pwc-archive/papers-with-abstracts huggingface dataset:
Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification
Authors: Yuxin Peng, Yunzhen Zhao, Junchao Zhang
Date: 2017-11-09 00:00:00
Tasks: General Classification, Optical Flow Estimation, Video Classification, Vocal Bursts Valence Prediction

Video classification is highly important with wide applications, such as
video search and intelligent surveillance. Video naturally consists of static
and motion information, which can be represented by frame and optical flow.
Recently, researchers generally adopt the deep networks to capture the static
and motion information \textbf{\emph{separately}}, which mainly has two
limitations: (1) Ignoring the coexistence relationship between spatial and
temporal attention, while they should be jointly modelled as the spatial and
temporal evolutions of video, thus discriminative video features can be
extracted.(2) Ignoring the strong complementarity between static and motion
information coexisted in video, while they should be collaboratively learned to
boost each other. For addressing the above two limitations, this paper proposes
the approach of two-stream collaborative learning with spatial-temporal
attention (TCLSTA), which consists of two models: (1) Spatial-temporal
attention model: The spatial-level attention emphasizes the salient regions in
frame, and the temporal-level attention exploits the discriminative frames in
video. They are jointly learned and mutually boosted to learn the
discriminative static and motion features for better classification
performance. (2) Static-motion collaborative model: It not only achieves mutual
guidance on static and motion information to boost the feature learning, but
also adaptively learns the fusion weights of static and motion streams, so as
to exploit the strong complementarity between static and motion information to
promote video classification. Experiments on 4 widely-used datasets show that
our TCLSTA approach achieves the best performance compared with more than 10
state-of-the-art methods.
On Estimation of $L_{r}$-Norms in Gaussian White Noise Models
Authors: Yanjun Han, Jiantao Jiao, Rajarshi Mukherjee
Date: 2017-10-11 00:00:00

We provide a complete picture of asymptotically minimax estimation of $L_r$-norms (for any $r\ge 1$) of the mean in Gaussian white noise model over Nikolskii-Besov spaces. In this regard, we complement the work of Lepski, Nemirovski and Spokoiny (1999), who considered the cases of $r=1$ (with poly-logarithmic gap between upper and lower bounds) and $r$ even (with asymptotically sharp upper and lower bounds) over H\"{o}lder spaces. We additionally consider the case of asymptotically adaptive minimax estimation and demonstrate a difference between even and non-even $r$ in terms of an investigator's ability to produce asymptotically adaptive minimax estimators without paying a penalty.
Predicting Scene Parsing and Motion Dynamics in the Future
Authors: Xiaojie Jin, Huaxin Xiao, Xiaohui Shen, Jimei Yang, Zhe Lin, Yunpeng Chen, Zequn Jie, Jiashi Feng, Shuicheng Yan
Date: 2017-11-09 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Autonomous Vehicles, motion prediction, Optical Flow Estimation, Scene Parsing

The ability of predicting the future is important for intelligent systems,
e.g. autonomous vehicles and robots to plan early and make decisions
accordingly. Future scene parsing and optical flow estimation are two key tasks
that help agents better understand their environments as the former provides
dense semantic information, i.e. what objects will be present and where they
will appear, while the latter provides dense motion information, i.e. how the
objects will move. In this paper, we propose a novel model to simultaneously
predict scene parsing and optical flow in unobserved future video frames. To
our best knowledge, this is the first attempt in jointly predicting scene
parsing and motion dynamics. In particular, scene parsing enables structured
motion prediction by decomposing optical flow into different groups while
optical flow estimation brings reliable pixel-wise correspondence to scene
parsing. By exploiting this mutually beneficial relationship, our model shows
significantly better parsing and motion prediction results when compared to
well-established baselines and individual prediction models on the large-scale
Cityscapes dataset. In addition, we also demonstrate that our model can be used
to predict the steering angle of the vehicles, which further verifies the
ability of our model to learn latent representations of scene dynamics.
Language Modeling for Code-Switched Data: Challenges and Approaches
Authors: Ganji Sreeram, Rohit Sinha
Date: 2017-11-09 00:00:00
Tasks: Language Modeling, Language Modelling, POS

Lately, the problem of code-switching has gained a lot of attention and has
emerged as an active area of research. In bilingual communities, the speakers
commonly embed the words and phrases of a non-native language into the syntax
of a native language in their day-to-day communications. The code-switching is
a global phenomenon among multilingual communities, still very limited acoustic
and linguistic resources are available as yet. For developing effective speech
based applications, the ability of the existing language technologies to deal
with the code-switched data can not be over emphasized. The code-switching is
broadly classified into two modes: inter-sentential and intra-sentential
code-switching. In this work, we have studied the intra-sentential problem in
the context of code-switching language modeling task. The salient contributions
of this paper includes: (i) the creation of Hindi-English code-switching text
corpus by crawling a few blogging sites educating about the usage of the
Internet (ii) the exploration of the parts-of-speech features towards more
effective modeling of Hindi-English code-switched data by the monolingual
language model (LM) trained on native (Hindi) language data, and (iii) the
proposal of a novel textual factor referred to as the code-switch factor
(CS-factor), which allows the LM to predict the code-switching instances. In
the context of recognition of the code-switching data, the substantial
reduction in the PPL is achieved with the use of POS factors and also the
proposed CS-factor provides independent as well as additive gain in the PPL.
Deep Embedding Convolutional Neural Network for Synthesizing CT Image from T1-Weighted MR Image
Authors: Lei Xiang, Qian Wang, Xiyao Jin, Dong Nie, Yu Qiao, Dinggang Shen
Date: 2017-09-07 00:00:00
Tasks: Computed Tomography (CT), Image Generation

Recently, more and more attention is drawn to the field of medical image
synthesis across modalities. Among them, the synthesis of computed tomography
(CT) image from T1-weighted magnetic resonance (MR) image is of great
importance, although the mapping between them is highly complex due to large
gaps of appearances of the two modalities. In this work, we aim to tackle this
MR-to-CT synthesis by a novel deep embedding convolutional neural network
(DECNN). Specifically, we generate the feature maps from MR images, and then
transform these feature maps forward through convolutional layers in the
network. We can further compute a tentative CT synthesis from the midway of the
flow of feature maps, and then embed this tentative CT synthesis back to the
feature maps. This embedding operation results in better feature maps, which
are further transformed forward in DECNN. After repeat-ing this embedding
procedure for several times in the network, we can eventually synthesize a
final CT image in the end of the DECNN. We have validated our proposed method
on both brain and prostate datasets, by also compar-ing with the
state-of-the-art methods. Experimental results suggest that our DECNN (with
repeated embedding op-erations) demonstrates its superior performances, in
terms of both the perceptive quality of the synthesized CT image and the
run-time cost for synthesizing a CT image.
SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness
Authors: Mustafa A. Kocak, David Ramirez, Elza Erkip, Dennis E. Shasha
Date: 2017-08-21 00:00:00
Tasks: BIG-bench Machine Learning

SafePredict is a novel meta-algorithm that works with any base prediction
algorithm for online data to guarantee an arbitrarily chosen correctness rate,
$1-\epsilon$, by allowing refusals. Allowing refusals means that the
meta-algorithm may refuse to emit a prediction produced by the base algorithm
on occasion so that the error rate on non-refused predictions does not exceed
$\epsilon$. The SafePredict error bound does not rely on any assumptions on the
data distribution or the base predictor. When the base predictor happens not to
exceed the target error rate $\epsilon$, SafePredict refuses only a finite
number of times. When the error rate of the base predictor changes through time
SafePredict makes use of a weight-shifting heuristic that adapts to these
changes without knowing when the changes occur yet still maintains the
correctness guarantee. Empirical results show that (i) SafePredict compares
favorably with state-of-the art confidence based refusal mechanisms which fail
to offer robust error guarantees; and (ii) combining SafePredict with such
refusal mechanisms can in many cases further reduce the number of refusals. Our
software (currently in Python) is included in the supplementary material.
Efficient Multiple Incremental Computation for Kernel Ridge Regression with Bayesian Uncertainty Modeling
Authors: Bo-Wei Chen, Nik Nailah Binti Abdullah, Sangoh Park
Date: 2016-08-01 00:00:00
Tasks: regression

This study presents an efficient incremental/decremental approach for big
streams based on Kernel Ridge Regression (KRR), a frequently used data analysis
in cloud centers. To avoid reanalyzing the whole dataset whenever sensors
receive new training data, typical incremental KRR used a single-instance
mechanism for updating an existing system. However, this inevitably increased
redundant computational time, not to mention applicability to big streams. To
this end, the proposed mechanism supports incremental/decremental processing
for both single and multiple samples (i.e., batch processing). A large scale of
data can be divided into batches, processed by a machine, without sacrificing
the accuracy. Moreover, incremental/decremental analyses in empirical and
intrinsic space are also proposed in this study to handle different types of
data either with a large number of samples or high feature dimensions, whereas
typical methods focused only on one type. At the end of this study, we further
the proposed mechanism to statistical Kernelized Bayesian Regression, so that
uncertainty modeling with incremental/decremental computation becomes
applicable. Experimental results showed that computational time was
significantly reduced, better than the original nonincremental design and the
typical single incremental method. Furthermore, the accuracy of the proposed
method remained the same as the baselines. This implied that the system
enhanced efficiency without sacrificing the accuracy. These findings proved
that the proposed method was appropriate for variable streaming data analysis,
thereby demonstrating the effectiveness of the proposed method.
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
Authors: Anirbit Mukherjee, Amitabh Basu
Date: 2017-11-08 00:00:00

Motivated by the resurgence of neural networks in being able to solve complex
learning tasks we undertake a study of high depth networks using ReLU gates
which implement the function $x \mapsto \max\{0,x\}$. We try to understand the
role of depth in such neural networks by showing size lowerbounds against such
network architectures in parameter regimes hitherto unexplored. In particular
we show the following two main results about neural nets computing Boolean
functions of input dimension $n$,
  1. We use the method of random restrictions to show almost linear,
$\Omega(\epsilon^{2(1-\delta)}n^{1-\delta})$, lower bound for completely weight
unrestricted LTF-of-ReLU circuits to match the Andreev function on at least
$\frac{1}{2} +\epsilon$ fraction of the inputs for $\epsilon >
\sqrt{2\frac{\log^{\frac {2}{2-\delta}}(n)}{n}}$ for any $\delta \in (0,\frac 1
2)$
  2. We use the method of sign-rank to show exponential in dimension lower
bounds for ReLU circuits ending in a LTF gate and of depths upto $O(n^{\xi})$
with $\xi < \frac{1}{8}$ with some restrictions on the weights in the bottom
most layer. All other weights in these circuits are kept unrestricted. This in
turns also implies the same lowerbounds for LTF circuits with the same
architecture and the same weight restrictions on their bottom most layer.
  Along the way we also show that there exists a $\mathbb{R}^ n\rightarrow
\mathbb{R}$ Sum-of-ReLU-of-ReLU function which Sum-of-ReLU neural nets can
never represent no matter how large they are allowed to be.
Autonomous Racing using Learning Model Predictive Control
Authors: Ugo Rosolia, Ashwin Carvalho, Francesco Borrelli
Date: 2016-10-20 00:00:00
Tasks: Autonomous Racing, model, Model Predictive Control

A novel learning Model Predictive Control technique is applied to the
autonomous racing problem. The goal of the controller is to minimize the time
to complete a lap. The proposed control strategy uses the data from previous
laps to improve its performance while satisfying safety requirements. Moreover,
a system identification technique is proposed to estimate the vehicle dynamics.
Simulation results with the high fidelity simulator software CarSim show the
effectiveness of the proposed control scheme.
CogSciK: Clustering for Cognitive Science Motivated Decision Making
Authors: Dr. W. A. Rivera, James C. Wu
Date: 2017-11-09 00:00:00
Tasks: Clustering, Decision Making, General Classification

Computational models of decisionmaking must contend with the variance of
context and any number of possible decisions that a defined strategic actor can
make at a given time. Relying on cognitive science theory, the authors have
created an algorithm that captures the orientation of the actor towards an
object and arrays the possible decisions available to that actor based on their
given intersubjective orientation. This algorithm, like a traditional K-means
clustering algorithm, relies on a core-periphery structure that gives the
likelihood of moves as those closest to the cluster's centroid. The result is
an algorithm that enables unsupervised classification of an array of decision
points belonging to an actor's present state and deeply rooted in cognitive
science theory.
Linear Convergence of a Frank-Wolfe Type Algorithm over Trace-Norm Balls
Authors: Zeyuan Allen-Zhu, Elad Hazan, Wei Hu, Yuanzhi Li
Date: 2017-08-07 00:00:00
Proceeding: NeurIPS 2017 12

We propose a rank-$k$ variant of the classical Frank-Wolfe algorithm to solve
convex optimization over a trace-norm ball. Our algorithm replaces the top
singular-vector computation ($1$-SVD) in Frank-Wolfe with a top-$k$
singular-vector computation ($k$-SVD), which can be done by repeatedly applying
$1$-SVD $k$ times. Alternatively, our algorithm can be viewed as a rank-$k$
restricted version of projected gradient descent. We show that our algorithm
has a linear convergence rate when the objective function is smooth and
strongly convex, and the optimal solution has rank at most $k$. This improves
the convergence rate and the total time complexity of the Frank-Wolfe method
and its variants.
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks
Authors: Yelong Shen, Xiaodong Liu, Kevin Duh, Jianfeng Gao
Date: 2017-11-09 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Descriptive, Reading Comprehension, Reinforcement Learning, Reinforcement Learning (RL)

Reading comprehension (RC) is a challenging task that requires synthesis of
information across sentences and multiple turns of reasoning. Using a
state-of-the-art RC model, we empirically investigate the performance of
single-turn and multiple-turn reasoning on the SQuAD and MS MARCO datasets. The
RC model is an end-to-end neural network with iterative attention, and uses
reinforcement learning to dynamically control the number of turns. We find that
multiple-turn reasoning outperforms single-turn reasoning for all question and
answer types; further, we observe that enabling a flexible number of turns
generally improves upon a fixed multiple-turn strategy. %across all question
types, and is particularly beneficial to questions with lengthy, descriptive
answers. We achieve results competitive to the state-of-the-art on these two
datasets.
Towards Deep Learning Models Resistant to Adversarial Attacks
Authors: Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Adrian Vladu
Date: 2017-06-19 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Attack, Adversarial Defense, Adversarial Robustness, Deep Learning, Image Classification, Object Detection, Part-Of-Speech Tagging, Robust classification, Sound Event Detection, Video Quality Assessment

Recent work has demonstrated that deep neural networks are vulnerable to adversarial examples---inputs that are almost indistinguishable from natural data and yet classified incorrectly by the network. In fact, some of the latest findings suggest that the existence of adversarial attacks may be an inherent weakness of deep learning models. To address this problem, we study the adversarial robustness of neural networks through the lens of robust optimization. This approach provides us with a broad and unifying view on much of the prior work on this topic. Its principled nature also enables us to identify methods for both training and attacking neural networks that are reliable and, in a certain sense, universal. In particular, they specify a concrete security guarantee that would protect against any adversary. These methods let us train networks with significantly improved resistance to a wide range of adversarial attacks. They also suggest the notion of security against a first-order adversary as a natural and broad security guarantee. We believe that robustness against such well-defined classes of adversaries is an important stepping stone towards fully resistant deep learning models. Code and pre-trained models are available at https://github.com/MadryLab/mnist_challenge and https://github.com/MadryLab/cifar10_challenge.
Reinforcement Learning under Model Mismatch
Authors: Aurko Roy, Huan Xu, Sebastian Pokutta
Date: 2017-06-15 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: model, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We study reinforcement learning under model misspecification, where we do not
have access to the true environment but only to a reasonably close
approximation to it. We address this problem by extending the framework of
robust MDPs to the model-free Reinforcement Learning setting, where we do not
have access to the model parameters, but can only sample states from it. We
define robust versions of Q-learning, SARSA, and TD-learning and prove
convergence to an approximately optimal robust policy and approximate value
function respectively. We scale up the robust algorithms to large MDPs via
function approximation and prove convergence under two different settings. We
prove convergence of robust approximate policy iteration and robust approximate
value iteration for linear architectures (under mild assumptions). We also
define a robust loss function, the mean squared robust projected Bellman error
and give stochastic gradient descent algorithms that are guaranteed to converge
to a local minimum.
Fingerprint Orientation Refinement through Iterative Smoothing
Authors: Pierluigi Maponi, Riccardo Piergallini, Filippo Santarelli
Date: 2017-11-09 00:00:00

We propose a new gradient-based method for the extraction of the orientation
field associated to a fingerprint, and a regularisation procedure to improve
the orientation field computed from noisy fingerprint images. The
regularisation algorithm is based on three new integral operators, introduced
and discussed in this paper. A pre-processing technique is also proposed to
achieve better performances of the algorithm. The results of a numerical
experiment are reported to give an evidence of the efficiency of the proposed
algorithm.
Information Directed Sampling for Stochastic Bandits with Graph Feedback
Authors: Fang Liu, Swapna Buccapatnam, Ness Shroff
Date: 2017-11-08 00:00:00
Tasks: Decision Making, Thompson Sampling

We consider stochastic multi-armed bandit problems with graph feedback, where
the decision maker is allowed to observe the neighboring actions of the chosen
action. We allow the graph structure to vary with time and consider both
deterministic and Erd\H{o}s-R\'enyi random graph models. For such a graph
feedback model, we first present a novel analysis of Thompson sampling that
leads to tighter performance bound than existing work. Next, we propose new
Information Directed Sampling based policies that are graph-aware in their
decision making. Under the deterministic graph case, we establish a Bayesian
regret bound for the proposed policies that scales with the clique cover number
of the graph instead of the number of actions. Under the random graph case, we
provide a Bayesian regret bound for the proposed policies that scales with the
ratio of the number of actions over the expected number of observations per
iteration. To the best of our knowledge, this is the first analytical result
for stochastic bandits with random graph feedback. Finally, using numerical
evaluations, we demonstrate that our proposed IDS policies outperform existing
approaches, including adaptions of upper confidence bound, $\epsilon$-greedy
and Exp3 algorithms.
Multi-stage Suture Detection for Robot Assisted Anastomosis based on Deep Learning
Authors: Yang Hu, Yun Gu, Jie Yang, Guang-Zhong Yang
Date: 2017-11-08 00:00:00

In robotic surgery, task automation and learning from demonstration combined
with human supervision is an emerging trend for many new surgical robot
platforms. One such task is automated anastomosis, which requires bimanual
needle handling and suture detection. Due to the complexity of the surgical
environment and varying patient anatomies, reliable suture detection is
difficult, which is further complicated by occlusion and thread topologies. In
this paper, we propose a multi-stage framework for suture thread detection
based on deep learning. Fully convolutional neural networks are used to obtain
the initial detection and the overlapping status of suture thread, which are
later fused with the original image to learn a gradient road map of the thread.
Based on the gradient road map, multiple segments of the thread are extracted
and linked to form the whole thread using a curvilinear structure detector.
Experiments on two different types of sutures demonstrate the accuracy of the
proposed framework.
Discovery of potential collaboration networks from open knowledge sources
Authors: Nelson Piedra, Janneth Chicaiza, Jorge Lopez-Vargas, Edmundo Tovar
Date: 2017-11-08 00:00:00

Scientific publishing conveys the outputs of an academic or research
activity, in this sense; it also reflects the efforts and issues in which
people engage. To identify potential collaborative networks one of the simplest
approaches is to leverage the co-authorship relations. In this approach,
semantic and hierarchic relationships defined by a Knowledge Organization
System are used in order to improve the system's ability to recommend potential
networks beyond the lexical or syntactic analysis of the topics or concepts
that are of interest to academics.