. In particular, we show that among the considered distance
measures the Earth Mover's Distance seems the most beneficial for the
localization task. Combined with kernel density estimation we were able to
retain the topological structure of rooms in a real-world office scenario.
Identifying Generalization Properties in Neural Networks
Authors: Huan Wang, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
Date: 2018-09-19 00:00:00
Proceeding: ICLR 2019 5

While it has not yet been proven, empirical evidence suggests that model
generalization is related to local properties of the optima which can be
described via the Hessian. We connect model generalization with the local
property of a solution under the PAC-Bayes paradigm. In particular, we prove
that model generalization ability is related to the Hessian, the higher-order
"smoothness" terms characterized by the Lipschitz constant of the Hessian, and
the scales of the parameters. Guided by the proof, we propose a metric to score
the generalization capability of the model, as well as an algorithm that
optimizes the perturbed model accordingly.
Nonisometric Surface Registration via Conformal Laplace-Beltrami Basis Pursuit
Authors: Stefan C. Schonsheck, Michael M. Bronstein, Rongjie Lai
Date: 2018-09-19 00:00:00

Surface registration is one of the most fundamental problems in geometry
processing. Many approaches have been developed to tackle this problem in cases
where the surfaces are nearly isometric. However, it is much more challenging
to compute correspondence between surfaces which are intrinsically less
similar. In this paper, we propose a variational model to align the
Laplace-Beltrami (LB) eigensytems of two non-isometric genus zero shapes via
conformal deformations. This method enables us compute to geometric meaningful
point-to-point maps between non-isometric shapes. Our model is based on a novel
basis pursuit scheme whereby we simultaneously compute a conformal deformation
of a 'target shape' and its deformed LB eigensytem. We solve the model using an
proximal alternating minimization algorithm hybridized with the augmented
Lagrangian method which produces accurate correspondences given only a few
landmark points. We also propose a reinitialization scheme to overcome some of
the difficulties caused by the non-convexity of the variational problem.
Intensive numerical experiments illustrate the effectiveness and robustness of
the proposed method to handle non-isometric surfaces with large deformation
with respect to both noise on the underlying manifolds and errors within the
given landmarks.
Improving Subseasonal Forecasting in the Western U.S. with Machine Learning
Authors: Jessica Hwang, Paulo Orenstein, Judah Cohen, Karl Pfeiffer, Lester Mackey
Date: 2018-09-19 00:00:00
Tasks: BIG-bench Machine Learning, Model Selection, regression

Water managers in the western United States (U.S.) rely on longterm forecasts of temperature and precipitation to prepare for droughts and other wet weather extremes. To improve the accuracy of these longterm forecasts, the U.S. Bureau of Reclamation and the National Oceanic and Atmospheric Administration (NOAA) launched the Subseasonal Climate Forecast Rodeo, a year-long real-time forecasting challenge in which participants aimed to skillfully predict temperature and precipitation in the western U.S. two to four weeks and four to six weeks in advance. Here we present and evaluate our machine learning approach to the Rodeo and release our SubseasonalRodeo dataset, collected to train and evaluate our forecasting system. Our system is an ensemble of two regression models. The first integrates the diverse collection of meteorological measurements and dynamic model forecasts in the SubseasonalRodeo dataset and prunes irrelevant predictors using a customized multitask model selection procedure. The second uses only historical measurements of the target variable (temperature or precipitation) and introduces multitask nearest neighbor features into a weighted local linear regression. Each model alone is significantly more accurate than the debiased operational U.S. Climate Forecasting System (CFSv2), and our ensemble skill exceeds that of the top Rodeo competitor for each target variable and forecast horizon. Moreover, over 2011-2018, an ensemble of our regression models and debiased CFSv2 improves debiased CFSv2 skill by 40-50% for temperature and 129-169% for precipitation. We hope that both our dataset and our methods will help to advance the state of the art in subseasonal forecasting.
A Dataset for Document Grounded Conversations
Authors: Kangyan Zhou, Shrimai Prabhumoye, Alan W. black
Date: 2018-09-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles

This paper introduces a document grounded dataset for text conversations. We
define "Document Grounded Conversations" as conversations that are about the
contents of a specified document. In this dataset the specified documents were
Wikipedia articles about popular movies. The dataset contains 4112
conversations with an average of 21.43 turns per conversation. This positions
this dataset to not only provide a relevant chat history while generating
responses but also provide a source of information that the models could use.
We describe two neural architectures that provide benchmark performance on the
task of generating the next response. We also evaluate our models for
engagement and fluency, and find that the information from the document helps
in generating more engaging and fluent responses.