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2,700 | Efficient Probabilistic Performance Bounds for Inverse Reinforcement
Learning | cs.AI | In the field of reinforcement learning there has been recent progress towards
safety and high-confidence bounds on policy performance. However, to our
knowledge, no practical methods exist for determining high-confidence policy
performance bounds in the inverse reinforcement learning setting---where the
true reward fun... | computer science |
2,701 | Kernel Feature Selection via Conditional Covariance Minimization | stat.ML | We propose a framework for feature selection that employs kernel-based
measures of independence to find a subset of covariates that is maximally
predictive of the response. Building on past work in kernel dimension
reduction, we formulate our approach as a constrained optimization problem
involving the trace of the con... | computer science |
2,702 | Convergence Analysis of Optimization Algorithms | stat.ML | The regret bound of an optimization algorithms is one of the basic criteria
for evaluating the performance of the given algorithm. By inspecting the
differences between the regret bounds of traditional algorithms and adaptive
one, we provide a guide for choosing an optimizer with respect to the given
data set and the l... | computer science |
2,703 | GLSR-VAE: Geodesic Latent Space Regularization for Variational
AutoEncoder Architectures | cs.LG | VAEs (Variational AutoEncoders) have proved to be powerful in the context of
density modeling and have been used in a variety of contexts for creative
purposes. In many settings, the data we model possesses continuous attributes
that we would like to take into account at generation time. We propose in this
paper GLSR-V... | computer science |
2,704 | On consistency of optimal pricing algorithms in repeated posted-price
auctions with strategic buyer | cs.GT | We study revenue optimization learning algorithms for repeated posted-price
auctions where a seller interacts with a single strategic buyer that holds a
fixed private valuation for a good and seeks to maximize his cumulative
discounted surplus. For this setting, first, we propose a novel algorithm that
never decreases ... | computer science |
2,705 | Robust Bayesian Optimization with Student-t Likelihood | cs.LG | Bayesian optimization has recently attracted the attention of the automatic
machine learning community for its excellent results in hyperparameter tuning.
BO is characterized by the sample efficiency with which it can optimize
expensive black-box functions. The efficiency is achieved in a similar fashion
to the learnin... | computer science |
2,706 | A Distributional Perspective on Reinforcement Learning | cs.LG | In this paper we argue for the fundamental importance of the value
distribution: the distribution of the random return received by a reinforcement
learning agent. This is in contrast to the common approach to reinforcement
learning which models the expectation of this return, or value. Although there
is an established ... | computer science |
2,707 | Learning Sparse Representations in Reinforcement Learning with Sparse
Coding | cs.AI | A variety of representation learning approaches have been investigated for
reinforcement learning; much less attention, however, has been given to
investigating the utility of sparse coding. Outside of reinforcement learning,
sparse coding representations have been widely used, with non-convex objectives
that result in... | computer science |
2,708 | DARLA: Improving Zero-Shot Transfer in Reinforcement Learning | stat.ML | Domain adaptation is an important open problem in deep reinforcement learning
(RL). In many scenarios of interest data is hard to obtain, so agents may learn
a source policy in a setting where data is readily available, with the hope
that it generalises well to the target domain. We propose a new multi-stage RL
agent, ... | computer science |
2,709 | Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers | cs.LG | Low-rank modeling has many important applications in computer vision and
machine learning. While the matrix rank is often approximated by the convex
nuclear norm, the use of nonconvex low-rank regularizers has demonstrated
better empirical performance. However, the resulting optimization problem is
much more challengin... | computer science |
2,710 | Hidden Physics Models: Machine Learning of Nonlinear Partial
Differential Equations | cs.AI | While there is currently a lot of enthusiasm about "big data", useful data is
usually "small" and expensive to acquire. In this paper, we present a new
paradigm of learning partial differential equations from {\em small} data. In
particular, we introduce \emph{hidden physics models}, which are essentially
data-efficien... | computer science |
2,711 | Fairness-aware machine learning: a perspective | cs.AI | Algorithms learned from data are increasingly used for deciding many aspects
in our life: from movies we see, to prices we pay, or medicine we get. Yet
there is growing evidence that decision making by inappropriately trained
algorithms may unintentionally discriminate people. For example, in automated
matching of cand... | computer science |
2,712 | Independently Controllable Factors | cs.LG | It has been postulated that a good representation is one that disentangles
the underlying explanatory factors of variation. However, it remains an open
question what kind of training framework could potentially achieve that.
Whereas most previous work focuses on the static setting (e.g., with images),
we postulate that... | computer science |
2,713 | An Information-Theoretic Optimality Principle for Deep Reinforcement
Learning | cs.AI | We methodologically address the problem of Q-value overestimation in deep
reinforcement learning to handle high-dimensional state spaces efficiently. By
adapting concepts from information theory, we introduce an intrinsic penalty
signal encouraging reduced Q-value estimates. The resultant algorithm
encompasses a wide r... | computer science |
2,714 | Stochastic Optimization with Bandit Sampling | cs.LG | Many stochastic optimization algorithms work by estimating the gradient of
the cost function on the fly by sampling datapoints uniformly at random from a
training set. However, the estimator might have a large variance, which
inadvertently slows down the convergence rate of the algorithms. One way to
reduce this varian... | computer science |
2,715 | Multi-Generator Generative Adversarial Nets | cs.LG | We propose a new approach to train the Generative Adversarial Nets (GANs)
with a mixture of generators to overcome the mode collapsing problem. The main
intuition is to employ multiple generators, instead of using a single one as in
the original GAN. The idea is simple, yet proven to be extremely effective at
covering ... | computer science |
2,716 | Graph Classification via Deep Learning with Virtual Nodes | cs.LG | Learning representation for graph classification turns a variable-size graph
into a fixed-size vector (or matrix). Such a representation works nicely with
algebraic manipulations. Here we introduce a simple method to augment an
attributed graph with a virtual node that is bidirectionally connected to all
existing nodes... | computer science |
2,717 | Theoretical Foundation of Co-Training and Disagreement-Based Algorithms | cs.LG | Disagreement-based approaches generate multiple classifiers and exploit the
disagreement among them with unlabeled data to improve learning performance.
Co-training is a representative paradigm of them, which trains two classifiers
separately on two sufficient and redundant views; while for the applications
where there... | computer science |
2,718 | Scalable Joint Models for Reliable Uncertainty-Aware Event Prediction | stat.ML | Missing data and noisy observations pose significant challenges for reliably
predicting events from irregularly sampled multivariate time series
(longitudinal) data. Imputation methods, which are typically used for
completing the data prior to event prediction, lack a principled mechanism to
account for the uncertainty... | computer science |
2,719 | Weighted parallel SGD for distributed unbalanced-workload training
system | cs.LG | Stochastic gradient descent (SGD) is a popular stochastic optimization method
in machine learning. Traditional parallel SGD algorithms, e.g., SimuParallel
SGD, often require all nodes to have the same performance or to consume equal
quantities of data. However, these requirements are difficult to satisfy when
the paral... | computer science |
2,720 | The Mean and Median Criterion for Automatic Kernel Bandwidth Selection
for Support Vector Data Description | cs.LG | Support vector data description (SVDD) is a popular technique for detecting
anomalies. The SVDD classifier partitions the whole space into an inlier
region, which consists of the region near the training data, and an outlier
region, which consists of points away from the training data. The computation
of the SVDD class... | computer science |
2,721 | Learning to Transfer | cs.AI | Transfer learning borrows knowledge from a source domain to facilitate
learning in a target domain. Two primary issues to be addressed in transfer
learning are what and how to transfer. For a pair of domains, adopting
different transfer learning algorithms results in different knowledge
transferred between them. To dis... | computer science |
2,722 | Neural Block Sampling | cs.AI | Efficient Monte Carlo inference often requires manual construction of
model-specific proposals. We propose an approach to automated proposal
construction by training neural networks to provide fast approximations to
block Gibbs conditionals. The learned proposals generalize to occurrences of
common structural motifs bo... | computer science |
2,723 | ChemGAN challenge for drug discovery: can AI reproduce natural chemical
diversity? | stat.ML | Generating molecules with desired chemical properties is important for drug
discovery. The use of generative neural networks is promising for this task.
However, from visual inspection, it often appears that generated samples lack
diversity. In this paper, we quantify this internal chemical diversity, and we
raise the ... | computer science |
2,724 | Incorporating Feedback into Tree-based Anomaly Detection | cs.LG | Anomaly detectors are often used to produce a ranked list of statistical
anomalies, which are examined by human analysts in order to extract the actual
anomalies of interest. Unfortunately, in realworld applications, this process
can be exceedingly difficult for the analyst since a large fraction of
high-ranking anomal... | computer science |
2,725 | Linking Generative Adversarial Learning and Binary Classification | cs.LG | In this note, we point out a basic link between generative adversarial (GA)
training and binary classification -- any powerful discriminator essentially
computes an (f-)divergence between real and generated samples. The result,
repeatedly re-derived in decision theory, has implications for GA Networks
(GANs), providing... | computer science |
2,726 | Inferring Generative Model Structure with Static Analysis | cs.LG | Obtaining enough labeled data to robustly train complex discriminative models
is a major bottleneck in the machine learning pipeline. A popular solution is
combining multiple sources of weak supervision using generative models. The
structure of these models affects training label quality, but is difficult to
learn with... | computer science |
2,727 | On better training the infinite restricted Boltzmann machines | cs.LG | The infinite restricted Boltzmann machine (iRBM) is an extension of the
classic RBM. It enjoys a good property of automatically deciding the size of
the hidden layer according to specific training data. With sufficient training,
the iRBM can achieve a competitive performance with that of the classic RBM.
However, the c... | computer science |
2,728 | Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network | cs.LG | The prediction of organic reaction outcomes is a fundamental problem in
computational chemistry. Since a reaction may involve hundreds of atoms, fully
exploring the space of possible transformations is intractable. The current
solution utilizes reaction templates to limit the space, but it suffers from
coverage and eff... | computer science |
2,729 | A Framework for Generalizing Graph-based Representation Learning Methods | stat.ML | Random walks are at the heart of many existing deep learning algorithms for
graph data. However, such algorithms have many limitations that arise from the
use of random walks, e.g., the features resulting from these methods are unable
to transfer to new nodes and graphs as they are tied to node identity. In this
work, ... | computer science |
2,730 | On Inductive Abilities of Latent Factor Models for Relational Learning | cs.LG | Latent factor models are increasingly popular for modeling multi-relational
knowledge graphs. By their vectorial nature, it is not only hard to interpret
why this class of models works so well, but also to understand where they fail
and how they might be improved. We conduct an experimental survey of
state-of-the-art m... | computer science |
2,731 | Human Understandable Explanation Extraction for Black-box Classification
Models Based on Matrix Factorization | cs.AI | In recent years, a number of artificial intelligent services have been
developed such as defect detection system or diagnosis system for customer
services. Unfortunately, the core in these services is a black-box in which
human cannot understand the underlying decision making logic, even though the
inspection of the lo... | computer science |
2,732 | Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy
Regularization for Reinforcement Learning | cs.LG | In this paper, a sparse Markov decision process (MDP) with novel causal
sparse Tsallis entropy regularization is proposed.The proposed policy
regularization induces a sparse and multi-modal optimal policy distribution of
a sparse MDP. The full mathematical analysis of the proposed sparse MDP is
provided.We first analyz... | computer science |
2,733 | Interactive Music Generation with Positional Constraints using
Anticipation-RNNs | cs.AI | Recurrent Neural Networks (RNNS) are now widely used on sequence generation
tasks due to their ability to learn long-range dependencies and to generate
sequences of arbitrary length. However, their left-to-right generation
procedure only allows a limited control from a potential user which makes them
unsuitable for int... | computer science |
2,734 | Summable Reparameterizations of Wasserstein Critics in the
One-Dimensional Setting | cs.LG | Generative adversarial networks (GANs) are an exciting alternative to
algorithms for solving density estimation problems---using data to assess how
likely samples are to be drawn from the same distribution. Instead of
explicitly computing these probabilities, GANs learn a generator that can match
the given probabilisti... | computer science |
2,735 | Verifying Properties of Binarized Deep Neural Networks | stat.ML | Understanding properties of deep neural networks is an important challenge in
deep learning. In this paper, we take a step in this direction by proposing a
rigorous way of verifying properties of a popular class of neural networks,
Binarized Neural Networks, using the well-developed means of Boolean
satisfiability. Our... | computer science |
2,736 | MRNet-Product2Vec: A Multi-task Recurrent Neural Network for Product
Embeddings | cs.AI | E-commerce websites such as Amazon, Alibaba, Flipkart, and Walmart sell
billions of products. Machine learning (ML) algorithms involving products are
often used to improve the customer experience and increase revenue, e.g.,
product similarity, recommendation, and price estimation. The products are
required to be repres... | computer science |
2,737 | On overfitting and asymptotic bias in batch reinforcement learning with
partial observability | stat.ML | This paper stands in the context of reinforcement learning with partial
observability and limited data. In this setting, we focus on the tradeoff
between asymptotic bias (suboptimality with unlimited data) and overfitting
(additional suboptimality due to limited data), and theoretically show that
while potentially incr... | computer science |
2,738 | Cross-modal Recurrent Models for Weight Objective Prediction from
Multimodal Time-series Data | stat.ML | We analyse multimodal time-series data corresponding to weight, sleep and
steps measurements. We focus on predicting whether a user will successfully
achieve his/her weight objective. For this, we design several deep long
short-term memory (LSTM) architectures, including a novel cross-modal LSTM
(X-LSTM), and demonstra... | computer science |
2,739 | Glass-Box Program Synthesis: A Machine Learning Approach | cs.LG | Recently proposed models which learn to write computer programs from data use
either input/output examples or rich execution traces. Instead, we argue that a
novel alternative is to use a glass-box loss function, given as a program
itself that can be directly inspected. Glass-box optimization covers a wide
range of pro... | computer science |
2,740 | On formalizing fairness in prediction with machine learning | cs.LG | Machine learning algorithms for prediction are increasingly being used in
critical decisions affecting human lives. Various fairness formalizations, with
no firm consensus yet, are employed to prevent such algorithms from
systematically discriminating against people based on certain attributes
protected by law. The aim... | computer science |
2,741 | On- and Off-Policy Monotonic Policy Improvement | cs.AI | Monotonic policy improvement and off-policy learning are two main desirable
properties for reinforcement learning algorithms. In this paper, by lower
bounding the performance difference of two policies, we show that the monotonic
policy improvement is guaranteed from on- and off-policy mixture samples. An
optimization ... | computer science |
2,742 | Towards Scalable Spectral Clustering via Spectrum-Preserving
Sparsification | cs.LG | The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is
the main computational bottleneck in spectral clustering. In this work, we
introduce a highly-scalable, spectrum-preserving graph sparsification algorithm
that enables to build ultra-sparse NN (u-NN) graphs with guaranteed
preservation of the or... | computer science |
2,743 | Bayesian Hypernetworks | stat.ML | We propose Bayesian hypernetworks: a framework for approximate Bayesian
inference in neural networks. A Bayesian hypernetwork, $h$, is a neural network
which learns to transform a simple noise distribution, $p(\epsilon) =
\mathcal{N}(0,I)$, to a distribution $q(\theta) \doteq q(h(\epsilon))$ over the
parameters $\theta... | computer science |
2,744 | Deep Learning for Case-Based Reasoning through Prototypes: A Neural
Network that Explains Its Predictions | cs.AI | Deep neural networks are widely used for classification. These deep models
often suffer from a lack of interpretability -- they are particularly difficult
to understand because of their non-linear nature. As a result, neural networks
are often treated as "black box" models, and in the past, have been trained
purely to ... | computer science |
2,745 | Learning Infinite RBMs with Frank-Wolfe | cs.LG | In this work, we propose an infinite restricted Boltzmann machine~(RBM),
whose maximum likelihood estimation~(MLE) corresponds to a constrained convex
optimization. We consider the Frank-Wolfe algorithm to solve the program, which
provides a sparse solution that can be interpreted as inserting a hidden unit
at each ite... | computer science |
2,746 | The Effects of Memory Replay in Reinforcement Learning | cs.AI | Experience replay is a key technique behind many recent advances in deep
reinforcement learning. Allowing the agent to learn from earlier memories can
speed up learning and break undesirable temporal correlations. Despite its
wide-spread application, very little is understood about the properties of
experience replay. ... | computer science |
2,747 | A Novel Stochastic Stratified Average Gradient Method: Convergence Rate
and Its Complexity | cs.LG | SGD (Stochastic Gradient Descent) is a popular algorithm for large scale
optimization problems due to its low iterative cost. However, SGD can not
achieve linear convergence rate as FGD (Full Gradient Descent) because of the
inherent gradient variance. To attack the problem, mini-batch SGD was proposed
to get a trade-o... | computer science |
2,748 | Deep Neural Network Approximation using Tensor Sketching | stat.ML | Deep neural networks are powerful learning models that achieve
state-of-the-art performance on many computer vision, speech, and language
processing tasks. In this paper, we study a fundamental question that arises
when designing deep network architectures: Given a target network architecture
can we design a smaller ne... | computer science |
2,749 | Exploiting generalization in the subspaces for faster model-based
learning | stat.ML | Due to the lack of enough generalization in the state-space, common methods
in Reinforcement Learning (RL) suffer from slow learning speed especially in
the early learning trials. This paper introduces a model-based method in
discrete state-spaces for increasing learning speed in terms of required
experience (but not r... | computer science |
2,750 | Regularization approaches for support vector machines with applications
to biomedical data | cs.LG | The support vector machine (SVM) is a widely used machine learning tool for
classification based on statistical learning theory. Given a set of training
data, the SVM finds a hyperplane that separates two different classes of data
points by the largest distance. While the standard form of SVM uses L2-norm
regularizatio... | computer science |
2,751 | Transfer Learning to Learn with Multitask Neural Model Search | cs.AI | Deep learning models require extensive architecture design exploration and
hyperparameter optimization to perform well on a given task. The exploration of
the model design space is often made by a human expert, and optimized using a
combination of grid search and search heuristics over a large space of possible
choices... | computer science |
2,752 | Graph Attention Networks | stat.ML | We present graph attention networks (GATs), novel neural network
architectures that operate on graph-structured data, leveraging masked
self-attentional layers to address the shortcomings of prior methods based on
graph convolutions or their approximations. By stacking layers in which nodes
are able to attend over thei... | computer science |
2,753 | Fast and Scalable Learning of Sparse Changes in High-Dimensional
Gaussian Graphical Model Structure | cs.LG | We focus on the problem of estimating the change in the dependency structures
of two $p$-dimensional Gaussian Graphical models (GGMs). Previous studies for
sparse change estimation in GGMs involve expensive and difficult non-smooth
optimization. We propose a novel method, DIFFEE for estimating DIFFerential
networks via... | computer science |
2,754 | Learning Graph Convolution Filters from Data Manifold | cs.LG | Convolution Neural Network (CNN) has gained tremendous success in computer
vision tasks with its outstanding ability to capture the local latent features.
Recently, there has been an increasing interest in extending CNNs to the
general spatial domain. Although various types of graph and geometric
convolution methods ha... | computer science |
2,755 | Fraternal Dropout | stat.ML | Recurrent neural networks (RNNs) are important class of architectures among
neural networks useful for language modeling and sequential prediction.
However, optimizing RNNs is known to be harder compared to feed-forward neural
networks. A number of techniques have been proposed in literature to address
this problem. In... | computer science |
2,756 | Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer
Ordering | cs.LG | Existing deep multitask learning (MTL) approaches align layers shared between
tasks in a parallel ordering. Such an organization significantly constricts the
types of shared structure that can be learned. The necessity of parallel
ordering for deep MTL is first tested by comparing it with permuted ordering of
shared la... | computer science |
2,757 | Pomegranate: fast and flexible probabilistic modeling in python | cs.AI | We present pomegranate, an open source machine learning package for
probabilistic modeling in Python. Probabilistic modeling encompasses a wide
range of methods that explicitly describe uncertainty using probability
distributions. Three widely used probabilistic models implemented in
pomegranate are general mixture mod... | computer science |
2,758 | Minimal Exploration in Structured Stochastic Bandits | stat.ML | This paper introduces and addresses a wide class of stochastic bandit
problems where the function mapping the arm to the corresponding reward
exhibits some known structural properties. Most existing structures (e.g.
linear, Lipschitz, unimodal, combinatorial, dueling, ...) are covered by our
framework. We derive an asy... | computer science |
2,759 | The Case for Meta-Cognitive Machine Learning: On Model Entropy and
Concept Formation in Deep Learning | cs.AI | Machine learning is usually defined in behaviourist terms, where external
validation is the primary mechanism of learning. In this paper, I argue for a
more holistic interpretation in which finding more probable, efficient and
abstract representations is as central to learning as performance. In other
words, machine le... | computer science |
2,760 | Fisher-Rao Metric, Geometry, and Complexity of Neural Networks | cs.LG | We study the relationship between geometry and capacity measures for deep
neural networks from an invariance viewpoint. We introduce a new notion of
capacity --- the Fisher-Rao norm --- that possesses desirable invariance
properties and is motivated by Information Geometry. We discover an analytical
characterization of... | computer science |
2,761 | Adaptive Bayesian Sampling with Monte Carlo EM | cs.LG | We present a novel technique for learning the mass matrices in samplers
obtained from discretized dynamics that preserve some energy function. Existing
adaptive samplers use Riemannian preconditioning techniques, where the mass
matrices are functions of the parameters being sampled. This leads to
significant complexiti... | computer science |
2,762 | Alpha-expansion is Exact on Stable Instances | stat.ML | Approximate algorithms for structured prediction problems---such as the
popular alpha-expansion algorithm (Boykov et al. 2001) in computer
vision---typically far exceed their theoretical performance guarantees on
real-world instances. These algorithms often find solutions that are very close
to optimal. The goal of thi... | computer science |
2,763 | Learning Overcomplete HMMs | cs.LG | We study the problem of learning overcomplete HMMs---those that have many
hidden states but a small output alphabet. Despite having significant practical
importance, such HMMs are poorly understood with no known positive or negative
results for efficient learning. In this paper, we present several new
results---both po... | computer science |
2,764 | Deep Fault Analysis and Subset Selection in Solar Power Grids | cs.LG | Non-availability of reliable and sustainable electric power is a major
problem in the developing world. Renewable energy sources like solar are not
very lucrative in the current stage due to various uncertainties like weather,
storage, land use among others. There also exists various other issues like
mis-commitment of... | computer science |
2,765 | Scalable Log Determinants for Gaussian Process Kernel Learning | stat.ML | For applications as varied as Bayesian neural networks, determinantal point
processes, elliptical graphical models, and kernel learning for Gaussian
processes (GPs), one must compute a log determinant of an $n \times n$ positive
definite matrix, and its derivatives - leading to prohibitive
$\mathcal{O}(n^3)$ computatio... | computer science |
2,766 | A Change-Detection based Framework for Piecewise-stationary Multi-Armed
Bandit Problem | cs.LG | The multi-armed bandit problem has been extensively studied under the
stationary assumption. However in reality, this assumption often does not hold
because the distributions of rewards themselves may change over time. In this
paper, we propose a change-detection (CD) based framework for multi-armed
bandit problems und... | computer science |
2,767 | Medical Diagnosis From Laboratory Tests by Combining Generative and
Discriminative Learning | cs.AI | A primary goal of computational phenotype research is to conduct medical
diagnosis. In hospital, physicians rely on massive clinical data to make
diagnosis decisions, among which laboratory tests are one of the most important
resources. However, the longitudinal and incomplete nature of laboratory test
data casts a sig... | computer science |
2,768 | Simple And Efficient Architecture Search for Convolutional Neural
Networks | stat.ML | Neural networks have recently had a lot of success for many tasks. However,
neural network architectures that perform well are still typically designed
manually by experts in a cumbersome trial-and-error process. We propose a new
method to automatically search for well-performing CNN architectures based on a
simple hil... | computer science |
2,769 | Budget-Constrained Multi-Armed Bandits with Multiple Plays | cs.LG | We study the multi-armed bandit problem with multiple plays and a budget
constraint for both the stochastic and the adversarial setting. At each round,
exactly $K$ out of $N$ possible arms have to be played (with $1\leq K \leq N$).
In addition to observing the individual rewards for each arm played, the player
also lea... | computer science |
2,770 | Towards Deep Learning Models for Psychological State Prediction using
Smartphone Data: Challenges and Opportunities | cs.LG | There is an increasing interest in exploiting mobile sensing technologies and
machine learning techniques for mental health monitoring and intervention.
Researchers have effectively used contextual information, such as mobility,
communication and mobile phone usage patterns for quantifying individuals' mood
and wellbei... | computer science |
2,771 | Run, skeleton, run: skeletal model in a physics-based simulation | cs.AI | In this paper, we present our approach to solve a physics-based reinforcement
learning challenge "Learning to Run" with objective to train
physiologically-based human model to navigate a complex obstacle course as
quickly as possible. The environment is computationally expensive, has a
high-dimensional continuous actio... | computer science |
2,772 | Classification with Costly Features using Deep Reinforcement Learning | cs.AI | We study a classification problem where each feature can be acquired for a
cost and the goal is to optimize the trade-off between classification precision
and the total feature cost. We frame the problem as a sequential
decision-making problem, where we classify one sample in each episode. At each
step, an agent can us... | computer science |
2,773 | The Promise and Peril of Human Evaluation for Model Interpretability | cs.AI | Transparency, user trust, and human comprehension are popular ethical
motivations for interpretable machine learning. In support of these goals,
researchers evaluate model explanation performance using humans and real world
applications. This alone presents a challenge in many areas of artificial
intelligence. In this ... | computer science |
2,774 | Modular Continual Learning in a Unified Visual Environment | cs.LG | A core aspect of human intelligence is the ability to learn new tasks quickly
and switch between them flexibly. Here, we describe a modular continual
reinforcement learning paradigm inspired by these abilities. We first introduce
a visual interaction environment that allows many types of tasks to be unified
in a single... | computer science |
2,775 | Teaching a Machine to Read Maps with Deep Reinforcement Learning | cs.RO | The ability to use a 2D map to navigate a complex 3D environment is quite
remarkable, and even difficult for many humans. Localization and navigation is
also an important problem in domains such as robotics, and has recently become
a focus of the deep reinforcement learning community. In this paper we teach a
reinforce... | computer science |
2,776 | Transferring Agent Behaviors from Videos via Motion GANs | cs.LG | A major bottleneck for developing general reinforcement learning agents is
determining rewards that will yield desirable behaviors under various
circumstances. We introduce a general mechanism for automatically specifying
meaningful behaviors from raw pixels. In particular, we train a generative
adversarial network to ... | computer science |
2,777 | Deep Learning for Physical Processes: Incorporating Prior Scientific
Knowledge | cs.AI | We consider the use of Deep Learning methods for modeling complex phenomena
like those occurring in natural physical processes. With the large amount of
data gathered on these phenomena the data intensive paradigm could begin to
challenge more traditional approaches elaborated over the years in fields like
maths or phy... | computer science |
2,778 | Safer Classification by Synthesis | cs.LG | The discriminative approach to classification using deep neural networks has
become the de-facto standard in various fields. Complementing recent
reservations about safety against adversarial examples, we show that
conventional discriminative methods can easily be fooled to provide incorrect
labels with very high confi... | computer science |
2,779 | Generalizing Hamiltonian Monte Carlo with Neural Networks | stat.ML | We present a general-purpose method to train Markov chain Monte Carlo
kernels, parameterized by deep neural networks, that converge and mix quickly
to their target distribution. Our method generalizes Hamiltonian Monte Carlo
and is trained to maximize expected squared jumped distance, a proxy for mixing
speed. We demon... | computer science |
2,780 | Distilling a Neural Network Into a Soft Decision Tree | cs.LG | Deep neural networks have proved to be a very effective way to perform
classification tasks. They excel when the input data is high dimensional, the
relationship between the input and the output is complicated, and the number of
labeled training examples is large. But it is hard to explain why a learned
network makes a... | computer science |
2,781 | Tensor Completion Algorithms in Big Data Analytics | stat.ML | Tensor completion is a problem of filling the missing or unobserved entries
of partially observed tensors. Due to the multidimensional character of tensors
in describing complex datasets, tensor completion algorithms and their
applications have received wide attention and achievement in data mining,
computer vision, si... | computer science |
2,782 | Quantitative CBA: Small and Comprehensible Association Rule
Classification Models | stat.ML | Quantitative CBA is a postprocessing algorithm for association rule
classification algorithm CBA (Liu et al, 1998). QCBA uses original,
undiscretized numerical attributes to optimize the discovered association
rules, refining the boundaries of literals in the antecedent of the rules
produced by CBA. Some rules as well ... | computer science |
2,783 | Learning to Rank based on Analogical Reasoning | stat.ML | Object ranking or "learning to rank" is an important problem in the realm of
preference learning. On the basis of training data in the form of a set of
rankings of objects represented as feature vectors, the goal is to learn a
ranking function that predicts a linear order of any new set of objects. In
this paper, we pr... | computer science |
2,784 | TensorFlow Distributions | cs.LG | The TensorFlow Distributions library implements a vision of probability
theory adapted to the modern deep-learning paradigm of end-to-end
differentiable computation. Building on two basic abstractions, it offers
flexible building blocks for probabilistic computation. Distributions provide
fast, numerically stable metho... | computer science |
2,785 | Deep Reinforcement Learning for De-Novo Drug Design | cs.AI | We propose a novel computational strategy based on deep and reinforcement
learning techniques for de-novo design of molecules with desired properties.
This strategy integrates two deep neural networks -generative and predictive -
that are trained separately but employed jointly to generate novel chemical
structures wit... | computer science |
2,786 | A Semantic Loss Function for Deep Learning with Symbolic Knowledge | cs.AI | This paper develops a novel methodology for using symbolic knowledge in deep
learning. From first principles, we derive a semantic loss function that
bridges between neural output vectors and logical constraints. This loss
function captures how close the neural network is to satisfying the constraints
on its output. An... | computer science |
2,787 | Variational Deep Q Network | cs.LG | We propose a framework that directly tackles the probability distribution of
the value function parameters in Deep Q Network (DQN), with powerful
variational inference subroutines to approximate the posterior of the
parameters. We will establish the equivalence between our proposed surrogate
objective and variational i... | computer science |
2,788 | An Equivalence of Fully Connected Layer and Convolutional Layer | cs.LG | This article demonstrates that convolutional operation can be converted to
matrix multiplication, which has the same calculation way with fully connected
layer. The article is helpful for the beginners of the neural network to
understand how fully connected layer and the convolutional layer work in the
backend. To be c... | computer science |
2,789 | Stochastic Dual Coordinate Descent with Bandit Sampling | cs.LG | Coordinate descent methods minimize a cost function by updating a single
decision variable (corresponding to one coordinate) at a time. Ideally, one
would update the decision variable that yields the largest marginal decrease in
the cost function. However, finding this coordinate would require checking all
of them, whi... | computer science |
2,790 | Deep Echo State Network (DeepESN): A Brief Survey | cs.LG | The study of deep recurrent neural networks (RNNs) and, in particular, of
deep Reservoir Computing (RC) is gaining an increasing research attention in
the neural networks community. The recently introduced deep Echo State Network
(deepESN) model opened the way to an extremely efficient approach for designing
deep neura... | computer science |
2,791 | Ray: A Distributed Framework for Emerging AI Applications | cs.DC | The next generation of AI applications will continuously interact with the
environment and learn from these interactions. These applications impose new
and demanding systems requirements, both in terms of performance and
flexibility. In this paper, we consider these requirements and present Ray---a
distributed system t... | computer science |
2,792 | Nonparametric Inference for Auto-Encoding Variational Bayes | stat.ML | We would like to learn latent representations that are low-dimensional and
highly interpretable. A model that has these characteristics is the Gaussian
Process Latent Variable Model. The benefits and negative of the GP-LVM are
complementary to the Variational Autoencoder, the former provides interpretable
low-dimension... | computer science |
2,793 | Safe Policy Improvement with Baseline Bootstrapping | cs.LG | A common goal in Reinforcement Learning is to derive a good strategy given a
limited batch of data. In this paper, we adopt the safe policy improvement
(SPI) approach: we compute a target policy guaranteed to perform at least as
well as a given baseline policy. Our SPI strategy, inspired by the
knows-what-it-knows para... | computer science |
2,794 | Block-diagonal Hessian-free Optimization for Training Neural Networks | cs.LG | Second-order methods for neural network optimization have several advantages
over methods based on first-order gradient descent, including better scaling to
large mini-batch sizes and fewer updates needed for convergence. But they are
rarely applied to deep learning in practice because of high computational cost
and th... | computer science |
2,795 | f-Divergence constrained policy improvement | cs.LG | To ensure stability of learning, state-of-the-art generalized policy
iteration algorithms augment the policy improvement step with a trust region
constraint bounding the information loss. The size of the trust region is
commonly determined by the Kullback-Leibler (KL) divergence, which not only
captures the notion of d... | computer science |
2,796 | Deep Learning for Identifying Potential Conceptual Shifts for
Co-creative Drawing | cs.LG | We present a system for identifying conceptual shifts between visual
categories, which will form the basis for a co-creative drawing system to help
users draw more creative sketches. The system recognizes human sketches and
matches them to structurally similar sketches from categories to which they do
not belong. This ... | computer science |
2,797 | Convergence Analysis of Gradient Descent Algorithms with Proportional
Updates | cs.LG | The rise of deep learning in recent years has brought with it increasingly
clever optimization methods to deal with complex, non-linear loss functions.
These methods are often designed with convex optimization in mind, but have
been shown to work well in practice even for the highly non-convex optimization
associated w... | computer science |
2,798 | Paranom: A Parallel Anomaly Dataset Generator | cs.LG | In this paper, we present Paranom, a parallel anomaly dataset generator. We
discuss its design and provide brief experimental results demonstrating its
usefulness in improving the classification correctness of LSTM-AD, a
state-of-the-art anomaly detection model. | computer science |
2,799 | Theoretical Impediments to Machine Learning With Seven Sparks from the
Causal Revolution | cs.LG | Current machine learning systems operate, almost exclusively, in a
statistical, or model-free mode, which entails severe theoretical limits on
their power and performance. Such systems cannot reason about interventions and
retrospection and, therefore, cannot serve as the basis for strong AI. To
achieve human level int... | computer science |
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