pwc-archive/papers-with-abstracts huggingface dataset:
OpenTag: Open Attribute Value Extraction from Product Profiles [Deep Learning, Active Learning, Named Entity Recognition]
Authors: Guineng Zheng, Subhabrata Mukherjee, Xin Luna Dong, Fei-Fei Li
Date: 2018-06-01 00:00:00
Tasks: Active Learning, Attribute, Attribute Value Extraction, Missing Values, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Extraction of missing attribute values is to find values describing an
attribute of interest from a free text input. Most past related work on
extraction of missing attribute values work with a closed world assumption with
the possible set of values known beforehand, or use dictionaries of values and
hand-crafted features. How can we discover new attribute values that we have
never seen before? Can we do this with limited human annotation or supervision?
We study this problem in the context of product catalogs that often have
missing values for many attributes of interest.
  In this work, we leverage product profile information such as titles and
descriptions to discover missing values of product attributes. We develop a
novel deep tagging model OpenTag for this extraction problem with the following
contributions: (1) we formalize the problem as a sequence tagging task, and
propose a joint model exploiting recurrent neural networks (specifically,
bidirectional LSTM) to capture context and semantics, and Conditional Random
Fields (CRF) to enforce tagging consistency, (2) we develop a novel attention
mechanism to provide interpretable explanation for our model's decisions, (3)
we propose a novel sampling strategy exploring active learning to reduce the
burden of human annotation. OpenTag does not use any dictionary or hand-crafted
features as in prior works. Extensive experiments in real-life datasets in
different domains show that OpenTag with our active learning strategy discovers
new attribute values from as few as 150 annotated samples (reduction in 3.3x
amount of annotation effort) with a high F-score of 83%, outperforming
state-of-the-art models.
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Authors: Maohua Zhu, Jason Clemons, Jeff Pool, Minsoo Rhu, Stephen W. Keckler, Yuan Xie
Date: 2018-06-01 00:00:00

Exploiting sparsity enables hardware systems to run neural networks faster
and more energy-efficiently. However, most prior sparsity-centric optimization
techniques only accelerate the forward pass of neural networks and usually
require an even longer training process with iterative pruning and retraining.
We observe that artificially inducing sparsity in the gradients of the gates in
an LSTM cell has little impact on the training quality. Further, we can enforce
structured sparsity in the gate gradients to make the LSTM backward pass up to
45% faster than the state-of-the-art dense approach and 168% faster than the
state-of-the-art sparsifying method on modern GPUs. Though the structured
sparsifying method can impact the accuracy of a model, this performance gap can
be eliminated by mixing our sparse training method and the standard dense
training method. Experimental results show that the mixed method can achieve
comparable results in a shorter time span than using purely dense training.
Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation
Authors: Mohammad Akbari, Jie Liang
Date: 2018-06-01 00:00:00
Tasks: Music Generation

A semi-recurrent hybrid VAE-GAN model for generating sequential data is
introduced. In order to consider the spatial correlation of the data in each
frame of the generated sequence, CNNs are utilized in the encoder, generator,
and discriminator. The subsequent frames are sampled from the latent
distributions obtained by encoding the previous frames. As a result, the
dependencies between the frames are maintained. Two testing frameworks for
synthesizing a sequence with any number of frames are also proposed. The
promising experimental results on piano music generation indicates the
potential of the proposed framework in modeling other sequential data such as
video.
AGIL: Learning Attention from Human for Visuomotor Tasks
Authors: Ruohan Zhang, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, Dana H. Ballard
Date: 2018-06-01 00:00:00
Proceeding: ECCV 2018 9
Tasks: Atari Games, Decision Making, Imitation Learning

When intelligent agents learn visuomotor behaviors from human demonstrations,
they may benefit from knowing where the human is allocating visual attention,
which can be inferred from their gaze. A wealth of information regarding
intelligent decision making is conveyed by human gaze allocation; hence,
exploiting such information has the potential to improve the agents'
performance. With this motivation, we propose the AGIL (Attention Guided
Imitation Learning) framework. We collect high-quality human action and gaze
data while playing Atari games in a carefully controlled experimental setting.
Using these data, we first train a deep neural network that can predict human
gaze positions and visual attention with high accuracy (the gaze network) and
then train another network to predict human actions (the policy network).
Incorporating the learned attention model from the gaze network into the policy
network significantly improves the action prediction accuracy and task
performance.
Backpropagation for Implicit Spectral Densities
Authors: Aditya Ramesh, Yann Lecun
Date: 2018-06-01 00:00:00

Most successful machine intelligence systems rely on gradient-based learning,
which is made possible by backpropagation. Some systems are designed to aid us
in interpreting data when explicit goals cannot be provided. These unsupervised
systems are commonly trained by backpropagating through a likelihood function.
We introduce a tool that allows us to do this even when the likelihood is not
explicitly set, by instead using the implicit likelihood of the model.
Explicitly defining the likelihood often entails making heavy-handed
assumptions that impede our ability to solve challenging tasks. On the other
hand, the implicit likelihood of the model is accessible without the need for
such assumptions. Our tool, which we call spectral backpropagation, allows us
to optimize it in much greater generality than what has been attempted before.
GANs can also be viewed as a technique for optimizing implicit likelihoods. We
study them using spectral backpropagation in order to demonstrate robustness
for high-dimensional problems, and identify two novel properties of the
generator G: (1) there exist aberrant, nonsensical outputs to which G assigns
very high likelihood, and (2) the eigenvectors of the metric induced by G over
latent space correspond to quasi-disentangled explanatory factors.
Implicit Bias of Gradient Descent on Linear Convolutional Networks
Authors: Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro
Date: 2018-06-01 00:00:00
Proceeding: NeurIPS 2018 12

We show that gradient descent on full-width linear convolutional networks of
depth $L$ converges to a linear predictor related to the $\ell_{2/L}$ bridge
penalty in the frequency domain. This is in contrast to linearly fully
connected networks, where gradient descent converges to the hard margin linear
support vector machine solution, regardless of depth.
Surgical Activity Recognition in Robot-Assisted Radical Prostatectomy using Deep Learning
Authors: Aneeq Zia, Andrew Hung, Irfan Essa, Anthony Jarc
Date: 2018-06-01 00:00:00
Tasks: Activity Recognition

Adverse surgical outcomes are costly to patients and hospitals. Approaches to
benchmark surgical care are often limited to gross measures across the entire
procedure despite the performance of particular tasks being largely responsible
for undesirable outcomes. In order to produce metrics from tasks as opposed to
the whole procedure, methods to recognize automatically individual surgical
tasks are needed. In this paper, we propose several approaches to recognize
surgical activities in robot-assisted minimally invasive surgery using deep
learning. We collected a clinical dataset of 100 robot-assisted radical
prostatectomies (RARP) with 12 tasks each and propose `RP-Net', a modified
version of InceptionV3 model, for image based surgical activity recognition. We
achieve an average precision of 80.9% and average recall of 76.7% across all
tasks using RP-Net which out-performs all other RNN and CNN based models
explored in this paper. Our results suggest that automatic surgical activity
recognition during RARP is feasible and can be the foundation for advanced
analytics.
Adversarial Semantic Alignment for Improved Image Captions
Authors: Pierre L. Dognin, Igor Melnyk, Youssef Mroueh, Jarret Ross, Tom Sercu
Date: 2018-04-30 00:00:00
Tasks: Image Captioning

In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically focus on the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST, even without accessing discriminator gradients directly. We also address the problem of automatic evaluation for captioning models and introduce a new semantic score, and show its correlation to human judgement. As an evaluation paradigm, we argue that an important criterion for a captioner is the ability to generalize to compositions of objects that do not usually co-occur together. To this end, we introduce a small captioned Out of Context (OOC) test set. The OOC set, combined with our semantic score, are the proposed new diagnosis tools for the captioning community. When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training.
Adversarial quantum circuit learning for pure state approximation
Authors: Marcello Benedetti, Edward Grant, Leonard Wossnig, Simone Severini
Date: 2018-06-01 00:00:00
Tasks: Quantum State Tomography

Adversarial learning is one of the most successful approaches to modelling
high-dimensional probability distributions from data. The quantum computing
community has recently begun to generalize this idea and to look for potential
applications. In this work, we derive an adversarial algorithm for the problem
of approximating an unknown quantum pure state. Although this could be done on
universal quantum computers, the adversarial formulation enables us to execute
the algorithm on near-term quantum computers. Two parametrized circuits are
optimized in tandem: One tries to approximate the target state, the other tries
to distinguish between target and approximated state. Supported by numerical
simulations, we show that resilient backpropagation algorithms perform
remarkably well in optimizing the two circuits. We use the bipartite
entanglement entropy to design an efficient heuristic for the stopping
criterion. Our approach may find application in quantum state tomography.
Multi-objective Contextual Multi-armed Bandit with a Dominant Objective
Authors: Cem Tekin, Eralp Turgay
Date: 2017-08-18 00:00:00
Tasks: Medical Diagnosis, Recommendation Systems

In this paper, we propose a new multi-objective contextual multi-armed bandit
(MAB) problem with two objectives, where one of the objectives dominates the
other objective. Unlike single-objective MAB problems in which the learner
obtains a random scalar reward for each arm it selects, in the proposed
problem, the learner obtains a random reward vector, where each component of
the reward vector corresponds to one of the objectives and the distribution of
the reward depends on the context that is provided to the learner at the
beginning of each round. We call this problem contextual multi-armed bandit
with a dominant objective (CMAB-DO). In CMAB-DO, the goal of the learner is to
maximize its total reward in the non-dominant objective while ensuring that it
maximizes its total reward in the dominant objective. In this case, the optimal
arm given a context is the one that maximizes the expected reward in the
non-dominant objective among all arms that maximize the expected reward in the
dominant objective. First, we show that the optimal arm lies in the Pareto
front. Then, we propose the multi-objective contextual multi-armed bandit
algorithm (MOC-MAB), and define two performance measures: the 2-dimensional
(2D) regret and the Pareto regret. We show that both the 2D regret and the
Pareto regret of MOC-MAB are sublinear in the number of rounds. We also compare
the performance of the proposed algorithm with other state-of-the-art methods
in synthetic and real-world datasets. The proposed model and the algorithm have
a wide range of real-world applications that involve multiple and possibly
conflicting objectives ranging from wireless communication to medical diagnosis
and recommender systems.
Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Authors: Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar
Date: 2018-06-01 00:00:00

Machine learning is currently dominated by largely experimental work focused
on improvements in a few key tasks. However, the impressive accuracy numbers of
the best performing models are questionable because the same test sets have
been used to select these models for multiple years now. To understand the
danger of overfitting, we measure the accuracy of CIFAR-10 classifiers by
creating a new test set of truly unseen images. Although we ensure that the new
test set is as close to the original data distribution as possible, we find a
large drop in accuracy (4% to 10%) for a broad range of deep learning models.
Yet more recent models with higher original accuracy show a smaller drop and
better overall performance, indicating that this drop is likely not due to
overfitting based on adaptivity. Instead, we view our results as evidence that
current accuracy numbers are brittle and susceptible to even minute natural
variations in the data distribution.
Learning to Bid Without Knowing your Value
Authors: Zhe Feng, Chara Podimata, Vasilis Syrgkanis
Date: 2017-11-03 00:00:00

We address online learning in complex auction settings, such as sponsored
search auctions, where the value of the bidder is unknown to her, evolving in
an arbitrary manner and observed only if the bidder wins an allocation. We
leverage the structure of the utility of the bidder and the partial feedback
that bidders typically receive in auctions, in order to provide algorithms with
regret rates against the best fixed bid in hindsight, that are exponentially
faster in convergence in terms of dependence on the action space, than what
would have been derived by applying a generic bandit algorithm and almost
equivalent to what would have been achieved in the full information setting.
Our results are enabled by analyzing a new online learning setting with
outcome-based feedback, which generalizes learning with feedback graphs. We
provide an online learning algorithm for this setting, of independent interest,
with regret that grows only logarithmically with the number of actions and
linearly only in the number of potential outcomes (the latter being very small
in most auction settings). Last but not least, we show that our algorithm
outperforms the bandit approach experimentally and that this performance is
robust to dropping some of our theoretical assumptions or introducing noise in
the feedback that the bidder receives.
A Guide to Constraining Effective Field Theories with Machine Learning
Authors: Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez
Date: 2018-04-30 00:00:00
Tasks: BIG-bench Machine Learning

We develop, discuss, and compare several inference techniques to constrain
theory parameters in collider experiments. By harnessing the latent-space
structure of particle physics processes, we extract extra information from the
simulator. This augmented data can be used to train neural networks that
precisely estimate the likelihood ratio. The new methods scale well to many
observables and high-dimensional parameter spaces, do not require any
approximations of the parton shower and detector response, and can be evaluated
in microseconds. Using weak-boson-fusion Higgs production as an example
process, we compare the performance of several techniques. The best results are
found for likelihood ratio estimators trained with extra information about the
score, the gradient of the log likelihood function with respect to the theory
parameters. The score also provides sufficient statistics that contain all the
information needed for inference in the neighborhood of the Standard Model.
These methods enable us to put significantly stronger bounds on effective
dimension-six operators than the traditional approach based on histograms. They
also outperform generic machine learning methods that do not make use of the
particle physics structure, demonstrating their potential to substantially
improve the new physics reach of the LHC legacy results.
Large-Margin Classification in Hyperbolic Space
Authors: Hyunghoon Cho, Benjamin DeMeo, Jian Peng, Bonnie Berger
Date: 2018-06-01 00:00:00
Tasks: Classification, General Classification

Representing data in hyperbolic space can effectively capture latent
hierarchical relationships. With the goal of enabling accurate classification
of points in hyperbolic space while respecting their hyperbolic geometry, we
introduce hyperbolic SVM, a hyperbolic formulation of support vector machine
classifiers, and elucidate through new theoretical work its connection to the
Euclidean counterpart. We demonstrate the performance improvement of hyperbolic
SVM for multi-class prediction tasks on real-world complex networks as well as
simulated datasets. Our work allows analytic pipelines that take the inherent
hyperbolic geometry of the data into account in an end-to-end fashion without
resorting to ill-fitting tools developed for Euclidean space.
Constraining Effective Field Theories with Machine Learning
Authors: Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez
Date: 2018-04-30 00:00:00
Tasks: BIG-bench Machine Learning

We present powerful new analysis techniques to constrain effective field
theories at the LHC. By leveraging the structure of particle physics processes,
we extract extra information from Monte-Carlo simulations, which can be used to
train neural network models that estimate the likelihood ratio. These methods
scale well to processes with many observables and theory parameters, do not
require any approximations of the parton shower or detector response, and can
be evaluated in microseconds. We show that they allow us to put significantly
stronger bounds on dimension-six operators than existing methods, demonstrating
their potential to improve the precision of the LHC legacy constraints.
A Classification approach towards Unsupervised Learning of Visual Representations
Authors: Aditya Vora
Date: 2018-06-01 00:00:00
Tasks: Classification, General Classification, Object Recognition

In this paper, we present a technique for unsupervised learning of visual
representations. Specifically, we train a model for foreground and background
classification task, in the process of which it learns visual representations.
Foreground and background patches for training come af- ter mining for such
patches from hundreds and thousands of unlabelled videos available on the web
which we ex- tract using a proposed patch extraction algorithm. With- out using
any supervision, with just using 150, 000 unla- belled videos and the PASCAL
VOC 2007 dataset, we train a object recognition model that achieves 45.3 mAP
which is close to the best performing unsupervised feature learn- ing technique
whereas better than many other proposed al- gorithms. The code for patch
extraction is implemented in Matlab and available open source at the following
link .
Solving the Kolmogorov PDE by means of deep learning
Authors: Christian Beck, Sebastian Becker, Philipp Grohs, Nor Jaafari, Arnulf Jentzen
Date: 2018-06-01 00:00:00
Tasks: Deep Learning

Stochastic differential equations (SDEs) and the Kolmogorov partial differential equations (PDEs) associated to them have been widely used in models from engineering, finance, and the natural sciences. In particular, SDEs and Kolmogorov PDEs, respectively, are highly employed in models for the approximative pricing of financial derivatives. Kolmogorov PDEs and SDEs, respectively, can typically not be solved explicitly and it has been and still is an active topic of research to design and analyze numerical methods which are able to approximately solve Kolmogorov PDEs and SDEs, respectively. Nearly all approximation methods for Kolmogorov PDEs in the literature suffer under the curse of dimensionality or only provide approximations of the solution of the PDE at a single fixed space-time point. In this paper we derive and propose a numerical approximation method which aims to overcome both of the above mentioned drawbacks and intends to deliver a numerical approximation of the Kolmogorov PDE on an entire region $[a,b]^d$ without suffering from the curse of dimensionality. Numerical results on examples including the heat equation, the Black-Scholes model, the stochastic Lorenz equation, and the Heston model suggest that the proposed approximation algorithm is quite effective in high dimensions in terms of both accuracy and speed.
Whitening and Coloring batch transform for GANs
Authors: Aliaksandr Siarohin, Enver Sangineto, Nicu Sebe
Date: 2018-06-01 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image Generation

Batch Normalization (BN) is a common technique used to speed-up and stabilize
training. On the other hand, the learnable parameters of BN are commonly used
in conditional Generative Adversarial Networks (cGANs) for representing
class-specific information using conditional Batch Normalization (cBN). In this
paper we propose to generalize both BN and cBN using a Whitening and Coloring
based batch normalization. We show that our conditional Coloring can represent
categorical conditioning information which largely helps the cGAN qualitative
results. Moreover, we show that full-feature whitening is important in a
general GAN scenario in which the training process is known to be highly
unstable. We test our approach on different datasets and using different GAN
networks and training protocols, showing a consistent improvement in all the
tested frameworks. Our CIFAR-10 conditioned results are higher than all
previous works on this dataset.
End-to-end Active Object Tracking via Reinforcement Learning
Authors: Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang
Date: 2017-05-30 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Reinforcement Learning, Object, Object Tracking, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study active object tracking, where a tracker takes as input the visual
observation (i.e., frame sequence) and produces the camera control signal
(e.g., move forward, turn left, etc.). Conventional methods tackle the tracking
and the camera control separately, which is challenging to tune jointly. It
also incurs many human efforts for labeling and many expensive trial-and-errors
in realworld. To address these issues, we propose, in this paper, an end-to-end
solution via deep reinforcement learning, where a ConvNet-LSTM function
approximator is adopted for the direct frame-toaction prediction. We further
propose an environment augmentation technique and a customized reward function,
which are crucial for a successful training. The tracker trained in simulators
(ViZDoom, Unreal Engine) shows good generalization in the case of unseen object
moving path, unseen object appearance, unseen background, and distracting
object. It can restore tracking when occasionally losing the target. With the
experiments over the VOT dataset, we also find that the tracking ability,
obtained solely from simulators, can potentially transfer to real-world
scenarios.