pwc-archive/papers-with-abstracts huggingface dataset:
Learning Deterministic Policy with Target for Power Control in Wireless Networks
Authors: Yujiao Lu, Hancheng Lu, Liangliang Cao, Feng Wu, Daren Zhu
Date: 2019-02-21 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Inter-Cell Interference Coordination (ICIC) is a promising way to improve
energy efficiency in wireless networks, especially where small base stations
are densely deployed. However, traditional optimization based ICIC schemes
suffer from severe performance degradation with complex interference pattern.
To address this issue, we propose a Deep Reinforcement Learning with
Deterministic Policy and Target (DRL-DPT) framework for ICIC in wireless
networks. DRL-DPT overcomes the main obstacles in applying reinforcement
learning and deep learning in wireless networks, i.e. continuous state space,
continuous action space and convergence. Firstly, a Deep Neural Network (DNN)
is involved as the actor to obtain deterministic power control actions in
continuous space. Then, to guarantee the convergence, an online training
process is presented, which makes use of a dedicated reward function as the
target rule and a policy gradient descent algorithm to adjust DNN weights.
Experimental results show that the proposed DRL-DPT framework consistently
outperforms existing schemes in terms of energy efficiency and throughput under
different wireless interference scenarios. More specifically, it improves up to
15% of energy efficiency with faster convergence rate.
Wasserstein Adversarial Examples via Projected Sinkhorn Iterations
Authors: Eric Wong, Frank R. Schmidt, J. Zico Kolter
Date: 2019-02-21 00:00:00
Tasks: Adversarial Attack, Adversarial Defense, Adversarial Robustness, General Classification, image-classification, Image Classification, Translation

A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works have focused primarily on threat models defined by $\ell_p$ norm-bounded perturbations. In this paper, we propose a new threat model for adversarial attacks based on the Wasserstein distance. In the image classification setting, such distances measure the cost of moving pixel mass, which naturally cover "standard" image manipulations such as scaling, rotation, translation, and distortion (and can potentially be applied to other settings as well). To generate Wasserstein adversarial examples, we develop a procedure for projecting onto the Wasserstein ball, based upon a modified version of the Sinkhorn iteration. The resulting algorithm can successfully attack image classification models, bringing traditional CIFAR10 models down to 3% accuracy within a Wasserstein ball with radius 0.1 (i.e., moving 10% of the image mass 1 pixel), and we demonstrate that PGD-based adversarial training can improve this adversarial accuracy to 76%. In total, this work opens up a new direction of study in adversarial robustness, more formally considering convex metrics that accurately capture the invariances that we typically believe should exist in classifiers. Code for all experiments in the paper is available at https://github.com/locuslab/projected_sinkhorn.
Bayesian optimisation under uncertain inputs
Authors: Rafael Oliveira, Lionel Ott, Fabio Ramos
Date: 2019-02-21 00:00:00
Tasks: Bayesian Optimisation

Bayesian optimisation (BO) has been a successful approach to optimise
functions which are expensive to evaluate and whose observations are noisy.
Classical BO algorithms, however, do not account for errors about the location
where observations are taken, which is a common issue in problems with physical
components. In these cases, the estimation of the actual query location is also
subject to uncertainty. In this context, we propose an upper confidence bound
(UCB) algorithm for BO problems where both the outcome of a query and the true
query location are uncertain. The algorithm employs a Gaussian process model
that takes probability distributions as inputs. Theoretical results are
provided for both the proposed algorithm and a conventional UCB approach within
the uncertain-inputs setting. Finally, we evaluate each method's performance
experimentally, comparing them to other input noise aware BO approaches on
simulated scenarios involving synthetic and real data.
An information criterion for auxiliary variable selection in incomplete data analysis
Authors: Shinpei Imori, Hidetoshi Shimodaira
Date: 2019-02-21 00:00:00
Tasks: Model Selection, Variable Selection

Statistical inference is considered for variables of interest, called primary
variables, when auxiliary variables are observed along with the primary
variables. We consider the setting of incomplete data analysis, where some
primary variables are not observed. Utilizing a parametric model of joint
distribution of primary and auxiliary variables, it is possible to improve the
estimation of parametric model for the primary variables when the auxiliary
variables are closely related to the primary variables. However, the estimation
accuracy reduces when the auxiliary variables are irrelevant to the primary
variables. For selecting useful auxiliary variables, we formulate the problem
as model selection, and propose an information criterion for predicting primary
variables by leveraging auxiliary variables. The proposed information criterion
is an asymptotically unbiased estimator of the Kullback-Leibler divergence for
complete data of primary variables under some reasonable conditions. We also
clarify an asymptotic equivalence between the proposed information criterion
and a variant of leave-one-out cross validation. Performance of our method is
demonstrated via a simulation study and a real data example.
Deep Learning Multidimensional Projections
Authors: Mateus Espadoto, Nina S. T. Hirata, Alexandru C. Telea
Date: 2019-02-21 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Dimensionality Reduction

Dimensionality reduction methods, also known as projections, are frequently
used for exploring multidimensional data in machine learning, data science, and
information visualization. Among these, t-SNE and its variants have become very
popular for their ability to visually separate distinct data clusters. However,
such methods are computationally expensive for large datasets, suffer from
stability problems, and cannot directly handle out-of-sample data. We propose a
learning approach to construct such projections. We train a deep neural network
based on a collection of samples from a given data universe, and their
corresponding projections, and next use the network to infer projections of
data from the same, or similar, universes. Our approach generates projections
with similar characteristics as the learned ones, is computationally two to
three orders of magnitude faster than SNE-class methods, has no complex-to-set
user parameters, handles out-of-sample data in a stable manner, and can be used
to learn any projection technique. We demonstrate our proposal on several
real-world high dimensional datasets from machine learning.
Learning representations of irregular particle-detector geometry with distance-weighted graph networks
Authors: Shah Rukh Qasim, Jan Kieseler, Yutaro Iiyama, Maurizio Pierini
Date: 2019-02-21 00:00:00
Tasks: Clustering, Representation Learning

We explore the use of graph networks to deal with irregular-geometry detectors in the context of particle reconstruction. Thanks to their representation-learning capabilities, graph networks can exploit the full detector granularity, while natively managing the event sparsity and arbitrarily complex detector geometries. We introduce two distance-weighted graph network architectures, dubbed GarNet and GravNet layers, and apply them to a typical particle reconstruction task. The performance of the new architectures is evaluated on a data set of simulated particle interactions on a toy model of a highly granular calorimeter, loosely inspired by the endcap calorimeter to be installed in the CMS detector for the High-Luminosity LHC phase. We study the clustering of energy depositions, which is the basis for calorimetric particle reconstruction, and provide a quantitative comparison to alternative approaches. The proposed algorithms provide an interesting alternative to existing methods, offering equally performing or less resource-demanding solutions with less underlying assumptions on the detector geometry and, consequently, the possibility to generalize to other detectors.
Mitigation of Adversarial Examples in RF Deep Classifiers Utilizing AutoEncoder Pre-training
Authors: Silvija Kokalj-Filipovic, Rob Miller, Nicholas Chang, Chi Leung Lau
Date: 2019-02-16 00:00:00
Tasks: Deep Learning

Adversarial examples in machine learning for images are widely publicized and
explored. Illustrations of misclassifications caused by slightly perturbed
inputs are abundant and commonly known (e.g., a picture of panda imperceptibly
perturbed to fool the classifier into incorrectly labeling it as a gibbon).
Similar attacks on deep learning (DL) for radio frequency (RF) signals and
their mitigation strategies are scarcely addressed in the published work. Yet,
RF adversarial examples (AdExs) with minimal waveform perturbations can cause
drastic, targeted misclassification results, particularly against spectrum
sensing/survey applications (e.g. BPSK is mistaken for 8-PSK). Our research on
deep learning AdExs and proposed defense mechanisms are RF-centric, and
incorporate physical world, over-the-air (OTA) effects. We herein present
defense mechanisms based on pre-training the target classifier using an
autoencoder. Our results validate this approach as a viable mitigation method
to subvert adversarial attacks against deep learning-based communications and
radar sensing systems.
Multi-Player Bandits: The Adversarial Case
Authors: Pragnya Alatur, Kfir. Y. Levy, Andreas Krause
Date: 2019-02-21 00:00:00

We consider a setting where multiple players sequentially choose among a
common set of actions (arms). Motivated by a cognitive radio networks
application, we assume that players incur a loss upon colliding, and that
communication between players is not possible. Existing approaches assume that
the system is stationary. Yet this assumption is often violated in practice,
e.g., due to signal strength fluctuations. In this work, we design the first
Multi-player Bandit algorithm that provably works in arbitrarily changing
environments, where the losses of the arms may even be chosen by an adversary.
This resolves an open problem posed by Rosenski, Shamir, and Szlak (2016).
Curiosity-Driven Experience Prioritization via Density Estimation
Authors: Rui Zhao, Volker Tresp
Date: 2019-02-20 00:00:00
Tasks: Density Estimation, OpenAI Gym, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Robot Manipulation

In Reinforcement Learning (RL), an agent explores the environment and collects trajectories into the memory buffer for later learning. However, the collected trajectories can easily be imbalanced with respect to the achieved goal states. The problem of learning from imbalanced data is a well-known problem in supervised learning, but has not yet been thoroughly researched in RL. To address this problem, we propose a novel Curiosity-Driven Prioritization (CDP) framework to encourage the agent to over-sample those trajectories that have rare achieved goal states. The CDP framework mimics the human learning process and focuses more on relatively uncommon events. We evaluate our methods using the robotic environment provided by OpenAI Gym. The environment contains six robot manipulation tasks. In our experiments, we combined CDP with Deep Deterministic Policy Gradient (DDPG) with or without Hindsight Experience Replay (HER). The experimental results show that CDP improves both performance and sample-efficiency of reinforcement learning agents, compared to state-of-the-art methods.
Towards Reliable, Automated General Movement Assessment for Perinatal Stroke Screening in Infants Using Wearable Accelerometers
Authors: Yan Gao, Yang Long, Yu Guan, Anna Basu, Jessica Baggaley, Thomas Ploetz
Date: 2019-02-21 00:00:00

Perinatal stroke (PS) is a serious condition that, if undetected and thus
untreated, often leads to life-long disability, in particular Cerebral Palsy
(CP). In clinical settings, Prechtl's General Movement Assessment (GMA) can be
used to classify infant movements using a Gestalt approach, identifying infants
at high risk of developing PS. Training and maintenance of assessment skills
are essential and expensive for the correct use of GMA, yet many practitioners
lack these skills, preventing larger-scale screening and leading to significant
risks of missing opportunities for early detection and intervention for
affected infants. We present an automated approach to GMA, based on body-worn
accelerometers and a novel sensor data analysis method-Discriminative Pattern
Discovery (DPD)-that is designed to cope with scenarios where only coarse
annotations of data are available for model training. We demonstrate the
effectiveness of our approach in a study with 34 newborns (21 typically
developing infants and 13 PS infants with abnormal movements). Our method is
able to correctly recognise the trials with abnormal movements with at least
the accuracy that is required by newly trained human annotators (75%), which is
encouraging towards our ultimate goal of an automated PS screening system that
can be used population-wide.
Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities
Authors: Octavian-Eugen Ganea, Sylvain Gelly, Gary Bécigneul, Aliaksei Severyn
Date: 2019-02-21 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

The Softmax function on top of a final linear layer is the de facto method to output probability distributions in neural networks. In many applications such as language models or text generation, this model has to produce distributions over large output vocabularies. Recently, this has been shown to have limited representational capacity due to its connection with the rank bottleneck in matrix factorization. However, little is known about the limitations of Linear-Softmax for quantities of practical interest such as cross entropy or mode estimation, a direction that we explore here. As an efficient and effective solution to alleviate this issue, we propose to learn parametric monotonic functions on top of the logits. We theoretically investigate the rank increasing capabilities of such monotonic functions. Empirically, our method improves in two different quality metrics over the traditional Linear-Softmax layer in synthetic and real language model experiments, adding little time or memory overhead, while being comparable to the more computationally expensive mixture of Softmaxes.
Evaluating the Search Phase of Neural Architecture Search
Authors: Kaicheng Yu, Christian Sciuto, Martin Jaggi, Claudiu Musat, Mathieu Salzmann
Date: 2019-02-21 00:00:00
Proceeding: ICLR 2020 1
Tasks: Neural Architecture Search

Neural Architecture Search (NAS) aims to facilitate the design of deep networks for new tasks. Existing techniques rely on two stages: searching over the architecture space and validating the best architecture. NAS algorithms are currently compared solely based on their results on the downstream task. While intuitive, this fails to explicitly evaluate the effectiveness of their search strategies. In this paper, we propose to evaluate the NAS search phase. To this end, we compare the quality of the solutions obtained by NAS search policies with that of random architecture selection. We find that: (i) On average, the state-of-the-art NAS algorithms perform similarly to the random policy; (ii) the widely-used weight sharing strategy degrades the ranking of the NAS candidates to the point of not reflecting their true performance, thus reducing the effectiveness of the search process. We believe that our evaluation framework will be key to designing NAS strategies that consistently discover architectures superior to random ones.
Learned Step Size Quantization
Authors: Steven K. Esser, Jeffrey L. McKinstry, Deepika Bablani, Rathinakumar Appuswamy, Dharmendra S. Modha
Date: 2019-02-21 00:00:00
Proceeding: ICLR 2020 1
Tasks: Model Compression, Quantization

Deep networks run with low precision operations at inference time offer power and space advantages over high precision alternatives, but need to overcome the challenge of maintaining high accuracy as precision decreases. Here, we present a method for training such networks, Learned Step Size Quantization, that achieves the highest accuracy to date on the ImageNet dataset when using models, from a variety of architectures, with weights and activations quantized to 2-, 3- or 4-bits of precision, and that can train 3-bit models that reach full precision baseline accuracy. Our approach builds upon existing methods for learning weights in quantized networks by improving how the quantizer itself is configured. Specifically, we introduce a novel means to estimate and scale the task loss gradient at each weight and activation layer's quantizer step size, such that it can be learned in conjunction with other network parameters. This approach works using different levels of precision as needed for a given system and requires only a simple modification of existing training code.
Topology of Learning in Artificial Neural Networks
Authors: Maxime Gabella
Date: 2019-02-21 00:00:00
Tasks: Topological Data Analysis

Understanding how neural networks learn remains one of the central challenges in machine learning research. From random at the start of training, the weights of a neural network evolve in such a way as to be able to perform a variety of tasks, like classifying images. Here we study the emergence of structure in the weights by applying methods from topological data analysis. We train simple feedforward neural networks on the MNIST dataset and monitor the evolution of the weights. When initialized to zero, the weights follow trajectories that branch off recurrently, thus generating trees that describe the growth of the effective capacity of each layer. When initialized to tiny random values, the weights evolve smoothly along two-dimensional surfaces. We show that natural coordinates on these learning surfaces correspond to important factors of variation.
A Dictionary Based Generalization of Robust PCA
Authors: Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Date: 2019-02-21 00:00:00

We analyze the decomposition of a data matrix, assumed to be a superposition
of a low-rank component and a component which is sparse in a known dictionary,
using a convex demixing method. We provide a unified analysis, encompassing
both undercomplete and overcomplete dictionary cases, and show that the
constituent components can be successfully recovered under some relatively mild
assumptions up to a certain $\textit{global}$ sparsity level. Further, we
corroborate our theoretical results by presenting empirical evaluations in
terms of phase transitions in rank and sparsity for various dictionary sizes.
Online Sampling from Log-Concave Distributions
Authors: Holden Lee, Oren Mangoubi, Nisheeth K. Vishnoi
Date: 2019-02-21 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: regression

Given a sequence of convex functions $f_0, f_1, \ldots, f_T$, we study the problem of sampling from the Gibbs distribution $\pi_t \propto e^{-\sum_{k=0}^tf_k}$ for each epoch $t$ in an online manner. Interest in this problem derives from applications in machine learning, Bayesian statistics, and optimization where, rather than obtaining all the observations at once, one constantly acquires new data, and must continuously update the distribution. Our main result is an algorithm that generates roughly independent samples from $\pi_t$ for every epoch $t$ and, under mild assumptions, makes $\mathrm{polylog}(T)$ gradient evaluations per epoch. All previous results imply a bound on the number of gradient or function evaluations which is at least linear in $T$. Motivated by real-world applications, we assume that functions are smooth, their associated distributions have a bounded second moment, and their minimizer drifts in a bounded manner, but do not assume they are strongly convex. In particular, our assumptions hold for online Bayesian logistic regression, when the data satisfy natural regularity properties, giving a sampling algorithm with updates that are poly-logarithmic in $T$. In simulations, our algorithm achieves accuracy comparable to an algorithm specialized to logistic regression. Key to our algorithm is a novel stochastic gradient Langevin dynamics Markov chain with a carefully designed variance reduction step and constant batch size. Technically, lack of strong convexity is a significant barrier to analysis and, here, our main contribution is a martingale exit time argument that shows our Markov chain remains in a ball of radius roughly poly-logarithmic in $T$ for enough time to reach within $\varepsilon$ of $\pi_t$.
Unsupervised Grounding of Plannable First-Order Logic Representation from Images
Authors: Masataro Asai
Date: 2019-02-21 00:00:00
Tasks: Reinforcement Learning

Recently, there is an increasing interest in obtaining the relational
structures of the environment in the Reinforcement Learning community. However,
the resulting "relations" are not the discrete, logical predicates compatible
to the symbolic reasoning such as classical planning or goal recognition.
Meanwhile, Latplan (Asai and Fukunaga 2018) bridged the gap between
deep-learning perceptual systems and symbolic classical planners. One key
component of the system is a Neural Network called State AutoEncoder (SAE),
which encodes an image-based input into a propositional representation
compatible to classical planning. To get the best of both worlds, we propose
First-Order State AutoEncoder, an unsupervised architecture for grounding the
first-order logic predicates and facts. Each predicate models a relationship
between objects by taking the interpretable arguments and returning a
propositional value. In the experiment using 8-Puzzle and a photo-realistic
Blocksworld environment, we show that (1) the resulting predicates capture the
interpretable relations (e.g. spatial), (2) they help obtaining the compact,
abstract model of the environment, and finally, (3) the resulting model is
compatible to symbolic classical planning.
Founded World Views with Autoepistemic Equilibrium Logic
Authors: Pedro Cabalar, Jorge Fandinno, Luis Fariñas
Date: 2019-02-20 00:00:00

Defined by Gelfond in 1991 (G91), epistemic specifications (or programs) are
an extension of logic programming under stable models semantics that
introducessubjective literals. A subjective literal al-lows checking whether
some regular literal is true in all (or in some of) the stable models of the
program, being those models collected in a setcalledworld view. One epistemic
program may yield several world views but, under the original G91 semantics,
some of them resulted from self-supported derivations. During the last eight
years, several alternative approaches have been proposed to get rid of these
self-supported worldviews. Unfortunately, their success could only be measured
by studying their behaviour on a set of common examples in the literature,
since no formal property of "self-supportedness" had been defined. To fill this
gap, we extend in this paper the idea of unfounded set from standard logic
programming to the epistemic case. We define when a world view is founded with
respect to some program and propose the foundedness property for any semantics
whose world views are always founded. Using counterexamples, we explain that
the previous approaches violate foundedness, and proceed to propose a new
semantics based on a combination of Moore's Autoepistemic Logic and Pearce's
Equilibrium Logic. The main result proves that this new semantics precisely
captures the set of founded G91 world views.