pwc-archive/papers-with-abstracts huggingface dataset:
Deep Quality-Value (DQV) Learning
Authors: Matthia Sabatelli, Gilles Louppe, Pierre Geurts, Marco A. Wiering
Date: 2018-09-30 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a novel Deep Reinforcement Learning (DRL) algorithm called Deep
Quality-Value (DQV) Learning. DQV uses temporal-difference learning to train a
Value neural network and uses this network for training a second Quality-value
network that learns to estimate state-action values. We first test DQV's update
rules with Multilayer Perceptrons as function approximators on two classic RL
problems, and then extend DQV with the use of Deep Convolutional Neural
Networks, `Experience Replay' and `Target Neural Networks' for tackling four
games of the Atari Arcade Learning environment. Our results show that DQV
learns significantly faster and better than Deep Q-Learning and Double Deep
Q-Learning, suggesting that our algorithm can potentially be a better
performing synchronous temporal difference algorithm than what is currently
present in DRL.
Multi-Level Contextual Network for Biomedical Image Segmentation
Authors: Amirhossein Dadashzadeh, Alireza Tavakoli Targhi
Date: 2018-09-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Accurate and reliable image segmentation is an essential part of biomedical
image analysis. In this paper, we consider the problem of biomedical image
segmentation using deep convolutional neural networks. We propose a new
end-to-end network architecture that effectively integrates local and global
contextual patterns of histologic primitives to obtain a more reliable
segmentation result. Specifically, we introduce a deep fully convolution
residual network with a new skip connection strategy to control the contextual
information passed forward. Moreover, our trained model is also computationally
inexpensive due to its small number of network parameters. We evaluate our
method on two public datasets for epithelium segmentation and tubule
segmentation tasks. Our experimental results show that the proposed method
provides a fast and effective way of producing a pixel-wise dense prediction of
biomedical images.
Can Adversarially Robust Learning Leverage Computational Hardness?
Authors: Saeed Mahloujifar, Mohammad Mahmoody
Date: 2018-10-02 00:00:00

Making learners robust to adversarial perturbation at test time (i.e.,
evasion attacks) or training time (i.e., poisoning attacks) has emerged as a
challenging task. It is known that for some natural settings, sublinear
perturbations in the training phase or the testing phase can drastically
decrease the quality of the predictions. These negative results, however, are
information theoretic and only prove the existence of such successful
adversarial perturbations. A natural question for these settings is whether or
not we can make classifiers computationally robust to polynomial-time attacks.
  In this work, we prove strong barriers against achieving such envisioned
computational robustness both for evasion and poisoning attacks. In particular,
we show that if the test instances come from a product distribution (e.g.,
uniform over $\{0,1\}^n$ or $[0,1]^n$, or isotropic $n$-variate Gaussian) and
that there is an initial constant error, then there exists a polynomial-time
attack that finds adversarial examples of Hamming distance $O(\sqrt n)$. For
poisoning attacks, we prove that for any learning algorithm with sample
complexity $m$ and any efficiently computable "predicate" defining some "bad"
property $B$ for the produced hypothesis (e.g., failing on a particular test)
that happens with an initial constant probability, there exist polynomial-time
online poisoning attacks that tamper with $O (\sqrt m)$ many examples, replace
them with other correctly labeled examples, and increases the probability of
the bad event $B$ to $\approx 1$.
  Both of our poisoning and evasion attacks are black-box in how they access
their corresponding components of the system (i.e., the hypothesis, the
concept, and the learning algorithm) and make no further assumptions about the
classifier or the learning algorithm producing the classifier.
Super-Resolution via Conditional Implicit Maximum Likelihood Estimation
Authors: Ke Li, Shichong Peng, Jitendra Malik
Date: 2018-10-02 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Single-image super-resolution (SISR) is a canonical problem with diverse
applications. Leading methods like SRGAN produce images that contain various
artifacts, such as high-frequency noise, hallucinated colours and shape
distortions, which adversely affect the realism of the result. In this paper,
we propose an alternative approach based on an extension of the method of
Implicit Maximum Likelihood Estimation (IMLE). We demonstrate greater
effectiveness at noise reduction and preservation of the original colours and
shapes, yielding more realistic super-resolved images.
Attention Models with Random Features for Multi-layered Graph Embeddings
Authors: Uday Shankar Shanthamallu, Jayaraman J. Thiagarajan, Huan Song, Andreas Spanias
Date: 2018-10-02 00:00:00
Tasks: Network Embedding, Node Classification

Modern data analysis pipelines are becoming increasingly complex due to the
presence of multi-view information sources. While graphs are effective in
modeling complex relationships, in many scenarios a single graph is rarely
sufficient to succinctly represent all interactions, and hence multi-layered
graphs have become popular. Though this leads to richer representations,
extending solutions from the single-graph case is not straightforward.
Consequently, there is a strong need for novel solutions to solve classical
problems, such as node classification, in the multi-layered case. In this
paper, we consider the problem of semi-supervised learning with multi-layered
graphs. Though deep network embeddings, e.g. DeepWalk, are widely adopted for
community discovery, we argue that feature learning with random node
attributes, using graph neural networks, can be more effective. To this end, we
propose to use attention models for effective feature learning, and develop two
novel architectures, GrAMME-SG and GrAMME-Fusion, that exploit the inter-layer
dependencies for building multi-layered graph embeddings. Using empirical
studies on several benchmark datasets, we evaluate the proposed approaches and
demonstrate significant performance improvements in comparison to
state-of-the-art network embedding strategies. The results also show that using
simple random features is an effective choice, even in cases where explicit
node attributes are not available.
GLAD: GLocalized Anomaly Detection via Human-in-the-Loop Learning
Authors: Md. Rakibul Islam, Shubhomoy Das, Janardhan Rao Doppa, Sriraam Natarajan
Date: 2018-10-02 00:00:00
Tasks: Anomaly Detection

Human analysts that use anomaly detection systems in practice want to retain the use of simple and explainable global anomaly detectors. In this paper, we propose a novel human-in-the-loop learning algorithm called GLAD (GLocalized Anomaly Detection) that supports global anomaly detectors. GLAD automatically learns their local relevance to specific data instances using label feedback from human analysts. The key idea is to place a uniform prior on the relevance of each member of the anomaly detection ensemble over the input feature space via a neural network trained on unlabeled instances. Subsequently, weights of the neural network are tuned to adjust the local relevance of each ensemble member using all labeled instances. GLAD also provides explanations which can improve the understanding of end-users about anomalies. Our experiments on synthetic and real-world data show the effectiveness of GLAD in learning the local relevance of ensemble members and discovering anomalies via label feedback.
Sketching for Latent Dirichlet-Categorical Models
Authors: Joseph Tassarotti, Jean-Baptiste Tristan, Michael Wick
Date: 2018-10-02 00:00:00
Tasks: Bayesian Inference

Recent work has explored transforming data sets into smaller, approximate
summaries in order to scale Bayesian inference. We examine a related problem in
which the parameters of a Bayesian model are very large and expensive to store
in memory, and propose more compact representations of parameter values that
can be used during inference. We focus on a class of graphical models that we
refer to as latent Dirichlet-Categorical models, and show how a combination of
two sketching algorithms known as count-min sketch and approximate counters
provide an efficient representation for them. We show that this sketch
combination -- which, despite having been used before in NLP applications, has
not been previously analyzed -- enjoys desirable properties. We prove that for
this class of models, when the sketches are used during Markov Chain Monte
Carlo inference, the equilibrium of sketched MCMC converges to that of the
exact chain as sketch parameters are tuned to reduce the error rate.
Optimal Completion Distillation for Sequence Learning
Authors: Sara Sabour, William Chan, Mohammad Norouzi
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Position, speech-recognition, Speech Recognition

We present Optimal Completion Distillation (OCD), a training procedure for
optimizing sequence to sequence models based on edit distance. OCD is
efficient, has no hyper-parameters of its own, and does not require pretraining
or joint optimization with conditional log-likelihood. Given a partial sequence
generated by the model, we first identify the set of optimal suffixes that
minimize the total edit distance, using an efficient dynamic programming
algorithm. Then, for each position of the generated sequence, we use a target
distribution that puts equal probability on the first token of all the optimal
suffixes. OCD achieves the state-of-the-art performance on end-to-end speech
recognition, on both Wall Street Journal and Librispeech datasets, achieving
$9.3\%$ WER and $4.5\%$ WER respectively.
Phasebook and Friends: Leveraging Discrete Representations for Source Separation
Authors: Jonathan Le Roux, Gordon Wichern, Shinji Watanabe, Andy Sarroff, John R. Hershey
Date: 2018-10-02 00:00:00
Tasks: Speaker Separation, Speech Enhancement

Deep learning based speech enhancement and source separation systems have
recently reached unprecedented levels of quality, to the point that performance
is reaching a new ceiling. Most systems rely on estimating the magnitude of a
target source by estimating a real-valued mask to be applied to a
time-frequency representation of the mixture signal. A limiting factor in such
approaches is a lack of phase estimation: the phase of the mixture is most
often used when reconstructing the estimated time-domain signal. Here, we
propose "magbook", "phasebook", and "combook", three new types of layers based
on discrete representations that can be used to estimate complex time-frequency
masks. Magbook layers extend classical sigmoidal units and a recently
introduced convex softmax activation for mask-based magnitude estimation.
Phasebook layers use a similar structure to give an estimate of the phase mask
without suffering from phase wrapping issues. Combook layers are an alternative
to the magbook-phasebook combination that directly estimate complex masks. We
present various training and inference schemes involving these representations,
and explain in particular how to include them in an end-to-end learning
framework. We also present an oracle study to assess upper bounds on
performance for various types of masks using discrete phase representations. We
evaluate the proposed methods on the wsj0-2mix dataset, a well-studied corpus
for single-channel speaker-independent speaker separation, matching the
performance of state-of-the-art mask-based approaches without requiring
additional phase reconstruction steps.
WAIC, but Why? Generative Ensembles for Robust Anomaly Detection
Authors: Hyunsun Choi, Eric Jang, Alexander A. Alemi
Date: 2018-10-02 00:00:00
Tasks: Anomaly Detection, Out of Distribution (OOD) Detection

Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data. One proposal to scale OoD detection to high-dimensional data is to learn a tractable likelihood approximation of the training distribution, and use it to reject unlikely inputs. However, likelihood models on natural data are themselves susceptible to OoD errors, and even assign large likelihoods to samples from other datasets. To mitigate this problem, we propose Generative Ensembles, which robustify density-based OoD detection by way of estimating epistemic uncertainty of the likelihood model. We present a puzzling observation in need of an explanation -- although likelihood measures cannot account for the typical set of a distribution, and therefore should not be suitable on their own for OoD detection, WAIC performs surprisingly well in practice.
A Knowledge Hunting Framework for Common Sense Reasoning
Authors: Ali Emami, Noelia De La Cruz, Adam Trischler, Kaheer Suleman, Jackie Chi Kit Cheung
Date: 2018-10-02 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Common Sense Reasoning, Coreference Resolution

We introduce an automatic system that achieves state-of-the-art results on
the Winograd Schema Challenge (WSC), a common sense reasoning task that
requires diverse, complex forms of inference and knowledge. Our method uses a
knowledge hunting module to gather text from the web, which serves as evidence
for candidate problem resolutions. Given an input problem, our system generates
relevant queries to send to a search engine, then extracts and classifies
knowledge from the returned results and weighs them to make a resolution. Our
approach improves F1 performance on the full WSC by 0.21 over the previous best
and represents the first system to exceed 0.5 F1. We further demonstrate that
the approach is competitive on the Choice of Plausible Alternatives (COPA)
task, which suggests that it is generally applicable.
Multi-scale Convolution Aggregation and Stochastic Feature Reuse for DenseNets
Authors: Mingjie Wang, Jun Zhou, Wendong Mao, Minglun Gong
Date: 2018-10-02 00:00:00

Recently, Convolution Neural Networks (CNNs) obtained huge success in
numerous vision tasks. In particular, DenseNets have demonstrated that feature
reuse via dense skip connections can effectively alleviate the difficulty of
training very deep networks and that reusing features generated by the initial
layers in all subsequent layers has strong impact on performance. To feed even
richer information into the network, a novel adaptive Multi-scale Convolution
Aggregation module is presented in this paper. Composed of layers for
multi-scale convolutions, trainable cross-scale aggregation, maxout, and
concatenation, this module is highly non-linear and can boost the accuracy of
DenseNet while using much fewer parameters. In addition, due to high model
complexity, the network with extremely dense feature reuse is prone to
overfitting. To address this problem, a regularization method named Stochastic
Feature Reuse is also presented. Through randomly dropping a set of feature
maps to be reused for each mini-batch during the training phase, this
regularization method reduces training costs and prevents co-adaptation.
Experimental results on CIFAR-10, CIFAR-100 and SVHN benchmarks demonstrated
the effectiveness of the proposed methods.
Efficient Dialog Policy Learning via Positive Memory Retention
Authors: Rui Zhao, Volker Tresp
Date: 2018-10-02 00:00:00
Tasks: Goal-Oriented Dialog, Object Discovery, Reinforcement Learning, Reinforcement Learning (RL)

This paper is concerned with the training of recurrent neural networks as goal-oriented dialog agents using reinforcement learning. Training such agents with policy gradients typically requires a large amount of samples. However, the collection of the required data in form of conversations between chat-bots and human agents is time-consuming and expensive. To mitigate this problem, we describe an efficient policy gradient method using positive memory retention, which significantly increases the sample-efficiency. We show that our method is 10 times more sample-efficient than policy gradients in extensive experiments on a new synthetic number guessing game. Moreover, in a real-word visual object discovery game, the proposed method is twice as sample-efficient as policy gradients and shows state-of-the-art performance.
Semi-dense Stereo Matching using Dual CNNs
Authors: Wendong Mao, Mingjie Wang, Jun Zhou, Minglun Gong
Date: 2018-10-02 00:00:00
Tasks: Stereo Matching, Stereo Matching Hand

A robust solution for semi-dense stereo matching is presented. It utilizes
two CNN models for computing stereo matching cost and performing
confidence-based filtering, respectively. Compared to existing CNNs-based
matching cost generation approaches, our method feeds additional global
information into the network so that the learned model can better handle
challenging cases, such as lighting changes and lack of textures. Through
utilizing non-parametric transforms, our method is also more self-reliant than
most existing semi-dense stereo approaches, which rely highly on the adjustment
of parameters. The experimental results based on Middlebury Stereo dataset
demonstrate that the proposed approach outperforms the state-of-the-art
semi-dense stereo approaches.
FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models
Authors: Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5
Tasks: Density Estimation, Form, Image Generation, Variational Inference

A promising class of generative models maps points from a simple distribution
to a complex distribution through an invertible neural network.
Likelihood-based training of these models requires restricting their
architectures to allow cheap computation of Jacobian determinants.
Alternatively, the Jacobian trace can be used if the transformation is
specified by an ordinary differential equation. In this paper, we use
Hutchinson's trace estimator to give a scalable unbiased estimate of the
log-density. The result is a continuous-time invertible generative model with
unbiased density estimation and one-pass sampling, while allowing unrestricted
neural network architectures. We demonstrate our approach on high-dimensional
density estimation, image generation, and variational inference, achieving the
state-of-the-art among exact likelihood methods with efficient sampling.
On Self Modulation for Generative Adversarial Networks
Authors: Ting Chen, Mario Lucic, Neil Houlsby, Sylvain Gelly
Date: 2018-10-02 00:00:00
Proceeding: ICLR 2019 5

Training Generative Adversarial Networks (GANs) is notoriously challenging.
We propose and study an architectural modification, self-modulation, which
improves GAN performance across different data sets, architectures, losses,
regularizers, and hyperparameter settings. Intuitively, self-modulation allows
the intermediate feature maps of a generator to change as a function of the
input noise vector. While reminiscent of other conditioning techniques, it
requires no labeled data. In a large-scale empirical study we observe a
relative decrease of $5\%-35\%$ in FID. Furthermore, all else being equal,
adding this modification to the generator leads to improved performance in
$124/144$ ($86\%$) of the studied settings. Self-modulation is a simple
architectural change that requires no additional parameter tuning, which
suggests that it can be applied readily to any GAN.
Energy-Based Hindsight Experience Prioritization
Authors: Rui Zhao, Volker Tresp
Date: 2018-10-02 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In Hindsight Experience Replay (HER), a reinforcement learning agent is trained by treating whatever it has achieved as virtual goals. However, in previous work, the experience was replayed at random, without considering which episode might be the most valuable for learning. In this paper, we develop an energy-based framework for prioritizing hindsight experience in robotic manipulation tasks. Our approach is inspired by the work-energy principle in physics. We define a trajectory energy function as the sum of the transition energy of the target object over the trajectory. We hypothesize that replaying episodes that have high trajectory energy is more effective for reinforcement learning in robotics. To verify our hypothesis, we designed a framework for hindsight experience prioritization based on the trajectory energy of goal states. The trajectory energy function takes the potential, kinetic, and rotational energy into consideration. We evaluate our Energy-Based Prioritization (EBP) approach on four challenging robotic manipulation tasks in simulation. Our empirical results show that our proposed method surpasses state-of-the-art approaches in terms of both performance and sample-efficiency on all four tasks, without increasing computational time. A video showing experimental results is available at https://youtu.be/jtsF2tTeUGQ
Unsupervised Emergence of Spatial Structure from Sensorimotor Prediction
Authors: Alban Laflaquière, Michael Garcia Ortiz
Date: 2018-10-02 00:00:00
Tasks: Prediction

Despite its omnipresence in robotics application, the nature of spatial
knowledge and the mechanisms that underlie its emergence in autonomous agents
are still poorly understood. Recent theoretical work suggests that the concept
of space can be grounded by capturing invariants induced by the structure of
space in an agent's raw sensorimotor experience. Moreover, it is hypothesized
that capturing these invariants is beneficial for a naive agent trying to
predict its sensorimotor experience. Under certain exploratory conditions,
spatial representations should thus emerge as a byproduct of learning to
predict. We propose a simple sensorimotor predictive scheme, apply it to
different agents and types of exploration, and evaluate the pertinence of this
hypothesis. We show that a naive agent can capture the topology and metric
regularity of its spatial configuration without any a priori knowledge, nor
extraneous supervision.
FutureGAN: Anticipating the Future Frames of Video Sequences using Spatio-Temporal 3d Convolutions in Progressively Growing GANs
Authors: Sandra Aigner, Marco Körner
Date: 2018-10-02 00:00:00
Tasks: Decoder, Video Prediction

We introduce a new encoder-decoder GAN model, FutureGAN, that predicts future
frames of a video sequence conditioned on a sequence of past frames. During
training, the networks solely receive the raw pixel values as an input, without
relying on additional constraints or dataset specific conditions. To capture
both the spatial and temporal components of a video sequence, spatio-temporal
3d convolutions are used in all encoder and decoder modules. Further, we
utilize concepts of the existing progressively growing GAN (PGGAN) that
achieves high-quality results on generating high-resolution single images. The
FutureGAN model extends this concept to the complex task of video prediction.
We conducted experiments on three different datasets, MovingMNIST, KTH Action,
and Cityscapes. Our results show that the model learned representations to
transform the information of an input sequence into a plausible future sequence
effectively for all three datasets. The main advantage of the FutureGAN
framework is that it is applicable to various different datasets without
additional changes, whilst achieving stable results that are competitive to the
state-of-the-art in video prediction. Our code is available at
https://github.com/TUM-LMF/FutureGAN.