pwc-archive/papers-with-abstracts huggingface dataset:
A Well-Tempered Landscape for Non-convex Robust Subspace Recovery
Authors: Tyler Maunu, Teng Zhang, Gilad Lerman
Date: 2017-06-13 00:00:00

We present a mathematical analysis of a non-convex energy landscape for
robust subspace recovery. We prove that an underlying subspace is the only
stationary point and local minimizer in a specified neighborhood under a
deterministic condition on a dataset. If the deterministic condition is
satisfied, we further show that a geodesic gradient descent method over the
Grassmannian manifold can exactly recover the underlying subspace when the
method is properly initialized. Proper initialization by principal component
analysis is guaranteed with a simple deterministic condition. Under slightly
stronger assumptions, the gradient descent method with a piecewise constant
step-size scheme achieves linear convergence. The practicality of the
deterministic condition is demonstrated on some statistical models of data, and
the method achieves almost state-of-the-art recovery guarantees on the Haystack
Model for different regimes of sample size and ambient dimension. In
particular, when the ambient dimension is fixed and the sample size is large
enough, we show that our gradient method can exactly recover the underlying
subspace for any fixed fraction of outliers (less than 1).
LCNN: Lookup-based Convolutional Neural Network
Authors: Hessam Bagherinezhad, Mohammad Rastegari, Ali Farhadi
Date: 2016-11-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Few-Shot Learning

Porting state of the art deep learning algorithms to resource constrained
compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose
a fast, compact, and accurate model for convolutional neural networks that
enables efficient learning and inference. We introduce LCNN, a lookup-based
convolutional neural network that encodes convolutions by few lookups to a
dictionary that is trained to cover the space of weights in CNNs. Training LCNN
involves jointly learning a dictionary and a small set of linear combinations.
The size of the dictionary naturally traces a spectrum of trade-offs between
efficiency and accuracy. Our experimental results on ImageNet challenge show
that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using
AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while
maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at
inference, but it also enables efficient training. In this paper, we show the
benefits of LCNN in few-shot learning and few-iteration learning, two crucial
aspects of on-device training of deep learning models.
Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN
Authors: Lvmin Zhang, Yi Ji, Xin Lin
Date: 2017-06-11 00:00:00
Tasks: Colorization, Generative Adversarial Network, Style Transfer

Recently, with the revolutionary neural style transferring methods,
creditable paintings can be synthesized automatically from content images and
style images. However, when it comes to the task of applying a painting's style
to an anime sketch, these methods will just randomly colorize sketch lines as
outputs and fail in the main task: specific style tranfer. In this paper, we
integrated residual U-net to apply the style to the gray-scale sketch with
auxiliary classifier generative adversarial network (AC-GAN). The whole process
is automatic and fast, and the results are creditable in the quality of art
style as well as colorization.
Skeleton based action recognition using translation-scale invariant image mapping and multi-scale deep cnn
Authors: Bo Li, Mingyi He, Xuelian Cheng, Yu-cheng Chen, Yuchao Dai
Date: 2017-04-19 00:00:00
Tasks: Action Recognition, image-classification, Image Classification, Skeleton Based Action Recognition, Temporal Action Localization, Translation

This paper presents an image classification based approach for skeleton-based
video action recognition problem. Firstly, A dataset independent
translation-scale invariant image mapping method is proposed, which transformes
the skeleton videos to colour images, named skeleton-images. Secondly, A
multi-scale deep convolutional neural network (CNN) architecture is proposed
which could be built and fine-tuned on the powerful pre-trained CNNs, e.g.,
AlexNet, VGGNet, ResNet etal.. Even though the skeleton-images are very
different from natural images, the fine-tune strategy still works well. At
last, we prove that our method could also work well on 2D skeleton video data.
We achieve the state-of-the-art results on the popular benchmard datasets e.g.
NTU RGB+D, UTD-MHAD, MSRC-12, and G3D. Especially on the largest and challenge
NTU RGB+D, UTD-MHAD, and MSRC-12 dataset, our method outperforms other methods
by a large margion, which proves the efficacy of the proposed method.
Axiomatic Attribution for Deep Networks
Authors: Mukund Sundararajan, Ankur Taly, Qiqi Yan
Date: 2017-03-04 00:00:00
Proceeding: ICML 2017 8
Tasks: Explainable artificial intelligence, Image Attribution, Interpretability Techniques for Deep Learning, Interpretable Machine Learning

We study the problem of attributing the prediction of a deep network to its
input features, a problem previously studied by several other works. We
identify two fundamental axioms---Sensitivity and Implementation Invariance
that attribution methods ought to satisfy. We show that they are not satisfied
by most known attribution methods, which we consider to be a fundamental
weakness of those methods. We use the axioms to guide the design of a new
attribution method called Integrated Gradients. Our method requires no
modification to the original network and is extremely simple to implement; it
just needs a few calls to the standard gradient operator. We apply this method
to a couple of image models, a couple of text models and a chemistry model,
demonstrating its ability to debug networks, to extract rules from a network,
and to enable users to engage with models better.
Multilevel Clustering via Wasserstein Means
Authors: Nhat Ho, XuanLong Nguyen, Mikhail Yurochkin, Hung Hai Bui, Viet Huynh, Dinh Phung
Date: 2017-06-13 00:00:00
Proceeding: ICML 2017 8
Tasks: Clustering

We propose a novel approach to the problem of multilevel clustering, which
aims to simultaneously partition data in each group and discover grouping
patterns among groups in a potentially large hierarchically structured corpus
of data. Our method involves a joint optimization formulation over several
spaces of discrete probability measures, which are endowed with Wasserstein
distance metrics. We propose a number of variants of this problem, which admit
fast optimization algorithms, by exploiting the connection to the problem of
finding Wasserstein barycenters. Consistency properties are established for the
estimates of both local and global clusters. Finally, experiment results with
both synthetic and real data are presented to demonstrate the flexibility and
scalability of the proposed approach.
MNL-Bandit: A Dynamic Learning Approach to Assortment Selection
Authors: Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi
Date: 2017-06-13 00:00:00

We consider a dynamic assortment selection problem, where in every round the
retailer offers a subset (assortment) of $N$ substitutable products to a
consumer, who selects one of these products according to a multinomial logit
(MNL) choice model. The retailer observes this choice and the objective is to
dynamically learn the model parameters, while optimizing cumulative revenues
over a selling horizon of length $T$. We refer to this exploration-exploitation
formulation as the MNL-Bandit problem. Existing methods for this problem follow
an "explore-then-exploit" approach, which estimate parameters to a desired
accuracy and then, treating these estimates as if they are the correct
parameter values, offers the optimal assortment based on these estimates. These
approaches require certain a priori knowledge of "separability", determined by
the true parameters of the underlying MNL model, and this in turn is critical
in determining the length of the exploration period. (Separability refers to
the distinguishability of the true optimal assortment from the other
sub-optimal alternatives.) In this paper, we give an efficient algorithm that
simultaneously explores and exploits, achieving performance independent of the
underlying parameters. The algorithm can be implemented in a fully online
manner, without knowledge of the horizon length $T$. Furthermore, the algorithm
is adaptive in the sense that its performance is near-optimal in both the "well
separated" case, as well as the general parameter setting where this separation
need not hold.
Autonomous Reactive Mission Scheduling and Task-Path Planning Architecture for Autonomous Underwater Vehicle
Authors: Somaiyeh Mahmoud. Zadeh
Date: 2017-06-13 00:00:00
Tasks: Scheduling

An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a
limited time interval. Since existing AUVs have limited battery capacity and
restricted endurance, they should autonomously manage mission time and the
resources to perform effective persistent deployment in longer missions. Task
assignment requires making decisions subject to resource constraints, while
tasks are assigned with costs and/or values that are budgeted in advance. Tasks
are distributed in a particular operation zone and mapped by a waypoint covered
network. Thus, design an efficient routing-task priority assign framework
considering vehicle's availabilities and properties is essential for increasing
mission productivity and on-time mission completion. This depends strongly on
the order and priority of the tasks that are located between node-like
waypoints in an operation network. On the other hand, autonomous operation of
AUVs in an unfamiliar dynamic underwater and performing quick response to
sudden environmental changes is a complicated process. Water current
instabilities can deflect the vehicle to an undesired direction and perturb
AUVs safety. The vehicle's robustness to strong environmental variations is
extremely crucial for its safe and optimum operations in an uncertain and
dynamic environment. To this end, the AUV needs to have a general overview of
the environment in top level to perform an autonomous action selection (task
selection) and a lower level local motion planner to operate successfully in
dealing with continuously changing situations. This research deals with
developing a novel reactive control architecture to provide a higher level of
decision autonomy for the AUV operation that enables a single vehicle to
accomplish multiple tasks in a single mission in the face of periodic
disturbances in a turbulent and highly uncertain environment.
Contrast Enhancement Estimation for Digital Image Forensics
Authors: Longyin Wen, Honggang Qi, Siwei Lyu
Date: 2017-06-13 00:00:00
Tasks: Image Forensics

Inconsistency in contrast enhancement can be used to expose image forgeries.
In this work, we describe a new method to estimate contrast enhancement from a
single image. Our method takes advantage of the nature of contrast enhancement
as a mapping between pixel values, and the distinct characteristics it
introduces to the image pixel histogram. Our method recovers the original pixel
histogram and the contrast enhancement simultaneously from a single image with
an iterative algorithm. Unlike previous methods, our method is robust in the
presence of additive noise perturbations that are used to hide the traces of
contrast enhancement. Furthermore, we also develop an e effective method to to
detect image regions undergone contrast enhancement transformations that are
different from the rest of the image, and use this method to detect composite
images. We perform extensive experimental evaluations to demonstrate the
efficacy and efficiency of our method method.
Six Challenges for Neural Machine Translation
Authors: Philipp Koehn, Rebecca Knowles
Date: 2017-06-12 00:00:00
Proceeding: WS 2017 8
Tasks: Machine Translation, Translation, Word Alignment

We explore six challenges for neural machine translation: domain mismatch,
amount of training data, rare words, long sentences, word alignment, and beam
search. We show both deficiencies and improvements over the quality of
phrase-based statistical machine translation.
Criteria Sliders: Learning Continuous Database Criteria via Interactive Ranking
Authors: James Tompkin, Kwang In Kim, Hanspeter Pfister, Christian Theobalt
Date: 2017-06-12 00:00:00

Large databases are often organized by hand-labeled metadata, or criteria,
which are expensive to collect. We can use unsupervised learning to model
database variation, but these models are often high dimensional, complex to
parameterize, or require expert knowledge. We learn low-dimensional continuous
criteria via interactive ranking, so that the novice user need only describe
the relative ordering of examples. This is formed as semi-supervised label
propagation in which we maximize the information gained from a limited number
of examples. Further, we actively suggest data points to the user to rank in a
more informative way than existing work. Our efficient approach allows users to
interactively organize thousands of data points along 1D and 2D continuous
sliders. We experiment with datasets of imagery and geometry to demonstrate
that our tool is useful for quickly assessing and organizing the content of
large databases.
Geometry-Based Next Frame Prediction from Monocular Video
Authors: Reza Mahjourian, Martin Wicke, Anelia Angelova
Date: 2016-09-20 00:00:00
Tasks: Autonomous Driving, Benchmarking, Depth Estimation, Depth Prediction, Prediction

We consider the problem of next frame prediction from video input. A
recurrent convolutional neural network is trained to predict depth from
monocular video input, which, along with the current video image and the camera
trajectory, can then be used to compute the next frame. Unlike prior next-frame
prediction approaches, we take advantage of the scene geometry and use the
predicted depth for generating the next frame prediction. Our approach can
produce rich next frame predictions which include depth information attached to
each pixel. Another novel aspect of our approach is that it predicts depth from
a sequence of images (e.g. in a video), rather than from a single still image.
We evaluate the proposed approach on the KITTI dataset, a standard dataset for
benchmarking tasks relevant to autonomous driving. The proposed method produces
results which are visually and numerically superior to existing methods that
directly predict the next frame. We show that the accuracy of depth prediction
improves as more prior frames are considered.
Unsupervised Perceptual Rewards for Imitation Learning
Authors: Pierre Sermanet, Kelvin Xu, Sergey Levine
Date: 2016-12-20 00:00:00
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

Reward function design and exploration time are arguably the biggest
obstacles to the deployment of reinforcement learning (RL) agents in the real
world. In many real-world tasks, designing a reward function takes considerable
hand engineering and often requires additional sensors to be installed just to
measure whether the task has been executed successfully. Furthermore, many
interesting tasks consist of multiple implicit intermediate steps that must be
executed in sequence. Even when the final outcome can be measured, it does not
necessarily provide feedback on these intermediate steps. To address these
issues, we propose leveraging the abstraction power of intermediate visual
representations learned by deep models to quickly infer perceptual reward
functions from small numbers of demonstrations. We present a method that is
able to identify key intermediate steps of a task from only a handful of
demonstration sequences, and automatically identify the most discriminative
features for identifying these steps. This method makes use of the features in
a pre-trained deep model, but does not require any explicit specification of
sub-goals. The resulting reward functions can then be used by an RL agent to
learn to perform the task in real-world settings. To evaluate the learned
reward, we present qualitative results on two real-world tasks and a
quantitative evaluation against a human-designed reward function. We also show
that our method can be used to learn a real-world door opening skill using a
real robot, even when the demonstration used for reward learning is provided by
a human using their own hand. To our knowledge, these are the first results
showing that complex robotic manipulation skills can be learned directly and
without supervised labels from a video of a human performing the task.
Supplementary material and data are available at
https://sermanet.github.io/rewards
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks
Authors: Christos Louizos, Max Welling
Date: 2017-03-06 00:00:00
Proceeding: ICML 2017 8

We reinterpret multiplicative noise in neural networks as auxiliary random
variables that augment the approximate posterior in a variational setting for
Bayesian neural networks. We show that through this interpretation it is both
efficient and straightforward to improve the approximation by employing
normalizing flows while still allowing for local reparametrizations and a
tractable lower bound. In experiments we show that with this new approximation
we can significantly improve upon classical mean field for Bayesian neural
networks on both predictive accuracy as well as predictive uncertainty.
Neural Message Passing for Quantum Chemistry
Authors: Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl
Date: 2017-04-04 00:00:00
Proceeding: ICML 2017 8
Tasks: Drug Discovery, Formation Energy, Graph Regression, Molecular Property Prediction, Node Classification, Property Prediction

Supervised learning on molecules has incredible potential to be useful in
chemistry, drug discovery, and materials science. Luckily, several promising
and closely related neural network models invariant to molecular symmetries
have already been described in the literature. These models learn a message
passing algorithm and aggregation procedure to compute a function of their
entire input graph. At this point, the next step is to find a particularly
effective variant of this general approach and apply it to chemical prediction
benchmarks until we either solve them or reach the limits of the approach. In
this paper, we reformulate existing models into a single common framework we
call Message Passing Neural Networks (MPNNs) and explore additional novel
variations within this framework. Using MPNNs we demonstrate state of the art
results on an important molecular property prediction benchmark; these results
are strong enough that we believe future work should focus on datasets with
larger molecules or more accurate ground truth labels.
Input Switched Affine Networks: An RNN Architecture Designed for Interpretability
Authors: Jakob N. Foerster, Justin Gilmer, Jan Chorowski, Jascha Sohl-Dickstein, David Sussillo
Date: 2016-11-28 00:00:00
Proceeding: ICML 2017 8
Tasks: Computational Efficiency

There exist many problem domains where the interpretability of neural network
models is essential for deployment. Here we introduce a recurrent architecture
composed of input-switched affine transformations - in other words an RNN
without any explicit nonlinearities, but with input-dependent recurrent
weights. This simple form allows the RNN to be analyzed via straightforward
linear methods: we can exactly characterize the linear contribution of each
input to the model predictions; we can use a change-of-basis to disentangle
input, output, and computational hidden unit subspaces; we can fully
reverse-engineer the architecture's solution to a simple task. Despite this
ease of interpretation, the input switched affine network achieves reasonable
performance on a text modeling tasks, and allows greater computational
efficiency than networks with standard nonlinearities.
Continual Learning Through Synaptic Intelligence
Authors: Friedemann Zenke, Ben Poole, Surya Ganguli
Date: 2017-03-13 00:00:00
Proceeding: ICML 2017 8
Tasks: Computational Efficiency, Continual Learning, General Classification

While deep learning has led to remarkable advances across diverse
applications, it struggles in domains where the data distribution changes over
the course of learning. In stark contrast, biological neural networks
continually adapt to changing domains, possibly by leveraging complex molecular
machinery to solve many tasks simultaneously. In this study, we introduce
intelligent synapses that bring some of this biological complexity into
artificial neural networks. Each synapse accumulates task relevant information
over time, and exploits this information to rapidly store new memories without
forgetting old ones. We evaluate our approach on continual learning of
classification tasks, and show that it dramatically reduces forgetting while
maintaining computational efficiency.
SmoothGrad: removing noise by adding noise
Authors: Daniel Smilkov, Nikhil Thorat, Been Kim, Fernanda Viégas, Martin Wattenberg
Date: 2017-06-12 00:00:00
Tasks: Interpretable Machine Learning, Sensitivity

Explaining the output of a deep network remains a challenge. In the case of
an image classifier, one type of explanation is to identify pixels that
strongly influence the final decision. A starting point for this strategy is
the gradient of the class score function with respect to the input image. This
gradient can be interpreted as a sensitivity map, and there are several
techniques that elaborate on this basic idea. This paper makes two
contributions: it introduces SmoothGrad, a simple method that can help visually
sharpen gradient-based sensitivity maps, and it discusses lessons in the
visualization of these maps. We publish the code for our experiments and a
website with our results.
Attention-based Vocabulary Selection for NMT Decoding
Authors: Baskaran Sankaran, Markus Freitag, Yaser Al-Onaizan
Date: 2017-06-12 00:00:00
Tasks: Machine Translation, NMT, Sentence, Translation

Neural Machine Translation (NMT) models usually use large target vocabulary
sizes to capture most of the words in the target language. The vocabulary size
is a big factor when decoding new sentences as the final softmax layer
normalizes over all possible target words. To address this problem, it is
widely common to restrict the target vocabulary with candidate lists based on
the source sentence. Usually, the candidate lists are a combination of external
word-to-word aligner, phrase table entries or most frequent words. In this
work, we propose a simple and yet novel approach to learn candidate lists
directly from the attention layer during NMT training. The candidate lists are
highly optimized for the current NMT model and do not need any external
computation of the candidate pool. We show significant decoding speedup
compared with using the entire vocabulary, without losing any translation
quality for two language pairs.