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5,000 | Local Gaussian Processes for Efficient Fine-Grained Traffic Speed
Prediction | cs.AI | Traffic speed is a key indicator for the efficiency of an urban
transportation system. Accurate modeling of the spatiotemporally varying
traffic speed thus plays a crucial role in urban planning and development. This
paper addresses the problem of efficient fine-grained traffic speed prediction
using big traffic data o... | computer science |
5,001 | A Generic Approach for Escaping Saddle points | cs.LG | A central challenge to using first-order methods for optimizing nonconvex
problems is the presence of saddle points. First-order methods often get stuck
at saddle points, greatly deteriorating their performance. Typically, to escape
from saddles one has to use second-order methods. However, most works on
second-order m... | computer science |
5,002 | Formulation of Deep Reinforcement Learning Architecture Toward
Autonomous Driving for On-Ramp Merge | cs.LG | Multiple automakers have in development or in production automated driving
systems (ADS) that offer freeway-pilot functions. This type of ADS is typically
limited to restricted-access freeways only, that is, the transition from manual
to automated modes takes place only after the ramp merging process is completed
manua... | computer science |
5,003 | Representation Learning for Visual-Relational Knowledge Graphs | cs.LG | A visual-relational knowledge graph (KG) is a multi-relational graph whose
entities are associated with images. We introduce ImageGraph, a KG with 1,330
relation types, 14,870 entities, and 829,931 images. Visual-relational KGs lead
to novel probabilistic query types where images are treated as first-class
citizens. Bo... | computer science |
5,004 | Semantic Preserving Embeddings for Generalized Graphs | cs.AI | A new approach to the study of Generalized Graphs as semantic data structures
using machine learning techniques is presented. We show how vector
representations maintaining semantic characteristics of the original data can
be obtained from a given graph using neural encoding architectures and
considering the topologica... | computer science |
5,005 | TensorFlow Agents: Efficient Batched Reinforcement Learning in
TensorFlow | cs.LG | We introduce TensorFlow Agents, an efficient infrastructure paradigm for
building parallel reinforcement learning algorithms in TensorFlow. We simulate
multiple environments in parallel, and group them to perform the neural network
computation on a batch rather than individual observations. This allows the
TensorFlow e... | computer science |
5,006 | Gigamachine: incremental machine learning on desktop computers | cs.AI | We present a concrete design for Solomonoff's incremental machine learning
system suitable for desktop computers. We use R5RS Scheme and its standard
library with a few omissions as the reference machine. We introduce a Levin
Search variant based on a stochastic Context Free Grammar together with new
update algorithms ... | computer science |
5,007 | RRA: Recurrent Residual Attention for Sequence Learning | cs.LG | In this paper, we propose a recurrent neural network (RNN) with residual
attention (RRA) to learn long-range dependencies from sequential data. We
propose to add residual connections across timesteps to RNN, which explicitly
enhances the interaction between current state and hidden states that are
several timesteps apa... | computer science |
5,008 | Meta-QSAR: a large-scale application of meta-learning to drug design and
discovery | cs.AI | We investigate the learning of quantitative structure activity relationships
(QSARs) as a case-study of meta-learning. This application area is of the
highest societal importance, as it is a key step in the development of new
medicines. The standard QSAR learning problem is: given a target (usually a
protein) and a set... | computer science |
5,009 | Pre-training Neural Networks with Human Demonstrations for Deep
Reinforcement Learning | cs.LG | Deep reinforcement learning (deep RL) has achieved superior performance in
complex sequential tasks by using a deep neural network as its function
approximator and by learning directly from raw images. A drawback of using raw
images is that deep RL must learn the state feature representation from the raw
images in addi... | computer science |
5,010 | Action Schema Networks: Generalised Policies with Deep Learning | cs.AI | In this paper, we introduce the Action Schema Network (ASNet): a neural
network architecture for learning generalised policies for probabilistic
planning problems. By mimicking the relational structure of planning problems,
ASNets are able to adopt a weight-sharing scheme which allows the network to be
applied to any p... | computer science |
5,011 | Shared Learning : Enhancing Reinforcement in $Q$-Ensembles | cs.LG | Deep Reinforcement Learning has been able to achieve amazing successes in a
variety of domains from video games to continuous control by trying to maximize
the cumulative reward. However, most of these successes rely on algorithms that
require a large amount of data to train in order to obtain results on par with
human... | computer science |
5,012 | Disentangled Variational Auto-Encoder for Semi-supervised Learning | cs.LG | In this paper, we develop a novel approach for semi-supervised VAE without
classifier. Specifically, we propose a new model called SDVAE, which encodes
the input data into disentangled representation and non-interpretable
representation, then the category information is directly utilized to
regularize the disentangled ... | computer science |
5,013 | A Generic Framework for Interesting Subspace Cluster Detection in
Multi-attributed Networks | cs.LG | Detection of interesting (e.g., coherent or anomalous) clusters has been
studied extensively on plain or univariate networks, with various applications.
Recently, algorithms have been extended to networks with multiple attributes
for each node in the real-world. In a multi-attributed network, often, a
cluster of nodes ... | computer science |
5,014 | Leveraging Distributional Semantics for Multi-Label Learning | cs.LG | We present a novel and scalable label embedding framework for large-scale
multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using
Distributional Semantics). Our approach draws inspiration from ideas rooted in
distributional semantics, specifically the Skip Gram Negative Sampling (SGNS)
approach, widely us... | computer science |
5,015 | Deep Graph Attention Model | cs.LG | Graph classification is a problem with practical applications in many
different domains. Most of the existing methods take the entire graph into
account when calculating graph features. In a graphlet-based approach, for
instance, the entire graph is processed to get the total count of different
graphlets or sub-graphs.... | computer science |
5,016 | Predicting Runtime Distributions using Deep Neural Networks | cs.AI | Many state-of-the-art algorithms for solving hard combinatorial problems
include elements of stochasticity that lead to high variations in runtime, even
for a fixed problem instance, across runs with different pseudo-random number
seeds. Knowledge about the runtime distributions (RTDs) of algorithms on given
problem in... | computer science |
5,017 | Non-iterative Label Propagation on Optimal Leading Forest | cs.LG | Graph based semi-supervised learning (GSSL) has intuitive representation and
can be improved by exploiting the matrix calculation. However, it has to
perform iterative optimization to achieve a preset objective, which usually
leads to low efficiency. Another inconvenience lying in GSSL is that when new
data come, the g... | computer science |
5,018 | FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for
Classification | cs.LG | This paper introduces a novel real-time Fuzzy Supervised Learning with Binary
Meta-Feature (FSL-BM) for big data classification task. The study of real-time
algorithms addresses several major concerns, which are namely: accuracy, memory
consumption, and ability to stretch assumptions and time complexity. Attaining
a fa... | computer science |
5,019 | Deep TAMER: Interactive Agent Shaping in High-Dimensional State Spaces | cs.AI | While recent advances in deep reinforcement learning have allowed autonomous
learning agents to succeed at a variety of complex tasks, existing algorithms
generally require a lot of training data. One way to increase the speed at
which agents are able to learn to perform tasks is by leveraging the input of
human traine... | computer science |
5,020 | Deep Abstract Q-Networks | cs.LG | We examine the problem of learning and planning on high-dimensional domains
with long horizons and sparse rewards. Recent approaches have shown great
successes in many Atari 2600 domains. However, domains with long horizons and
sparse rewards, such as Montezuma's Revenge and Venture, remain challenging for
existing met... | computer science |
5,021 | Dilated Recurrent Neural Networks | cs.AI | Learning with recurrent neural networks (RNNs) on long sequences is a
notoriously difficult task. There are three major challenges: 1) complex
dependencies, 2) vanishing and exploding gradients, and 3) efficient
parallelization. In this paper, we introduce a simple yet effective RNN
connection structure, the DilatedRNN... | computer science |
5,022 | Rainbow: Combining Improvements in Deep Reinforcement Learning | cs.AI | The deep reinforcement learning community has made several independent
improvements to the DQN algorithm. However, it is unclear which of these
extensions are complementary and can be fruitfully combined. This paper
examines six extensions to the DQN algorithm and empirically studies their
combination. Our experiments ... | computer science |
5,023 | Random Projection and Its Applications | cs.LG | Random Projection is a foundational research topic that connects a bunch of
machine learning algorithms under a similar mathematical basis. It is used to
reduce the dimensionality of the dataset by projecting the data points
efficiently to a smaller dimensions while preserving the original relative
distance between the... | computer science |
5,024 | Continuous Adaptation via Meta-Learning in Nonstationary and Competitive
Environments | cs.LG | Ability to continuously learn and adapt from limited experience in
nonstationary environments is an important milestone on the path towards
general intelligence. In this paper, we cast the problem of continuous
adaptation into the learning-to-learn framework. We develop a simple
gradient-based meta-learning algorithm s... | computer science |
5,025 | Sign-Constrained Regularized Loss Minimization | cs.LG | In practical analysis, domain knowledge about analysis target has often been
accumulated, although, typically, such knowledge has been discarded in the
statistical analysis stage, and the statistical tool has been applied as a
black box. In this paper, we introduce sign constraints that are a handy and
simple represent... | computer science |
5,026 | Mental Sampling in Multimodal Representations | cs.LG | Both resources in the natural environment and concepts in a semantic space
are distributed "patchily", with large gaps in between the patches. To describe
people's internal and external foraging behavior, various random walk models
have been proposed. In particular, internal foraging has been modeled as
sampling: in or... | computer science |
5,027 | Eigenoption Discovery through the Deep Successor Representation | cs.LG | Options in reinforcement learning allow agents to hierarchically decompose a
task into subtasks, having the potential to speed up learning and planning.
However, autonomously learning effective sets of options is still a major
challenge in the field. In this paper we focus on the recently introduced idea
of using repre... | computer science |
5,028 | Regret Minimization for Partially Observable Deep Reinforcement Learning | cs.LG | Deep reinforcement learning algorithms that estimate state and state-action
value functions have been shown to be effective in a variety of challenging
domains, including learning control strategies from raw image pixels. However,
algorithms that estimate state and state-action value functions typically
assume a fully ... | computer science |
5,029 | Piecewise Linear Neural Network verification: A comparative study | cs.AI | The success of Deep Learning and its potential use in many important safety-
critical applications has motivated research on formal verification of Neural
Network (NN) models. Despite the reputation of learned NN models to behave as
black boxes and the theoretical hardness of proving their properties,
researchers have ... | computer science |
5,030 | Strategies for Conceptual Change in Convolutional Neural Networks | cs.LG | A remarkable feature of human beings is their capacity for creative
behaviour, referring to their ability to react to problems in ways that are
novel, surprising, and useful. Transformational creativity is a form of
creativity where the creative behaviour is induced by a transformation of the
actor's conceptual space, ... | computer science |
5,031 | Learning Solving Procedure for Artificial Neural Network | cs.AI | It is expected that progress toward true artificial intelligence will be
achieved through the emergence of a system that integrates representation
learning and complex reasoning (LeCun et al. 2015). In response to this
prediction, research has been conducted on implementing the symbolic reasoning
of a von Neumann compu... | computer science |
5,032 | Online Tool Condition Monitoring Based on Parsimonious Ensemble+ | cs.LG | Accurate diagnosis of tool wear in metal turning process remains an open
challenge for both scientists and industrial practitioners because of
inhomogeneities in workpiece material, nonstationary machining settings to suit
production requirements, and nonlinear relations between measured variables and
tool wear. Common... | computer science |
5,033 | Inverse Reward Design | cs.AI | Autonomous agents optimize the reward function we give them. What they don't
know is how hard it is for us to design a reward function that actually
captures what we want. When designing the reward, we might think of some
specific training scenarios, and make sure that the reward will lead to the
right behavior in thos... | computer science |
5,034 | Deep-ESN: A Multiple Projection-encoding Hierarchical Reservoir
Computing Framework | cs.LG | As an efficient recurrent neural network (RNN) model, reservoir computing
(RC) models, such as Echo State Networks, have attracted widespread attention
in the last decade. However, while they have had great success with time series
data [1], [2], many time series have a multiscale structure, which a
single-hidden-layer... | computer science |
5,035 | Exploiting Layerwise Convexity of Rectifier Networks with Sign
Constrained Weights | cs.LG | By introducing sign constraints on the weights, this paper proposes sign
constrained rectifier networks (SCRNs), whose training can be solved
efficiently by the well known majorization-minimization (MM) algorithms. We
prove that the proposed two-hidden-layer SCRNs, which exhibit negative weights
in the second hidden la... | computer science |
5,036 | An Iterative Closest Points Approach to Neural Generative Models | cs.LG | We present a simple way to learn a transformation that maps samples of one
distribution to the samples of another distribution. Our algorithm comprises an
iteration of 1) drawing samples from some simple distribution and transforming
them using a neural network, 2) determining pairwise correspondences between
the trans... | computer science |
5,037 | Is prioritized sweeping the better episodic control? | cs.AI | Episodic control has been proposed as a third approach to reinforcement
learning, besides model-free and model-based control, by analogy with the three
types of human memory. i.e. episodic, procedural and semantic memory. But the
theoretical properties of episodic control are not well investigated. Here I
show that in ... | computer science |
5,038 | Implementing the Deep Q-Network | cs.LG | The Deep Q-Network proposed by Mnih et al. [2015] has become a benchmark and
building point for much deep reinforcement learning research. However,
replicating results for complex systems is often challenging since original
scientific publications are not always able to describe in detail every
important parameter sett... | computer science |
5,039 | Constructive Preference Elicitation over Hybrid Combinatorial Spaces | cs.AI | Peference elicitation is the task of suggesting a highly preferred
configuration to a decision maker. The preferences are typically learned by
querying the user for choice feedback over pairs or sets of objects. In its
constructive variant, new objects are synthesized "from scratch" by maximizing
an estimate of the use... | computer science |
5,040 | Posterior Sampling for Large Scale Reinforcement Learning | cs.LG | Posterior sampling for reinforcement learning (PSRL) is a popular algorithm
for learning to control an unknown Markov decision process (MDP). PSRL
maintains a distribution over MDP parameters and in an episodic fashion samples
MDP parameters, computes the optimal policy for them and executes it. A special
case of PSRL ... | computer science |
5,041 | Deterministic Policy Optimization by Combining Pathwise and Score
Function Estimators for Discrete Action Spaces | cs.AI | Policy optimization methods have shown great promise in solving complex
reinforcement and imitation learning tasks. While model-free methods are
broadly applicable, they often require many samples to optimize complex
policies. Model-based methods greatly improve sample-efficiency but at the cost
of poor generalization,... | computer science |
5,042 | An influence-based fast preceding questionnaire model for elderly
assessments | cs.AI | To improve the efficiency of elderly assessments, an influence-based fast
preceding questionnaire model (FPQM) is proposed. Compared with traditional
assessments, the FPQM optimizes questionnaires by reordering their attributes.
The values of low-ranking attributes can be predicted by the values of the
high-ranking att... | computer science |
5,043 | Action Branching Architectures for Deep Reinforcement Learning | cs.LG | Discrete-action algorithms have been central to numerous recent successes of
deep reinforcement learning. However, applying these algorithms to
high-dimensional action tasks requires tackling the combinatorial increase of
the number of possible actions with the number of action dimensions. This
problem is further exace... | computer science |
5,044 | Deep Reinforcement Learning for Sepsis Treatment | cs.AI | Sepsis is a leading cause of mortality in intensive care units and costs
hospitals billions annually. Treating a septic patient is highly challenging,
because individual patients respond very differently to medical interventions
and there is no universally agreed-upon treatment for sepsis. In this work, we
propose an a... | computer science |
5,045 | AI Safety Gridworlds | cs.LG | We present a suite of reinforcement learning environments illustrating
various safety properties of intelligent agents. These problems include safe
interruptibility, avoiding side effects, absent supervisor, reward gaming, safe
exploration, as well as robustness to self-modification, distributional shift,
and adversari... | computer science |
5,046 | Learnings Options End-to-End for Continuous Action Tasks | cs.LG | We present new results on learning temporally extended actions for
continuoustasks, using the options framework (Suttonet al.[1999b], Precup
[2000]). In orderto achieve this goal we work with the option-critic
architecture (Baconet al.[2017])using a deliberation cost and train it with
proximal policy optimization (Schu... | computer science |
5,047 | Comparing Deep Reinforcement Learning and Evolutionary Methods in
Continuous Control | cs.LG | Reinforcement Learning and the Evolutionary Strategy are two major approaches
in addressing complicated control problems. Both are strong contenders and have
their own devotee communities. Both groups have been very active in developing
new advances in their own domain and devising, in recent years, leading-edge
techni... | computer science |
5,048 | On the Real-time Vehicle Placement Problem | cs.AI | Motivated by ride-sharing platforms' efforts to reduce their riders' wait
times for a vehicle, this paper introduces a novel problem of placing vehicles
to fulfill real-time pickup requests in a spatially and temporally changing
environment. The real-time nature of this problem makes it fundamentally
different from oth... | computer science |
5,049 | A Deeper Look at Experience Replay | cs.LG | Recently experience replay is widely used in various deep reinforcement
learning (RL) algorithms, however in this paper we showcase that it is not as
good as people think. To be more specific, experience replay will significantly
hurt the learning process if the size of replay buffer is not well tuned.
Although experie... | computer science |
5,050 | Mastering Chess and Shogi by Self-Play with a General Reinforcement
Learning Algorithm | cs.AI | The game of chess is the most widely-studied domain in the history of
artificial intelligence. The strongest programs are based on a combination of
sophisticated search techniques, domain-specific adaptations, and handcrafted
evaluation functions that have been refined by human experts over several
decades. In contrast... | computer science |
5,051 | Bayesian Q-learning with Assumed Density Filtering | cs.LG | While off-policy temporal difference methods have been broadly used in
reinforcement learning due to their efficiency and simple implementation, their
Bayesian counterparts have been relatively understudied. This is mainly because
the max operator in the Bellman optimality equation brings non-linearity and
inconsistent... | computer science |
5,052 | StackInsights: Cognitive Learning for Hybrid Cloud Readiness | cs.LG | Hybrid cloud is an integrated cloud computing environment utilizing a mix of
public cloud, private cloud, and on-premise traditional IT infrastructures.
Workload awareness, defined as a detailed full range understanding of each
individual workload, is essential in implementing the hybrid cloud. While it is
critical to ... | computer science |
5,053 | Reachable Set Computation and Safety Verification for Neural Networks
with ReLU Activations | cs.LG | Neural networks have been widely used to solve complex real-world problems.
Due to the complicate, nonlinear, non-convex nature of neural networks, formal
safety guarantees for the output behaviors of neural networks will be crucial
for their applications in safety-critical systems.In this paper, the output
reachable s... | computer science |
5,054 | Multi-task learning of time series and its application to the travel
demand | cs.LG | We address the problem of modeling and prediction of a set of temporal events
in the context of intelligent transportation systems. To leverage the
information shared by different events, we propose a multi-task learning
framework. We develop a support vector regression model for joint learning of
mutually dependent ti... | computer science |
5,055 | Faster Deep Q-learning using Neural Episodic Control | cs.LG | The research on deep reinforcement learning which estimates Q-value by deep
learning has been attracted the interest of researchers recently. In deep
reinforcement learning, it is important to efficiently learn the experiences
that an agent has collected by exploring environment. In this research, we
propose NEC2DQN th... | computer science |
5,056 | Building Generalizable Agents with a Realistic and Rich 3D Environment | cs.LG | Towards bridging the gap between machine and human intelligence, it is of
utmost importance to introduce environments that are visually realistic and
rich in content. In such environments, one can evaluate and improve a crucial
property of practical intelligent systems, namely \emph{generalization}. In
this work, we bu... | computer science |
5,057 | Sample-Efficient Reinforcement Learning through Transfer and
Architectural Priors | cs.LG | Recent work in deep reinforcement learning has allowed algorithms to learn
complex tasks such as Atari 2600 games just from the reward provided by the
game, but these algorithms presently require millions of training steps in
order to learn, making them approximately five orders of magnitude slower than
humans. One rea... | computer science |
5,058 | Interactive Learning of Acyclic Conditional Preference Networks | cs.AI | Learning of user preferences, as represented by, for example, Conditional
Preference Networks (CP-nets), has become a core issue in AI research. Recent
studies investigate learning of CP-nets from randomly chosen examples or from
membership and equivalence queries. To assess the optimality of learning
algorithms as wel... | computer science |
5,059 | Comparative Study on Generative Adversarial Networks | cs.LG | In recent years, there have been tremendous advancements in the field of
machine learning. These advancements have been made through both academic as
well as industrial research. Lately, a fair amount of research has been
dedicated to the usage of generative models in the field of computer vision and
image classificati... | computer science |
5,060 | GitGraph - Architecture Search Space Creation through Frequent
Computational Subgraph Mining | cs.LG | The dramatic success of deep neural networks across multiple application
areas often relies on experts painstakingly designing a network architecture
specific to each task. To simplify this process and make it more accessible, an
emerging research effort seeks to automate the design of neural network
architectures, usi... | computer science |
5,061 | Learning Features For Relational Data | cs.AI | Feature engineering is one of the most important but tedious tasks in data
science projects. This work studies automation of feature learning for
relational data. We first theoretically proved that learning relevant features
from relational data for a given predictive analytics problem is NP-hard.
However, it is possib... | computer science |
5,062 | Solutions to problems with deep learning | cs.LG | Despite the several successes of deep learning systems, there are concerns
about their limitations, discussed most recently by Gary Marcus. This paper
discusses Marcus's concerns and some others, together with solutions to several
of these problems provided by the "P theory of intelligence" and its
realisation in the "... | computer science |
5,063 | Unseen Class Discovery in Open-world Classification | cs.LG | This paper concerns open-world classification, where the classifier not only
needs to classify test examples into seen classes that have appeared in
training but also reject examples from unseen or novel classes that have not
appeared in training. Specifically, this paper focuses on discovering the
hidden unseen classe... | computer science |
5,064 | Intrinsic dimension of concept lattices | cs.AI | Geometric analysis is a very capable theory to understand the influence of
the high dimensionality of the input data in machine learning (ML) and
knowledge discovery (KD). With our approach we can assess how far the
application of a specific KD/ML-algorithm to a concrete data set is prone to
the curse of dimensionality... | computer science |
5,065 | On the Inter-relationships among Drift rate, Forgetting rate,
Bias/variance profile and Error | cs.LG | We propose two general and falsifiable hypotheses about expectations on
generalization error when learning in the context of concept drift. One posits
that as drift rate increases, the forgetting rate that minimizes generalization
error will also increase and vice versa. The other posits that as a learner's
forgetting ... | computer science |
5,066 | Deep Learning Approach for Very Similar Objects Recognition Application
on Chihuahua and Muffin Problem | cs.AI | We address the problem to tackle the very similar objects like Chihuahua or
muffin problem to recognize at least in human vision level. Our regular deep
structured machine learning still does not solve it. We saw many times for
about year in our community the problem. Today we proposed the state-of-the-art
solution for... | computer science |
5,067 | The Intriguing Properties of Model Explanations | cs.LG | Linear approximations to the decision boundary of a complex model have become
one of the most popular tools for interpreting predictions. In this paper, we
study such linear explanations produced either post-hoc by a few recent methods
or generated along with predictions with contextual explanation networks
(CENs). We ... | computer science |
5,068 | Personalized Survival Prediction with Contextual Explanation Networks | cs.LG | Accurate and transparent prediction of cancer survival times on the level of
individual patients can inform and improve patient care and treatment
practices. In this paper, we design a model that concurrently learns to
accurately predict patient-specific survival distributions and to explain its
predictions in terms of... | computer science |
5,069 | Elements of Effective Deep Reinforcement Learning towards Tactical
Driving Decision Making | cs.AI | Tactical driving decision making is crucial for autonomous driving systems
and has attracted considerable interest in recent years. In this paper, we
propose several practical components that can speed up deep reinforcement
learning algorithms towards tactical decision making tasks: 1) non-uniform
action skipping as a ... | computer science |
5,070 | Memory Fusion Network for Multi-view Sequential Learning | cs.LG | Multi-view sequential learning is a fundamental problem in machine learning
dealing with multi-view sequences. In a multi-view sequence, there exists two
forms of interactions between different views: view-specific interactions and
cross-view interactions. In this paper, we present a new neural architecture
for multi-v... | computer science |
5,071 | IMPALA: Scalable Distributed Deep-RL with Importance Weighted
Actor-Learner Architectures | cs.LG | In this work we aim to solve a large collection of tasks using a single
reinforcement learning agent with a single set of parameters. A key challenge
is to handle the increased amount of data and extended training time. We have
developed a new distributed agent IMPALA (Importance Weighted Actor-Learner
Architecture) th... | computer science |
5,072 | Utility Decomposition with Deep Corrections for Scalable Planning under
Uncertainty | cs.LG | Decomposition methods have been proposed in the past to approximate solutions
to large sequential decision making problems. In contexts where an agent
interacts with multiple entities, utility decomposition can be used where each
individual entity is considered independently. The individual utility functions
are then c... | computer science |
5,073 | Neural Dynamic Programming for Musical Self Similarity | cs.AI | We present a neural sequence model designed specifically for symbolic music.
The model is based on a learned edit distance mechanism which generalises a
classic recursion from computer science, leading to a neural dynamic program.
Repeated motifs are detected by learning the transformations between them. We
represent t... | computer science |
5,074 | A note on reinforcement learning with Wasserstein distance
regularisation, with applications to multipolicy learning | cs.LG | In this note we describe an application of Wasserstein distance to
Reinforcement Learning. The Wasserstein distance in question is between the
distribution of mappings of trajectories of a policy into some metric space,
and some other fixed distribution (which may, for example, come from another
policy). Different poli... | computer science |
5,075 | Evolved Policy Gradients | cs.LG | We propose a meta-learning approach for learning gradient-based reinforcement
learning (RL) algorithms. The idea is to evolve a differentiable loss function,
such that an agent, which optimizes its policy to minimize this loss, will
achieve high rewards. The loss is parametrized via temporal convolutions over
the agent... | computer science |
5,076 | Probabilistic Warnings in National Security Crises: Pearl Harbor
Revisited | cs.AI | Imagine a situation where a group of adversaries is preparing an attack on
the United States or U.S. interests. An intelligence analyst has observed some
signals, but the situation is rapidly changing. The analyst faces the decision
to alert a principal decision maker that an attack is imminent, or to wait
until more i... | computer science |
5,077 | Reactive Reinforcement Learning in Asynchronous Environments | cs.AI | The relationship between a reinforcement learning (RL) agent and an
asynchronous environment is often ignored. Frequently used models of the
interaction between an agent and its environment, such as Markov Decision
Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the
fact that, in an asynchronou... | computer science |
5,078 | A Deep Q-Learning Agent for the L-Game with Variable Batch Training | cs.LG | We employ the Deep Q-Learning algorithm with Experience Replay to train an
agent capable of achieving a high-level of play in the L-Game while
self-learning from low-dimensional states. We also employ variable batch size
for training in order to mitigate the loss of the rare reward signal and
significantly accelerate t... | computer science |
5,079 | Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance
Loss | cs.LG | Hierarchical classification is supervised multi-class classification problem
over the set of class labels organized according to a hierarchy. In this
report, we study the work by Ramaswamy et. al. on hierarchical classification
over symmetric tree distance loss. We extend the consistency of hierarchical
classification ... | computer science |
5,080 | Fourier Policy Gradients | cs.LG | We propose a new way of deriving policy gradient updates for reinforcement
learning. Our technique, based on Fourier analysis, recasts integrals that
arise with expected policy gradients as convolutions and turns them into
multiplications. The obtained analytical solutions allow us to capture the low
variance benefits ... | computer science |
5,081 | L2-Nonexpansive Neural Networks | cs.AI | This paper proposes a class of well-conditioned neural networks in which a
unit amount of change in the inputs causes at most a unit amount of change in
the outputs or any of the internal layers. We develop the known methodology of
controlling Lipschitz constants to realize its full potential in maximizing
robustness: ... | computer science |
5,082 | Modeling Others using Oneself in Multi-Agent Reinforcement Learning | cs.AI | We consider the multi-agent reinforcement learning setting with imperfect
information in which each agent is trying to maximize its own utility. The
reward function depends on the hidden state (or goal) of both agents, so the
agents must infer the other players' hidden goals from their observed behavior
in order to sol... | computer science |
5,083 | Investigating Human Priors for Playing Video Games | cs.AI | What makes humans so good at solving seemingly complex video games? Unlike
computers, humans bring in a great deal of prior knowledge about the world,
enabling efficient decision making. This paper investigates the role of human
priors for solving video games. Given a sample game, we conduct a series of
ablation studie... | computer science |
5,084 | Computational Theories of Curiosity-Driven Learning | cs.AI | What are the functions of curiosity? What are the mechanisms of
curiosity-driven learning? We approach these questions using concepts and tools
from machine learning and developmental robotics. We argue that
curiosity-driven learning enables organisms to make discoveries to solve
complex problems with rare or deceptive... | computer science |
5,085 | Separators and Adjustment Sets in Causal Graphs: Complete Criteria and
an Algorithmic Framework | cs.AI | Principled reasoning about the identifiability of causal effects from
non-experimental data is an important application of graphical causal models.
We present an algorithmic framework for efficiently testing, constructing, and
enumerating $m$-separators in ancestral graphs (AGs), a class of graphical
causal models that... | computer science |
5,086 | Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal
Exploration | cs.LG | Intrinsically motivated goal exploration algorithms enable machines to
discover repertoires of policies that produce a diversity of effects in complex
environments. These exploration algorithms have been shown to allow real world
robots to acquire skills such as tool use in high-dimensional continuous state
and action ... | computer science |
5,087 | SAFE: Spectral Evolution Analysis Feature Extraction for Non-Stationary
Time Series Prediction | cs.LG | This paper presents a practical approach for detecting non-stationarity in
time series prediction. This method is called SAFE and works by monitoring the
evolution of the spectral contents of time series through a distance function.
This method is designed to work in combination with state-of-the-art machine
learning m... | computer science |
5,088 | N-body Networks: a Covariant Hierarchical Neural Network Architecture
for Learning Atomic Potentials | cs.LG | We describe N-body networks, a neural network architecture for learning the
behavior and properties of complex many body physical systems. Our specific
application is to learn atomic potential energy surfaces for use in molecular
dynamics simulations. Our architecture is novel in that (a) it is based on a
hierarchical ... | computer science |
5,089 | Smoothed Action Value Functions for Learning Gaussian Policies | cs.LG | State-action value functions (i.e., Q-values) are ubiquitous in reinforcement
learning (RL), giving rise to popular algorithms such as SARSA and Q-learning.
We propose a new notion of action value defined by a Gaussian smoothed version
of the expected Q-value. We show that such smoothed Q-values still satisfy a
Bellman... | computer science |
5,090 | Satisficing in Time-Sensitive Bandit Learning | cs.LG | Much of the recent literature on bandit learning focuses on algorithms that
aim to converge on an optimal action. One shortcoming is that this orientation
does not account for time sensitivity, which can play a crucial role when
learning an optimal action requires much more information than near-optimal
ones. Indeed, p... | computer science |
5,091 | A New Model for Evaluating Range-Based Anomaly Detection Algorithms | cs.LG | Classical anomaly detection (AD) is principally concerned with point-based
anomalies, anomalies that occur at a single point in time. While point-based
anomalies are useful, many real-world anomalies are range-based, meaning they
occur over a period of time. Therefore, applying classical point-based accuracy
measures t... | computer science |
5,092 | Learning to Explore with Meta-Policy Gradient | cs.LG | The performance of off-policy learning, including deep Q-learning and deep
deterministic policy gradient (DDPG), critically depends on the choice of the
exploration policy. Existing exploration methods are mostly based on adding
noise to the on-going actor policy and can only explore \emph{local} regions
close to what ... | computer science |
5,093 | Learning to Play General Video-Games via an Object Embedding Network | cs.LG | Deep reinforcement learning (DRL) has proven to be an effective tool for
creating general video-game AI. However most current DRL video-game agents
learn end-to-end from the video-output of the game, which is superfluous for
many applications and creates a number of additional problems. More
importantly, directly worki... | computer science |
5,094 | PAC-Reasoning in Relational Domains | cs.AI | We consider the problem of predicting plausible missing facts in relational
data, given a set of imperfect logical rules. In particular, our aim is to
provide bounds on the (expected) number of incorrect inferences that are made
in this way. Since for classical inference it is in general impossible to bound
this number... | computer science |
5,095 | Structural Health Monitoring Using Neural Network Based Vibrational
System Identification | cs.NE | Composite fabrication technologies now provide the means for producing
high-strength, low-weight panels, plates, spars and other structural components
which use embedded fiber optic sensors and piezoelectric transducers. These
materials, often referred to as smart structures, make it possible to sense
internal characte... | computer science |
5,096 | Generalized Discriminant Analysis algorithm for feature reduction in
Cyber Attack Detection System | cs.CR | This Generalized Discriminant Analysis (GDA) has provided an extremely
powerful approach to extracting non linear features. The network traffic data
provided for the design of intrusion detection system always are large with
ineffective information, thus we need to remove the worthless information from
the original hig... | computer science |
5,097 | Geometric operations implemented by conformal geometric algebra neural
nodes | cs.CV | Geometric algebra is an optimal frame work for calculating with vectors. The
geometric algebra of a space includes elements that represent all the its
subspaces (lines, planes, volumes, ...). Conformal geometric algebra expands
this approach to elementary representations of arbitrary points, point pairs,
lines, circles... | computer science |
5,098 | Non-constant bounded holomorphic functions of hyperbolic numbers -
Candidates for hyperbolic activation functions | cs.NE | The Liouville theorem states that bounded holomorphic complex functions are
necessarily constant. Holomorphic functions fulfill the socalled Cauchy-Riemann
(CR) conditions. The CR conditions mean that a complex $z$-derivative is
independent of the direction. Holomorphic functions are ideal for activation
functions of c... | computer science |
5,099 | Learning Features and their Transformations by Spatial and Temporal
Spherical Clustering | cs.NE | Learning features invariant to arbitrary transformations in the data is a
requirement for any recognition system, biological or artificial. It is now
widely accepted that simple cells in the primary visual cortex respond to
features while the complex cells respond to features invariant to different
transformations. We ... | computer science |
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