pwc-archive/papers-with-abstracts huggingface dataset:
NPE: Neural Personalized Embedding for Collaborative Filtering
Authors: ThaiBinh Nguyen, Atsuhiro Takasu
Date: 2018-05-17 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Matrix factorization is one of the most efficient approaches in recommender
systems. However, such algorithms, which rely on the interactions between users
and items, perform poorly for "cold-users" (users with little history of such
interactions) and at capturing the relationships between closely related items.
To address these problems, we propose a neural personalized embedding (NPE)
model, which improves the recommendation performance for cold-users and can
learn effective representations of items. It models a user's click to an item
in two terms: the personal preference of the user for the item, and the
relationships between this item and other items clicked by the user. We show
that NPE outperforms competing methods for top-N recommendations, specially for
cold-user recommendations. We also performed a qualitative analysis that shows
the effectiveness of the representations learned by the model.
Guiding Policies with Language via Meta-Learning
Authors: John D. Co-Reyes, Abhishek Gupta, Suvansh Sanjeev, Nick Altieri, Jacob Andreas, John DeNero, Pieter Abbeel, Sergey Levine
Date: 2018-11-19 00:00:00
Proceeding: ICLR 2019 5
Tasks: Imitation Learning, Instruction Following, Meta-Learning, Reinforcement Learning

Behavioral skills or policies for autonomous agents are conventionally
learned from reward functions, via reinforcement learning, or from
demonstrations, via imitation learning. However, both modes of task
specification have their disadvantages: reward functions require manual
engineering, while demonstrations require a human expert to be able to actually
perform the task in order to generate the demonstration. Instruction following
from natural language instructions provides an appealing alternative: in the
same way that we can specify goals to other humans simply by speaking or
writing, we would like to be able to specify tasks for our machines. However, a
single instruction may be insufficient to fully communicate our intent or, even
if it is, may be insufficient for an autonomous agent to actually understand
how to perform the desired task. In this work, we propose an interactive
formulation of the task specification problem, where iterative language
corrections are provided to an autonomous agent, guiding it in acquiring the
desired skill. Our proposed language-guided policy learning algorithm can
integrate an instruction and a sequence of corrections to acquire new skills
very quickly. In our experiments, we show that this method can enable a policy
to follow instructions and corrections for simulated navigation and
manipulation tasks, substantially outperforming direct, non-interactive
instruction following.
Scalable agent alignment via reward modeling: a research direction
Authors: Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, Shane Legg
Date: 2018-11-19 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One obstacle to applying reinforcement learning algorithms to real-world
problems is the lack of suitable reward functions. Designing such reward
functions is difficult in part because the user only has an implicit
understanding of the task objective. This gives rise to the agent alignment
problem: how do we create agents that behave in accordance with the user's
intentions? We outline a high-level research direction to solve the agent
alignment problem centered around reward modeling: learning a reward function
from interaction with the user and optimizing the learned reward function with
reinforcement learning. We discuss the key challenges we expect to face when
scaling reward modeling to complex and general domains, concrete approaches to
mitigate these challenges, and ways to establish trust in the resulting agents.
Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine
Authors: Patrick Klose, Rudolf Mester
Date: 2018-11-19 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In the field of Autonomous Driving, the system controlling the vehicle can be
seen as an agent acting in a complex environment and thus naturally fits into
the modern framework of Reinforcement Learning. However, learning to drive can
be a challenging task and current results are often restricted to simplified
driving environments. To advance the field, we present a method to adaptively
restrict the action space of the agent according to its current driving
situation and show that it can be used to swiftly learn to drive in a realistic
environment based on the Deep Q-Network algorithm.
Non-submodular Function Maximization subject to a Matroid Constraint, with Applications
Authors: Khashayar Gatmiry, Manuel Gomez-Rodriguez
Date: 2018-11-19 00:00:00
Tasks: Point Processes

The standard greedy algorithm has been recently shown to enjoy approximation guarantees for constrained non-submodular nondecreasing set function maximization. While these recent results allow to better characterize the empirical success of the greedy algorithm, they are only applicable to simple cardinality constraints. In this paper, we study the problem of maximizing a non-submodular nondecreasing set function subject to a general matroid constraint. We first show that the standard greedy algorithm offers an approximation factor of $\frac{0.4 {\gamma}^{2}}{\sqrt{\gamma r} + 1}$, where $\gamma$ is the submodularity ratio of the function and $r$ is the rank of the matroid. Then, we show that the same greedy algorithm offers a constant approximation factor of $(1 + 1/(1-\alpha))^{-1}$, where $\alpha$ is the generalized curvature of the function. In addition, we demonstrate that these approximation guarantees are applicable to several real-world applications in which the submodularity ratio and the generalized curvature can be bounded. Finally, we show that our greedy algorithm does achieve a competitive performance in practice using a variety of experiments on synthetic and real-world data.
OrthoSeg: A Deep Multimodal Convolutional Neural Network for Semantic Segmentation of Orthoimagery
Authors: Pankaj Bodani, Kumar Shreshtha, Shashikant Sharma
Date: 2018-11-19 00:00:00
Tasks: 2D Semantic Segmentation, Decoder, General Classification, Segmentation, Semantic Segmentation, Semantic Segmentation Of Orthoimagery

This paper addresses the task of semantic segmentation of orthoimagery using
multimodal data e.g. optical RGB, infrared and digital surface model. We
propose a deep convolutional neural network architecture termed OrthoSeg for
semantic segmentation using multimodal, orthorectified and coregistered data.
We also propose a training procedure for supervised training of OrthoSeg. The
training procedure complements the inherent architectural characteristics of
OrthoSeg for preventing complex co-adaptations of learned features, which may
arise due to probable high dimensionality and spatial correlation in multimodal
and/or multispectral coregistered data. OrthoSeg consists of parallel encoding
networks for independent encoding of multimodal feature maps and a decoder
designed for efficiently fusing independently encoded multimodal feature maps.
A softmax layer at the end of the network uses the features generated by the
decoder for pixel-wise classification. The decoder fuses feature maps from the
parallel encoders locally as well as contextually at multiple scales to
generate per-pixel feature maps for final pixel-wise classification resulting
in segmented output. We experimentally show the merits of OrthoSeg by
demonstrating state-of-the-art accuracy on the ISPRS Potsdam 2D Semantic
Segmentation dataset. Adaptability is one of the key motivations behind
OrthoSeg so that it serves as a useful architectural option for a wide range of
problems involving the task of semantic segmentation of coregistered multimodal
and/or multispectral imagery. Hence, OrthoSeg is designed to enable independent
scaling of parallel encoder networks and decoder network to better match
application requirements, such as the number of input channels, the effective
field-of-view, and model capacity.
The Mafiascum Dataset: A Large Text Corpus for Deception Detection
Authors: Bob de Ruiter, George Kachergis
Date: 2018-11-19 00:00:00
Tasks: Deception Detection

Detecting deception in natural language has a wide variety of applications, but because of its hidden nature there are currently no public, large-scale sources of labeled deceptive text. This work introduces the Mafiascum dataset [1], a collection of over 700 games of Mafia, in which players are randomly assigned either deceptive or non-deceptive roles and then interact via forum postings. Over 9000 documents were compiled from the dataset, which each contained all messages written by a single player in a single game. This corpus was used to construct a set of hand-picked linguistic features based on prior deception research, as well as a set of average word vectors enriched with subword information. A logistic regression classifier fit on a combination of these feature sets achieved an average precision of 0.39 (chance = 0.26) and an AUROC of 0.68 on 5000+ word documents. On 50+ word documents, an average precision of 0.29 (chance = 0.23) and an AUROC of 0.59 was achieved. [1] https://bitbucket.org/bopjesvla/thesis/src
Behavioral Malware Classification using Convolutional Recurrent Neural Networks
Authors: Bander Alsulami, Spiros Mancoridis
Date: 2018-11-19 00:00:00
Tasks: Behavioral Malware Classification, Behavioral Malware Detection, Classification, Diversity, General Classification, Malware Classification, Malware Detection

Behavioral malware detection aims to improve on the performance of static
signature-based techniques used by anti-virus systems, which are less effective
against modern polymorphic and metamorphic malware. Behavioral malware
classification aims to go beyond the detection of malware by also identifying a
malware's family according to a naming scheme such as the ones used by
anti-virus vendors. Behavioral malware classification techniques use run-time
features, such as file system or network activities, to capture the behavioral
characteristic of running processes. The increasing volume of malware samples,
diversity of malware families, and the variety of naming schemes given to
malware samples by anti-virus vendors present challenges to behavioral malware
classifiers. We describe a behavioral classifier that uses a Convolutional
Recurrent Neural Network and data from Microsoft Windows Prefetch files. We
demonstrate the model's improvement on the state-of-the-art using a large
dataset of malware families and four major anti-virus vendor naming schemes.
The model is effective in classifying malware samples that belong to common and
rare malware families and can incrementally accommodate the introduction of new
malware samples and families.
Event-Based Features Selection and Tracking from Intertwined Estimation of Velocity and Generative Contours
Authors: Laurent Dardelet, Sio-Hoi Ieng, Ryad Benosman
Date: 2018-11-19 00:00:00

This paper presents a new event-based method for detecting and tracking
features from the output of an event-based camera. Unlike many tracking
algorithms from the computer vision community, this process does not aim for
particular predefined shapes such as corners. It relies on a dual intertwined
iterative continuous -- pure event-based -- estimation of the velocity vector
and a bayesian description of the generative feature contours. By projecting
along estimated speeds updated for each incoming event it is possible to
identify and determine the spatial location and generative contour of the
tracked feature while iteratively updating the estimation of the velocity
vector. Results on several environments are shown taking into account large
variations in terms of luminosity, speed, nature and size of the tracked
features. The usage of speed instead of positions allows for a much faster
feedback allowing for very fast convergence rates.
Efficient random graph matching via degree profiles
Authors: Jian Ding, Zongming Ma, Yihong Wu, Jiaming Xu
Date: 2018-11-19 00:00:00
Tasks: Graph Matching

Random graph matching refers to recovering the underlying vertex
correspondence between two random graphs with correlated edges; a prominent
example is when the two random graphs are given by Erd\H{o}s-R\'{e}nyi graphs
$G(n,\frac{d}{n})$. This can be viewed as an average-case and noisy version of
the graph isomorphism problem. Under this model, the maximum likelihood
estimator is equivalent to solving the intractable quadratic assignment
problem. This work develops an $\tilde{O}(n d^2+n^2)$-time algorithm which
perfectly recovers the true vertex correspondence with high probability,
provided that the average degree is at least $d = \Omega(\log^2 n)$ and the two
graphs differ by at most $\delta = O( \log^{-2}(n) )$ fraction of edges. For
dense graphs and sparse graphs, this can be improved to $\delta = O(
\log^{-2/3}(n) )$ and $\delta = O( \log^{-2}(d) )$ respectively, both in
polynomial time. The methodology is based on appropriately chosen distance
statistics of the degree profiles (empirical distribution of the degrees of
neighbors). Before this work, the best known result achieves $\delta=O(1)$ and
$n^{o(1)} \leq d \leq n^c$ for some constant $c$ with an $n^{O(\log n)}$-time
algorithm \cite{barak2018nearly} and $\delta=\tilde O((d/n)^4)$ and $d =
\tilde{\Omega}(n^{4/5})$ with a polynomial-time algorithm
\cite{dai2018performance}.
Learning Actionable Representations with Goal-Conditioned Policies
Authors: Dibya Ghosh, Abhishek Gupta, Sergey Levine
Date: 2018-11-19 00:00:00
Tasks: Decision Making, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Representation learning is a central challenge across a range of machine
learning areas. In reinforcement learning, effective and functional
representations have the potential to tremendously accelerate learning progress
and solve more challenging problems. Most prior work on representation learning
has focused on generative approaches, learning representations that capture all
underlying factors of variation in the observation space in a more disentangled
or well-ordered manner. In this paper, we instead aim to learn functionally
salient representations: representations that are not necessarily complete in
terms of capturing all factors of variation in the observation space, but
rather aim to capture those factors of variation that are important for
decision making -- that are "actionable." These representations are aware of
the dynamics of the environment, and capture only the elements of the
observation that are necessary for decision making rather than all factors of
variation, without explicit reconstruction of the observation. We show how
these representations can be useful to improve exploration for sparse reward
problems, to enable long horizon hierarchical reinforcement learning, and as a
state representation for learning policies for downstream tasks. We evaluate
our method on a number of simulated environments, and compare it to prior
methods for representation learning, exploration, and hierarchical
reinforcement learning.
Deeper Interpretability of Deep Networks
Authors: Tian Xu, Jiayu Zhan, Oliver G. B. Garrod, Philip H. S. Torr, Song-Chun Zhu, Robin A. A. Ince, Philippe G. Schyns
Date: 2018-11-19 00:00:00

Deep Convolutional Neural Networks (CNNs) have been one of the most
influential recent developments in computer vision, particularly for
categorization. There is an increasing demand for explainable AI as these
systems are deployed in the real world. However, understanding the information
represented and processed in CNNs remains in most cases challenging. Within
this paper, we explore the use of new information theoretic techniques
developed in the field of neuroscience to enable novel understanding of how a
CNN represents information. We trained a 10-layer ResNet architecture to
identify 2,000 face identities from 26M images generated using a rigorously
controlled 3D face rendering model that produced variations of intrinsic (i.e.
face morphology, gender, age, expression and ethnicity) and extrinsic factors
(i.e. 3D pose, illumination, scale and 2D translation). With our methodology,
we demonstrate that unlike human's network overgeneralizes face identities even
with extreme changes of face shape, but it is more sensitive to changes of
texture. To understand the processing of information underlying these
counterintuitive properties, we visualize the features of shape and texture
that the network processes to identify faces. Then, we shed a light into the
inner workings of the black box and reveal how hidden layers represent these
features and whether the representations are invariant to pose. We hope that
our methodology will provide an additional valuable tool for interpretability
of CNNs.
Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities
Authors: Jean-Matthieu Maro, Ryad Benosman
Date: 2018-11-19 00:00:00
Tasks: Gesture Recognition

This paper introduces a framework of gesture recognition operating on the output of an event based camera using the computational resources of a mobile phone. We will introduce a new development around the concept of time-surfaces modified and adapted to run on the limited computational resources of a mobile platform. We also introduce a new method to remove dynamically backgrounds that makes full use of the high temporal resolution of event-based cameras. We assess the performances of the framework by operating on several dynamic scenarios in uncontrolled lighting conditions indoors and outdoors. We also introduce a new publicly available event-based dataset for gesture recognition selected through a clinical process to allow human-machine interactions for the visually-impaired and the elderly. We finally report comparisons with prior works that tackled event-based gesture recognition reporting comparable if not superior results if taking into account the limited computational and memory constraints of the used hardware.
Distributed Learning of Average Belief Over Networks Using Sequential Observations
Authors: Kaiqing Zhang, Yang Liu, Ji Liu, Mingyan Liu, Tamer Başar
Date: 2018-11-19 00:00:00

This paper addresses the problem of distributed learning of average belief
with sequential observations, in which a network of $n>1$ agents aim to reach a
consensus on the average value of their beliefs, by exchanging information only
with their neighbors. Each agent has sequentially arriving samples of its
belief in an online manner. The neighbor relationships among the $n$ agents are
described by a graph which is possibly time-varying, whose vertices correspond
to agents and whose edges depict neighbor relationships. Two distributed online
algorithms are introduced for undirected and directed graphs, which are both
shown to converge to the average belief almost surely. Moreover, the sequences
generated by both algorithms are shown to reach consensus with an $O(1/t)$ rate
with high probability, where $t$ is the number of iterations. For undirected
graphs, the corresponding algorithm is modified for the case with quantized
communication and limited precision of the division operation. It is shown that
the modified algorithm causes all $n$ agents to either reach a quantized
consensus or enter a small neighborhood around the average of their beliefs.
Numerical simulations are then provided to corroborate the theoretical results.
DeepIR: A Deep Semantics Driven Framework for Image Retargeting
Authors: Jianxin Lin, Tiankuang Zhou, Zhibo Chen
Date: 2018-11-19 00:00:00
Tasks: Image Retargeting

We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of input image with a deep convolutional neural network. Then a uniform re-sampling that suits for semantic structure preserving is devised to resize feature maps to target aspect ratio at each feature layer. The final retargeting result is generated by coarse-to-fine nearest neighbor field search and step-by-step nearest neighbor field fusion. We empirically demonstrate the effectiveness of our model with both qualitative and quantitative results on widely used RetargetMe dataset.
Deep Shape-from-Template: Wide-Baseline, Dense and Fast Registration and Deformable Reconstruction from a Single Image
Authors: David Fuentes-Jimenez, David Casillas-Perez, Daniel Pizarro, Toby Collins, Adrien Bartoli
Date: 2018-11-19 00:00:00
Tasks: 3D Reconstruction, Object

We present Deep Shape-from-Template (DeepSfT), a novel Deep Neural Network (DNN) method for solving real-time automatic registration and 3D reconstruction of a deformable object viewed in a single monocular image.DeepSfT advances the state-of-the-art in various aspects. Compared to existing DNN SfT methods, it is the first fully convolutional real-time approach that handles an arbitrary object geometry, topology and surface representation. It also does not require ground truth registration with real data and scales well to very complex object models with large numbers of elements. Compared to previous non-DNN SfT methods, it does not involve numerical optimization at run-time, and is a dense, wide-baseline solution that does not demand, and does not suffer from, feature-based matching. It is able to process a single image with significant deformation and viewpoint changes, and handles well the core challenges of occlusions, weak texture and blur. DeepSfT is based on residual encoder-decoder structures and refining blocks. It is trained end-to-end with a novel combination of supervised learning from simulated renderings of the object model and semi-supervised automatic fine-tuning using real data captured with a standard RGB-D camera. The cameras used for fine-tuning and run-time can be different, making DeepSfT practical for real-world use. We show that DeepSfT significantly outperforms state-of-the-art wide-baseline approaches for non-trivial templates, with quantitative and qualitative evaluation.
Explicit Bias Discovery in Visual Question Answering Models
Authors: Varun Manjunatha, Nirat Saini, Larry S. Davis
Date: 2018-11-19 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Researchers have observed that Visual Question Answering (VQA) models tend to
answer questions by learning statistical biases in the data. For example, their
answer to the question "What is the color of the grass?" is usually "Green",
whereas a question like "What is the title of the book?" cannot be answered by
inferring statistical biases. It is of interest to the community to explicitly
discover such biases, both for understanding the behavior of such models, and
towards debugging them. Our work address this problem. In a database, we store
the words of the question, answer and visual words corresponding to regions of
interest in attention maps. By running simple rule mining algorithms on this
database, we discover human-interpretable rules which give us unique insight
into the behavior of such models. Our results also show examples of unusual
behaviors learned by models in attempting VQA tasks.