pwc-archive/papers-with-abstracts huggingface dataset:
Crowdsourcing Lightweight Pyramids for Manual Summary Evaluation
Authors: Ori Shapira, David Gabay, Yang Gao, Hadar Ronen, Ramakanth Pasunuru, Mohit Bansal, Yael Amsterdamer, Ido Dagan
Date: 2019-04-11 00:00:00
Proceeding: NAACL 2019 6

Conducting a manual evaluation is considered an essential part of summary
evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively
compares system summaries to references, has been perceived as very reliable,
providing objective scores. Yet, due to the high cost of the Pyramid method and
the required expertise, researchers resorted to cheaper and less thorough
manual evaluation methods, such as Responsiveness and pairwise comparison,
attainable via crowdsourcing. We revisit the Pyramid approach, proposing a
lightweight sampling-based version that is crowdsourcable. We analyze the
performance of our method in comparison to original expert-based Pyramid
evaluations, showing higher correlation relative to the common Responsiveness
method. We release our crowdsourced Summary-Content-Units, along with all
crowdsourcing scripts, for future evaluations.
Synthetic Examples Improve Generalization for Rare Classes
Authors: Sara Beery, Yang Liu, Dan Morris, Jim Piavis, Ashish Kapoor, Markus Meister, Neel Joshi, Pietro Perona
Date: 2019-04-11 00:00:00
Tasks: Few-Shot Learning, Self-Driving Cars

The ability to detect and classify rare occurrences in images has important applications - for example, counting rare and endangered species when studying biodiversity, or detecting infrequent traffic scenarios that pose a danger to self-driving cars. Few-shot learning is an open problem: current computer vision systems struggle to categorize objects they have seen only rarely during training, and collecting a sufficient number of training examples of rare events is often challenging and expensive, and sometimes outright impossible. We explore in depth an approach to this problem: complementing the few available training images with ad-hoc simulated data. Our testbed is animal species classification, which has a real-world long-tailed distribution. We analyze the effect of different axes of variation in simulation, such as pose, lighting, model, and simulation method, and we prescribe best practices for efficiently incorporating simulated data for real-world performance gain. Our experiments reveal that synthetic data can considerably reduce error rates for classes that are rare, that as the amount of simulated data is increased, accuracy on the target class improves, and that high variation of simulated data provides maximum performance gain.
Inferring the quantum density matrix with machine learning
Authors: Kyle Cranmer, Siavash Golkar, Duccio Pappadopulo
Date: 2019-04-11 00:00:00
Tasks: BIG-bench Machine Learning, Variational Inference

We introduce two methods for estimating the density matrix for a quantum
system: Quantum Maximum Likelihood and Quantum Variational Inference. In these
methods, we construct a variational family to model the density matrix of a
mixed quantum state. We also introduce quantum flows, the quantum analog of
normalizing flows, which can be used to increase the expressivity of this
variational family. The eigenstates and eigenvalues of interest are then
derived by optimizing an appropriate loss function. The approach is
qualitatively different than traditional lattice techniques that rely on the
time dependence of correlation functions that summarize the lattice
configurations. The resulting estimate of the density matrix can then be used
to evaluate the expectation of an arbitrary operator, which opens the door to
new possibilities.
Experimental neural network enhanced quantum tomography
Authors: Adriano Macarone Palmieri, Egor Kovlakov, Federico Bianchi, Dmitry Yudin, Stanislav Straupe, Jacob Biamonte, Sergei Kulik
Date: 2019-04-11 00:00:00

Quantum tomography is currently ubiquitous for testing any implementation of
a quantum information processing device. Various sophisticated procedures for
state and process reconstruction from measured data are well developed and
benefit from precise knowledge of the model describing state preparation and
the measurement apparatus. However, physical models suffer from intrinsic
limitations as actual measurement operators and trial states cannot be known
precisely. This scenario inevitably leads to state-preparation-and-measurement
(SPAM) errors degrading reconstruction performance. Here we develop and
experimentally implement a machine learning based protocol reducing SPAM
errors. We trained a supervised neural network to filter the experimental data
and hence uncovered salient patterns that characterize the measurement
probabilities for the original state and the ideal experimental apparatus free
from SPAM errors. We compared the neural network state reconstruction protocol
with a protocol treating SPAM errors by process tomography, as well as to a
SPAM-agnostic protocol with idealized measurements. The average reconstruction
fidelity is shown to be enhanced by 10\% and 27\%, respectively. The presented
methods apply to the vast range of quantum experiments which rely on
tomography.
Compressing deep neural networks by matrix product operators
Authors: Ze-Feng Gao, Song Cheng, Rong-Qiang He, Z. Y. Xie, Hui-Hai Zhao, Zhong-Yi Lu, Tao Xiang
Date: 2019-04-11 00:00:00

A deep neural network is a parametrization of a multilayer mapping of signals in terms of many alternatively arranged linear and nonlinear transformations. The linear transformations, which are generally used in the fully connected as well as convolutional layers, contain most of the variational parameters that are trained and stored. Compressing a deep neural network to reduce its number of variational parameters but not its prediction power is an important but challenging problem toward the establishment of an optimized scheme in training efficiently these parameters and in lowering the risk of overfitting. Here we show that this problem can be effectively solved by representing linear transformations with matrix product operators (MPOs), which is a tensor network originally proposed in physics to characterize the short-range entanglement in one-dimensional quantum states. We have tested this approach in five typical neural networks, including FC2, LeNet-5, VGG, ResNet, and DenseNet on two widely used data sets, namely, MNIST and CIFAR-10, and found that this MPO representation indeed sets up a faithful and efficient mapping between input and output signals, which can keep or even improve the prediction accuracy with a dramatically reduced number of parameters. Our method greatly simplifies the representations in deep learning, and opens a possible route toward establishing a framework of modern neural networks which might be simpler and cheaper, but more efficient.
On Machine Learning DoS Attack Identification from Cloud Computing Telemetry
Authors: João Henrique Corrêa, Patrick Marques Ciarelli, Moises R. N. Ribeiro, Rodolfo da Silva Villaca
Date: 2019-04-11 00:00:00
Tasks: BIG-bench Machine Learning, Cloud Computing

The detection of Denial of Service (DoS) attacks remains a challenge for the
cloud environment, affecting a massive number of services and applications
hosted by such virtualized infrastructures. Typically, in the literature, the
detection of DoS attacks is performed solely by analyzing the traffic of
packets in the network. This work advocates for the use of telemetry from the
cloud to detect DoS attacks using Machine Learning algorithms. Our hypothesis
is based on richness of such native data collection services, with metrics from
both physical and virtual hosts. Our preliminary results demonstrate that DoS
can be identified accurately with k-Nearest Neighbors (kNN) and decision tree
(CART).
Topological signature for periodic motion recognition
Authors: Javier Lamar-Leon, Rocio Gonzalez-Diaz, Edel Garcia-Reyes
Date: 2019-04-11 00:00:00
Tasks: Gait Recognition

In this paper, we present an algorithm that computes the topological
signature for a given periodic motion sequence. Such signature consists of a
vector obtained by persistent homology which captures the topological and
geometric changes of the object that models the motion. Two topological
signatures are compared simply by the angle between the corresponding vectors.
With respect to gait recognition, we have tested our method using only the
lowest fourth part of the body's silhouette. In this way, the impact of
variations in the upper part of the body, which are very frequent in real
scenarios, decreases considerably. We have also tested our method using other
periodic motions such as running or jumping. Finally, we formally prove that
our method is robust to small perturbations in the input data and does not
depend on the number of periods contained in the periodic motion sequence.
Simulation of hyperelastic materials in real-time using Deep Learning
Authors: Andrea Mendizabal, Pablo Márquez-Neila, Stéphane Cotin
Date: 2019-04-10 00:00:00
Tasks: Cantilever Beam, Deep Learning

The finite element method (FEM) is among the most commonly used numerical methods for solving engineering problems. Due to its computational cost, various ideas have been introduced to reduce computation times, such as domain decomposition, parallel computing, adaptive meshing, and model order reduction. In this paper we present U-Mesh: a data-driven method based on a U-Net architecture that approximates the non-linear relation between a contact force and the displacement field computed by a FEM algorithm. We show that deep learning, one of the latest machine learning methods based on artificial neural networks, can enhance computational mechanics through its ability to encode highly non-linear models in a compact form. Our method is applied to two benchmark examples: a cantilever beam and an L-shape subject to moving punctual loads. A comparison between our method and proper orthogonal decomposition (POD) is done through the paper. The results show that U-Mesh can perform very fast simulations on various geometries, mesh resolutions and number of input forces with very small errors.
Discovering Hidden Factors of Variation in DeepNetworks
Authors: Brian Cheung, Jesse A. Livezey, Arjun K. Bansal, Bruno A. Olshausen
Date: 2015-06-17 00:00:00
Proceeding: arXiv 2015 6
Tasks: General Classification

Deep learning has enjoyed a great deal of success because of its ability to learnuseful  features  for  tasks  such  as  classification.   But  there  has  been  less  explo-ration in learning the factors of variation apart from the classification signal.  Byaugmenting  autoencoders  with  simple  regularization  terms  during  training,  wedemonstrate  that  standard  deep  architectures  can  discover  and  explicitly  repre-sent factors of variation beyond those relevant for categorization.  We introducea cross-covariance penalty (XCov) as a method to disentangle factors like hand-writing style for digits and subject identity in faces.  We demonstrate this on theMNIST handwritten digit database,  the Toronto Faces Database (TFD) and theMulti-PIE dataset by generating manipulated instances of the data.  Furthermore,we demonstrate these deep networks can extrapolate ‘hidden’ variation in the su-pervised signal
High-frequency trading in a limit order book
Authors: MARCO AVELLANEDA, SASHA STOIKOV
Date: 2008-04-28 00:00:00
Proceeding: Quantitative Finance 2008 4
Tasks: Form, Vocal Bursts Intensity Prediction

High-frequency trading in a limit order book
MARCO AVELLANEDA and SASHA STOIKOV*
Mathematics, New York University, 251 Mercer Street, New York, NY 10012, USA
(Received 24 April 2006; in final form 3 April 2007)
µKanren: A Minimal Functional Core for Relational Programming
Authors: Jason Hemann, Daniel P. Friedman
Date: 2013-11-13 00:00:00
Proceeding: Scheme '13 2013 11

This paper presents µKanren, a minimalist language in the miniKanren family of relational (logic) programming languages. Its implementation comprises fewer than 40 lines of Scheme. We motivate the need for a minimalist miniKanren language, and iteratively develop a complete search strategy. Finally, we demonstrate that through sufficient user-level features one regains much of the expressiveness of other miniKanren languages. In our opinion its brevity and simple semantics make µKanren uniquely elegant.
Siamese neural networks for one-shot image recognition
Authors: Gregory Koch, Richard Zemel, Ruslan Salakhutdinov
Date: 2015-07-10 00:00:00
Proceeding: ICML deep learning workshop, vol. 2. 2015. 2015 7
Tasks: One-Shot Learning

The process of learning good features for machine learning applications can be very computationally expensive and may prove difficult in cases where little data is available. A prototypical example of this is the one-shot learning setting, in which we must correctly make predictions given only a single example of each new class. In this paper, we explore a method for learning siamese neural networks which employ a unique structure to naturally rank similarity between inputs. Once a network has been tuned, we can then capitalize on powerful discriminative features to generalize the predictive power of the network not just to new data, but to entirely new classes from unknown distributions. Using a convolutional architecture, we are able to achieve strong results which exceed those of other deep learning models with near state-of-the-art performance on one-shot classification tasks.
Order book model with herd behavior exhibiting long-range memory
Authors: Aleksejus Kononovicius, Julius Ruseckas
Date: 2018-09-08 00:00:00
Tasks: Time Series, Time Series Analysis

In this work, we propose an order book model with herd behavior. The proposed
model is built upon two distinct approaches: a recent empirical study of the
detailed order book records by Kanazawa et al. [Phys. Rev. Lett. 120, 138301]
and financial herd behavior model. Combining these approaches allows us to
propose a model that replicates the long-range memory of absolute returns and
trading activity. We compare the statistical properties of the model against
the empirical statistical properties of the Bitcoin exchange rates and New York
stock exchange tickers. We also show that the fracture in the spectral density
of the high-frequency absolute return time series might be related to the
mechanism of convergence towards the equilibrium price.
A Repository of Conversational Datasets
Authors: Matthew Henderson, Paweł Budzianowski, Iñigo Casanueva, Sam Coope, Daniela Gerz, Girish Kumar, Nikola Mrkšić, Georgios Spithourakis, Pei-Hao Su, Ivan Vulić, Tsung-Hsien Wen
Date: 2019-04-13 00:00:00
Proceeding: WS 2019 8
Tasks: BIG-bench Machine Learning, Conversational Response Selection, Dialogue Understanding

Progress in Machine Learning is often driven by the availability of large datasets, and consistent evaluation metrics for comparing modeling approaches. To this end, we present a repository of conversational datasets consisting of hundreds of millions of examples, and a standardised evaluation procedure for conversational response selection models using '1-of-100 accuracy'. The repository contains scripts that allow researchers to reproduce the standard datasets, or to adapt the pre-processing and data filtering steps to their needs. We introduce and evaluate several competitive baselines for conversational response selection, whose implementations are shared in the repository, as well as a neural encoder model that is trained on the entire training set.
CEDR: Contextualized Embeddings for Document Ranking
Authors: Sean MacAvaney, Andrew Yates, Arman Cohan, Nazli Goharian
Date: 2019-04-15 00:00:00
Tasks: Ad-Hoc Information Retrieval, Document Ranking, General Classification

Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several existing neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.
Learning Discriminative Model Prediction for Tracking
Authors: Goutam Bhat, Martin Danelljan, Luc van Gool, Radu Timofte
Date: 2019-04-15 00:00:00
Proceeding: ICCV 2019 10
Tasks: model, Object Tracking, Prediction, Video Object Tracking, Visual Object Tracking, Visual Tracking

The current strive towards end-to-end trainable computer vision systems imposes major challenges for the task of visual tracking. In contrast to most other vision problems, tracking requires the learning of a robust target-specific appearance model online, during the inference stage. To be end-to-end trainable, the online learning of the target model thus needs to be embedded in the tracking architecture itself. Due to the imposed challenges, the popular Siamese paradigm simply predicts a target feature template, while ignoring the background appearance information during inference. Consequently, the predicted model possesses limited target-background discriminability. We develop an end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction. Our architecture is derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations. Furthermore, our approach is able to learn key aspects of the discriminative loss itself. The proposed tracker sets a new state-of-the-art on 6 tracking benchmarks, achieving an EAO score of 0.440 on VOT2018, while running at over 40 FPS. The code and models are available at https://github.com/visionml/pytracking.
VORNet: Spatio-temporally Consistent Video Inpainting for Object Removal
Authors: Ya-Liang Chang, Zhe Yu Liu, Winston Hsu
Date: 2019-04-14 00:00:00
Tasks: Image Inpainting, Object, One-shot visual object segmentation, Optical Flow Estimation, Video Inpainting, Video Segmentation, Video Semantic Segmentation

Video object removal is a challenging task in video processing that often
requires massive human efforts. Given the mask of the foreground object in each
frame, the goal is to complete (inpaint) the object region and generate a video
without the target object. While recently deep learning based methods have
achieved great success on the image inpainting task, they often lead to
inconsistent results between frames when applied to videos. In this work, we
propose a novel learning-based Video Object Removal Network (VORNet) to solve
the video object removal task in a spatio-temporally consistent manner, by
combining the optical flow warping and image-based inpainting model.
Experiments are done on our Synthesized Video Object Removal (SVOR) dataset
based on the YouTube-VOS video segmentation dataset, and both the objective and
subjective evaluation demonstrate that our VORNet generates more spatially and
temporally consistent videos compared with existing methods.
Joint Discriminative and Generative Learning for Person Re-identification
Authors: Zhedong Zheng, Xiaodong Yang, Zhiding Yu, Liang Zheng, Yi Yang, Jan Kautz
Date: 2019-04-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, Image-to-Image Translation, Person Re-Identification, Unsupervised Domain Adaptation, Unsupervised Person Re-Identification

Person re-identification (re-id) remains challenging due to significant intra-class variations across different cameras. Recently, there has been a growing interest in using generative models to augment training data and enhance the invariance to input changes. The generative pipelines in existing methods, however, stay relatively separate from the discriminative re-id learning stages. Accordingly, re-id models are often trained in a straightforward manner on the generated data. In this paper, we seek to improve learned re-id embeddings by better leveraging the generated data. To this end, we propose a joint learning framework that couples re-id learning and data generation end-to-end. Our model involves a generative module that separately encodes each person into an appearance code and a structure code, and a discriminative module that shares the appearance encoder with the generative module. By switching the appearance or structure codes, the generative module is able to generate high-quality cross-id composed images, which are online fed back to the appearance encoder and used to improve the discriminative module. The proposed joint learning framework renders significant improvement over the baseline without using generated data, leading to the state-of-the-art performance on several benchmark datasets.
Attention-Passing Models for Robust and Data-Efficient End-to-End Speech Translation
Authors: Matthias Sperber, Graham Neubig, Jan Niehues, Alex Waibel
Date: 2019-04-15 00:00:00
Proceeding: TACL 2019 3
Tasks: Machine Translation, speech-recognition, Speech Recognition, Translation

Speech translation has traditionally been approached through cascaded models
consisting of a speech recognizer trained on a corpus of transcribed speech,
and a machine translation system trained on parallel texts. Several recent
works have shown the feasibility of collapsing the cascade into a single,
direct model that can be trained in an end-to-end fashion on a corpus of
translated speech. However, experiments are inconclusive on whether the cascade
or the direct model is stronger, and have only been conducted under the
unrealistic assumption that both are trained on equal amounts of data, ignoring
other available speech recognition and machine translation corpora.
  In this paper, we demonstrate that direct speech translation models require
more data to perform well than cascaded models, and while they allow including
auxiliary data through multi-task training, they are poor at exploiting such
data, putting them at a severe disadvantage. As a remedy, we propose the use of
end-to-end trainable models with two attention mechanisms, the first
establishing source speech to source text alignments, the second modeling
source to target text alignment. We show that such models naturally decompose
into multi-task-trainable recognition and translation tasks and propose an
attention-passing technique that alleviates error propagation issues in a
previous formulation of a model with two attention stages. Our proposed model
outperforms all examined baselines and is able to exploit auxiliary training
data much more effectively than direct attentional models.