pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Event-Triggered Control
Authors: Dominik Baumann, Jia-Jie Zhu, Georg Martius, Sebastian Trimpe
Date: 2018-09-13 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Event-triggered control (ETC) methods can achieve high-performance control
with a significantly lower number of samples compared to usual, time-triggered
methods. These frameworks are often based on a mathematical model of the system
and specific designs of controller and event trigger. In this paper, we show
how deep reinforcement learning (DRL) algorithms can be leveraged to
simultaneously learn control and communication behavior from scratch, and
present a DRL approach that is particularly suitable for ETC. To our knowledge,
this is the first work to apply DRL to ETC. We validate the approach on
multiple control tasks and compare it to model-based event-triggering
frameworks. In particular, we demonstrate that it can, other than many
model-based ETC designs, be straightforwardly applied to nonlinear systems.
Gaussian Process Classification for Variable Fidelity Data
Authors: Nikita Klyuchnikov, Evgeny Burnaev
Date: 2018-09-13 00:00:00
Tasks: Classification, General Classification

In this paper we address a classification problem where two sources of labels with different levels of fidelity are available. Our approach is to combine data from both sources by applying a co-kriging schema on latent functions, which allows the model to account item-dependent labeling discrepancy. We provide an extension of Laplace inference for Gaussian process classification, that takes into account multi-fidelity data. We evaluate the proposed method on real and synthetic datasets and show that it is more resistant to different levels of discrepancy between sources than other approaches for data fusion. Our method can provide accuracy/cost trade-off for a number of practical tasks such as crowd-sourced data annotation and feasibility regions construction in engineering design.
A Deep Learning and Gamification Approach to Energy Conservation at Nanyang Technological University
Authors: Ioannis C. Konstantakopoulos, Andrew R. Barkan, Shiying He, Tanya Veeravalli, Huihan Liu, Costas Spanos
Date: 2018-09-13 00:00:00
Tasks: Discrete Choice Models

The implementation of smart building technology in the form of smart
infrastructure applications has great potential to improve sustainability and
energy efficiency by leveraging humans-in-the-loop strategy. However, human
preference in regard to living conditions is usually unknown and heterogeneous
in its manifestation as control inputs to a building. Furthermore, the
occupants of a building typically lack the independent motivation necessary to
contribute to and play a key role in the control of smart building
infrastructure. Moreover, true human actions and their integration with
sensing/actuation platforms remains unknown to the decision maker tasked with
improving operational efficiency. By modeling user interaction as a sequential
discrete game between non-cooperative players, we introduce a gamification
approach for supporting user engagement and integration in a human-centric
cyber-physical system. We propose the design and implementation of a
large-scale network game with the goal of improving the energy efficiency of a
building through the utilization of cutting-edge Internet of Things (IoT)
sensors and cyber-physical systems sensing/actuation platforms. A benchmark
utility learning framework that employs robust estimations for classical
discrete choice models provided for the derived high dimensional imbalanced
data. To improve forecasting performance, we extend the benchmark utility
learning scheme by leveraging Deep Learning end-to-end training with Deep
bi-directional Recurrent Neural Networks. We apply the proposed methods to high
dimensional data from a social game experiment designed to encourage energy
efficient behavior among smart building occupants in Nanyang Technological
University (NTU) residential housing. Using occupant-retrieved actions for
resources such as lighting and A/C, we simulate the game defined by the
estimated utility functions.
Choosing to Rank
Authors: Stephen Ragain, Johan Ugander
Date: 2018-09-13 00:00:00
Tasks: Car Racing

Ranking data arises in a wide variety of application areas but remains
difficult to model, learn from, and predict. Datasets often exhibit
multimodality, intransitivity, or incomplete rankings---particularly when
generated by humans---yet popular probabilistic models are often too rigid to
capture such complexities. In this work we leverage recent progress on similar
challenges in discrete choice modeling to form flexible and tractable
choice-based models for ranking data. We study choice representations, maps
from rankings (complete or top-$k$) to collections of choices, as a way of
forming ranking models from choice models. We focus on the repeated selection
(RS) choice representation, first used to form the Plackett-Luce ranking model
from the conditional multinomial logit choice model. We fully characterize, for
a prime number of alternatives, the choice representations that admit ranking
distributions with unit normalization, a desirably property that greatly
simplifies maximum likelihood estimation. We further show that only specific
minor variations on repeated selection exhibit this property. Our choice-based
ranking models provide higher out-of-sample likelihood when compared to
Plackett-Luce and Mallows models on a broad collection of ranking tasks
including food preferences, ranked-choice elections, car racing, and search
engine relevance tasks.
IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks
Authors: Khushmeen Sakloth, Wesley Beckner, Jim Pfaendtner, Garrett B. Goh
Date: 2018-09-13 00:00:00
Tasks: Automated Feature Engineering, Feature Engineering, Representation Learning

Deep neural networks (DNN) excel at extracting patterns. Through
representation learning and automated feature engineering on large datasets,
such models have been highly successful in computer vision and natural language
applications. Designing optimal network architectures from a principled or
rational approach however has been less than successful, with the best
successful approaches utilizing an additional machine learning algorithm to
tune the network hyperparameters. However, in many technical fields, there
exist established domain knowledge and understanding about the subject matter.
In this work, we develop a novel furcated neural network architecture that
utilizes domain knowledge as high-level design principles of the network. We
demonstrate proof-of-concept by developing IL-Net, a furcated network for
predicting the properties of ionic liquids, which is a class of complex
multi-chemicals entities. Compared to existing state-of-the-art approaches, we
show that furcated networks can improve model accuracy by approximately 20-35%,
without using additional labeled data. Lastly, we distill two key design
principles for furcated networks that can be adapted to other domains.
Negative Update Intervals in Deep Multi-Agent Reinforcement Learning
Authors: Gregory Palmer, Rahul Savani, Karl Tuyls
Date: 2018-09-13 00:00:00
Tasks: Multi-agent Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In Multi-Agent Reinforcement Learning (MA-RL), independent cooperative learners must overcome a number of pathologies to learn optimal joint policies. Addressing one pathology often leaves approaches vulnerable towards others. For instance, hysteretic Q-learning addresses miscoordination while leaving agents vulnerable towards misleading stochastic rewards. Other methods, such as leniency, have proven more robust when dealing with multiple pathologies simultaneously. However, leniency has predominately been studied within the context of strategic form games (bimatrix games) and fully observable Markov games consisting of a small number of probabilistic state transitions. This raises the question of whether these findings scale to more complex domains. For this purpose we implement a temporally extend version of the Climb Game, within which agents must overcome multiple pathologies simultaneously, including relative overgeneralisation, stochasticity, the alter-exploration and moving target problems, while learning from a large observation space. We find that existing lenient and hysteretic approaches fail to consistently learn near optimal joint-policies in this environment. To address these pathologies we introduce Negative Update Intervals-DDQN (NUI-DDQN), a Deep MA-RL algorithm which discards episodes yielding cumulative rewards outside the range of expanding intervals. NUI-DDQN consistently gravitates towards optimal joint-policies in our environment, overcoming the outlined pathologies.
An FPGA Implementation of a Time Delay Reservoir Using Stochastic Logic
Authors: Lisa Loomis, Nathan McDonald, Cory Merkel
Date: 2018-09-12 00:00:00
Tasks: General Classification

This paper presents and demonstrates a stochastic logic time delay reservoir
design in FPGA hardware. The reservoir network approach is analyzed using a
number of metrics, such as kernel quality, generalization rank, performance on
simple benchmarks, and is also compared to a deterministic design. A novel
re-seeding method is introduced to reduce the adverse effects of stochastic
noise, which may also be implemented in other stochastic logic reservoir
computing designs, such as echo state networks. Benchmark results indicate that
the proposed design performs well on noise-tolerant classification problems,
but more work needs to be done to improve the stochastic logic time delay
reservoirs robustness for regression problems. In addition, we show that the
stochastic design can significantly reduce area cost if the conversion between
binary and stochastic representations implemented efficiently.
Style Transfer Through Multilingual and Feedback-Based Back-Translation
Authors: Shrimai Prabhumoye, Yulia Tsvetkov, Alan W. black, Ruslan Salakhutdinov
Date: 2018-09-17 00:00:00
Tasks: Attribute, Sentence, Style Transfer, Translation

Style transfer is the task of transferring an attribute of a sentence (e.g.,
formality) while maintaining its semantic content. The key challenge in style
transfer is to strike a balance between the competing goals, one to preserve
meaning and the other to improve the style transfer accuracy. Prior research
has identified that the task of meaning preservation is generally harder to
attain and evaluate. This paper proposes two extensions of the state-of-the-art
style transfer models aiming at improving the meaning preservation in style
transfer. Our evaluation shows that these extensions help to ground meaning
better while improving the transfer accuracy.
Unsupervised Stylish Image Description Generation via Domain Layer Norm
Authors: Cheng Kuan Chen, Zhu Feng Pan, Min Sun, Ming-Yu Liu
Date: 2018-09-11 00:00:00
Tasks: Image Description

Most of the existing works on image description focus on generating
expressive descriptions. The only few works that are dedicated to generating
stylish (e.g., romantic, lyric, etc.) descriptions suffer from limited style
variation and content digression. To address these limitations, we propose a
controllable stylish image description generation model. It can learn to
generate stylish image descriptions that are more related to image content and
can be trained with the arbitrary monolingual corpus without collecting new
paired image and stylish descriptions. Moreover, it enables users to generate
various stylish descriptions by plugging in style-specific parameters to
include new styles into the existing model. We achieve this capability via a
novel layer normalization layer design, which we will refer to as the Domain
Layer Norm (DLN). Extensive experimental validation and user study on various
stylish image description generation tasks are conducted to show the
competitive advantages of the proposed model.
Multi-Label Image Classification via Knowledge Distillation from Weakly-Supervised Detection
Authors: Yongcheng Liu, Lu Sheng, Jing Shao, Junjie Yan, Shiming Xiang, Chunhong Pan
Date: 2018-09-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Knowledge Distillation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Image Classification, Object

Multi-label image classification is a fundamental but challenging task
towards general visual understanding. Existing methods found the region-level
cues (e.g., features from RoIs) can facilitate multi-label classification.
Nevertheless, such methods usually require laborious object-level annotations
(i.e., object labels and bounding boxes) for effective learning of the
object-level visual features. In this paper, we propose a novel and efficient
deep framework to boost multi-label classification by distilling knowledge from
weakly-supervised detection task without bounding box annotations.
Specifically, given the image-level annotations, (1) we first develop a
weakly-supervised detection (WSD) model, and then (2) construct an end-to-end
multi-label image classification framework augmented by a knowledge
distillation module that guides the classification model by the WSD model
according to the class-level predictions for the whole image and the
object-level visual features for object RoIs. The WSD model is the teacher
model and the classification model is the student model. After this cross-task
knowledge distillation, the performance of the classification model is
significantly improved and the efficiency is maintained since the WSD model can
be safely discarded in the test phase. Extensive experiments on two large-scale
datasets (MS-COCO and NUS-WIDE) show that our framework achieves superior
performances over the state-of-the-art methods on both performance and
efficiency.
Focal Loss in 3D Object Detection
Authors: Peng Yun, Lei Tai, Yu-An Wang, Chengju Liu, Ming Liu
Date: 2018-09-17 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Autonomous Vehicles, Object, object-detection, Object Detection

3D object detection is still an open problem in autonomous driving scenes.
When recognizing and localizing key objects from sparse 3D inputs, autonomous
vehicles suffer from a larger continuous searching space and higher
fore-background imbalance compared to image-based object detection. In this
paper, we aim to solve this fore-background imbalance in 3D object detection.
Inspired by the recent use of focal loss in image-based object detection, we
extend this hard-mining improvement of binary cross entropy to
point-cloud-based object detection and conduct experiments to show its
performance based on two different 3D detectors: 3D-FCN and VoxelNet. The
evaluation results show up to 11.2AP gains through the focal loss in a wide
range of hyperparameters for 3D object detection.
DeepEfficiency - optimal efficiency inversion in higher dimensions at the LHC
Authors: Mikael Mieskolainen
Date: 2018-09-17 00:00:00

We introduce a new high dimensional algorithm for efficiency corrected,
maximally Monte Carlo event generator independent fiducial measurements at the
LHC and beyond. The approach is driven probabilistically using a Deep Neural
Network on an event-by-event basis, trained using detector simulation and even
only pure phase space distributed events. This approach gives also a glimpse
into the future of high energy physics, where experiments publish new type of
measurements in a radically multidimensional way.
Generalizing Across Multi-Objective Reward Functions in Deep Reinforcement Learning
Authors: Eli Friedman, Fred Fontaine
Date: 2018-09-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many reinforcement-learning researchers treat the reward function as a part
of the environment, meaning that the agent can only know the reward of a state
if it encounters that state in a trial run. However, we argue that this is an
unnecessary limitation and instead, the reward function should be provided to
the learning algorithm. The advantage is that the algorithm can then use the
reward function to check the reward for states that the agent hasn't even
encountered yet. In addition, the algorithm can simultaneously learn policies
for multiple reward functions. For each state, the algorithm would calculate
the reward using each of the reward functions and add the rewards to its
experience replay dataset. The Hindsight Experience Replay algorithm developed
by Andrychowicz et al. (2017) does just this, and learns to generalize across a
distribution of sparse, goal-based rewards. We extend this algorithm to
linearly-weighted, multi-objective rewards and learn a single policy that can
generalize across all linear combinations of the multi-objective reward.
Whereas other multi-objective algorithms teach the Q-function to generalize
across the reward weights, our algorithm enables the policy to generalize, and
can thus be used with continuous actions.
Apple Flower Detection using Deep Convolutional Networks
Authors: Philipe A. Dias, Amy Tabb, Henry Medeiros
Date: 2018-09-17 00:00:00

To optimize fruit production, a portion of the flowers and fruitlets of apple
trees must be removed early in the growing season. The proportion to be removed
is determined by the bloom intensity, i.e., the number of flowers present in
the orchard. Several automated computer vision systems have been proposed to
estimate bloom intensity, but their overall performance is still far from
satisfactory even in relatively controlled environments. With the goal of
devising a technique for flower identification which is robust to clutter and
to changes in illumination, this paper presents a method in which a pre-trained
convolutional neural network is fine-tuned to become specially sensitive to
flowers. Experimental results on a challenging dataset demonstrate that our
method significantly outperforms three approaches that represent the state of
the art in flower detection, with recall and precision rates higher than
$90\%$. Moreover, a performance assessment on three additional datasets
previously unseen by the network, which consist of different flower species and
were acquired under different conditions, reveals that the proposed method
highly surpasses baseline approaches in terms of generalization capability.
Powerful, transferable representations for molecules through intelligent task selection in deep multitask networks
Authors: Clyde Fare, Lukas Turcani, Edward O. Pyzer-Knapp
Date: 2018-09-17 00:00:00
Tasks: Drug Discovery, Multi-Task Learning, Transfer Learning

Chemical representations derived from deep learning are emerging as a
powerful tool in areas such as drug discovery and materials innovation.
Currently, this methodology has three major limitations - the cost of
representation generation, risk of inherited bias, and the requirement for
large amounts of data. We propose the use of multi-task learning in tandem with
transfer learning to address these limitations directly. In order to avoid
introducing unknown bias into multi-task learning through the task selection
itself, we calculate task similarity through pairwise task affinity, and use
this measure to programmatically select tasks. We test this methodology on
several real-world data sets to demonstrate its potential for execution in
complex and low-data environments. Finally, we utilise the task similarity to
further probe the expressiveness of the learned representation through a
comparison to a commonly used cheminformatics fingerprint, and show that the
deep representation is able to capture more expressive task-based information.
Efficient Dense Modules of Asymmetric Convolution for Real-Time Semantic Segmentation
Authors: Shao-Yuan Lo, Hsueh-Ming Hang, Sheng-Wei Chan, Jing-Jhih Lin
Date: 2018-09-17 00:00:00
Tasks: Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation

Real-time semantic segmentation plays an important role in practical applications such as self-driving and robots. Most semantic segmentation research focuses on improving estimation accuracy with little consideration on efficiency. Several previous studies that emphasize high-speed inference often fail to produce high-accuracy segmentation results. In this paper, we propose a novel convolutional network named Efficient Dense modules with Asymmetric convolution (EDANet), which employs an asymmetric convolution structure and incorporates dilated convolution and dense connectivity to achieve high efficiency at low computational cost and model size. EDANet is 2.7 times faster than the existing fast segmentation network, ICNet, while it achieves a similar mIoU score without any additional context module, post-processing scheme, and pretrained model. We evaluate EDANet on Cityscapes and CamVid datasets, and compare it with the other state-of-art systems. Our network can run with the high-resolution inputs at the speed of 108 FPS on one GTX 1080Ti.
Commonsense for Generative Multi-Hop Question Answering Tasks
Authors: Lisa Bauer, Yicheng Wang, Mohit Bansal
Date: 2018-09-17 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Implicit Relations, Multi-hop Question Answering, Question Answering, Reading Comprehension

Reading comprehension QA tasks have seen a recent surge in popularity, yet most works have focused on fact-finding extractive QA. We instead focus on a more challenging multi-hop generative task (NarrativeQA), which requires the model to reason, gather, and synthesize disjoint pieces of information within the context to generate an answer. This type of multi-step reasoning also often requires understanding implicit relations, which humans resolve via external, background commonsense knowledge. We first present a strong generative baseline that uses a multi-attention mechanism to perform multiple hops of reasoning and a pointer-generator decoder to synthesize the answer. This model performs substantially better than previous generative models, and is competitive with current state-of-the-art span prediction models. We next introduce a novel system for selecting grounded multi-hop relational commonsense information from ConceptNet via a pointwise mutual information and term-frequency based scoring function. Finally, we effectively use this extracted commonsense information to fill in gaps of reasoning between context hops, using a selectively-gated attention mechanism. This boosts the model's performance significantly (also verified via human evaluation), establishing a new state-of-the-art for the task. We also show promising initial results of the generalizability of our background knowledge enhancements by demonstrating some improvement on QAngaroo-WikiHop, another multi-hop reasoning dataset.
Automata Guided Reinforcement Learning With Demonstrations
Authors: Xiao Li, Yao Ma, Calin Belta
Date: 2018-09-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Tasks with complex temporal structures and long horizons pose a challenge for
reinforcement learning agents due to the difficulty in specifying the tasks in
terms of reward functions as well as large variances in the learning signals.
We propose to address these problems by combining temporal logic (TL) with
reinforcement learning from demonstrations. Our method automatically generates
intrinsic rewards that align with the overall task goal given a TL task
specification. The policy resulting from our framework has an interpretable and
hierarchical structure. We validate the proposed method experimentally on a set
of robotic manipulation tasks.