pwc-archive/papers-with-abstracts huggingface dataset:
AdGraph: A Graph-Based Approach to Ad and Tracker Blocking
Authors: Umar Iqbal, Peter Snyder, Shitong Zhu, Benjamin Livshits, Zhiyun Qian, Zubair Shafiq
Date: 2018-05-22 00:00:00
Tasks: Blocking

User demand for blocking advertising and tracking online is large and growing. Existing tools, both deployed and described in research, have proven useful, but lack either the completeness or robustness needed for a general solution. Existing detection approaches generally focus on only one aspect of advertising or tracking (e.g. URL patterns, code structure), making existing approaches susceptible to evasion. In this work we present AdGraph, a novel graph-based machine learning approach for detecting advertising and tracking resources on the web. AdGraph differs from existing approaches by building a graph representation of the HTML structure, network requests, and JavaScript behavior of a webpage, and using this unique representation to train a classifier for identifying advertising and tracking resources. Because AdGraph considers many aspects of the context a network request takes place in, it is less susceptible to the single-factor evasion techniques that flummox existing approaches. We evaluate AdGraph on the Alexa top-10K websites, and find that it is highly accurate, able to replicate the labels of human-generated filter lists with 95.33% accuracy, and can even identify many mistakes in filter lists. We implement AdGraph as a modification to Chromium. AdGraph adds only minor overhead to page loading and execution, and is actually faster than stock Chromium on 42% of websites and AdBlock Plus on 78% of websites. Overall, we conclude that AdGraph is both accurate enough and performant enough for online use, breaking comparable or fewer websites than popular filter list based approaches.
Verifiable Reinforcement Learning via Policy Extraction
Authors: Osbert Bastani, Yewen Pu, Armando Solar-Lezama
Date: 2018-05-22 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, Imitation Learning, Model Compression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

While deep reinforcement learning has successfully solved many challenging
control tasks, its real-world applicability has been limited by the inability
to ensure the safety of learned policies. We propose an approach to verifiable
reinforcement learning by training decision tree policies, which can represent
complex policies (since they are nonparametric), yet can be efficiently
verified using existing techniques (since they are highly structured). The
challenge is that decision tree policies are difficult to train. We propose
VIPER, an algorithm that combines ideas from model compression and imitation
learning to learn decision tree policies guided by a DNN policy (called the
oracle) and its Q-function, and show that it substantially outperforms two
baselines. We use VIPER to (i) learn a provably robust decision tree policy for
a variant of Atari Pong with a symbolic state space, (ii) learn a decision tree
policy for a toy game based on Pong that provably never loses, and (iii) learn
a provably stable decision tree policy for cart-pole. In each case, the
decision tree policy achieves performance equal to that of the original DNN
policy.
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Authors: Ashia C. Wilson, Rebecca Roelofs, Mitchell Stern, Nathan Srebro, Benjamin Recht
Date: 2017-05-23 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: BIG-bench Machine Learning, Binary Classification

Adaptive optimization methods, which perform local optimization with a metric
constructed from the history of iterates, are becoming increasingly popular for
training deep neural networks. Examples include AdaGrad, RMSProp, and Adam. We
show that for simple overparameterized problems, adaptive methods often find
drastically different solutions than gradient descent (GD) or stochastic
gradient descent (SGD). We construct an illustrative binary classification
problem where the data is linearly separable, GD and SGD achieve zero test
error, and AdaGrad, Adam, and RMSProp attain test errors arbitrarily close to
half. We additionally study the empirical generalization capability of adaptive
methods on several state-of-the-art deep learning models. We observe that the
solutions found by adaptive methods generalize worse (often significantly
worse) than SGD, even when these solutions have better training performance.
These results suggest that practitioners should reconsider the use of adaptive
methods to train neural networks.
Disentangling Controllable and Uncontrollable Factors of Variation by Interacting with the World
Authors: Yoshihide Sawada
Date: 2018-04-19 00:00:00
Tasks: Disentanglement, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a method to disentangle controllable and uncontrollable factors
of variation by interacting with the world. Disentanglement leads to good
representations and is important when applying deep neural networks (DNNs) in
fields where explanations are required. This study attempts to improve an
existing reinforcement learning (RL) approach to disentangle controllable and
uncontrollable factors of variation, because the method lacks a mechanism to
represent uncontrollable obstacles. To address this problem, we train two DNNs
simultaneously: one that represents the controllable object and another that
represents uncontrollable obstacles. For stable training, we applied a
pretraining approach using a model robust against uncontrollable obstacles.
Simulation experiments demonstrate that the proposed model can disentangle
independently controllable and uncontrollable factors without annotated data.
Robust Gradient Descent via Moment Encoding with LDPC Codes
Authors: Raj Kumar Maity, Ankit Singh Rawat, Arya Mazumdar
Date: 2018-05-22 00:00:00
Tasks: Distributed Computing

This paper considers the problem of implementing large-scale gradient descent
algorithms in a distributed computing setting in the presence of {\em
straggling} processors. To mitigate the effect of the stragglers, it has been
previously proposed to encode the data with an erasure-correcting code and
decode at the master server at the end of the computation. We, instead, propose
to encode the second-moment of the data with a low density parity-check (LDPC)
code. The iterative decoding algorithms for LDPC codes have very low
computational overhead and the number of decoding iterations can be made to
automatically adjust with the number of stragglers in the system. We show that
for a random model for stragglers, the proposed moment encoding based gradient
descent method can be viewed as the stochastic gradient descent method. This
allows us to obtain convergence guarantees for the proposed solution.
Furthermore, the proposed moment encoding based method is shown to outperform
the existing schemes in a real distributed computing setup.
Measurement-wise Occlusion in Multi-object Tracking
Authors: Michael Motro, Joydeep Ghosh
Date: 2018-05-21 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking, Visual Tracking

Handling object interaction is a fundamental challenge in practical
multi-object tracking, even for simple interactive effects such as one object
temporarily occluding another. We formalize the problem of occlusion in
tracking with two different abstractions. In object-wise occlusion, objects
that are occluded by other objects do not generate measurements. In
measurement-wise occlusion, a previously unstudied approach, all objects may
generate measurements but some measurements may be occluded by others. While
the relative validity of each abstraction depends on the situation and sensor,
measurement-wise occlusion fits into probabilistic multi-object tracking
algorithms with much looser assumptions on object interaction. Its value is
demonstrated by showing that it naturally derives a popular approximation for
lidar tracking, and by an example of visual tracking in image space.
DS-MLR: Exploiting Double Separability for Scaling up Distributed Multinomial Logistic Regression
Authors: Parameswaran Raman, Sriram Srinivasan, Shin Matsushima, Xinhua Zhang, Hyokun Yun, S. V. N. Vishwanathan
Date: 2016-04-16 00:00:00
Tasks: Blocking, Multi-class Classification, regression

Scaling multinomial logistic regression to datasets with very large number of
data points and classes is challenging. This is primarily because one needs to
compute the log-partition function on every data point. This makes distributing
the computation hard. In this paper, we present a distributed stochastic
gradient descent based optimization method (DS-MLR) for scaling up multinomial
logistic regression problems to massive scale datasets without hitting any
storage constraints on the data and model parameters. Our algorithm exploits
double-separability, an attractive property that allows us to achieve both data
as well as model parallelism simultaneously. In addition, we introduce a
non-blocking and asynchronous variant of our algorithm that avoids
bulk-synchronization. We demonstrate the versatility of DS-MLR to various
scenarios in data and model parallelism, through an extensive empirical study
using several real-world datasets. In particular, we demonstrate the
scalability of DS-MLR by solving an extreme multi-class classification problem
on the Reddit dataset (159 GB data, 358 GB parameters) where, to the best of
our knowledge, no other existing methods apply.
Extreme Stochastic Variational Inference: Distributed and Asynchronous
Authors: Jiong Zhang, Parameswaran Raman, Shihao Ji, Hsiang-Fu Yu, S. V. N. Vishwanathan, Inderjit S. Dhillon
Date: 2016-05-31 00:00:00
Tasks: Variational Inference

Stochastic variational inference (SVI), the state-of-the-art algorithm for
scaling variational inference to large-datasets, is inherently serial.
Moreover, it requires the parameters to fit in the memory of a single
processor; this is problematic when the number of parameters is in billions. In
this paper, we propose extreme stochastic variational inference (ESVI), an
asynchronous and lock-free algorithm to perform variational inference for
mixture models on massive real world datasets. ESVI overcomes the limitations
of SVI by requiring that each processor only access a subset of the data and a
subset of the parameters, thus providing data and model parallelism
simultaneously. We demonstrate the effectiveness of ESVI by running Latent
Dirichlet Allocation (LDA) on UMBC-3B, a dataset that has a vocabulary of 3
million and a token size of 3 billion. In our experiments, we found that ESVI
not only outperforms VI and SVI in wallclock-time, but also achieves a better
quality solution. In addition, we propose a strategy to speed up computation
and save memory when fitting large number of topics.
A New Finitely Controllable Class of Tuple Generating Dependencies: The Triangularly-Guarded Class
Authors: Vernon Asuncion, Yan Zhang
Date: 2018-05-21 00:00:00

In this paper we introduce a new class of tuple-generating dependencies
(TGDs) called triangularly-guarded (TG) TGDs. We show that conjunctive query
answering under this new class of TGDs is decidable since this new class of
TGDs also satisfies the finite controllability (FC) property. We further show
that this new class strictly contains some other decidable classes such as
weak-acyclic, guarded, sticky and shy. In this sense, the class TG provides a
unified representation of all these aforementioned classes of TGDs.
Linear-Time Constituency Parsing with RNNs and Dynamic Programming
Authors: Juneki Hong, Liang Huang
Date: 2018-05-17 00:00:00
Proceeding: ACL 2018 7
Tasks: Boundary Detection, Constituency Parsing, Discourse Parsing, Sentence

Recently, span-based constituency parsing has achieved competitive accuracies
with extremely simple models by using bidirectional RNNs to model "spans".
However, the minimal span parser of Stern et al (2017a) which holds the current
state of the art accuracy is a chart parser running in cubic time, $O(n^3)$,
which is too slow for longer sentences and for applications beyond sentence
boundaries such as end-to-end discourse parsing and joint sentence boundary
detection and parsing. We propose a linear-time constituency parser with RNNs
and dynamic programming using graph-structured stack and beam search, which
runs in time $O(n b^2)$ where $b$ is the beam size. We further speed this up to
$O(n b\log b)$ by integrating cube pruning. Compared with chart parsing
baselines, this linear-time parser is substantially faster for long sentences
on the Penn Treebank and orders of magnitude faster for discourse parsing, and
achieves the highest F1 accuracy on the Penn Treebank among single model
end-to-end systems.
Two Hilbert schemes in computer vision
Authors: Max Lieblich, Lucas Van Meter
Date: 2017-07-28 00:00:00
Tasks: Vocal Bursts Valence Prediction

We study multiview moduli problems that arise in computer vision. We show that these moduli spaces are always smooth and irreducible, in both the calibrated and uncalibrated cases, for any number of views. We also show that these moduli spaces always admit open immersions into Hilbert schemes for more than two views, extending and refining work of Aholt-Sturmfels-Thomas. We use these moduli spaces to study and extend the classical twisted pair covering of the essential variety.
Self-Attention Generative Adversarial Networks
Authors: Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena
Date: 2018-05-21 00:00:00
Proceeding: arXiv 2018 5
Tasks: Conditional Image Generation, Generative Adversarial Network, Image Generation

In this paper, we propose the Self-Attention Generative Adversarial Network (SAGAN) which allows attention-driven, long-range dependency modeling for image generation tasks. Traditional convolutional GANs generate high-resolution details as a function of only spatially local points in lower-resolution feature maps. In SAGAN, details can be generated using cues from all feature locations. Moreover, the discriminator can check that highly detailed features in distant portions of the image are consistent with each other. Furthermore, recent work has shown that generator conditioning affects GAN performance. Leveraging this insight, we apply spectral normalization to the GAN generator and find that this improves training dynamics. The proposed SAGAN achieves the state-of-the-art results, boosting the best published Inception score from 36.8 to 52.52 and reducing Frechet Inception distance from 27.62 to 18.65 on the challenging ImageNet dataset. Visualization of the attention layers shows that the generator leverages neighborhoods that correspond to object shapes rather than local regions of fixed shape.
Learning long-range spatial dependencies with horizontal gated-recurrent units
Authors: Drew Linsley, Junkyung Kim, Vijay Veerabadran, Thomas Serre
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018
Tasks: Contour Detection

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching -- and sometimes even surpassing -- human accuracy on a variety of visual recognition tasks. Here, however, we show that these neural networks and their recent extensions struggle in recognition tasks where co-dependent visual features must be detected over long spatial ranges. We introduce the horizontal gated-recurrent unit (hGRU) to learn intrinsic horizontal connections -- both within and across feature columns. We demonstrate that a single hGRU layer matches or outperforms all tested feedforward hierarchical baselines including state-of-the-art architectures which have orders of magnitude more free parameters. We further discuss the biological plausibility of the hGRU in comparison to anatomical data from the visual cortex as well as human behavioral data on a classic contour detection task.
Learning Safe Policies with Expert Guidance
Authors: Jessie Huang, Fa Wu, Doina Precup, Yang Cai
Date: 2018-05-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a framework for ensuring safe behavior of a reinforcement learning
agent when the reward function may be difficult to specify. In order to do
this, we rely on the existence of demonstrations from expert policies, and we
provide a theoretical framework for the agent to optimize in the space of
rewards consistent with its existing knowledge. We propose two methods to solve
the resulting optimization: an exact ellipsoid-based method and a method in the
spirit of the "follow-the-perturbed-leader" algorithm. Our experiments
demonstrate the behavior of our algorithm in both discrete and continuous
problems. The trained agent safely avoids states with potential negative
effects while imitating the behavior of the expert in the other states.
AgileNet: Lightweight Dictionary-based Few-shot Learning
Authors: Mohammad Ghasemzadeh, Fang Lin, Bita Darvish Rouhani, Farinaz Koushanfar, Ke Huang
Date: 2018-05-21 00:00:00
Tasks: Few-Shot Learning

The success of deep learning models is heavily tied to the use of massive
amount of labeled data and excessively long training time. With the emergence
of intelligent edge applications that use these models, the critical challenge
is to obtain the same inference capability on a resource-constrained device
while providing adaptability to cope with the dynamic changes in the data. We
propose AgileNet, a novel lightweight dictionary-based few-shot learning
methodology which provides reduced complexity deep neural network for efficient
execution at the edge while enabling low-cost updates to capture the dynamics
of the new data. Evaluations of state-of-the-art few-shot learning benchmarks
demonstrate the superior accuracy of AgileNet compared to prior arts.
Additionally, AgileNet is the first few-shot learning approach that prevents
model updates by eliminating the knowledge obtained from the primary training.
This property is ensured through the dictionaries learned by our novel
end-to-end structured decomposition, which also reduces the memory footprint
and computation complexity to match the edge device constraints.
AxTrain: Hardware-Oriented Neural Network Training for Approximate Inference
Authors: Xin He, Liu Ke, Wenyan Lu, Guihai Yan, Xuan Zhang
Date: 2018-05-21 00:00:00

The intrinsic error tolerance of neural network (NN) makes approximate
computing a promising technique to improve the energy efficiency of NN
inference. Conventional approximate computing focuses on balancing the
efficiency-accuracy trade-off for existing pre-trained networks, which can lead
to suboptimal solutions. In this paper, we propose AxTrain, a hardware-oriented
training framework to facilitate approximate computing for NN inference.
Specifically, AxTrain leverages the synergy between two orthogonal
methods---one actively searches for a network parameters distribution with high
error tolerance, and the other passively learns resilient weights by
numerically incorporating the noise distributions of the approximate hardware
in the forward pass during the training phase. Experimental results from
various datasets with near-threshold computing and approximation multiplication
strategies demonstrate AxTrain's ability to obtain resilient neural network
parameters and system energy efficiency improvement.
geomstats: a Python Package for Riemannian Geometry in Machine Learning
Authors: Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec
Date: 2018-05-21 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning, GPU, Riemannian optimization

We introduce geomstats, a python package that performs computations on
manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive
definite matrices and Lie groups of transformations. We provide efficient and
extensively unit-tested implementations of these manifolds, together with
useful Riemannian metrics and associated Exponential and Logarithm maps. The
corresponding geodesic distances provide a range of intuitive choices of
Machine Learning loss functions. We also give the corresponding Riemannian
gradients. The operations implemented in geomstats are available with different
computing backends such as numpy, tensorflow and keras. We have enabled GPU
implementation and integrated geomstats manifold computations into keras deep
learning framework. This paper also presents a review of manifolds in machine
learning and an overview of the geomstats package with examples demonstrating
its use for efficient and user-friendly Riemannian geometry.
Lip Movements Generation at a Glance
Authors: Lele Chen, Zhiheng Li, Ross K. Maddox, Zhiyao Duan, Chenliang Xu
Date: 2018-03-28 00:00:00
Proceeding: ECCV 2018 9

Cross-modality generation is an emerging topic that aims to synthesize data
in one modality based on information in a different modality. In this paper, we
consider a task of such: given an arbitrary audio speech and one lip image of
arbitrary target identity, generate synthesized lip movements of the target
identity saying the speech. To perform well in this task, it inevitably
requires a model to not only consider the retention of target identity,
photo-realistic of synthesized images, consistency and smoothness of lip images
in a sequence, but more importantly, learn the correlations between audio
speech and lip movements. To solve the collective problems, we explore the best
modeling of the audio-visual correlations in building and training a
lip-movement generator network. Specifically, we devise a method to fuse audio
and image embeddings to generate multiple lip images at once and propose a
novel correlation loss to synchronize lip changes and speech changes. Our final
model utilizes a combination of four losses for a comprehensive consideration
in generating lip movements; it is trained in an end-to-end fashion and is
robust to lip shapes, view angles and different facial characteristics.
Thoughtful experiments on three datasets ranging from lab-recorded to lips
in-the-wild show that our model significantly outperforms other
state-of-the-art methods extended to this task.
Deep Energy Estimator Networks
Authors: Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, Aapo Hyvärinen
Date: 2018-05-21 00:00:00
Tasks: Denoising, Density Estimation

Density estimation is a fundamental problem in statistical learning. This
problem is especially challenging for complex high-dimensional data due to the
curse of dimensionality. A promising solution to this problem is given here in
an inference-free hierarchical framework that is built on score matching. We
revisit the Bayesian interpretation of the score function and the Parzen score
matching, and construct a multilayer perceptron with a scalable objective for
learning the energy (i.e. the unnormalized log-density), which is then
optimized with stochastic gradient descent. In addition, the resulting deep
energy estimator network (DEEN) is designed as products of experts. We present
the utility of DEEN in learning the energy, the score function, and in
single-step denoising experiments for synthetic and high-dimensional data. We
also diagnose stability problems in the direct estimation of the score function
that had been observed for denoising autoencoders.