pwc-archive/papers-with-abstracts huggingface dataset:
Fast Video Classification via Adaptive Cascading of Deep Models
Authors: Haichen Shen, Seungyeop Han, Matthai Philipose, Arvind Krishnamurthy
Date: 2016-11-20 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, CPU, Decision Making, General Classification, GPU, Sequential Decision Making, Video Classification

Recent advances have enabled "oracle" classifiers that can classify across
many classes and input distributions with high accuracy without retraining.
However, these classifiers are relatively heavyweight, so that applying them to
classify video is costly. We show that day-to-day video exhibits highly skewed
class distributions over the short term, and that these distributions can be
classified by much simpler models. We formulate the problem of detecting the
short-term skews online and exploiting models based on it as a new sequential
decision making problem dubbed the Online Bandit Problem, and present a new
algorithm to solve it. When applied to recognizing faces in TV shows and
movies, we realize end-to-end classification speedups of 2.4-7.8x/2.6-11.2x (on
GPU/CPU) relative to a state-of-the-art convolutional neural network, at
competitive accuracy.
Physically-Based Rendering for Indoor Scene Understanding Using Convolutional Neural Networks
Authors: Yinda Zhang, Shuran Song, Ersin Yumer, Manolis Savva, Joon-Young Lee, Hailin Jin, Thomas Funkhouser
Date: 2016-12-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Boundary Detection, Edge Detection, Representation Learning, Robot Navigation, Scene Understanding, Semantic Segmentation

Indoor scene understanding is central to applications such as robot
navigation and human companion assistance. Over the last years, data-driven
deep neural networks have outperformed many traditional approaches thanks to
their representation learning capabilities. One of the bottlenecks in training
for better representations is the amount of available per-pixel ground truth
data that is required for core scene understanding tasks such as semantic
segmentation, normal prediction, and object edge detection. To address this
problem, a number of works proposed using synthetic data. However, a systematic
study of how such synthetic data is generated is missing. In this work, we
introduce a large-scale synthetic dataset with 400K physically-based rendered
images from 45K realistic 3D indoor scenes. We study the effects of rendering
methods and scene lighting on training for three computer vision tasks: surface
normal prediction, semantic segmentation, and object boundary detection. This
study provides insights into the best practices for training with synthetic
data (more realistic rendering is worth it) and shows that pretraining with our
new synthetic dataset can improve results beyond the current state of the art
on all three tasks.
Named Entity Disambiguation for Noisy Text
Authors: Yotam Eshel, Noam Cohen, Kira Radinsky, Shaul Markovitch, Ikuya Yamada, Omer Levy
Date: 2017-06-28 00:00:00
Proceeding: CONLL 2017 8
Tasks: Entity Disambiguation, Entity Embeddings

We address the task of Named Entity Disambiguation (NED) for noisy text. We
present WikilinksNED, a large-scale NED dataset of text fragments from the web,
which is significantly noisier and more challenging than existing news-based
datasets. To capture the limited and noisy local context surrounding each
mention, we design a neural model and train it with a novel method for sampling
informative negative examples. We also describe a new way of initializing word
and entity embeddings that significantly improves performance. Our model
significantly outperforms existing state-of-the-art methods on WikilinksNED
while achieving comparable performance on a smaller newswire dataset.
Efficient Correlated Topic Modeling with Topic Embedding
Authors: Junxian He, Zhiting Hu, Taylor Berg-Kirkpatrick, Ying Huang, Eric P. Xing
Date: 2017-07-01 00:00:00
Tasks: Document Classification, General Classification, Retrieval, Variational Inference

Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inference in the low-dimensional embedding space, reducing
previous cubic or quadratic time complexity to linear w.r.t the topic size. We
further speedup variational inference with a fast sampler to exploit sparsity
of topic occurrence. Extensive experiments show that our approach is capable of
handling model and data scales which are several orders of magnitude larger
than existing correlation results, without sacrificing modeling quality by
providing competitive or superior performance in document classification and
retrieval.
On Scalable Inference with Stochastic Gradient Descent
Authors: Yixin Fang, Jinfeng Xu, Lei Yang
Date: 2017-07-01 00:00:00
Tasks: quantile regression

In many applications involving large dataset or online updating, stochastic
gradient descent (SGD) provides a scalable way to compute parameter estimates
and has gained increasing popularity due to its numerical convenience and
memory efficiency. While the asymptotic properties of SGD-based estimators have
been established decades ago, statistical inference such as interval estimation
remains much unexplored. The traditional resampling method such as the
bootstrap is not computationally feasible since it requires to repeatedly draw
independent samples from the entire dataset. The plug-in method is not
applicable when there are no explicit formulas for the covariance matrix of the
estimator. In this paper, we propose a scalable inferential procedure for
stochastic gradient descent, which, upon the arrival of each observation,
updates the SGD estimate as well as a large number of randomly perturbed SGD
estimates. The proposed method is easy to implement in practice. We establish
its theoretical properties for a general class of models that includes
generalized linear models and quantile regression models as special cases. The
finite-sample performance and numerical utility is evaluated by simulation
studies and two real data applications.
Guided Optical Flow Learning
Authors: Yi Zhu, Zhenzhong Lan, Shawn Newsam, Alexander G. Hauptmann
Date: 2017-02-08 00:00:00
Tasks: Image Reconstruction, Optical Flow Estimation

We study the unsupervised learning of CNNs for optical flow estimation using
proxy ground truth data. Supervised CNNs, due to their immense learning
capacity, have shown superior performance on a range of computer vision
problems including optical flow prediction. They however require the ground
truth flow which is usually not accessible except on limited synthetic data.
Without the guidance of ground truth optical flow, unsupervised CNNs often
perform worse as they are naturally ill-conditioned. We therefore propose a
novel framework in which proxy ground truth data generated from classical
approaches is used to guide the CNN learning. The models are further refined in
an unsupervised fashion using an image reconstruction loss. Our guided learning
approach is competitive with or superior to state-of-the-art approaches on
three standard benchmark datasets yet is completely unsupervised and can run in
real time.
Thompson Sampling for the MNL-Bandit
Authors: Shipra Agrawal, Vashist Avadhanula, Vineet Goyal, Assaf Zeevi
Date: 2017-06-03 00:00:00
Tasks: Thompson Sampling

We consider a sequential subset selection problem under parameter
uncertainty, where at each time step, the decision maker selects a subset of
cardinality $K$ from $N$ possible items (arms), and observes a (bandit)
feedback in the form of the index of one of the items in said subset, or none.
Each item in the index set is ascribed a certain value (reward), and the
feedback is governed by a Multinomial Logit (MNL) choice model whose parameters
are a priori unknown. The objective of the decision maker is to maximize the
expected cumulative rewards over a finite horizon $T$, or alternatively,
minimize the regret relative to an oracle that knows the MNL parameters. We
refer to this as the MNL-Bandit problem. This problem is representative of a
larger family of exploration-exploitation problems that involve a combinatorial
objective, and arise in several important application domains. We present an
approach to adapt Thompson Sampling to this problem and show that it achieves
near-optimal regret as well as attractive numerical performance.
Local Binary Convolutional Neural Networks
Authors: Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides
Date: 2016-08-22 00:00:00
Proceeding: CVPR 2017 7

We propose local binary convolution (LBC), an efficient alternative to
convolutional layers in standard convolutional neural networks (CNN). The
design principles of LBC are motivated by local binary patterns (LBP). The LBC
layer comprises of a set of fixed sparse pre-defined binary convolutional
filters that are not updated during the training process, a non-linear
activation function and a set of learnable linear weights. The linear weights
combine the activated filter responses to approximate the corresponding
activated filter responses of a standard convolutional layer. The LBC layer
affords significant parameter savings, 9x to 169x in the number of learnable
parameters compared to a standard convolutional layer. Furthermore, the sparse
and binary nature of the weights also results in up to 9x to 169x savings in
model size compared to a standard convolutional layer. We demonstrate both
theoretically and experimentally that our local binary convolution layer is a
good approximation of a standard convolutional layer. Empirically, CNNs with
LBC layers, called local binary convolutional neural networks (LBCNN), achieves
performance parity with regular CNNs on a range of visual datasets (MNIST,
SVHN, CIFAR-10, and ImageNet) while enjoying significant computational savings.
BMOBench: Black-Box Multi-Objective Optimization Benchmarking Platform
Authors: Abdullah Al-Dujaili, S. Suresh
Date: 2016-05-23 00:00:00
Tasks: Benchmarking

This document briefly describes the Black-Box Multi-Objective Optimization
Benchmarking (BMOBench) platform. It presents the test problems, evaluation
procedure, and experimental setup. To this end, the BMOBench is demonstrated by
comparing recent multi-objective solvers from the literature, namely SMS-EMOA,
DMS, and MO-SOO.
A Closer Look at Memorization in Deep Networks
Authors: Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, Simon Lacoste-Julien
Date: 2017-06-16 00:00:00
Proceeding: ICML 2017 8
Tasks: Adversarial Robustness, Memorization

We examine the role of memorization in deep learning, drawing connections to
capacity, generalization, and adversarial robustness. While deep networks are
capable of memorizing noise data, our results suggest that they tend to
prioritize learning simple patterns first. In our experiments, we expose
qualitative differences in gradient-based optimization of deep neural networks
(DNNs) on noise vs. real data. We also demonstrate that for appropriately tuned
explicit regularization (e.g., dropout) we can degrade DNN training performance
on noise datasets without compromising generalization on real data. Our
analysis suggests that the notions of effective capacity which are dataset
independent are unlikely to explain the generalization performance of deep
networks when trained with gradient based methods because training data itself
plays an important role in determining the degree of memorization.
PixelCNN Models with Auxiliary Variables for Natural Image Modeling
Authors: Alexander Kolesnikov, Christoph H. Lampert
Date: 2016-12-24 00:00:00
Proceeding: ICML 2017 8
Tasks: Image Generation

We study probabilistic models of natural images and extend the autoregressive
family of PixelCNN architectures by incorporating auxiliary variables.
Subsequently, we describe two new generative image models that exploit
different image transformations as auxiliary variables: a quantized grayscale
view of the image or a multi-resolution image pyramid. The proposed models
tackle two known shortcomings of existing PixelCNN models: 1) their tendency to
focus on low-level image details, while largely ignoring high-level image
information, such as object shapes, and 2) their computationally costly
procedure for image sampling. We experimentally demonstrate benefits of the
proposed models, in particular showing that they produce much more
realistically looking image samples than previous state-of-the-art
probabilistic models.
Demystifying Neural Style Transfer
Authors: Yanghao Li, Naiyan Wang, Jiaying Liu, Xiaodi Hou
Date: 2017-01-04 00:00:00
Tasks: Domain Adaptation, Style Transfer

Neural Style Transfer has recently demonstrated very exciting results which
catches eyes in both academia and industry. Despite the amazing results, the
principle of neural style transfer, especially why the Gram matrices could
represent style remains unclear. In this paper, we propose a novel
interpretation of neural style transfer by treating it as a domain adaptation
problem. Specifically, we theoretically show that matching the Gram matrices of
feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with
the second order polynomial kernel. Thus, we argue that the essence of neural
style transfer is to match the feature distributions between the style images
and the generated images. To further support our standpoint, we experiment with
several other distribution alignment methods, and achieve appealing results. We
believe this novel interpretation connects these two important research fields,
and could enlighten future researches.
Minimax Regret Bounds for Reinforcement Learning
Authors: Mohammad Gheshlaghi Azar, Ian Osband, Rémi Munos
Date: 2017-03-16 00:00:00
Proceeding: ICML 2017 8
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider the problem of provably optimal exploration in reinforcement
learning for finite horizon MDPs. We show that an optimistic modification to
value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} +
H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states,
$A$ the number of actions and $T$ the number of time-steps. This result
improves over the best previous known bound $\tilde{O}(HS \sqrt{AT})$ achieved
by the UCRL2 algorithm of Jaksch et al., 2010. The key significance of our new
results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of
$\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of
$\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contains two key
insights. We use careful application of concentration inequalities to the
optimal value function as a whole, rather than to the transitions probabilities
(to improve scaling in $S$), and we define Bernstein-based "exploration
bonuses" that use the empirical variance of the estimated values at the next
states (to improve scaling in $H$).
Sub-sampled Cubic Regularization for Non-convex Optimization
Authors: Jonas Moritz Kohler, Aurelien Lucchi
Date: 2017-05-16 00:00:00
Proceeding: ICML 2017 8

We consider the minimization of non-convex functions that typically arise in
machine learning. Specifically, we focus our attention on a variant of trust
region methods known as cubic regularization. This approach is particularly
attractive because it escapes strict saddle points and it provides stronger
convergence guarantees than first- and second-order as well as classical trust
region methods. However, it suffers from a high computational complexity that
makes it impractical for large-scale learning. Here, we propose a novel method
that uses sub-sampling to lower this computational cost. By the use of
concentration inequalities we provide a sampling scheme that gives sufficiently
accurate gradient and Hessian approximations to retain the strong global and
local convergence guarantees of cubically regularized methods. To the best of
our knowledge this is the first work that gives global convergence guarantees
for a sub-sampled variant of cubic regularization on non-convex functions.
Furthermore, we provide experimental results supporting our theory.
Unsupervised Diverse Colorization via Generative Adversarial Networks
Authors: Yun Cao, Zhiming Zhou, Wei-Nan Zhang, Yong Yu
Date: 2017-02-22 00:00:00
Tasks: Colorization, Diversity

Colorization of grayscale images has been a hot topic in computer vision.
Previous research mainly focuses on producing a colored image to match the
original one. However, since many colors share the same gray value, an input
grayscale image could be diversely colored while maintaining its reality. In
this paper, we design a novel solution for unsupervised diverse colorization.
Specifically, we leverage conditional generative adversarial networks to model
the distribution of real-world item colors, in which we develop a fully
convolutional generator with multi-layer noise to enhance diversity, with
multi-layer condition concatenation to maintain reality, and with stride 1 to
keep spatial information. With such a novel network architecture, the model
yields highly competitive performance on the open LSUN bedroom dataset. The
Turing test of 80 humans further indicates our generated color schemes are
highly convincible.
A study of existing Ontologies in the IoT-domain
Authors: Garvita Bajaj, Rachit Agarwal, Pushpendra Singh, Nikolaos Georgantas, Valerie Issarny
Date: 2017-07-01 00:00:00
Tasks: Survey

Several domains have adopted the increasing use of IoT-based devices to
collect sensor data for generating abstractions and perceptions of the real
world. This sensor data is multi-modal and heterogeneous in nature. This
heterogeneity induces interoperability issues while developing cross-domain
applications, thereby restricting the possibility of reusing sensor data to
develop new applications. As a solution to this, semantic approaches have been
proposed in the literature to tackle problems related to interoperability of
sensor data. Several ontologies have been proposed to handle different aspects
of IoT-based sensor data collection, ranging from discovering the IoT sensors
for data collection to applying reasoning on the collected sensor data for
drawing inferences. In this paper, we survey these existing semantic ontologies
to provide an overview of the recent developments in this field. We highlight
the fundamental ontological concepts (e.g., sensor-capabilities and
context-awareness) required for an IoT-based application, and survey the
existing ontologies which include these concepts. Based on our study, we also
identify the shortcomings of currently available ontologies, which serves as a
stepping stone to state the need for a common unified ontology for the IoT
domain.
Efficient Attention using a Fixed-Size Memory Representation
Authors: Denny Britz, Melody Y. Guan, Minh-Thang Luong
Date: 2017-07-01 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Translation

The standard content-based attention mechanism typically used in
sequence-to-sequence models is computationally expensive as it requires the
comparison of large encoder and decoder states at each time step. In this work,
we propose an alternative attention mechanism based on a fixed size memory
representation that is more efficient. Our technique predicts a compact set of
K attention contexts during encoding and lets the decoder compute an efficient
lookup that does not need to consult the memory. We show that our approach
performs on-par with the standard attention mechanism while yielding inference
speedups of 20% for real-world translation tasks and more for tasks with longer
sequences. By visualizing attention scores we demonstrate that our models learn
distinct, meaningful alignments.
Some methods for heterogeneous treatment effect estimation in high-dimensions
Authors: Scott Powers, Junyang Qian, Kenneth Jung, Alejandro Schuler, Nigam H. Shah, Trevor Hastie, Robert Tibshirani
Date: 2017-07-01 00:00:00
Tasks: Heterogeneous Treatment Effect Estimation, Vocal Bursts Intensity Prediction

When devising a course of treatment for a patient, doctors often have little
quantitative evidence on which to base their decisions, beyond their medical
education and published clinical trials. Stanford Health Care alone has
millions of electronic medical records (EMRs) that are only just recently being
leveraged to inform better treatment recommendations. These data present a
unique challenge because they are high-dimensional and observational. Our goal
is to make personalized treatment recommendations based on the outcomes for
past patients similar to a new patient. We propose and analyze three methods
for estimating heterogeneous treatment effects using observational data. Our
methods perform well in simulations using a wide variety of treatment effect
functions, and we present results of applying the two most promising methods to
data from The SPRINT Data Analysis Challenge, from a large randomized trial of
a treatment for high blood pressure.
Structured Sparse Ternary Weight Coding of Deep Neural Networks for Efficient Hardware Implementations
Authors: Yoonho Boo, Wonyong Sung
Date: 2017-07-01 00:00:00

Deep neural networks (DNNs) usually demand a large amount of operations for
real-time inference. Especially, fully-connected layers contain a large number
of weights, thus they usually need many off-chip memory accesses for inference.
We propose a weight compression method for deep neural networks, which allows
values of +1 or -1 only at predetermined positions of the weights so that
decoding using a table can be conducted easily. For example, the structured
sparse (8,2) coding allows at most two non-zero values among eight weights.
This method not only enables multiplication-free DNN implementations but also
compresses the weight storage by up to x32 compared to floating-point networks.
Weight distribution normalization and gradual pruning techniques are applied to
mitigate the performance degradation. The experiments are conducted with
fully-connected deep neural networks and convolutional neural networks.
Learning from Ambiguously Labeled Face Images
Authors: Ching-Hui Chen, Vishal M. Patel, Rama Chellappa
Date: 2017-02-15 00:00:00
Tasks: Matrix Completion

Learning a classifier from ambiguously labeled face images is challenging
since training images are not always explicitly-labeled. For instance, face
images of two persons in a news photo are not explicitly labeled by their names
in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar)
method for predicting the actual labels from ambiguously labeled images. This
step is followed by learning a standard supervised classifier from the
disambiguated labels to classify new images. To prevent the majority labels
from dominating the result of MCar, we generalize MCar to a weighted MCar
(WMCar) that handles label imbalance. Since WMCar outputs a soft labeling
vector of reduced ambiguity for each instance, we can iteratively refine it by
feeding it as the input to WMCar. Nevertheless, such an iterative
implementation can be affected by the noisy soft labeling vectors, and thus the
performance may degrade. Our proposed Iterative Candidate Elimination (ICE)
procedure makes the iterative ambiguity resolution possible by gradually
eliminating a portion of least likely candidates in ambiguously labeled face.
We further extend MCar to incorporate the labeling constraints between
instances when such prior knowledge is available. Compared to existing methods,
our approach demonstrates improvement on several ambiguously labeled datasets.