pwc-archive/papers-with-abstracts huggingface dataset:
Toward A Neuro-inspired Creative Decoder
Authors: Payel Das, Brian Quanz, Pin-Yu Chen, Jae-wook Ahn, Dhruv Shah
Date: 2019-02-06 00:00:00
Tasks: Decoder

Creativity, a process that generates novel and meaningful ideas, involves increased association between task-positive (control) and task-negative (default) networks in the human brain. Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space. The proposed approach is fully unsupervised and can be used off-the-shelf. Several novelty metrics and human evaluation were used to evaluate the creative capacity of the deep decoder. Our experiments on different image datasets (MNIST, FMNIST, MNIST+FMNIST, WikiArt and CelebA) reveal that atypical co-activation of highly activated and weakly activated neurons in a deep decoder promotes generation of novel and meaningful artifacts.
Real-time 3D Traffic Cone Detection for Autonomous Driving
Authors: Ankit Dhall, Dengxin Dai, Luc van Gool
Date: 2019-02-06 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Autonomous Vehicles, object-detection, Object Detection, Position, Scene Understanding

Considerable progress has been made in semantic scene understanding of road scenes with monocular cameras. It is, however, mainly related to certain classes such as cars and pedestrians. This work investigates traffic cones, an object class crucial for traffic control in the context of autonomous vehicles. 3D object detection using images from a monocular camera is intrinsically an ill-posed problem. In this work, we leverage the unique structure of traffic cones and propose a pipelined approach to the problem. Specifically, we first detect cones in images by a tailored 2D object detector; then, the spatial arrangement of keypoints on a traffic cone are detected by our deep structural regression network, where the fact that the cross-ratio is projection invariant is leveraged for network regularization; finally, the 3D position of cones is recovered by the classical Perspective n-Point algorithm. Extensive experiments show that our approach can accurately detect traffic cones and estimate their position in the 3D world in real time. The proposed method is also deployed on a real-time, critical system. It runs efficiently on the low-power Jetson TX2, providing accurate 3D position estimates, allowing a race-car to map and drive autonomously on an unseen track indicated by traffic cones. With the help of robust and accurate perception, our race-car won both Formula Student Competitions held in Italy and Germany in 2018, cruising at a top-speed of 54 kmph. Visualization of the complete pipeline, mapping and navigation can be found on our project page.
Distributed Synthesis of Surveillance Strategies for Mobile Sensors
Authors: Suda Bharadwaj, Rayna Dimitrova, Ufuk Topcu
Date: 2019-02-06 00:00:00

We study the problem of synthesizing strategies for a mobile sensor network
to conduct surveillance in partnership with static alarm triggers. We formulate
the problem as a multi-agent reactive synthesis problem with surveillance
objectives specified as temporal logic formulas. In order to avoid the state
space blow-up arising from a centralized strategy computation, we propose a
method to decentralize the surveillance strategy synthesis by decomposing the
multi-agent game into subgames that can be solved independently. We also
decompose the global surveillance specification into local specifications for
each sensor, and show that if the sensors satisfy their local surveillance
specifications, then the sensor network as a whole will satisfy the global
surveillance objective. Thus, our method is able to guarantee global
surveillance properties in a mobile sensor network while synthesizing
completely decentralized strategies with no need for coordination between the
sensors. We also present a case study in which we demonstrate an application of
decentralized surveillance strategy synthesis.
Investigating Recurrent Neural Network Memory Structures using Neuro-Evolution
Authors: Alexander Ororbia, Ahmed Ahmed Elsaid, Travis Desell
Date: 2019-02-06 00:00:00
Tasks: CPU, Time Series, Time Series Analysis

This paper presents a new algorithm, Evolutionary eXploration of Augmenting
Memory Models (EXAMM), which is capable of evolving recurrent neural networks
(RNNs) using a wide variety of memory structures, such as Delta-RNN, GRU, LSTM,
MGU and UGRNN cells. EXAMM evolved RNNs to perform prediction of large-scale,
real world time series data from the aviation and power industries. These data
sets consist of very long time series (thousands of readings), each with a
large number of potentially correlated and dependent parameters. Four different
parameters were selected for prediction and EXAMM runs were performed using
each memory cell type alone, each cell type with feed forward nodes, and with
all possible memory cell types. Evolved RNN performance was measured using
repeated k-fold cross validation, resulting in 1210 EXAMM runs which evolved
2,420,000 RNNs in 12,100 CPU hours on a high performance computing cluster.
Generalization of the evolved RNNs was examined statistically, providing
interesting findings that can help refine the RNN memory cell design as well as
inform future neuro-evolution algorithms development.
Global Explanations of Neural Networks: Mapping the Landscape of Predictions
Authors: Mark Ibrahim, Melissa Louie, Ceena Modarres, John Paisley
Date: 2019-02-06 00:00:00

A barrier to the wider adoption of neural networks is their lack of
interpretability. While local explanation methods exist for one prediction,
most global attributions still reduce neural network decisions to a single set
of features. In response, we present an approach for generating global
attributions called GAM, which explains the landscape of neural network
predictions across subpopulations. GAM augments global explanations with the
proportion of samples that each attribution best explains and specifies which
samples are described by each attribution. Global explanations also have
tunable granularity to detect more or fewer subpopulations. We demonstrate that
GAM's global explanations 1) yield the known feature importances of simulated
data, 2) match feature weights of interpretable statistical models on real
data, and 3) are intuitive to practitioners through user studies. With more
transparent predictions, GAM can help ensure neural network decisions are
generated for the right reasons.
End-to-end Anchored Speech Recognition
Authors: Yiming Wang, Xing Fan, I-Fan Chen, Yuzong Liu, Tongfei Chen, Björn Hoffmeister
Date: 2019-02-06 00:00:00
Tasks: Decoder, Multi-Task Learning, speech-recognition, Speech Recognition

Voice-controlled house-hold devices, like Amazon Echo or Google Home, face
the problem of performing speech recognition of device-directed speech in the
presence of interfering background speech, i.e., background noise and
interfering speech from another person or media device in proximity need to be
ignored. We propose two end-to-end models to tackle this problem with
information extracted from the "anchored segment". The anchored segment refers
to the wake-up word part of an audio stream, which contains valuable speaker
information that can be used to suppress interfering speech and background
noise. The first method is called "Multi-source Attention" where the attention
mechanism takes both the speaker information and decoder state into
consideration. The second method directly learns a frame-level mask on top of
the encoder output. We also explore a multi-task learning setup where we use
the ground truth of the mask to guide the learner. Given that audio data with
interfering speech is rare in our training data set, we also propose a way to
synthesize "noisy" speech from "clean" speech to mitigate the mismatch between
training and test data. Our proposed methods show up to 15% relative reduction
in WER for Amazon Alexa live data with interfering background speech without
significantly degrading on clean speech.
Compression of Recurrent Neural Networks for Efficient Language Modeling
Authors: Artem M. Grachev, Dmitry I. Ignatov, Andrey V. Savchenko
Date: 2019-02-06 00:00:00
Tasks: Language Modeling, Language Modelling, Quantization

Recurrent neural networks have proved to be an effective method for
statistical language modeling. However, in practice their memory and run-time
complexity are usually too large to be implemented in real-time offline mobile
applications. In this paper we consider several compression techniques for
recurrent neural networks including Long-Short Term Memory models. We make
particular attention to the high-dimensional output problem caused by the very
large vocabulary size. We focus on effective compression methods in the context
of their exploitation on devices: pruning, quantization, and matrix
decomposition approaches (low-rank factorization and tensor train
decomposition, in particular). For each model we investigate the trade-off
between its size, suitability for fast inference and perplexity. We propose a
general pipeline for applying the most suitable methods to compress recurrent
neural networks for language modeling. It has been shown in the experimental
study with the Penn Treebank (PTB) dataset that the most efficient results in
terms of speed and compression-perplexity balance are obtained by matrix
decomposition techniques.
DiffEqFlux.jl - A Julia Library for Neural Differential Equations
Authors: Chris Rackauckas, Mike Innes, Yingbo Ma, Jesse Bettencourt, Lyndon White, Vaibhav Dixit
Date: 2019-02-06 00:00:00
Tasks: BIG-bench Machine Learning

DiffEqFlux.jl is a library for fusing neural networks and differential
equations. In this work we describe differential equations from the viewpoint
of data science and discuss the complementary nature between machine learning
models and differential equations. We demonstrate the ability to incorporate
DifferentialEquations.jl-defined differential equation problems into a
Flux-defined neural network, and vice versa. The advantages of being able to
use the entire DifferentialEquations.jl suite for this purpose is demonstrated
by counter examples where simple integration strategies fail, but the
sophisticated integration strategies provided by the DifferentialEquations.jl
library succeed. This is followed by a demonstration of delay differential
equations and stochastic differential equations inside of neural networks. We
show high-level functionality for defining neural ordinary differential
equations (neural networks embedded into the differential equation) and
describe the extra models in the Flux model zoo which includes neural
stochastic differential equations. We conclude by discussing the various
adjoint methods used for backpropogation of the differential equation solvers.
DiffEqFlux.jl is an important contribution to the area, as it allows the full
weight of the differential equation solvers developed from decades of research
in the scientific computing field to be readily applied to the challenges posed
by machine learning and data science.
Centroid-based deep metric learning for speaker recognition
Authors: Jixuan Wang, Kuan-Chieh Wang, Marc Law, Frank Rudzicz, Michael Brudno
Date: 2019-02-06 00:00:00
Tasks: Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Metric Learning, Speaker Recognition, Speaker Verification, Triplet

Speaker embedding models that utilize neural networks to map utterances to a
space where distances reflect similarity between speakers have driven recent
progress in the speaker recognition task. However, there is still a significant
performance gap between recognizing speakers in the training set and unseen
speakers. The latter case corresponds to the few-shot learning task, where a
trained model is evaluated on unseen classes. Here, we optimize a speaker
embedding model with prototypical network loss (PNL), a state-of-the-art
approach for the few-shot image classification task. The resulting embedding
model outperforms the state-of-the-art triplet loss based models in both
speaker verification and identification tasks, for both seen and unseen
speakers.
Modeling and Analysis of Tagging Networks in Stack Exchange Communities
Authors: Xiang Fu, Shangdi Yu, Austin R. Benson
Date: 2019-02-06 00:00:00
Tasks: TAG

Large Question-and-Answer (Q&A) platforms support diverse knowledge curation
on the Web. While researchers have studied user behavior on the platforms in a
variety of contexts, there is relatively little insight into important
by-products of user behavior that also encode knowledge. Here, we analyze and
model the macroscopic structure of tags applied by users to annotate and
catalog questions, using a collection of 168 Stack Exchange websites. We find
striking similarity in tagging structure across these Stack Exchange
communities, even though each community evolves independently (albeit under
similar guidelines). Using our empirical findings, we develop a simple
generative model that creates random bipartite graphs of tags and questions.
Our model accounts for the tag frequency distribution but does not explicitly
account for co-tagging correlations. Even under these constraints, we
demonstrate empirically and theoretically that our model can reproduce a number
of statistical properties of the co-tagging graph that links tags appearing in
the same post.
On the asymptotic optimality of the comb strategy for prediction with expert advice
Authors: Erhan Bayraktar, Ibrahim Ekren, Yili Zhang
Date: 2019-02-06 00:00:00

For the problem of prediction with expert advice in the adversarial setting with geometric stopping, we compute the exact leading order expansion for the long time behavior of the value function. Then, we use this expansion to prove that as conjectured in Gravin et al. [12], the comb strategies are indeed asymptotically optimal for the adversary in the case of 4 experts.
Negative eigenvalues of the Hessian in deep neural networks
Authors: Guillaume Alain, Nicolas Le Roux, Pierre-Antoine Manzagol
Date: 2019-02-06 00:00:00

The loss function of deep networks is known to be non-convex but the precise
nature of this nonconvexity is still an active area of research. In this work,
we study the loss landscape of deep networks through the eigendecompositions of
their Hessian matrix. In particular, we examine how important the negative
eigenvalues are and the benefits one can observe in handling them
appropriately.
The role of a layer in deep neural networks: a Gaussian Process perspective
Authors: Oded Ben-David, Zohar Ringel
Date: 2019-02-06 00:00:00
Tasks: Gaussian Processes

A fundamental question in deep learning concerns the role played by individual layers in a deep neural network (DNN) and the transferable properties of the data representations which they learn. To the extent that layers have clear roles, one should be able to optimize them separately using layer-wise loss functions. Such loss functions would describe what is the set of good data representations at each depth of the network and provide a target for layer-wise greedy optimization (LEGO). Here we derive a novel correspondence between Gaussian Processes and SGD trained deep neural networks. Leveraging this correspondence, we derive the Deep Gaussian Layer-wise loss functions (DGLs) which, we believe, are the first supervised layer-wise loss functions which are both explicit and competitive in terms of accuracy. Being highly structured and symmetric, the DGLs provide a promising analytic route to understanding the internal representations generated by DNNs.
SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A novel approach to train Convolutional Neural Networks on lung CT scans using binary labels only
Authors: Mario Zusag, Sujal Desai, Marcello Di Paolo, Thomas Semple, Anand Shah, Elsa Angelini
Date: 2019-02-06 00:00:00
Tasks: Computed Tomography (CT)

Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by
infection with Aspergillus. Computed tomography (CT) images are frequently
requested in patients with suspected and established disease, but the
radiological signs on CT are difficult to quantify making accurate follow-up
challenging. We propose a novel method to train Convolutional Neural Networks
using only regional labels on the presence of pathological signs, to not only
detect CPA, but also spatially localize pathological signs. We use average
intensity projections within different ranges of Hounsfield-unit (HU) values,
transforming input 3D CT scans into 2D RGB-like images. CNN architectures are
trained for hierarchical tasks, leading to precise activation maps of
pathological patterns. Results on a cohort of 352 subjects demonstrate high
classification accuracy, localization precision and predictive power of 2 year
survival. Such tool opens the way to CPA patient stratification and
quantitative follow-up of CPA pathological signs, for patients under drug
therapy.
Impact of Fully Connected Layers on Performance of Convolutional Neural Networks for Image Classification
Authors: S. H. Shabbeer Basha, Shiv Ram Dubey, Viswanath Pulabaigari, Snehasis Mukherjee
Date: 2019-01-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

The Convolutional Neural Networks (CNNs), in domains like computer vision, mostly reduced the need for handcrafted features due to its ability to learn the problem-specific features from the raw input data. However, the selection of dataset-specific CNN architecture, which mostly performed by either experience or expertise is a time-consuming and error-prone process. To automate the process of learning a CNN architecture, this paper attempts at finding the relationship between Fully Connected (FC) layers with some of the characteristics of the datasets. The CNN architectures, and recently datasets also, are categorized as deep, shallow, wide, etc. This paper tries to formalize these terms along with answering the following questions. (i) What is the impact of deeper/shallow architectures on the performance of the CNN w.r.t. FC layers?, (ii) How the deeper/wider datasets influence the performance of CNN w.r.t. FC layers?, and (iii) Which kind of architecture (deeper/ shallower) is better suitable for which kind of (deeper/ wider) datasets. To address these findings, we have performed experiments with three CNN architectures having different depths. The experiments are conducted by varying the number of FC layers. We used four widely used datasets including CIFAR-10, CIFAR-100, Tiny ImageNet, and CRCHistoPhenotypes to justify our findings in the context of the image classification problem. The source code of this research is available at https://github.com/shabbeersh/Impact-of-FC-layers.
Fast Transient Simulation of High-Speed Channels Using Recurrent Neural Network
Authors: Thong Nguyen, Tianjian Lu, Ken Wu, Jose Schutt-Aine
Date: 2019-01-25 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Generating eye diagrams by using a circuit simulator can be very
computationally intensive, especially in the presence of nonlinearities. It
often involves multiple Newton-like iterations at every time step when a
SPICE-like circuit simulator handles a nonlinear system in the transient
regime. In this paper, we leverage machine learning methods, to be specific,
the recurrent neural network (RNN), to generate black-box macromodels and
achieve significant reduction of computation time. Through the proposed
approach, an RNN model is first trained and then validated on a relatively
short sequence generated from a circuit simulator. Once the training completes,
the RNN can be used to make predictions on the remaining sequence in order to
generate an eye diagram. The training cost can also be amortized when the
trained RNN starts making predictions. Besides, the proposed approach requires
no complex circuit simulations nor substantial domain knowledge. We use two
high-speed link examples to demonstrate that the proposed approach provides
adequate accuracy while the computation time can be dramatically reduced. In
the high-speed link example with a PAM4 driver, the eye diagram generated by
RNN models shows good agreement with that obtained from a commercial circuit
simulator. This paper also investigates the impacts of various RNN topologies,
training schemes, and tunable parameters on both the accuracy and the
generalization capability of an RNN model. It is found out that the long
short-term memory (LSTM) network outperforms the vanilla RNN in terms of the
accuracy in predicting transient waveforms.
Neural Network for NILM Based on Operational State Change Classification
Authors: Peng Xiao, Samuel Cheng
Date: 2019-02-05 00:00:00
Tasks: Classification, General Classification

Energy disaggregation in a non-intrusive way estimates appliance level
electricity consumption from a single meter that measures the whole house
electricity demand. Recently, with the ongoing increment of energy data, there
are many data-driven deep learning architectures being applied to solve the
non-intrusive energy disaggregation problem. However, most proposed methods try
to estimate the on-off state or the power consumption of appliance, which need
not only large amount of parameters, but also hyper-parameter optimization
prior to training and even preprocessing of energy data for a specified
appliance. In this paper, instead of estimating on-off state or power
consumption, we adapt a neural network to estimate the operational state change
of appliance. Our proposed solution is more feasible across various appliances
and lower complexity comparing to previous methods. The simulated experiments
in the low sample rate dataset REDD show the competitive performance of the
designed method, with respect to other two benchmark methods, Hidden Markov
Model-based and Graph Signal processing-based approaches.
Neural TTS Stylization with Adversarial and Collaborative Games
Authors: Shuang Ma, Daniel McDuff, Yale Song
Date: 2019-05-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Disentanglement, Style Transfer

The modeling of style when synthesizing natural human speech from text has been the focus of significant attention. Some state-of-the-art approaches train an encoder-decoder network on paired text and audio samples (x_txt, x_aud) by encouraging its output to reconstruct x_aud. The synthesized audio waveform is expected to contain the verbal content of x_txt and the auditory style of x_aud. Unfortunately, modeling style in TTS is somewhat under-determined and training models with a reconstruction loss alone is insufficient to disentangle content and style from other factors of variation. In this work, we introduce an end-to-end TTS model that offers enhanced content-style disentanglement ability and controllability. We achieve this by combining a pairwise training procedure, an adversarial game, and a collaborative game into one training scheme. The adversarial game concentrates the true data distribution, and the collaborative game minimizes the distance between real samples and generated samples in both the original space and the latent space. As a result, the proposed model delivers a highly controllable generator, and a disentangled representation. Benefiting from the separate modeling of style and content, our model can generate human fidelity speech that satisfies the desired style conditions. Our model achieves start-of-the-art results across multiple tasks, including style transfer (content and style swapping), emotion modeling, and identity transfer (fitting a new speaker's voice).