pwc-archive/papers-with-abstracts huggingface dataset:
A Content-Based Late Fusion Approach Applied to Pedestrian Detection
Authors: Jessica Sena, Artur Jordao, William Robson Schwartz
Date: 2018-06-08 00:00:00
Tasks: Pedestrian Detection

The variety of pedestrians detectors proposed in recent years has encouraged
some works to fuse pedestrian detectors to achieve a more accurate detection.
The intuition behind is to combine the detectors based on its spatial
consensus. We propose a novel method called Content-Based Spatial Consensus
(CSBC), which, in addition to relying on spatial consensus, considers the
content of the detection windows to learn a weighted-fusion of pedestrian
detectors. The result is a reduction in false alarms and an enhancement in the
detection. In this work, we also demonstrate that there is small influence of
the feature used to learn the contents of the windows of each detector, which
enables our method to be efficient even employing simple features. The CSBC
overcomes state-of-the-art fusion methods in the ETH dataset and in the Caltech
dataset. Particularly, our method is more efficient since fewer detectors are
necessary to achieve expressive results.
Situated Mapping of Sequential Instructions to Actions with Single-step Reward Observation
Authors: Alane Suhr, Yoav Artzi
Date: 2018-05-25 00:00:00
Proceeding: ACL 2018 7

We propose a learning approach for mapping context-dependent sequential
instructions to actions. We address the problem of discourse and state
dependencies with an attention-based model that considers both the history of
the interaction and the state of the world. To train from start and goal states
without access to demonstrations, we propose SESTRA, a learning algorithm that
takes advantage of single-step reward observations and immediate expected
reward maximization. We evaluate on the SCONE domains, and show absolute
accuracy improvements of 9.8%-25.3% across the domains over approaches that use
high-level logical representations.
Measuring Conversational Productivity in Child Forensic Interviews
Authors: Victor Ardulov, Manoj Kumar, Shanna Williams, Thomas Lyon, Shrikanth Narayanan
Date: 2018-06-08 00:00:00
Tasks: Decision Making, Information Retrieval, Retrieval

Child Forensic Interviewing (FI) presents a challenge for effective
information retrieval and decision making. The high stakes associated with the
process demand that expert legal interviewers are able to effectively establish
a channel of communication and elicit substantive knowledge from the
child-client while minimizing potential for experiencing trauma. As a first
step toward computationally modeling and producing quality spoken interviewing
strategies and a generalized understanding of interview dynamics, we propose a
novel methodology to computationally model effectiveness criteria, by applying
summarization and topic modeling techniques to objectively measure and rank the
responsiveness and conversational productivity of a child during FI. We score
information retrieval by constructing an agenda to represent general topics of
interest and measuring alignment with a given response and leveraging lexical
entrainment for responsiveness. For comparison, we present our methods along
with traditional metrics of evaluation and discuss the use of prior information
for generating situational awareness.
Semi-Amortized Variational Autoencoders
Authors: Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush
Date: 2018-02-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Text Generation, Variational Inference

Amortized variational inference (AVI) replaces instance-specific local
inference with a global inference network. While AVI has enabled efficient
training of deep generative models such as variational autoencoders (VAE),
recent empirical work suggests that inference networks can produce suboptimal
variational parameters. We propose a hybrid approach, to use AVI to initialize
the variational parameters and run stochastic variational inference (SVI) to
refine them. Crucially, the local SVI procedure is itself differentiable, so
the inference network and generative model can be trained end-to-end with
gradient-based optimization. This semi-amortized approach enables the use of
rich generative models without experiencing the posterior-collapse phenomenon
common in training VAEs for problems like text generation. Experiments show
this approach outperforms strong autoregressive and variational baselines on
standard text and image datasets.
The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces
Authors: G. Zacharias Holland, Erin J. Talvitie, Michael Bowling
Date: 2018-06-05 00:00:00
Tasks: Atari Games, Model-based Reinforcement Learning, Reinforcement Learning

Dyna is a fundamental approach to model-based reinforcement learning (MBRL)
that interleaves planning, acting, and learning in an online setting. In the
most typical application of Dyna, the dynamics model is used to generate
one-step transitions from selected start states from the agent's history, which
are used to update the agent's value function or policy as if they were real
experiences. In this work, one-step Dyna was applied to several games from the
Arcade Learning Environment (ALE). We found that the model-based updates
offered surprisingly little benefit over simply performing more updates with
the agent's existing experience, even when using a perfect model. We
hypothesize that to get the most from planning, the model must be used to
generate unfamiliar experience. To test this, we experimented with the "shape"
of planning in multiple different concrete instantiations of Dyna, performing
fewer, longer rollouts, rather than many short rollouts. We found that planning
shape has a profound impact on the efficacy of Dyna for both perfect and
learned models. In addition to these findings regarding Dyna in general, our
results represent, to our knowledge, the first time that a learned dynamics
model has been successfully used for planning in the ALE, suggesting that Dyna
may be a viable approach to MBRL in the ALE and other high-dimensional
problems.
An Optimal Algorithm for Online Unconstrained Submodular Maximization
Authors: Tim Roughgarden, Joshua R. Wang
Date: 2018-06-08 00:00:00

We consider a basic problem at the interface of two fundamental fields:
submodular optimization and online learning. In the online unconstrained
submodular maximization (online USM) problem, there is a universe
$[n]=\{1,2,...,n\}$ and a sequence of $T$ nonnegative (not necessarily
monotone) submodular functions arrive over time. The goal is to design a
computationally efficient online algorithm, which chooses a subset of $[n]$ at
each time step as a function only of the past, such that the accumulated value
of the chosen subsets is as close as possible to the maximum total value of a
fixed subset in hindsight. Our main result is a polynomial-time no-$1/2$-regret
algorithm for this problem, meaning that for every sequence of nonnegative
submodular functions, the algorithm's expected total value is at least $1/2$
times that of the best subset in hindsight, up to an error term sublinear in
$T$. The factor of $1/2$ cannot be improved upon by any polynomial-time online
algorithm when the submodular functions are presented as value oracles.
Previous work on the offline problem implies that picking a subset uniformly at
random in each time step achieves zero $1/4$-regret.
  A byproduct of our techniques is an explicit subroutine for the two-experts
problem that has an unusually strong regret guarantee: the total value of its
choices is comparable to twice the total value of either expert on rounds it
did not pick that expert. This subroutine may be of independent interest.
DSSLIC: Deep Semantic Segmentation-based Layered Image Compression
Authors: Mohammad Akbari, Jie Liang, Jingning Han
Date: 2018-06-08 00:00:00
Tasks: Image Compression, Image Retrieval, MS-SSIM, Segmentation, Semantic Segmentation, SSIM

Deep learning has revolutionized many computer vision fields in the last few
years, including learning-based image compression. In this paper, we propose a
deep semantic segmentation-based layered image compression (DSSLIC) framework
in which the semantic segmentation map of the input image is obtained and
encoded as the base layer of the bit-stream. A compact representation of the
input image is also generated and encoded as the first enhancement layer. The
segmentation map and the compact version of the image are then employed to
obtain a coarse reconstruction of the image. The residual between the input and
the coarse reconstruction is additionally encoded as another enhancement layer.
Experimental results show that the proposed framework outperforms the
H.265/HEVC-based BPG and other codecs in both PSNR and MS-SSIM metrics across a
wide range of bit rates in RGB domain. Besides, since semantic segmentation map
is included in the bit-stream, the proposed scheme can facilitate many other
tasks such as image search and object-based adaptive image compression.
ASP: Learning to Forget with Adaptive Synaptic Plasticity in Spiking Neural Networks
Authors: Priyadarshini Panda, Jason M. Allred, Shriram Ramanathan, Kaushik Roy
Date: 2017-03-22 00:00:00
Tasks: Denoising

A fundamental feature of learning in animals is the "ability to forget" that
allows an organism to perceive, model and make decisions from disparate streams
of information and adapt to changing environments. Against this backdrop, we
present a novel unsupervised learning mechanism ASP (Adaptive Synaptic
Plasticity) for improved recognition with Spiking Neural Networks (SNNs) for
real time on-line learning in a dynamic environment. We incorporate an adaptive
weight decay mechanism with the traditional Spike Timing Dependent Plasticity
(STDP) learning to model adaptivity in SNNs. The leak rate of the synaptic
weights is modulated based on the temporal correlation between the spiking
patterns of the pre- and post-synaptic neurons. This mechanism helps in gradual
forgetting of insignificant data while retaining significant, yet old,
information. ASP, thus, maintains a balance between forgetting and immediate
learning to construct a stable-plastic self-adaptive SNN for continuously
changing inputs. We demonstrate that the proposed learning methodology
addresses catastrophic forgetting while yielding significantly improved
accuracy over the conventional STDP learning method for digit recognition
applications. Additionally, we observe that the proposed learning model
automatically encodes selective attention towards relevant features in the
input data while eliminating the influence of background noise (or denoising)
further improving the robustness of the ASP learning.
Discovering Signals from Web Sources to Predict Cyber Attacks
Authors: Palash Goyal, KSM Tozammel Hossain, Ashok Deb, Nazgol Tavabi, Nathan Bartley, Andr'es Abeliuk, Emilio Ferrara, Kristina Lerman
Date: 2018-06-08 00:00:00
Tasks: Time Series, Time Series Analysis

Cyber attacks are growing in frequency and severity. Over the past year alone
we have witnessed massive data breaches that stole personal information of
millions of people and wide-scale ransomware attacks that paralyzed critical
infrastructure of several countries. Combating the rising cyber threat calls
for a multi-pronged strategy, which includes predicting when these attacks will
occur. The intuition driving our approach is this: during the planning and
preparation stages, hackers leave digital traces of their activities on both
the surface web and dark web in the form of discussions on platforms like
hacker forums, social media, blogs and the like. These data provide predictive
signals that allow anticipating cyber attacks. In this paper, we describe
machine learning techniques based on deep neural networks and autoregressive
time series models that leverage external signals from publicly available Web
sources to forecast cyber attacks. Performance of our framework across ground
truth data over real-world forecasting tasks shows that our methods yield a
significant lift or increase of F1 for the top signals on predicted cyber
attacks. Our results suggest that, when deployed, our system will be able to
provide an effective line of defense against various types of targeted cyber
attacks.
Learning the Reward Function for a Misspecified Model
Authors: Erik Talvitie
Date: 2018-01-29 00:00:00
Proceeding: ICML 2018 7
Tasks: model, Model-based Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

In model-based reinforcement learning it is typical to decouple the problems
of learning the dynamics model and learning the reward function. However, when
the dynamics model is flawed, it may generate erroneous states that would never
occur in the true environment. It is not clear a priori what value the reward
function should assign to such states. This paper presents a novel error bound
that accounts for the reward model's behavior in states sampled from the model.
This bound is used to extend the existing Hallucinated DAgger-MC algorithm,
which offers theoretical performance guarantees in deterministic MDPs that do
not assume a perfect model can be learned. Empirically, this approach to reward
learning can yield dramatic improvements in control performance when the
dynamics model is flawed.
Randomized Prior Functions for Deep Reinforcement Learning
Authors: Ian Osband, John Aslanides, Albin Cassirer
Date: 2018-06-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dealing with uncertainty is essential for efficient reinforcement learning.
There is a growing literature on uncertainty estimation for deep learning from
fixed datasets, but many of the most popular approaches are poorly-suited to
sequential decision problems. Other methods, such as bootstrap sampling, have
no mechanism for uncertainty that does not come from the observed data. We
highlight why this can be a crucial shortcoming and propose a simple remedy
through addition of a randomized untrainable `prior' network to each ensemble
member. We prove that this approach is efficient with linear representations,
provide simple illustrations of its efficacy with nonlinear representations and
show that this approach scales to large-scale problems far better than previous
attempts.
Securing Distributed Gradient Descent in High Dimensional Statistical Learning
Authors: Lili Su, Jiaming Xu
Date: 2018-04-26 00:00:00
Tasks: Vocal Bursts Intensity Prediction

We consider unreliable distributed learning systems wherein the training data is kept confidential by external workers, and the learner has to interact closely with those workers to train a model. In particular, we assume that there exists a system adversary that can adaptively compromise some workers; the compromised workers deviate from their local designed specifications by sending out arbitrarily malicious messages. We assume in each communication round, up to $q$ out of the $m$ workers suffer Byzantine faults. Each worker keeps a local sample of size $n$ and the total sample size is $N=nm$. We propose a secured variant of the gradient descent method that can tolerate up to a constant fraction of Byzantine workers, i.e., $q/m = O(1)$. Moreover, we show the statistical estimation error of the iterates converges in $O(\log N)$ rounds to $O(\sqrt{q/N} + \sqrt{d/N})$, where $d$ is the model dimension. As long as $q=O(d)$, our proposed algorithm achieves the optimal error rate $O(\sqrt{d/N})$. Our results are obtained under some technical assumptions. Specifically, we assume strongly-convex population risk. Nevertheless, the empirical risk (sample version) is allowed to be non-convex. The core of our method is to robustly aggregate the gradients computed by the workers based on the filtering procedure proposed by Steinhardt et al. On the technical front, deviating from the existing literature on robustly estimating a finite-dimensional mean vector, we establish a {\em uniform} concentration of the sample covariance matrix of gradients, and show that the aggregated gradient, as a function of model parameter, converges uniformly to the true gradient function. To get a near-optimal uniform concentration bound, we develop a new matrix concentration inequality, which might be of independent interest.
Provable defenses against adversarial examples via the convex outer adversarial polytope
Authors: Eric Wong, J. Zico Kolter
Date: 2017-11-02 00:00:00
Proceeding: ICML 2018 7
Tasks: Adversarial Attack

We propose a method to learn deep ReLU-based classifiers that are provably
robust against norm-bounded adversarial perturbations on the training data. For
previously unseen examples, the approach is guaranteed to detect all
adversarial examples, though it may flag some non-adversarial examples as well.
The basic idea is to consider a convex outer approximation of the set of
activations reachable through a norm-bounded perturbation, and we develop a
robust optimization procedure that minimizes the worst case loss over this
outer region (via a linear program). Crucially, we show that the dual problem
to this linear program can be represented itself as a deep network similar to
the backpropagation network, leading to very efficient optimization approaches
that produce guaranteed bounds on the robust loss. The end result is that by
executing a few more forward and backward passes through a slightly modified
version of the original network (though possibly with much larger batch sizes),
we can learn a classifier that is provably robust to any norm-bounded
adversarial attack. We illustrate the approach on a number of tasks to train
classifiers with robust adversarial guarantees (e.g. for MNIST, we produce a
convolutional classifier that provably has less than 5.8% test error for any
adversarial attack with bounded $\ell_\infty$ norm less than $\epsilon = 0.1$),
and code for all experiments in the paper is available at
https://github.com/locuslab/convex_adversarial.
Learning to rank for censored survival data
Authors: Margaux Luck, Tristan Sylvain, Joseph Paul Cohen, Heloise Cardinal, Andrea Lodi, Yoshua Bengio
Date: 2018-06-06 00:00:00
Tasks: Learning-To-Rank, Survival Analysis

Survival analysis is a type of semi-supervised ranking task where the target
output (the survival time) is often right-censored. Utilizing this information
is a challenge because it is not obvious how to correctly incorporate these
censored examples into a model. We study how three categories of loss
functions, namely partial likelihood methods, rank methods, and our
classification method based on a Wasserstein metric (WM) and the non-parametric
Kaplan Meier estimate of the probability density to impute the labels of
censored examples, can take advantage of this information. The proposed method
allows us to have a model that predict the probability distribution of an
event. If a clinician had access to the detailed probability of an event over
time this would help in treatment planning. For example, determining if the
risk of kidney graft rejection is constant or peaked after some time. Also, we
demonstrate that this approach directly optimizes the expected C-index which is
the most common evaluation metric for ranking survival models.
Unsupervised Learning for Surgical Motion by Learning to Predict the Future
Authors: Robert DiPietro, Gregory D. Hager
Date: 2018-06-08 00:00:00
Tasks: Decoder, Future prediction, Information Retrieval, Retrieval

We show that it is possible to learn meaningful representations of surgical
motion, without supervision, by learning to predict the future. An architecture
that combines an RNN encoder-decoder and mixture density networks (MDNs) is
developed to model the conditional distribution over future motion given past
motion. We show that the learned encodings naturally cluster according to
high-level activities, and we demonstrate the usefulness of these learned
encodings in the context of information retrieval, where a database of surgical
motion is searched for suturing activity using a motion-based query. Future
prediction with MDNs is found to significantly outperform simpler baselines as
well as the best previously-published result for this task, advancing
state-of-the-art performance from an F1 score of 0.60 +- 0.14 to 0.77 +- 0.05.
Adversarial Meta-Learning
Authors: Chengxiang Yin, Jian Tang, Zhiyuan Xu, Yanzhi Wang
Date: 2018-06-08 00:00:00
Tasks: Meta-Learning

Meta-learning enables a model to learn from very limited data to undertake a new task. In this paper, we study the general meta-learning with adversarial samples. We present a meta-learning algorithm, ADML (ADversarial Meta-Learner), which leverages clean and adversarial samples to optimize the initialization of a learning model in an adversarial manner. ADML leads to the following desirable properties: 1) it turns out to be very effective even in the cases with only clean samples; 2) it is robust to adversarial samples, i.e., unlike other meta-learning algorithms, it only leads to a minor performance degradation when there are adversarial samples; 3) it sheds light on tackling the cases with limited and even contaminated samples. It has been shown by extensive experimental results that ADML consistently outperforms three representative meta-learning algorithms in the cases involving adversarial samples, on two widely-used image datasets, MiniImageNet and CIFAR100, in terms of both accuracy and robustness.
Stein Points
Authors: Wilson Ye Chen, Lester Mackey, Jackson Gorham, François-Xavier Briol, Chris. J. Oates
Date: 2018-03-27 00:00:00
Proceeding: ICML 2018 7

An important task in computational statistics and machine learning is to
approximate a posterior distribution $p(x)$ with an empirical measure supported
on a set of representative points $\{x_i\}_{i=1}^n$. This paper focuses on
methods where the selection of points is essentially deterministic, with an
emphasis on achieving accurate approximation when $n$ is small. To this end, we
present `Stein Points'. The idea is to exploit either a greedy or a conditional
gradient method to iteratively minimise a kernel Stein discrepancy between the
empirical measure and $p(x)$. Our empirical results demonstrate that Stein
Points enable accurate approximation of the posterior at modest computational
cost. In addition, theoretical results are provided to establish convergence of
the method.
Discriminability objective for training descriptive captions
Authors: Ruotian Luo, Brian Price, Scott Cohen, Gregory Shakhnarovich
Date: 2018-03-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Caption Generation, Descriptive, Image Captioning

One property that remains lacking in image captions generated by contemporary
methods is discriminability: being able to tell two images apart given the
caption for one of them. We propose a way to improve this aspect of caption
generation. By incorporating into the captioning training objective a loss
component directly related to ability (by a machine) to disambiguate
image/caption matches, we obtain systems that produce much more discriminative
caption, according to human evaluation. Remarkably, our approach leads to
improvement in other aspects of generated captions, reflected by a battery of
standard scores such as BLEU, SPICE etc. Our approach is modular and can be
applied to a variety of model/loss combinations commonly proposed for image
captioning.