pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Action Annotation in Weakly Labeled Videos
Authors: Waqas Sultani, Mubarak Shah
Date: 2016-05-26 00:00:00
Tasks: Optical Flow Estimation

Manual spatio-temporal annotation of human action in videos is laborious,
requires several annotators and contains human biases. In this paper, we
present a weakly supervised approach to automatically obtain spatio-temporal
annotations of an actor in action videos. We first obtain a large number of
action proposals in each video. To capture a few most representative action
proposals in each video and evade processing thousands of them, we rank them
using optical flow and saliency in a 3D-MRF based framework and select a few
proposals using MAP based proposal subset selection method. We demonstrate that
this ranking preserves the high quality action proposals. Several such
proposals are generated for each video of the same action. Our next challenge
is to iteratively select one proposal from each video so that all proposals are
globally consistent. We formulate this as Generalized Maximum Clique Graph
problem using shape, global and fine grained similarity of proposals across the
videos. The output of our method is the most action representative proposals
from each video. Our method can also annotate multiple instances of the same
action in a video. We have validated our approach on three challenging action
datasets: UCF Sport, sub-JHMDB and THUMOS'13 and have obtained promising
results compared to several baseline methods. Moreover, on UCF Sports, we
demonstrate that action classifiers trained on these automatically obtained
spatio-temporal annotations have comparable performance to the classifiers
trained on ground truth annotation.
Exact Exponent in Optimal Rates for Crowdsourcing
Authors: Chao Gao, Yu Lu, Dengyong Zhou
Date: 2016-05-25 00:00:00

In many machine learning applications, crowdsourcing has become the primary
means for label collection. In this paper, we study the optimal error rate for
aggregating labels provided by a set of non-expert workers. Under the classic
Dawid-Skene model, we establish matching upper and lower bounds with an exact
exponent $mI(\pi)$ in which $m$ is the number of workers and $I(\pi)$ the
average Chernoff information that characterizes the workers' collective
ability. Such an exact characterization of the error exponent allows us to
state a precise sample size requirement
$m>\frac{1}{I(\pi)}\log\frac{1}{\epsilon}$ in order to achieve an $\epsilon$
misclassification error. In addition, our results imply the optimality of
various EM algorithms for crowdsourcing initialized by consistent estimators.
Unitary Evolution Recurrent Neural Networks
Authors: Martin Arjovsky, Amar Shah, Yoshua Bengio
Date: 2015-11-20 00:00:00
Tasks: Sequential Image Classification

Recurrent neural networks (RNNs) are notoriously difficult to train. When the
eigenvalues of the hidden to hidden weight matrix deviate from absolute value
1, optimization becomes difficult due to the well studied issue of vanishing
and exploding gradients, especially when trying to learn long-term
dependencies. To circumvent this problem, we propose a new architecture that
learns a unitary weight matrix, with eigenvalues of absolute value exactly 1.
The challenge we address is that of parametrizing unitary matrices in a way
that does not require expensive computations (such as eigendecomposition) after
each weight update. We construct an expressive unitary weight matrix by
composing several structured matrices that act as building blocks with
parameters to be learned. Optimization with this parameterization becomes
feasible only when considering hidden states in the complex domain. We
demonstrate the potential of this architecture by achieving state of the art
results in several hard tasks involving very long-term dependencies.
Partition Functions from Rao-Blackwellized Tempered Sampling
Authors: David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski
Date: 2016-03-07 00:00:00

Partition functions of probability distributions are important quantities for
model evaluation and comparisons. We present a new method to compute partition
functions of complex and multimodal distributions. Such distributions are often
sampled using simulated tempering, which augments the target space with an
auxiliary inverse temperature variable. Our method exploits the multinomial
probability law of the inverse temperatures, and provides estimates of the
partition function in terms of a simple quotient of Rao-Blackwellized marginal
inverse temperature probability estimates, which are updated while sampling. We
show that the method has interesting connections with several alternative
popular methods, and offers some significant advantages. In particular, we
empirically find that the new method provides more accurate estimates than
Annealed Importance Sampling when calculating partition functions of large
Restricted Boltzmann Machines (RBM); moreover, the method is sufficiently
accurate to track training and validation log-likelihoods during learning of
RBMs, at minimal computational cost.
Real-Time Human Motion Capture with Multiple Depth Cameras
Authors: Alireza Shafaei, James J. Little
Date: 2016-05-25 00:00:00
Tasks: 3D Pose Estimation, Image Segmentation, Markerless Motion Capture, Pose Estimation, Semantic Segmentation

Commonly used human motion capture systems require intrusive attachment of
markers that are visually tracked with multiple cameras. In this work we
present an efficient and inexpensive solution to markerless motion capture
using only a few Kinect sensors. Unlike the previous work on 3d pose estimation
using a single depth camera, we relax constraints on the camera location and do
not assume a co-operative user. We apply recent image segmentation techniques
to depth images and use curriculum learning to train our system on purely
synthetic data. Our method accurately localizes body parts without requiring an
explicit shape model. The body joint locations are then recovered by combining
evidence from multiple views in real-time. We also introduce a dataset of ~6
million synthetic depth frames for pose estimation from multiple cameras and
exceed state-of-the-art results on the Berkeley MHAD dataset.
Low-Rank Matrices on Graphs: Generalized Recovery & Applications
Authors: Nauman Shahid, Nathanael Perraudin, Pierre Vandergheynst
Date: 2016-05-18 00:00:00

Many real world datasets subsume a linear or non-linear low-rank structure in
a very low-dimensional space. Unfortunately, one often has very little or no
information about the geometry of the space, resulting in a highly
under-determined recovery problem. Under certain circumstances,
state-of-the-art algorithms provide an exact recovery for linear low-rank
structures but at the expense of highly inscalable algorithms which use nuclear
norm. However, the case of non-linear structures remains unresolved. We revisit
the problem of low-rank recovery from a totally different perspective,
involving graphs which encode pairwise similarity between the data samples and
features. Surprisingly, our analysis confirms that it is possible to recover
many approximate linear and non-linear low-rank structures with recovery
guarantees with a set of highly scalable and efficient algorithms. We call such
data matrices as \textit{Low-Rank matrices on graphs} and show that many real
world datasets satisfy this assumption approximately due to underlying
stationarity. Our detailed theoretical and experimental analysis unveils the
power of the simple, yet very novel recovery framework \textit{Fast Robust PCA
on Graphs}
Multimodal Remote Sensing Image Registration with Accuracy Estimation at Local and Global Scales
Authors: M. L. Uss, B. Vozel, V. V. Lukin, K. Chehdi
Date: 2016-02-08 00:00:00
Tasks: Image Registration

This paper focuses on potential accuracy of remote sensing images
registration. We investigate how this accuracy can be estimated without ground
truth available and used to improve registration quality of mono- and
multi-modal pair of images. At the local scale of image fragments, the
Cramer-Rao lower bound (CRLB) on registration error is estimated for each local
correspondence between coarsely registered pair of images. This CRLB is defined
by local image texture and noise properties. Opposite to the standard approach,
where registration accuracy is only evaluated at the output of the registration
process, such valuable information is used by us as an additional input
knowledge. It greatly helps detecting and discarding outliers and refining the
estimation of geometrical transformation model parameters. Based on these
ideas, a new area-based registration method called RAE (Registration with
Accuracy Estimation) is proposed. In addition to its ability to automatically
register very complex multimodal image pairs with high accuracy, the RAE method
provides registration accuracy at the global scale as covariance matrix of
estimation error of geometrical transformation model parameters or as
point-wise registration Standard Deviation. This accuracy does not depend on
any ground truth availability and characterizes each pair of registered images
individually. Thus, the RAE method can identify image areas for which a
predefined registration accuracy is guaranteed. The RAE method is proved
successful with reaching subpixel accuracy while registering eight complex
mono/multimodal and multitemporal image pairs including optical to optical,
optical to radar, optical to Digital Elevation Model (DEM) images and DEM to
radar cases. Other methods employed in comparisons fail to provide in a stable
manner accurate results on the same test cases.
Dropout as a Bayesian Approximation: Appendix
Authors: Yarin Gal, Zoubin Ghahramani
Date: 2015-06-06 00:00:00
Tasks: Deep Learning

We show that a neural network with arbitrary depth and non-linearities, with
dropout applied before every weight layer, is mathematically equivalent to an
approximation to a well known Bayesian model. This interpretation might offer
an explanation to some of dropout's key properties, such as its robustness to
over-fitting. Our interpretation allows us to reason about uncertainty in deep
learning, and allows the introduction of the Bayesian machinery into existing
deep learning frameworks in a principled way.
  This document is an appendix for the main paper "Dropout as a Bayesian
Approximation: Representing Model Uncertainty in Deep Learning" by Gal and
Ghahramani, 2015.
Toward a general, scaleable framework for Bayesian teaching with applications to topic models
Authors: Baxter S. Eaves Jr, Patrick Shafto
Date: 2016-05-25 00:00:00
Tasks: Topic Models

Machines, not humans, are the world's dominant knowledge accumulators but
humans remain the dominant decision makers. Interpreting and disseminating the
knowledge accumulated by machines requires expertise, time, and is prone to
failure. The problem of how best to convey accumulated knowledge from computers
to humans is a critical bottleneck in the broader application of machine
learning. We propose an approach based on human teaching where the problem is
formalized as selecting a small subset of the data that will, with high
probability, lead the human user to the correct inference. This approach,
though successful for modeling human learning in simple laboratory experiments,
has failed to achieve broader relevance due to challenges in formulating
general and scalable algorithms. We propose general-purpose teaching via
pseudo-marginal sampling and demonstrate the algorithm by teaching topic
models. Simulation results show our sampling-based approach: effectively
approximates the probability where ground-truth is possible via enumeration,
results in data that are markedly different from those expected by random
sampling, and speeds learning especially for small amounts of data. Application
to movie synopsis data illustrates differences between teaching and random
sampling for teaching distributions and specific topics, and demonstrates gains
in scalability and applicability to real-world problems.
Efficient Distributed Learning with Sparsity
Authors: Jialei Wang, Mladen Kolar, Nathan Srebro, Tong Zhang
Date: 2016-05-25 00:00:00
Proceeding: ICML 2017 8
Tasks: General Classification, regression, Sparse Learning

We propose a novel, efficient approach for distributed sparse learning in
high-dimensions, where observations are randomly partitioned across machines.
Computationally, at each round our method only requires the master machine to
solve a shifted ell_1 regularized M-estimation problem, and other workers to
compute the gradient. In respect of communication, the proposed approach
provably matches the estimation error bound of centralized methods within
constant rounds of communications (ignoring logarithmic factors). We conduct
extensive experiments on both simulated and real world datasets, and
demonstrate encouraging performances on high-dimensional regression and
classification tasks.
Multi-Object Tracking and Identification over Sets
Authors: Aijun Bai
Date: 2016-05-25 00:00:00
Tasks: Multi-Object Tracking, Object, Object Tracking

The ability for an autonomous agent or robot to track and identify
potentially multiple objects in a dynamic environment is essential for many
applications, such as automated surveillance, traffic monitoring, human-robot
interaction, etc. The main challenge is due to the noisy and incomplete
perception including inevitable false negative and false positive errors from a
low-level detector. In this paper, we propose a novel multi-object tracking and
identification over sets approach to address this challenge. We define joint
states and observations both as finite sets, and develop motion and observation
functions accordingly. The object identification problem is then formulated and
solved by using expectation-maximization methods. The set formulation enables
us to avoid directly performing observation-to-object association. We
empirically confirm that the overall algorithm outperforms the state-of-the-art
in a popular PETS dataset.
Pure Exploration of Multi-armed Bandit Under Matroid Constraints
Authors: Lijie Chen, Anupam Gupta, Jian Li
Date: 2016-05-23 00:00:00

We study the pure exploration problem subject to a matroid constraint
(Best-Basis) in a stochastic multi-armed bandit game. In a Best-Basis instance,
we are given $n$ stochastic arms with unknown reward distributions, as well as
a matroid $\mathcal{M}$ over the arms. Let the weight of an arm be the mean of
its reward distribution. Our goal is to identify a basis of $\mathcal{M}$ with
the maximum total weight, using as few samples as possible.
  The problem is a significant generalization of the best arm identification
problem and the top-$k$ arm identification problem, which have attracted
significant attentions in recent years. We study both the exact and PAC
versions of Best-Basis, and provide algorithms with nearly-optimal sample
complexities for these versions. Our results generalize and/or improve on
several previous results for the top-$k$ arm identification problem and the
combinatorial pure exploration problem when the combinatorial constraint is a
matroid.
Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling
Authors: Byung-soo Kim, Hwanjo Yu, Gary Geunbae Lee
Date: 2016-05-25 00:00:00
Tasks: Implicit Relations, Open Information Extraction, Sentence

Previous studies in Open Information Extraction (Open IE) are mainly based on
extraction patterns. They manually define patterns or automatically learn them
from a large corpus. However, these approaches are limited when grasping the
context of a sentence, and they fail to capture implicit relations. In this
paper, we address this problem with the following methods. First, we exploit
long short-term memory (LSTM) networks to extract higher-level features along
the shortest dependency paths, connecting headwords of relations and arguments.
The path-level features from LSTM networks provide useful clues regarding
contextual information and the validity of arguments. Second, we constructed
samples to train LSTM networks without the need for manual labeling. In
particular, feedback negative sampling picks highly negative samples among
non-positive samples through a model trained with positive samples. The
experimental results show that our approach produces more precise and abundant
extractions than state-of-the-art open IE systems. To the best of our
knowledge, this is the first work to apply deep learning to Open IE.
Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey
Authors: Nabiha Asghar
Date: 2016-05-25 00:00:00
Tasks: Relation Extraction

Automatic extraction of cause-effect relationships from natural language
texts is a challenging open problem in Artificial Intelligence. Most of the
early attempts at its solution used manually constructed linguistic and
syntactic rules on small and domain-specific data sets. However, with the
advent of big data, the availability of affordable computing power and the
recent popularization of machine learning, the paradigm to tackle this problem
has slowly shifted. Machines are now expected to learn generic causal
extraction rules from labelled data with minimal supervision, in a domain
independent-manner. In this paper, we provide a comprehensive survey of causal
relation extraction techniques from both paradigms, and analyse their relative
strengths and weaknesses, with recommendations for future work.
Visual Concept Recognition and Localization via Iterative Introspection
Authors: Amir Rosenfeld, Shimon Ullman
Date: 2016-03-14 00:00:00
Tasks: General Classification

Convolutional neural networks have been shown to develop internal
representations, which correspond closely to semantically meaningful objects
and parts, although trained solely on class labels. Class Activation Mapping
(CAM) is a recent method that makes it possible to easily highlight the image
regions contributing to a network's classification decision. We build upon
these two developments to enable a network to re-examine informative image
regions, which we term introspection. We propose a weakly-supervised iterative
scheme, which shifts its center of attention to increasingly discriminative
regions as it progresses, by alternating stages of classification and
introspection. We evaluate our method and show its effectiveness over a range
of several datasets, where we obtain competitive or state-of-the-art results:
on Stanford-40 Actions, we set a new state-of the art of 81.74%. On
FGVC-Aircraft and the Stanford Dogs dataset, we show consistent improvements
over baselines, some of which include significantly more supervision.
Simultaneous Sparse Dictionary Learning and Pruning
Authors: Simeng Qu, Xiao Wang
Date: 2016-05-25 00:00:00
Tasks: Denoising, Dictionary Learning, Image Denoising

Dictionary learning is a cutting-edge area in imaging processing, that has
recently led to state-of-the-art results in many signal processing tasks. The
idea is to conduct a linear decomposition of a signal using a few atoms of a
learned and usually over-completed dictionary instead of a pre-defined basis.
Determining a proper size of the to-be-learned dictionary is crucial for both
precision and efficiency of the process, while most of the existing dictionary
learning algorithms choose the size quite arbitrarily. In this paper, a novel
regularization method called the Grouped Smoothly Clipped Absolute Deviation
(GSCAD) is employed for learning the dictionary. The proposed method can
simultaneously learn a sparse dictionary and select the appropriate dictionary
size. Efficient algorithm is designed based on the alternative direction method
of multipliers (ADMM) which decomposes the joint non-convex problem with the
non-convex penalty into two convex optimization problems. Several examples are
presented for image denoising and the experimental results are compared with
other state-of-the-art approaches.
One-Shot Generalization in Deep Generative Models
Authors: Danilo Jimenez Rezende, Shakir Mohamed, Ivo Danihelka, Karol Gregor, Daan Wierstra
Date: 2016-03-16 00:00:00
Tasks: BIG-bench Machine Learning, Density Estimation, Image Generation

Humans have an impressive ability to reason about new concepts and
experiences from just a single example. In particular, humans have an ability
for one-shot generalization: an ability to encounter a new concept, understand
its structure, and then be able to generate compelling alternative variations
of the concept. We develop machine learning systems with this important
capacity by developing new deep generative models, models that combine the
representational power of deep learning with the inferential power of Bayesian
reasoning. We develop a class of sequential generative models that are built on
the principles of feedback and attention. These two characteristics lead to
generative models that are among the state-of-the art in density estimation and
image generation. We demonstrate the one-shot generalization ability of our
models using three tasks: unconditional sampling, generating new exemplars of a
given concept, and generating new exemplars of a family of concepts. In all
cases our models are able to generate compelling and diverse samples---having
seen new examples just once---providing an important class of general-purpose
models for one-shot machine learning.
A Convolutional Attention Network for Extreme Summarization of Source Code
Authors: Miltiadis Allamanis, Hao Peng, Charles Sutton
Date: 2016-02-09 00:00:00
Tasks: Descriptive, Extreme Summarization, Translation

Attention mechanisms in neural networks have proved useful for problems in
which the input and output do not have fixed dimension. Often there exist
features that are locally translation invariant and would be valuable for
directing the model's attention, but previous attentional architectures are not
constructed to learn such features specifically. We introduce an attentional
neural network that employs convolution on the input tokens to detect local
time-invariant and long-range topical attention features in a context-dependent
way. We apply this architecture to the problem of extreme summarization of
source code snippets into short, descriptive function name-like summaries.
Using those features, the model sequentially generates a summary by
marginalizing over two attention mechanisms: one that predicts the next summary
token based on the attention weights of the input tokens and another that is
able to copy a code token as-is directly into the summary. We demonstrate our
convolutional attention neural network's performance on 10 popular Java
projects showing that it achieves better performance compared to previous
attentional mechanisms.
Unsupervised Word and Dependency Path Embeddings for Aspect Term Extraction
Authors: Yichun Yin, Furu Wei, Li Dong, Kaimeng Xu, Ming Zhang, Ming Zhou
Date: 2016-05-25 00:00:00
Tasks: Term Extraction

In this paper, we develop a novel approach to aspect term extraction based on
unsupervised learning of distributed representations of words and dependency
paths. The basic idea is to connect two words (w1 and w2) with the dependency
path (r) between them in the embedding space. Specifically, our method
optimizes the objective w1 + r = w2 in the low-dimensional space, where the
multi-hop dependency paths are treated as a sequence of grammatical relations
and modeled by a recurrent neural network. Then, we design the embedding
features that consider linear context and dependency context information, for
the conditional random field (CRF) based aspect term extraction. Experimental
results on the SemEval datasets show that, (1) with only embedding features, we
can achieve state-of-the-art results; (2) our embedding method which
incorporates the syntactic information among words yields better performance
than other representative ones in aspect term extraction.