pwc-archive/papers-with-abstracts huggingface dataset:
Distinction Graphs and Graphtropy: A Formalized Phenomenological Layer Underlying Classical and Quantum Entropy, Observational Semantics and Cognitive Computation
Authors: Ben Goertzel
Date: 2019-02-02 00:00:00

A new conceptual foundation for the notion of "information" is proposed,
based on the concept of a "distinction graph": a graph in which two nodes are
connected iff they cannot be distinguished by a particular observer. The
"graphtropy" of a distinction graph is defined as the average connection
probability of two nodes; in the case where the distinction graph is a composed
of disconnected components that are fully connected subgraphs, this is
equivalent to Ellerman's logical entropy, which has straightforward
relationships to Shannon entropy. Probabilistic distinction graphs and
probabilistic graphtropy are also considered, as well as connections between
graphtropy and thermodynamic and quantum entropy. The semantics of the Second
Law of Thermodynamics and the Maximum Entropy Production Principle are unfolded
in a novel way, via analysis of the cognitive processes underlying the making
of distinction graphs This evokes an interpretation in which complex
intelligence is seen to correspond to states of consciousness with intermediate
graphtropy, which are associated with memory imperfections that violate the
assumptions leading to derivation of the Second Law. In the case where nodes of
a distinction graph are labeled by computable entities, graphtropy is shown to
be monotonically related to the average algorithmic information of the nodes
(relative to to the algorithmic information of the observer). A
quantum-mechanical version of distinction graphs is considered, in which
distinctions can exist in a superposed state; this yields to graphtropy as a
measure of the impurity of a mixed state, and to a concept of "quangraphtropy."
Finally, a novel computational model called Dynamic Distinction Graphs (DDGs)
is formulated, via enhancing distinction graphs with additional links
expressing causal implications, enabling a distinction-based model of
"observers."
Self-Binarizing Networks
Authors: Fayez Lahoud, Radhakrishna Achanta, Pablo Márquez-Neila, Sabine Süsstrunk
Date: 2019-02-02 00:00:00
Tasks: Binarization

We present a method to train self-binarizing neural networks, that is,
networks that evolve their weights and activations during training to become
binary. To obtain similar binary networks, existing methods rely on the sign
activation function. This function, however, has no gradients for non-zero
values, which makes standard backpropagation impossible. To circumvent the
difficulty of training a network relying on the sign activation function, these
methods alternate between floating-point and binary representations of the
network during training, which is sub-optimal and inefficient. We approach the
binarization task by training on a unique representation involving a smooth
activation function, which is iteratively sharpened during training until it
becomes a binary representation equivalent to the sign activation function.
Additionally, we introduce a new technique to perform binary batch
normalization that simplifies the conventional batch normalization by
transforming it into a simple comparison operation. This is unlike existing
methods, which are forced to the retain the conventional floating-point-based
batch normalization. Our binary networks, apart from displaying advantages of
lower memory and computation as compared to conventional floating-point and
binary networks, also show higher classification accuracy than existing
state-of-the-art methods on multiple benchmark datasets.
Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing
Authors: Miguel Alonso Jr
Date: 2019-02-02 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Interactive Machine Learning is concerned with creating systems that operate
in environments alongside humans to achieve a task. A typical use is to extend
or amplify the capabilities of a human in cognitive or physical ways, requiring
the machine to adapt to the users' intentions and preferences. Often, this
takes the form of a human operator providing some type of feedback to the user,
which can be explicit feedback, implicit feedback, or a combination of both.
Explicit feedback, such as through a mouse click, carries a high cognitive
load. The focus of this study is to extend the current state of the art in
interactive machine learning by demonstrating that agents can learn a human
user's behavior and adapt to preferences with a reduced amount of explicit
human feedback in a mixed feedback setting. The learning agent perceives a
value of its own behavior from hand gestures given via a spatial interface.
This feedback mechanism is termed Spatial Interface Valuing. This method is
evaluated experimentally in a simulated environment for a grasping task using a
robotic arm with variable grip settings. Preliminary results indicate that
learning agents using spatial interface valuing can learn a value function
mapping spatial gestures to expected future rewards much more quickly as
compared to those same agents just receiving explicit feedback, demonstrating
that an agent perceiving feedback from a human user via a spatial interface can
serve as an effective complement to existing approaches.
When Collaborative Filtering Meets Reinforcement Learning
Authors: Yu Lei, Wenjie Li
Date: 2019-02-02 00:00:00
Tasks: Collaborative Filtering, Interactive Recommendation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we study a multi-step interactive recommendation problem,
where the item recommended at current step may affect the quality of future
recommendations. To address the problem, we develop a novel and effective
approach, named CFRL, which seamlessly integrates the ideas of both
collaborative filtering (CF) and reinforcement learning (RL). More
specifically, we first model the recommender-user interactive recommendation
problem as an agent-environment RL task, which is mathematically described by a
Markov decision process (MDP). Further, to achieve collaborative
recommendations for the entire user community, we propose a novel CF-based MDP
by encoding the states of all users into a shared latent vector space. Finally,
we propose an effective Q-network learning method to learn the agent's optimal
policy based on the CF-based MDP. The capability of CFRL is demonstrated by
comparing its performance against a variety of existing methods on real-world
datasets.
Evaluating MAP-Elites on Constrained Optimization Problems
Authors: Stefano Fioravanzo, Giovanni Iacca
Date: 2019-02-02 00:00:00

Constrained optimization problems are often characterized by multiple
constraints that, in the practice, must be satisfied with different tolerance
levels. While some constraints are hard and as such must be satisfied with
zero-tolerance, others may be soft, such that non-zero violations are
acceptable. Here, we evaluate the applicability of MAP-Elites to "illuminate"
constrained search spaces by mapping them into feature spaces where each
feature corresponds to a different constraint. On the one hand, MAP-Elites
implicitly preserves diversity, thus allowing a good exploration of the search
space. On the other hand, it provides an effective visualization that
facilitates a better understanding of how constraint violations correlate with
the objective function. We demonstrate the feasibility of this approach on a
large set of benchmark problems, in various dimensionalities, and with
different algorithmic configurations. As expected, numerical results show that
a basic version of MAP-Elites cannot compete on all problems (especially those
with equality constraints) with state-of-the-art algorithms that use gradient
information or advanced constraint handling techniques. Nevertheless, it has a
higher potential at finding constraint violations vs. objectives trade-offs and
providing new problem information. As such, it could be used in the future as
an effective building-block for designing new constrained optimization
algorithms.
Making a Case for Social Media Corpus for Detecting Depression
Authors: Adil Rajput, Samara Ahmed
Date: 2019-02-02 00:00:00

The social media platform provides an opportunity to gain valuable insights
into user behaviour. Users mimic their internal feelings and emotions in a
disinhibited fashion using natural language. Techniques in Natural Language
Processing have helped researchers decipher standard documents and cull
together inferences from massive amount of data. A representative corpus is a
prerequisite for NLP and one of the challenges we face today is the
non-standard and noisy language that exists on the internet. Our work focuses
on building a corpus from social media that is focused on detecting mental
illness. We use depression as a case study and demonstrate the effectiveness of
using such a corpus for helping practitioners detect such cases. Our results
show a high correlation between our Social Media Corpus and the standard corpus
for depression.
Supervised classification via minimax probabilistic transformations
Authors: Santiago Mazuelas, Andrea Zanoni, Aritz Perez
Date: 2019-02-02 00:00:00
Tasks: Classification, General Classification, Generalization Bounds

Conventional techniques for supervised classification constrain the classification rules considered and use surrogate losses for classification 0-1 loss. Favored families of classification rules are those that enjoy parametric representations suitable for surrogate loss minimization, and low complexity properties suitable for overfitting control. This paper presents classification techniques based on robust risk minimization (RRM) that we call linear probabilistic classifiers (LPCs). The proposed techniques consider unconstrained classification rules, optimize the classification 0-1 loss, and provide performance bounds during learning. LPCs enable efficient learning by using linear optimization, and avoid overffiting by using RRM over polyhedral uncertainty sets of distributions. We also provide finite-sample generalization bounds for LPCs and show their competitive performance with state-of-the-art techniques using benchmark datasets.
Medical Diagnosis with a Novel SVM-CoDOA Based Hybrid Approach
Authors: M. Hanefi Calp
Date: 2019-02-02 00:00:00
Tasks: BIG-bench Machine Learning, Medical Diagnosis

Machine Learning is an important sub-field of the Artificial Intelligence and
it has been become a very critical task to train Machine Learning techniques
via effective method or techniques. Recently, researchers try to use
alternative techniques to improve ability of Machine Learning techniques.
Moving from the explanations, objective of this study is to introduce a novel
SVM-CoDOA (Cognitive Development Optimization Algorithm trained Support Vector
Machines) system for general medical diagnosis. In detail, the system consists
of a SVM, which is trained by CoDOA, a newly developed optimization algorithm.
As it is known, use of optimization algorithms is an essential task to train
and improve Machine Learning techniques. In this sense, the study has provided
a medical diagnosis oriented problem scope in order to show effectiveness of
the SVM-CoDOA hybrid formation.
First-Order Bayesian Regret Analysis of Thompson Sampling
Authors: Sébastien Bubeck, Mark Sellke
Date: 2019-02-02 00:00:00
Tasks: Combinatorial Optimization, Thompson Sampling

We address online combinatorial optimization when the player has a prior over the adversary's sequence of losses. In this framework, Russo and Van Roy proposed an information-theoretic analysis of Thompson Sampling based on the information ratio, resulting in optimal worst-case regret bounds. In this paper we introduce three novel ideas to this line of work. First we propose a new quantity, the scale-sensitive information ratio, which allows us to obtain more refined first-order regret bounds (i.e., bounds of the form $\sqrt{L^*}$ where $L^*$ is the loss of the best combinatorial action). Second we replace the entropy over combinatorial actions by a coordinate entropy, which allows us to obtain the first optimal worst-case bound for Thompson Sampling in the combinatorial setting. Finally, we introduce a novel link between Bayesian agents and frequentist confidence intervals. Combining these ideas we show that the classical multi-armed bandit first-order regret bound $\tilde{O}(\sqrt{d L^*})$ still holds true in the more challenging and more general semi-bandit scenario. This latter result improves the previous state of the art bound $\tilde{O}(\sqrt{(d+m^3)L^*})$ by Lykouris, Sridharan and Tardos. Moreover we sharpen these results with two technical ingredients. The first leverages a recent insight of Zimmert and Lattimore to replace Shannon entropy with more refined potential functions in the analysis. The second is a \emph{Thresholded} Thompson sampling algorithm, which slightly modifies the original algorithm by never playing low-probability actions. This thresholding results in fully $T$-independent regret bounds when $L^*$ is almost surely upper-bounded, which we show does not hold for ordinary Thompson sampling.
Natural Language Processing, Sentiment Analysis and Clinical Analytics
Authors: Adil Rajput
Date: 2019-02-02 00:00:00
Tasks: Sentiment Analysis

Recent advances in Big Data has prompted health care practitioners to utilize
the data available on social media to discern sentiment and emotions
expression. Health Informatics and Clinical Analytics depend heavily on
information gathered from diverse sources. Traditionally, a healthcare
practitioner will ask a patient to fill out a questionnaire that will form the
basis of diagnosing the medical condition. However, medical practitioners have
access to many sources of data including the patients writings on various
media. Natural Language Processing (NLP) allows researchers to gather such data
and analyze it to glean the underlying meaning of such writings. The field of
sentiment analysis (applied to many other domains) depend heavily on techniques
utilized by NLP. This work will look into various prevalent theories underlying
the NLP field and how they can be leveraged to gather users sentiments on
social media. Such sentiments can be culled over a period of time thus
minimizing the errors introduced by data input and other stressors.
Furthermore, we look at some applications of sentiment analysis and application
of NLP to mental health. The reader will also learn about the NLTK toolkit that
implements various NLP theories and how they can make the data scavenging
process a lot easier.
Belief dynamics extraction
Authors: Arun Kumar, Zhengwei Wu, Xaq Pitkow, Paul Schrater
Date: 2019-02-02 00:00:00
Tasks: Model-based Reinforcement Learning, Reinforcement Learning

Animal behavior is not driven simply by its current observations, but is
strongly influenced by internal states. Estimating the structure of these
internal states is crucial for understanding the neural basis of behavior. In
principle, internal states can be estimated by inverting behavior models, as in
inverse model-based Reinforcement Learning. However, this requires careful
parameterization and risks model-mismatch to the animal. Here we take a
data-driven approach to infer latent states directly from observations of
behavior, using a partially observable switching semi-Markov process. This
process has two elements critical for capturing animal behavior: it captures
non-exponential distribution of times between observations, and transitions
between latent states depend on the animal's actions, features that require
more complex non-markovian models to represent. To demonstrate the utility of
our approach, we apply it to the observations of a simulated optimal agent
performing a foraging task, and find that latent dynamics extracted by the
model has correspondences with the belief dynamics of the agent. Finally, we
apply our model to identify latent states in the behaviors of monkey performing
a foraging task, and find clusters of latent states that identify periods of
time consistent with expectant waiting. This data-driven behavioral model will
be valuable for inferring latent cognitive states, and thereby for measuring
neural representations of those states.
Query-oriented text summarization based on hypergraph transversals
Authors: Hadrien Van Lierde, Tommy W. S. Chow
Date: 2019-02-02 00:00:00
Tasks: Document Summarization, Sentence, Text Summarization

Existing graph- and hypergraph-based algorithms for document summarization
represent the sentences of a corpus as the nodes of a graph or a hypergraph in
which the edges represent relationships of lexical similarities between
sentences. Each sentence of the corpus is then scored individually, using
popular node ranking algorithms, and a summary is produced by extracting highly
scored sentences. This approach fails to select a subset of jointly relevant
sentences and it may produce redundant summaries that are missing important
topics of the corpus. To alleviate this issue, a new hypergraph-based
summarizer is proposed in this paper, in which each node is a sentence and each
hyperedge is a theme, namely a group of sentences sharing a topic. Themes are
weighted in terms of their prominence in the corpus and their relevance to a
user-defined query. It is further shown that the problem of identifying a
subset of sentences covering the relevant themes of the corpus is equivalent to
that of finding a hypergraph transversal in our theme-based hypergraph. Two
extensions of the notion of hypergraph transversal are proposed for the purpose
of summarization, and polynomial time algorithms building on the theory of
submodular functions are proposed for solving the associated discrete
optimization problems. The worst-case time complexity of the proposed
algorithms is squared in the number of terms, which makes it cheaper than the
existing hypergraph-based methods. A thorough comparative analysis with related
models on DUC benchmark datasets demonstrates the effectiveness of our
approach, which outperforms existing graph- or hypergraph-based methods by at
least 6% of ROUGE-SU4 score.
A Layer-Based Sequential Framework for Scene Generation with GANs
Authors: Mehmet Ozgur Turkoglu, William Thong, Luuk Spreeuwers, Berkay Kicanaoglu
Date: 2019-02-02 00:00:00
Tasks: Conditional Image Generation, Image Generation, Scene Generation

The visual world we sense, interpret and interact everyday is a complex
composition of interleaved physical entities. Therefore, it is a very
challenging task to generate vivid scenes of similar complexity using
computers. In this work, we present a scene generation framework based on
Generative Adversarial Networks (GANs) to sequentially compose a scene,
breaking down the underlying problem into smaller ones. Different than the
existing approaches, our framework offers an explicit control over the elements
of a scene through separate background and foreground generators. Starting with
an initially generated background, foreground objects then populate the scene
one-by-one in a sequential manner. Via quantitative and qualitative experiments
on a subset of the MS-COCO dataset, we show that our proposed framework
produces not only more diverse images but also copes better with affine
transformations and occlusion artifacts of foreground objects than its
counterparts.
Hierarchical Photo-Scene Encoder for Album Storytelling
Authors: Bairui Wang, Lin Ma, Wei zhang, Wenhao Jiang, Feng Zhang
Date: 2019-02-02 00:00:00
Tasks: Decoder, Image-guided Story Ending Generation, Visual Storytelling

In this paper, we propose a novel model with a hierarchical photo-scene
encoder and a reconstructor for the task of album storytelling. The photo-scene
encoder contains two sub-encoders, namely the photo and scene encoders, which
are stacked together and behave hierarchically to fully exploit the structure
information of the photos within an album. Specifically, the photo encoder
generates semantic representation for each photo while exploiting temporal
relationships among them. The scene encoder, relying on the obtained photo
representations, is responsible for detecting the scene changes and generating
scene representations. Subsequently, the decoder dynamically and attentively
summarizes the encoded photo and scene representations to generate a sequence
of album representations, based on which a story consisting of multiple
coherent sentences is generated. In order to fully extract the useful semantic
information from an album, a reconstructor is employed to reproduce the
summarized album representations based on the hidden states of the decoder. The
proposed model can be trained in an end-to-end manner, which results in an
improved performance over the state-of-the-arts on the public visual
storytelling (VIST) dataset. Ablation studies further demonstrate the
effectiveness of the proposed hierarchical photo-scene encoder and
reconstructor.
Optimization of Project Scheduling Activities in Dynamic CPM and PERT Networks Using Genetic Algorithms
Authors: Muhammed Hanefi Calp, Muhammet Ali Akcayol
Date: 2019-02-02 00:00:00
Tasks: Management, Scheduling

Projects consist of interconnected dimensions such as objective, time,
resource and environment. Use of these dimensions in a controlled way and their
effective scheduling brings the project success. Project scheduling process
includes defining project activities, and estimation of time and resources to
be used for the activities. At this point, the project resource-scheduling
problems have begun to attract more attention after Program Evaluation and
Review Technique (PERT) and Critical Path Method (CPM) are developed one after
the other. However, complexity and difficulty of CPM and PERT processes led to
the use of these techniques through artificial intelligence methods such as
Genetic Algorithm (GA). In this study, an algorithm was proposed and developed,
which determines critical path, critical activities and project completion
duration by using GA, instead of CPM and PERT techniques used for network
analysis within the scope of project management. The purpose of using GA was
that these algorithms are an effective method for solution of complex
optimization problems. Therefore, correct decisions can be made for implemented
project activities by using obtained results. Thus, optimum results were
obtained in a shorter time than the CPM and PERT techniques by using the model
based on the dynamic algorithm. It is expected that this study will contribute
to the performance field (time, speed, low error etc.) of other studies.
Learned Indexes for Dynamic Workloads
Authors: Chuzhe Tang, Zhiyuan Dong, Minjie Wang, Zhaoguo Wang, Haibo Chen
Date: 2019-02-02 00:00:00

The recent proposal of learned index structures opens up a new perspective on
how traditional range indexes can be optimized. However, the current learned
indexes assume the data distribution is relatively static and the access
pattern is uniform, while real-world scenarios consist of skew query
distribution and evolving data. In this paper, we demonstrate that the missing
consideration of access patterns and dynamic data distribution notably hinders
the applicability of learned indexes. To this end, we propose solutions for
learned indexes for dynamic workloads (called Doraemon). To improve the latency
for skew queries, Doraemon augments the training data with access frequencies.
To address the slow model re-training when data distribution shifts, Doraemon
caches the previously-trained models and incrementally fine-tunes them for
similar access patterns and data distribution. Our preliminary result shows
that, Doraemon improves the query latency by 45.1% and reduces the model
re-training time to 1/20.