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4,900 | Associative Adversarial Networks | cs.LG | We propose a higher-level associative memory for learning adversarial
networks. Generative adversarial network (GAN) framework has a discriminator
and a generator network. The generator (G) maps white noise (z) to data samples
while the discriminator (D) maps data samples to a single scalar. To do so, G
learns how to m... | computer science |
4,901 | Variational Intrinsic Control | cs.LG | In this paper we introduce a new unsupervised reinforcement learning method
for discovering the set of intrinsic options available to an agent. This set is
learned by maximizing the number of different states an agent can reliably
reach, as measured by the mutual information between the set of options and
option termin... | computer science |
4,902 | Multiscale Inverse Reinforcement Learning using Diffusion Wavelets | cs.LG | This work presents a multiscale framework to solve an inverse reinforcement
learning (IRL) problem for continuous-time/state stochastic systems. We take
advantage of a diffusion wavelet representation of the associated Markov chain
to abstract the state space. This not only allows for effectively handling the
large (an... | computer science |
4,903 | Dynamic Key-Value Memory Networks for Knowledge Tracing | cs.AI | Knowledge Tracing (KT) is a task of tracing evolving knowledge state of
students with respect to one or more concepts as they engage in a sequence of
learning activities. One important purpose of KT is to personalize the practice
sequence to help students learn knowledge concepts efficiently. However,
existing methods ... | computer science |
4,904 | Improving Policy Gradient by Exploring Under-appreciated Rewards | cs.LG | This paper presents a novel form of policy gradient for model-free
reinforcement learning (RL) with improved exploration properties. Current
policy-based methods use entropy regularization to encourage undirected
exploration of the reward landscape, which is ineffective in high dimensional
spaces with sparse rewards. W... | computer science |
4,905 | C-RNN-GAN: Continuous recurrent neural networks with adversarial
training | cs.AI | Generative adversarial networks have been proposed as a way of efficiently
training deep generative neural networks. We propose a generative adversarial
model that works on continuous sequential data, and apply it by training it on
a collection of classical music. We conclude that it generates music that
sounds better ... | computer science |
4,906 | Unit Commitment using Nearest Neighbor as a Short-Term Proxy | cs.LG | We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be used as
a proxy for quickly approximating outcomes of short-term decisions, to make
tractable hierarchical long-term assessment and planning for large power
systems. Experimental results on updated versions of IEEE-RTS79 and IEEE-RTS96
show high accu... | computer science |
4,907 | The observer-assisted method for adjusting hyper-parameters in deep
learning algorithms | cs.LG | This paper presents a concept of a novel method for adjusting
hyper-parameters in Deep Learning (DL) algorithms. An external agent-observer
monitors a performance of a selected Deep Learning algorithm. The observer
learns to model the DL algorithm using a series of random experiments.
Consequently, it may be used for p... | computer science |
4,908 | Reinforcement Learning via Recurrent Convolutional Neural Networks | cs.LG | Deep Reinforcement Learning has enabled the learning of policies for complex
tasks in partially observable environments, without explicitly learning the
underlying model of the tasks. While such model-free methods achieve
considerable performance, they often ignore the structure of task. We present a
natural representa... | computer science |
4,909 | Agent-Agnostic Human-in-the-Loop Reinforcement Learning | cs.LG | Providing Reinforcement Learning agents with expert advice can dramatically
improve various aspects of learning. Prior work has developed teaching
protocols that enable agents to learn efficiently in complex environments; many
of these methods tailor the teacher's guidance to agents with a particular
representation or ... | computer science |
4,910 | Near Optimal Behavior via Approximate State Abstraction | cs.LG | The combinatorial explosion that plagues planning and reinforcement learning
(RL) algorithms can be moderated using state abstraction. Prohibitively large
task representations can be condensed such that essential information is
preserved, and consequently, solutions are tractably computable. However, exact
abstractions... | computer science |
4,911 | Vulnerability of Deep Reinforcement Learning to Policy Induction Attacks | cs.LG | Deep learning classifiers are known to be inherently vulnerable to
manipulation by intentionally perturbed inputs, named adversarial examples. In
this work, we establish that reinforcement learning techniques based on Deep
Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and
verify the transfer... | computer science |
4,912 | Thompson Sampling For Stochastic Bandits with Graph Feedback | cs.LG | We present a novel extension of Thompson Sampling for stochastic sequential
decision problems with graph feedback, even when the graph structure itself is
unknown and/or changing. We provide theoretical guarantees on the Bayesian
regret of the algorithm, linking its performance to the underlying properties
of the graph... | computer science |
4,913 | Efficient Rank Aggregation via Lehmer Codes | cs.LG | We propose a novel rank aggregation method based on converting permutations
into their corresponding Lehmer codes or other subdiagonal images. Lehmer
codes, also known as inversion vectors, are vector representations of
permutations in which each coordinate can take values not restricted by the
values of other coordina... | computer science |
4,914 | Similarity Preserving Representation Learning for Time Series Analysis | cs.AI | A considerable amount of machine learning algorithms take instance-feature
matrices as their inputs. As such, they cannot directly analyze time series
data due to its temporal nature, usually unequal lengths, and complex
properties. This is a great pity since many of these algorithms are effective,
robust, efficient, a... | computer science |
4,915 | Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses? | cs.LG | Non-technical losses (NTL) occur during the distribution of electricity in
power grids and include, but are not limited to, electricity theft and faulty
meters. In emerging countries, they may range up to 40% of the total
electricity distributed. In order to detect NTLs, machine learning methods are
used that learn irr... | computer science |
4,916 | Efficient Multi-task Feature and Relationship Learning | cs.LG | In this paper we propose a multi-convex framework for multi-task learning
that improves predictions by learning relationships both between tasks and
between features. Our framework is a generalization of related methods in
multi-task learning, that either learn task relationships, or feature
relationships, but not both... | computer science |
4,917 | On the Discrepancy Between Kleinberg's Clustering Axioms and $k$-Means
Clustering Algorithm Behavior | cs.LG | This paper investigates the validity of Kleinberg's axioms for clustering
functions with respect to the quite popular clustering algorithm called
$k$-means. While Kleinberg's axioms have been discussed heavily in the past, we
concentrate here on the case predominantly relevant for $k$-means algorithm,
that is behavior ... | computer science |
4,918 | A Spacetime Approach to Generalized Cognitive Reasoning in Multi-scale
Learning | cs.AI | In modern machine learning, pattern recognition replaces realtime semantic
reasoning. The mapping from input to output is learned with fixed semantics by
training outcomes deliberately. This is an expensive and static approach which
depends heavily on the availability of a very particular kind of prior raining
data to ... | computer science |
4,919 | Linear Time Computation of Moments in Sum-Product Networks | cs.LG | Bayesian online algorithms for Sum-Product Networks (SPNs) need to update
their posterior distribution after seeing one single additional instance. To do
so, they must compute moments of the model parameters under this distribution.
The best existing method for computing such moments scales quadratically in the
size of... | computer science |
4,920 | Beating the World's Best at Super Smash Bros. with Deep Reinforcement
Learning | cs.LG | There has been a recent explosion in the capabilities of game-playing
artificial intelligence. Many classes of RL tasks, from Atari games to motor
control to board games, are now solvable by fairly generic algorithms, based on
deep learning, that learn to play from experience with minimal knowledge of the
specific doma... | computer science |
4,921 | Sample Efficient Policy Search for Optimal Stopping Domains | cs.AI | Optimal stopping problems consider the question of deciding when to stop an
observation-generating process in order to maximize a return. We examine the
problem of simultaneously learning and planning in such domains, when data is
collected directly from the environment. We propose GFSE, a simple and flexible
model-fre... | computer science |
4,922 | Causal Inference by Stochastic Complexity | cs.LG | The algorithmic Markov condition states that the most likely causal direction
between two random variables X and Y can be identified as that direction with
the lowest Kolmogorov complexity. Due to the halting problem, however, this
notion is not computable.
We hence propose to do causal inference by stochastic comple... | computer science |
4,923 | Criticality & Deep Learning I: Generally Weighted Nets | cs.AI | Motivated by the idea that criticality and universality of phase transitions
might play a crucial role in achieving and sustaining learning and intelligent
behaviour in biological and artificial networks, we analyse a theoretical and a
pragmatic experimental set up for critical phenomena in deep learning. On the
theore... | computer science |
4,924 | Reinforcement Learning with Deep Energy-Based Policies | cs.LG | We propose a method for learning expressive energy-based policies for
continuous states and actions, which has been feasible only in tabular domains
before. We apply our method to learning maximum entropy policies, resulting
into a new algorithm, called soft Q-learning, that expresses the optimal policy
via a Boltzmann... | computer science |
4,925 | PMLB: A Large Benchmark Suite for Machine Learning Evaluation and
Comparison | cs.LG | The selection, development, or comparison of machine learning methods in data
mining can be a difficult task based on the target problem and goals of a
particular study. Numerous publicly available real-world and simulated
benchmark datasets have emerged from different sources, but their organization
and adoption as st... | computer science |
4,926 | A Laplacian Framework for Option Discovery in Reinforcement Learning | cs.LG | Representation learning and option discovery are two of the biggest
challenges in reinforcement learning (RL). Proto-value functions (PVFs) are a
well-known approach for representation learning in MDPs. In this paper we
address the option discovery problem by showing how PVFs implicitly define
options. We do it by intr... | computer science |
4,927 | Stochastic Separation Theorems | cs.LG | The problem of non-iterative one-shot and non-destructive correction of
unavoidable mistakes arises in all Artificial Intelligence applications in the
real world. Its solution requires robust separation of samples with errors from
samples where the system works properly. We demonstrate that in (moderately)
high dimensi... | computer science |
4,928 | Multi-step Reinforcement Learning: A Unifying Algorithm | cs.AI | Unifying seemingly disparate algorithmic ideas to produce better performing
algorithms has been a longstanding goal in reinforcement learning. As a primary
example, TD($\lambda$) elegantly unifies one-step TD prediction with Monte
Carlo methods through the use of eligibility traces and the trace-decay
parameter $\lambd... | computer science |
4,929 | Memory Enriched Big Bang Big Crunch Optimization Algorithm for Data
Clustering | cs.AI | Cluster analysis plays an important role in decision making process for many
knowledge-based systems. There exist a wide variety of different approaches for
clustering applications including the heuristic techniques, probabilistic
models, and traditional hierarchical algorithms. In this paper, a novel
heuristic approac... | computer science |
4,930 | Combining Bayesian Approaches and Evolutionary Techniques for the
Inference of Breast Cancer Networks | cs.LG | Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated ... | computer science |
4,931 | Learning the Probabilistic Structure of Cumulative Phenomena with
Suppes-Bayes Causal Networks | cs.LG | One of the critical issues when adopting Bayesian networks (BNs) to model
dependencies among random variables is to "learn" their structure, given the
huge search space of possible solutions, i.e., all the possible direct acyclic
graphs. This is a well-known NP-hard problem, which is also complicated by
known pitfalls ... | computer science |
4,932 | Learning Gradient Descent: Better Generalization and Longer Horizons | cs.LG | Training deep neural networks is a highly nontrivial task, involving
carefully selecting appropriate training algorithms, scheduling step sizes and
tuning other hyperparameters. Trying different combinations can be quite
labor-intensive and time consuming. Recently, researchers have tried to use
deep learning algorithm... | computer science |
4,933 | Task-based End-to-end Model Learning in Stochastic Optimization | cs.LG | With the increasing popularity of machine learning techniques, it has become
common to see prediction algorithms operating within some larger process.
However, the criteria by which we train these algorithms often differ from the
ultimate criteria on which we evaluate them. This paper proposes an end-to-end
approach fo... | computer science |
4,934 | Particle Value Functions | cs.LG | The policy gradients of the expected return objective can react slowly to
rare rewards. Yet, in some cases agents may wish to emphasize the low or high
returns regardless of their probability. Borrowing from the economics and
control literature, we review the risk-sensitive value function that arises
from an exponentia... | computer science |
4,935 | Overcoming Catastrophic Forgetting by Incremental Moment Matching | cs.LG | Catastrophic forgetting is a problem of neural networks that loses the
information of the first task after training the second task. Here, we propose
a method, i.e. incremental moment matching (IMM), to resolve this problem. IMM
incrementally matches the moment of the posterior distribution of the neural
network which ... | computer science |
4,936 | Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level
Coordination in Learning to Play StarCraft Combat Games | cs.AI | Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to co... | computer science |
4,937 | Enter the Matrix: A Virtual World Approach to Safely Interruptable
Autonomous Systems | cs.AI | Robots and autonomous systems that operate around humans will likely always
rely on kill switches that stop their execution and allow them to be
remote-controlled for the safety of humans or to prevent damage to the system.
It is theoretically possible for an autonomous system with sufficient sensor
and effector capabi... | computer science |
4,938 | Multi-Label Learning with Global and Local Label Correlation | cs.LG | It is well-known that exploiting label correlations is important to
multi-label learning. Existing approaches either assume that the label
correlations are global and shared by all instances; or that the label
correlations are local and shared only by a data subset. In fact, in the
real-world applications, both cases m... | computer science |
4,939 | Deep Q-learning from Demonstrations | cs.AI | Deep reinforcement learning (RL) has achieved several high profile successes
in difficult decision-making problems. However, these algorithms typically
require a huge amount of data before they reach reasonable performance. In
fact, their performance during learning can be extremely poor. This may be
acceptable for a s... | computer science |
4,940 | Deep API Programmer: Learning to Program with APIs | cs.AI | We present DAPIP, a Programming-By-Example system that learns to program with
APIs to perform data transformation tasks. We design a domain-specific language
(DSL) that allows for arbitrary concatenations of API outputs and constant
strings. The DSL consists of three family of APIs: regular expression-based
APIs, looku... | computer science |
4,941 | Effective Warm Start for the Online Actor-Critic Reinforcement Learning
based mHealth Intervention | cs.LG | Online reinforcement learning (RL) is increasingly popular for the
personalized mobile health (mHealth) intervention. It is able to personalize
the type and dose of interventions according to user's ongoing statuses and
changing needs. However, at the beginning of online learning, there are usually
too few samples to s... | computer science |
4,942 | Investigating Recurrence and Eligibility Traces in Deep Q-Networks | cs.AI | Eligibility traces in reinforcement learning are used as a bias-variance
trade-off and can often speed up training time by propagating knowledge back
over time-steps in a single update. We investigate the use of eligibility
traces in combination with recurrent networks in the Atari domain. We
illustrate the benefits of... | computer science |
4,943 | Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces | cs.AI | Graph models are relevant in many fields, such as distributed computing,
intelligent tutoring systems or social network analysis. In many cases, such
models need to take changes in the graph structure into account, i.e. a varying
number of nodes or edges. Predicting such changes within graphs can be expected
to yield i... | computer science |
4,944 | Leveraging Patient Similarity and Time Series Data in Healthcare
Predictive Models | cs.AI | Patient time series classification faces challenges in high degrees of
dimensionality and missingness. In light of patient similarity theory, this
study explores effective temporal feature engineering and reduction, missing
value imputation, and change point detection methods that can afford
similarity-based classifica... | computer science |
4,945 | PPMF: A Patient-based Predictive Modeling Framework for Early ICU
Mortality Prediction | cs.LG | To date, developing a good model for early intensive care unit (ICU)
mortality prediction is still challenging. This paper presents a patient based
predictive modeling framework (PPMF) to improve the performance of ICU
mortality prediction using data collected during the first 48 hours of ICU
admission. PPMF consists o... | computer science |
4,946 | Towards well-specified semi-supervised model-based classifiers via
structural adaptation | cs.LG | Semi-supervised learning plays an important role in large-scale machine
learning. Properly using additional unlabeled data (largely available nowadays)
often can improve the machine learning accuracy. However, if the machine
learning model is misspecified for the underlying true data distribution, the
model performance... | computer science |
4,947 | Analyzing Knowledge Transfer in Deep Q-Networks for Autonomously
Handling Multiple Intersections | cs.LG | We analyze how the knowledge to autonomously handle one type of intersection,
represented as a Deep Q-Network, translates to other types of intersections
(tasks). We view intersection handling as a deep reinforcement learning
problem, which approximates the state action Q function as a deep neural
network. Using a traf... | computer science |
4,948 | Lifelong Metric Learning | cs.LG | The state-of-the-art online learning approaches are only capable of learning
the metric for predefined tasks. In this paper, we consider lifelong learning
problem to mimic "human learning", i.e., endowing a new capability to the
learned metric for a new task from new online samples and incorporating
previous experience... | computer science |
4,949 | Metacontrol for Adaptive Imagination-Based Optimization | cs.LG | Many machine learning systems are built to solve the hardest examples of a
particular task, which often makes them large and expensive to run---especially
with respect to the easier examples, which might require much less computation.
For an agent with a limited computational budget, this "one-size-fits-all"
approach m... | computer science |
4,950 | A First Empirical Study of Emphatic Temporal Difference Learning | cs.AI | In this paper we present the first empirical study of the emphatic
temporal-difference learning algorithm (ETD), comparing it with conventional
temporal-difference learning, in particular, with linear TD(0), on on-policy
and off-policy variations of the Mountain Car problem. The initial motivation
for developing ETD wa... | computer science |
4,951 | Repeated Inverse Reinforcement Learning | cs.AI | We introduce a novel repeated Inverse Reinforcement Learning problem: the
agent has to act on behalf of a human in a sequence of tasks and wishes to
minimize the number of tasks that it surprises the human by acting suboptimally
with respect to how the human would have acted. Each time the human is
surprised, the agent... | computer science |
4,952 | Evolving Ensemble Fuzzy Classifier | cs.LG | The concept of ensemble learning offers a promising avenue in learning from
data streams under complex environments because it addresses the bias and
variance dilemma better than its single model counterpart and features a
reconfigurable structure, which is well suited to the given context. While
various extensions of ... | computer science |
4,953 | Online learnability of Statistical Relational Learning in anomaly
detection | cs.LG | Statistical Relational Learning (SRL) methods for anomaly detection are
introduced via a security-related application. Operational requirements for
online learning stability are outlined and compared to mathematical definitions
as applied to the learning process of a representative SRL method - Bayesian
Logic Programs ... | computer science |
4,954 | Feature Control as Intrinsic Motivation for Hierarchical Reinforcement
Learning | cs.LG | The problem of sparse rewards is one of the hardest challenges in
contemporary reinforcement learning. Hierarchical reinforcement learning (HRL)
tackles this problem by using a set of temporally-extended actions, or options,
each of which has its own subgoal. These subgoals are normally handcrafted for
specific tasks. ... | computer science |
4,955 | Learning to Factor Policies and Action-Value Functions: Factored Action
Space Representations for Deep Reinforcement learning | cs.LG | Deep Reinforcement Learning (DRL) methods have performed well in an
increasing numbering of high-dimensional visual decision making domains. Among
all such visual decision making problems, those with discrete action spaces
often tend to have underlying compositional structure in the said action space.
Such action space... | computer science |
4,956 | Learning to Mix n-Step Returns: Generalizing lambda-Returns for Deep
Reinforcement Learning | cs.LG | Reinforcement Learning (RL) can model complex behavior policies for
goal-directed sequential decision making tasks. A hallmark of RL algorithms is
Temporal Difference (TD) learning: value function for the current state is
moved towards a bootstrapped target that is estimated using next state's value
function. $\lambda$... | computer science |
4,957 | Selective Classification for Deep Neural Networks | cs.LG | Selective classification techniques (also known as reject option) have not
yet been considered in the context of deep neural networks (DNNs). These
techniques can potentially significantly improve DNNs prediction performance by
trading-off coverage. In this paper we propose a method to construct a
selective classifier ... | computer science |
4,958 | Principled Hybrids of Generative and Discriminative Domain Adaptation | cs.LG | We propose a probabilistic framework for domain adaptation that blends both
generative and discriminative modeling in a principled way. Under this
framework, generative and discriminative models correspond to specific choices
of the prior over parameters. This provides us a very general way to
interpolate between gener... | computer science |
4,959 | Human Trajectory Prediction using Spatially aware Deep Attention Models | cs.LG | Trajectory Prediction of dynamic objects is a widely studied topic in the
field of artificial intelligence. Thanks to a large number of applications like
predicting abnormal events, navigation system for the blind, etc. there have
been many approaches to attempt learning patterns of motion directly from data
using a wi... | computer science |
4,960 | Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation | cs.AI | Online music services are increasing in popularity. They enable us to analyze
people's music listening behavior based on play logs. Although it is known that
people listen to music based on topic (e.g., rock or jazz), we assume that when
a user is addicted to an artist, s/he chooses the artist's songs regardless of
top... | computer science |
4,961 | Good Semi-supervised Learning that Requires a Bad GAN | cs.LG | Semi-supervised learning methods based on generative adversarial networks
(GANs) obtained strong empirical results, but it is not clear 1) how the
discriminator benefits from joint training with a generator, and 2) why good
semi-supervised classification performance and a good generator cannot be
obtained at the same t... | computer science |
4,962 | Bayesian Unification of Gradient and Bandit-based Learning for
Accelerated Global Optimisation | cs.AI | Bandit based optimisation has a remarkable advantage over gradient based
approaches due to their global perspective, which eliminates the danger of
getting stuck at local optima. However, for continuous optimisation problems or
problems with a large number of actions, bandit based approaches can be
hindered by slow lea... | computer science |
4,963 | Deep Learning for Ontology Reasoning | cs.AI | In this work, we present a novel approach to ontology reasoning that is based
on deep learning rather than logic-based formal reasoning. To this end, we
introduce a new model for statistical relational learning that is built upon
deep recursive neural networks, and give experimental evidence that it can
easily compete ... | computer science |
4,964 | Multi-Labelled Value Networks for Computer Go | cs.AI | This paper proposes a new approach to a novel value network architecture for
the game Go, called a multi-labelled (ML) value network. In the ML value
network, different values (win rates) are trained simultaneously for different
settings of komi, a compensation given to balance the initiative of playing
first. The ML v... | computer science |
4,965 | Knowledge Base Completion: Baselines Strike Back | cs.LG | Many papers have been published on the knowledge base completion task in the
past few years. Most of these introduce novel architectures for relation
learning that are evaluated on standard datasets such as FB15k and WN18. This
paper shows that the accuracy of almost all models published on the FB15k can
be outperforme... | computer science |
4,966 | Semi-Supervised Learning for Detecting Human Trafficking | cs.LG | Human trafficking is one of the most atrocious crimes and among the
challenging problems facing law enforcement which demands attention of global
magnitude. In this study, we leverage textual data from the website "Backpage"-
used for classified advertisement- to discern potential patterns of human
trafficking activiti... | computer science |
4,967 | Generalized Value Iteration Networks: Life Beyond Lattices | cs.LG | In this paper, we introduce a generalized value iteration network (GVIN),
which is an end-to-end neural network planning module. GVIN emulates the value
iteration algorithm by using a novel graph convolution operator, which enables
GVIN to learn and plan on irregular spatial graphs. We propose three novel
differentiabl... | computer science |
4,968 | Setting Players' Behaviors in World of Warcraft through Semi-Supervised
Learning | cs.AI | Digital games are one of the major and most important fields on the
entertainment domain, which also involves cinema and music. Numerous attempts
have been done to improve the quality of the games including more realistic
artistic production and computer science. Assessing the player's behavior, a
task known as player ... | computer science |
4,969 | ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with
Deep Multi-agent Reinforcement Learning | cs.AI | Communication is a critical factor for the big multi-agent world to stay
organized and productive. Typically, most previous multi-agent
"learning-to-communicate" studies try to predefine the communication protocols
or use technologies such as tabular reinforcement learning and evolutionary
algorithm, which can not gene... | computer science |
4,970 | Target Curricula via Selection of Minimum Feature Sets: a Case Study in
Boolean Networks | cs.AI | We consider the effect of introducing a curriculum of targets when training
Boolean models on supervised Multi Label Classification (MLC) problems. In
particular, we consider how to order targets in the absence of prior knowledge,
and how such a curriculum may be enforced when using meta-heuristics to train
discrete no... | computer science |
4,971 | Device Placement Optimization with Reinforcement Learning | cs.LG | The past few years have witnessed a growth in size and computational
requirements for training and inference with neural networks. Currently, a
common approach to address these requirements is to use a heterogeneous
distributed environment with a mixture of hardware devices such as CPUs and
GPUs. Importantly, the decis... | computer science |
4,972 | Zero-Shot Task Generalization with Multi-Task Deep Reinforcement
Learning | cs.AI | As a step towards developing zero-shot task generalization capabilities in
reinforcement learning (RL), we introduce a new RL problem where the agent
should learn to execute sequences of instructions after learning useful skills
that solve subtasks. In this problem, we consider two types of generalizations:
to previous... | computer science |
4,973 | Structured Best Arm Identification with Fixed Confidence | cs.LG | We study the problem of identifying the best action among a set of possible
options when the value of each action is given by a mapping from a number of
noisy micro-observables in the so-called fixed confidence setting. Our main
motivation is the application to the minimax game search, which has been a
major topic of i... | computer science |
4,974 | Learning Hierarchical Information Flow with Recurrent Neural Modules | cs.LG | We propose ThalNet, a deep learning model inspired by neocortical
communication via the thalamus. Our model consists of recurrent neural modules
that send features through a routing center, endowing the modules with the
flexibility to share features over multiple time steps. We show that our model
learns to route infor... | computer science |
4,975 | VAIN: Attentional Multi-agent Predictive Modeling | cs.LG | Multi-agent predictive modeling is an essential step for understanding
physical, social and team-play systems. Recently, Interaction Networks (INs)
were proposed for the task of modeling multi-agent physical systems, INs scale
with the number of interactions in the system (typically quadratic or higher
order in the num... | computer science |
4,976 | Policy Gradient Methods for Reinforcement Learning with Function
Approximation and Action-Dependent Baselines | cs.AI | We show how an action-dependent baseline can be used by the policy gradient
theorem using function approximation, originally presented with
action-independent baselines by (Sutton et al. 2000). | computer science |
4,977 | Gradient Episodic Memory for Continual Learning | cs.LG | One major obstacle towards AI is the poor ability of models to solve new
problems quicker, and without forgetting previously acquired knowledge. To
better understand this issue, we study the problem of continual learning, where
the model observes, once and one by one, examples concerning a sequence of
tasks. First, we ... | computer science |
4,978 | Providing Effective Real-time Feedback in Simulation-based Surgical
Training | cs.AI | Virtual reality simulation is becoming popular as a training platform in
surgical education. However, one important aspect of simulation-based surgical
training that has not received much attention is the provision of automated
real-time performance feedback to support the learning process. Performance
feedback is acti... | computer science |
4,979 | Bridging the Gap between Probabilistic and Deterministic Models: A
Simulation Study on a Variational Bayes Predictive Coding Recurrent Neural
Network Model | cs.AI | The current paper proposes a novel variational Bayes predictive coding RNN
model, which can learn to generate fluctuated temporal patterns from exemplars.
The model learns to maximize the lower bound of the weighted sum of the
regularization and reconstruction error terms. We examined how this weighting
can affect deve... | computer science |
4,980 | Teacher-Student Curriculum Learning | cs.LG | We propose Teacher-Student Curriculum Learning (TSCL), a framework for
automatic curriculum learning, where the Student tries to learn a complex task
and the Teacher automatically chooses subtasks from a given set for the Student
to train on. We describe a family of Teacher algorithms that rely on the
intuition that th... | computer science |
4,981 | Unsupervised Submodular Rank Aggregation on Score-based Permutations | cs.LG | Unsupervised rank aggregation on score-based permutations, which is widely
used in many applications, has not been deeply explored yet. This work studies
the use of submodular optimization for rank aggregation on score-based
permutations in an unsupervised way. Specifically, we propose an unsupervised
approach based on... | computer science |
4,982 | Towards an automated method based on Iterated Local Search optimization
for tuning the parameters of Support Vector Machines | cs.AI | We provide preliminary details and formulation of an optimization strategy
under current development that is able to automatically tune the parameters of
a Support Vector Machine over new datasets. The optimization strategy is a
heuristic based on Iterated Local Search, a modification of classic hill
climbing which ite... | computer science |
4,983 | Similarity Search Over Graphs Using Localized Spectral Analysis | cs.AI | This paper provides a new similarity detection algorithm. Given an input set
of multi-dimensional data points, where each data point is assumed to be
multi-dimensional, and an additional reference data point for similarity
finding, the algorithm uses kernel method that embeds the data points into a
low dimensional mani... | computer science |
4,984 | Value Prediction Network | cs.AI | This paper proposes a novel deep reinforcement learning (RL) architecture,
called Value Prediction Network (VPN), which integrates model-free and
model-based RL methods into a single neural network. In contrast to typical
model-based RL methods, VPN learns a dynamics model whose abstract states are
trained to make opti... | computer science |
4,985 | A Brief Study of In-Domain Transfer and Learning from Fewer Samples
using A Few Simple Priors | cs.AI | Domain knowledge can often be encoded in the structure of a network, such as
convolutional layers for vision, which has been shown to increase
generalization and decrease sample complexity, or the number of samples
required for successful learning. In this study, we ask whether sample
complexity can be reduced for syst... | computer science |
4,986 | Normalized Gradient with Adaptive Stepsize Method for Deep Neural
Network Training | cs.LG | In this paper, we propose a generic and simple algorithmic framework for
first order optimization. The framework essentially contains two consecutive
steps in each iteration: 1) computing and normalizing the mini-batch stochastic
gradient; 2) selecting adaptive step size to update the decision variable
(parameter) towa... | computer science |
4,987 | Efficient Architecture Search by Network Transformation | cs.LG | Techniques for automatically designing deep neural network architectures such
as reinforcement learning based approaches have recently shown promising
results. However, their success is based on vast computational resources (e.g.
hundreds of GPUs), making them difficult to be widely used. A noticeable
limitation is tha... | computer science |
4,988 | TensorLog: Deep Learning Meets Probabilistic DBs | cs.AI | We present an implementation of a probabilistic first-order logic called
TensorLog, in which classes of logical queries are compiled into differentiable
functions in a neural-network infrastructure such as Tensorflow or Theano. This
leads to a close integration of probabilistic logical reasoning with
deep-learning infr... | computer science |
4,989 | RAIL: Risk-Averse Imitation Learning | cs.LG | Imitation learning algorithms learn viable policies by imitating an expert's
behavior when reward signals are not available. Generative Adversarial
Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies
when the expert's behavior is available as a fixed set of trajectories. We
evaluate in terms... | computer science |
4,990 | Likelihood Estimation for Generative Adversarial Networks | cs.LG | We present a simple method for assessing the quality of generated images in
Generative Adversarial Networks (GANs). The method can be applied in any kind
of GAN without interfering with the learning procedure or affecting the
learning objective. The central idea is to define a likelihood function that
correlates with t... | computer science |
4,991 | A Survey on Multi-Task Learning | cs.LG | Multi-Task Learning (MTL) is a learning paradigm in machine learning and its
aim is to leverage useful information contained in multiple related tasks to
help improve the generalization performance of all the tasks. In this paper, we
give a survey for MTL. First, we classify different MTL algorithms into several
catego... | computer science |
4,992 | Identification of Probabilities | cs.LG | Within psychology, neuroscience and artificial intelligence, there has been
increasing interest in the proposal that the brain builds probabilistic models
of sensory and linguistic input: that is, to infer a probabilistic model from a
sample. The practical problems of such inference are substantial: the brain has
limit... | computer science |
4,993 | Intrinsically Motivated Goal Exploration Processes with Automatic
Curriculum Learning | cs.AI | Intrinsically motivated spontaneous exploration is a key enabler of
autonomous lifelong learning in human children. It allows them to discover and
acquire large repertoires of skills through self-generation, self-selection,
self-ordering and self-experimentation of learning goals. We present the
unsupervised multi-goal... | computer science |
4,994 | Distance and Similarity Measures Effect on the Performance of K-Nearest
Neighbor Classifier - A Review | cs.LG | The K-nearest neighbor (KNN) classifier is one of the simplest and most
common classifiers, yet its performance competes with the most complex
classifiers in the literature. The core of this classifier depends mainly on
measuring the distance or similarity between the tested example and the
training examples. This rais... | computer science |
4,995 | StarCraft II: A New Challenge for Reinforcement Learning | cs.LG | This paper introduces SC2LE (StarCraft II Learning Environment), a
reinforcement learning environment based on the StarCraft II game. This domain
poses a new grand challenge for reinforcement learning, representing a more
difficult class of problems than considered in most prior work. It is a
multi-agent problem with m... | computer science |
4,996 | Induction of Decision Trees based on Generalized Graph Queries | cs.LG | Usually, decision tree induction algorithms are limited to work with non
relational data. Given a record, they do not take into account other objects
attributes even though they can provide valuable information for the learning
task. In this paper we present GGQ-ID3, a multi-relational decision tree
learning algorithm ... | computer science |
4,997 | Reinforcement Learning in POMDPs with Memoryless Options and
Option-Observation Initiation Sets | cs.AI | Many real-world reinforcement learning problems have a hierarchical nature,
and often exhibit some degree of partial observability. While hierarchy and
partial observability are usually tackled separately (for instance by combining
recurrent neural networks and options), we show that addressing both problems
simultaneo... | computer science |
4,998 | Anytime Neural Network: a Versatile Trade-off Between Computation and
Accuracy | cs.LG | Anytime predictors first produce crude results quickly, and then continuously
refine them until the test-time computational budget is depleted. Such
predictors are used in real-time vision systems and streaming-data processing
to efficiently utilize varying test-time budgets, and to reduce average
prediction cost via e... | computer science |
4,999 | On Relaxing Determinism in Arithmetic Circuits | cs.AI | The past decade has seen a significant interest in learning tractable
probabilistic representations. Arithmetic circuits (ACs) were among the first
proposed tractable representations, with some subsequent representations being
instances of ACs with weaker or stronger properties. In this paper, we provide
a formal basis... | computer science |
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