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2,200 | Online Edge Grafting for Efficient MRF Structure Learning | cs.LG | Incremental methods for structure learning of pairwise Markov random fields
(MRFs), such as grafting, improve scalability to large systems by avoiding
inference over the entire feature space in each optimization step. Instead,
inference is performed over an incrementally grown active set of features. In
this paper, we ... | computer science |
2,201 | Learning Causal Structures Using Regression Invariance | cs.LG | We study causal inference in a multi-environment setting, in which the
functional relations for producing the variables from their direct causes
remain the same across environments, while the distribution of exogenous noises
may vary. We introduce the idea of using the invariance of the functional
relations of the vari... | computer science |
2,202 | Multiple Source Domain Adaptation with Adversarial Training of Neural
Networks | cs.LG | While domain adaptation has been actively researched in recent years, most
theoretical results and algorithms focus on the single-source-single-target
adaptation setting. Naive application of such algorithms on multiple source
domain adaptation problem may lead to suboptimal solutions. As a step toward
bridging the gap... | computer science |
2,203 | Emergence of Invariance and Disentangling in Deep Representations | cs.LG | Using established principles from Information Theory and Statistics, we show
that in a deep neural network invariance to nuisance factors is equivalent to
information minimality of the learned representation, and that stacking layers
and injecting noise during training naturally bias the network towards learning
invari... | computer science |
2,204 | Deep reinforcement learning from human preferences | stat.ML | For sophisticated reinforcement learning (RL) systems to interact usefully
with real-world environments, we need to communicate complex goals to these
systems. In this work, we explore goals defined in terms of (non-expert) human
preferences between pairs of trajectory segments. We show that this approach
can effective... | computer science |
2,205 | Dex: Incremental Learning for Complex Environments in Deep Reinforcement
Learning | stat.ML | This paper introduces Dex, a reinforcement learning environment toolkit
specialized for training and evaluation of continual learning methods as well
as general reinforcement learning problems. We also present the novel continual
learning method of incremental learning, where a challenging environment is
solved using o... | computer science |
2,206 | A Useful Motif for Flexible Task Learning in an Embodied Two-Dimensional
Visual Environment | cs.LG | Animals (especially humans) have an amazing ability to learn new tasks
quickly, and switch between them flexibly. How brains support this ability is
largely unknown, both neuroscientifically and algorithmically. One reasonable
supposition is that modules drawing on an underlying general-purpose sensory
representation a... | computer science |
2,207 | There and Back Again: A General Approach to Learning Sparse Models | cs.LG | We propose a simple and efficient approach to learning sparse models. Our
approach consists of (1) projecting the data into a lower dimensional space,
(2) learning a dense model in the lower dimensional space, and then (3)
recovering the sparse model in the original space via compressive sensing. We
apply this approach... | computer science |
2,208 | Generative Bridging Network in Neural Sequence Prediction | cs.AI | In order to alleviate data sparsity and overfitting problems in maximum
likelihood estimation (MLE) for sequence prediction tasks, we propose the
Generative Bridging Network (GBN), in which a novel bridge module is introduced
to assist the training of the sequence prediction model (the generator
network). Unlike MLE di... | computer science |
2,209 | Probabilistic Active Learning of Functions in Structural Causal Models | stat.ML | We consider the problem of learning the functions computing children from
parents in a Structural Causal Model once the underlying causal graph has been
identified. This is in some sense the second step after causal discovery.
Taking a probabilistic approach to estimating these functions, we derive a
natural myopic act... | computer science |
2,210 | Causal Consistency of Structural Equation Models | stat.ML | Complex systems can be modelled at various levels of detail. Ideally, causal
models of the same system should be consistent with one another in the sense
that they agree in their predictions of the effects of interventions. We
formalise this notion of consistency in the case of Structural Equation Models
(SEMs) by intr... | computer science |
2,211 | Human-Level Intelligence or Animal-Like Abilities? | cs.AI | The vision systems of the eagle and the snake outperform everything that we
can make in the laboratory, but snakes and eagles cannot build an eyeglass or a
telescope or a microscope. (Judea Pearl) | computer science |
2,212 | A Machine Learning Approach for Evaluating Creative Artifacts | cs.LG | Much work has been done in understanding human creativity and defining
measures to evaluate creativity. This is necessary mainly for the reason of
having an objective and automatic way of quantifying creative artifacts. In
this work, we propose a regression-based learning framework which takes into
account quantitative... | computer science |
2,213 | Imagination-Augmented Agents for Deep Reinforcement Learning | cs.LG | We introduce Imagination-Augmented Agents (I2As), a novel architecture for
deep reinforcement learning combining model-free and model-based aspects. In
contrast to most existing model-based reinforcement learning and planning
methods, which prescribe how a model should be used to arrive at a policy, I2As
learn to inter... | computer science |
2,214 | An Infinite Hidden Markov Model With Similarity-Biased Transitions | stat.ML | We describe a generalization of the Hierarchical Dirichlet Process Hidden
Markov Model (HDP-HMM) which is able to encode prior information that state
transitions are more likely between "nearby" states. This is accomplished by
defining a similarity function on the state space and scaling transition
probabilities by pai... | computer science |
2,215 | Prediction-Constrained Training for Semi-Supervised Mixture and Topic
Models | stat.ML | Supervisory signals have the potential to make low-dimensional data
representations, like those learned by mixture and topic models, more
interpretable and useful. We propose a framework for training latent variable
models that explicitly balances two goals: recovery of faithful generative
explanations of high-dimensio... | computer science |
2,216 | Advantages and Limitations of using Successor Features for Transfer in
Reinforcement Learning | cs.AI | One question central to Reinforcement Learning is how to learn a feature
representation that supports algorithm scaling and re-use of learned
information from different tasks. Successor Features approach this problem by
learning a feature representation that satisfies a temporal constraint. We
present an implementation... | computer science |
2,217 | An Effective Training Method For Deep Convolutional Neural Network | cs.LG | In this paper, we propose the nonlinearity generation method to speed up and
stabilize the training of deep convolutional neural networks. The proposed
method modifies a family of activation functions as nonlinearity generators
(NGs). NGs make the activation functions linear symmetric for their inputs to
lower model ca... | computer science |
2,218 | Boosting Variational Inference: an Optimization Perspective | cs.LG | Variational inference is a popular technique to approximate a possibly
intractable Bayesian posterior with a more tractable one. Recently, boosting
variational inference has been proposed as a new paradigm to approximate the
posterior by a mixture of densities by greedily adding components to the
mixture. However, as i... | computer science |
2,219 | Training of Deep Neural Networks based on Distance Measures using
RMSProp | cs.LG | The vanishing gradient problem was a major obstacle for the success of deep
learning. In recent years it was gradually alleviated through multiple
different techniques. However the problem was not really overcome in a
fundamental way, since it is inherent to neural networks with activation
functions based on dot produc... | computer science |
2,220 | The Tensor Memory Hypothesis | cs.AI | We discuss memory models which are based on tensor decompositions using
latent representations of entities and events. We show how episodic memory and
semantic memory can be realized and discuss how new memory traces can be
generated from sensory input: Existing memories are the basis for perception
and new memories ar... | computer science |
2,221 | Automatic Selection of t-SNE Perplexity | cs.AI | t-Distributed Stochastic Neighbor Embedding (t-SNE) is one of the most widely
used dimensionality reduction methods for data visualization, but it has a
perplexity hyperparameter that requires manual selection. In practice, proper
tuning of t-SNE perplexity requires users to understand the inner working of
the method a... | computer science |
2,222 | Learning from Noisy Label Distributions | cs.LG | In this paper, we consider a novel machine learning problem, that is,
learning a classifier from noisy label distributions. In this problem, each
instance with a feature vector belongs to at least one group. Then, instead of
the true label of each instance, we observe the label distribution of the
instances associated ... | computer science |
2,223 | Geometric Enclosing Networks | cs.LG | Training model to generate data has increasingly attracted research attention
and become important in modern world applications. We propose in this paper a
new geometry-based optimization approach to address this problem. Orthogonal to
current state-of-the-art density-based approaches, most notably VAE and GAN, we
pres... | computer science |
2,224 | On the Compressive Power of Deep Rectifier Networks for High Resolution
Representation of Class Boundaries | cs.LG | This paper provides a theoretical justification of the superior
classification performance of deep rectifier networks over shallow rectifier
networks from the geometrical perspective of piecewise linear (PWL) classifier
boundaries. We show that, for a given threshold on the approximation error, the
required number of b... | computer science |
2,225 | Mean Actor Critic | stat.ML | We propose a new algorithm, Mean Actor-Critic (MAC), for discrete-action
continuous-state reinforcement learning. MAC is a policy gradient algorithm
that uses the agent's explicit representation of all action values to estimate
the gradient of the policy, rather than using only the actions that were
actually executed. ... | computer science |
2,226 | Budgeted Experiment Design for Causal Structure Learning | cs.LG | We study the problem of causal structure learning when the experimenter is
limited to perform at most $k$ non-adaptive experiments of size $1$. We
formulate the problem of finding the best intervention target set as an
optimization problem, which aims to maximize the average number of edges whose
directions are resolve... | computer science |
2,227 | A Practically Competitive and Provably Consistent Algorithm for Uplift
Modeling | cs.LG | Randomized experiments have been critical tools of decision making for
decades. However, subjects can show significant heterogeneity in response to
treatments in many important applications. Therefore it is not enough to simply
know which treatment is optimal for the entire population. What we need is a
model that corr... | computer science |
2,228 | Supervising Unsupervised Learning | cs.AI | We introduce a framework to leverage knowledge acquired from a repository of
(heterogeneous) supervised datasets to new unsupervised datasets. Our
perspective avoids the subjectivity inherent in unsupervised learning by
reducing it to supervised learning, and provides a principled way to evaluate
unsupervised algorithm... | computer science |
2,229 | The Uncertainty Bellman Equation and Exploration | cs.AI | We consider the exploration/exploitation problem in reinforcement learning.
For exploitation, it is well known that the Bellman equation connects the value
at any time-step to the expected value at subsequent time-steps. In this paper
we consider a similar uncertainty Bellman equation (UBE), which connects the
uncertai... | computer science |
2,230 | Learning to update Auto-associative Memory in Recurrent Neural Networks
for Improving Sequence Memorization | cs.AI | Learning to remember long sequences remains a challenging task for recurrent
neural networks. Register memory and attention mechanisms were both proposed to
resolve the issue with either high computational cost to retain memory
differentiability, or by discounting the RNN representation learning towards
encoding shorte... | computer science |
2,231 | Feature Engineering for Predictive Modeling using Reinforcement Learning | cs.AI | Feature engineering is a crucial step in the process of predictive modeling.
It involves the transformation of given feature space, typically using
mathematical functions, with the objective of reducing the modeling error for a
given target. However, there is no well-defined basis for performing effective
feature engin... | computer science |
2,232 | Neural Optimizer Search with Reinforcement Learning | cs.AI | We present an approach to automate the process of discovering optimization
methods, with a focus on deep learning architectures. We train a Recurrent
Neural Network controller to generate a string in a domain specific language
that describes a mathematical update equation based on a list of primitive
functions, such as... | computer science |
2,233 | An Optimal Online Method of Selecting Source Policies for Reinforcement
Learning | cs.AI | Transfer learning significantly accelerates the reinforcement learning
process by exploiting relevant knowledge from previous experiences. The problem
of optimally selecting source policies during the learning process is of great
importance yet challenging. There has been little theoretical analysis of this
problem. In... | computer science |
2,234 | The Consciousness Prior | cs.LG | A new prior is proposed for representation learning, which can be combined
with other priors in order to help disentangling abstract factors from each
other. It is inspired by the phenomenon of consciousness seen as the formation
of a low-dimensional combination of a few concepts constituting a conscious
thought, i.e.,... | computer science |
2,235 | Learning Graphical Models from a Distributed Stream | cs.AI | A current challenge for data management systems is to support the
construction and maintenance of machine learning models over data that is
large, multi-dimensional, and evolving. While systems that could support these
tasks are emerging, the need to scale to distributed, streaming data requires
new models and algorith... | computer science |
2,236 | Stacked Structure Learning for Lifted Relational Neural Networks | cs.LG | Lifted Relational Neural Networks (LRNNs) describe relational domains using
weighted first-order rules which act as templates for constructing feed-forward
neural networks. While previous work has shown that using LRNNs can lead to
state-of-the-art results in various ILP tasks, these results depended on
hand-crafted ru... | computer science |
2,237 | Learnable Explicit Density for Continuous Latent Space and Variational
Inference | cs.LG | In this paper, we study two aspects of the variational autoencoder (VAE): the
prior distribution over the latent variables and its corresponding posterior.
First, we decompose the learning of VAEs into layerwise density estimation, and
argue that having a flexible prior is beneficial to both sample generation and
infer... | computer science |
2,238 | An Analysis of the Value of Information when Exploring Stochastic,
Discrete Multi-Armed Bandits | cs.AI | In this paper, we propose an information-theoretic exploration strategy for
stochastic, discrete multi-armed bandits that achieves optimal regret. Our
strategy is based on the value of information criterion. This criterion
measures the trade-off between policy information and obtainable rewards. High
amounts of policy ... | computer science |
2,239 | Function space analysis of deep learning representation layers | cs.AI | In this paper we propose a function space approach to Representation Learning
and the analysis of the representation layers in deep learning architectures.
We show how to compute a weak-type Besov smoothness index that quantifies the
geometry of the clustering in the feature space. This approach was already
applied suc... | computer science |
2,240 | Coresets for Dependency Networks | cs.AI | Many applications infer the structure of a probabilistic graphical model from
data to elucidate the relationships between variables. But how can we train
graphical models on a massive data set? In this paper, we show how to construct
coresets -compressed data sets which can be used as proxy for the original data
and ha... | computer science |
2,241 | Mixed Precision Training | cs.AI | Deep neural networks have enabled progress in a wide variety of applications.
Growing the size of the neural network typically results in improved accuracy.
As model sizes grow, the memory and compute requirements for training these
models also increases. We introduce a technique to train deep neural networks
using hal... | computer science |
2,242 | Is Epicurus the father of Reinforcement Learning? | cs.LG | The Epicurean Philosophy is commonly thought as simplistic and hedonistic.
Here I discuss how this is a misconception and explore its link to
Reinforcement Learning. Based on the letters of Epicurus, I construct an
objective function for hedonism which turns out to be equivalent of the
Reinforcement Learning objective ... | computer science |
2,243 | Two-stage Algorithm for Fairness-aware Machine Learning | stat.ML | Algorithmic decision making process now affects many aspects of our lives.
Standard tools for machine learning, such as classification and regression, are
subject to the bias in data, and thus direct application of such off-the-shelf
tools could lead to a specific group being unfairly discriminated. Removing
sensitive ... | computer science |
2,244 | Manifold Regularization for Kernelized LSTD | cs.LG | Policy evaluation or value function or Q-function approximation is a key
procedure in reinforcement learning (RL). It is a necessary component of policy
iteration and can be used for variance reduction in policy gradient methods.
Therefore its quality has a significant impact on most RL algorithms. Motivated
by manifol... | computer science |
2,245 | Auditing Black-Box Models Using Transparent Model Distillation With Side
Information | stat.ML | Black-box risk scoring models permeate our lives, yet are typically
proprietary or opaque. We propose a transparent model distillation approach to
audit such models. Model distillation was first introduced to transfer
knowledge from a large, complex teacher model to a faster, simpler student
model without significant l... | computer science |
2,246 | A Bayesian Perspective on Generalization and Stochastic Gradient Descent | cs.LG | We consider two questions at the heart of machine learning; how can we
predict if a minimum will generalize to the test set, and why does stochastic
gradient descent find minima that generalize well? Our work responds to Zhang
et al. (2016), who showed deep neural networks can easily memorize randomly
labeled training ... | computer science |
2,247 | Low Precision RNNs: Quantizing RNNs Without Losing Accuracy | cs.LG | Similar to convolution neural networks, recurrent neural networks (RNNs)
typically suffer from over-parameterization. Quantizing bit-widths of weights
and activations results in runtime efficiency on hardware, yet it often comes
at the cost of reduced accuracy. This paper proposes a quantization approach
that increases... | computer science |
2,248 | A Learning-to-Infer Method for Real-Time Power Grid Topology
Identification | cs.LG | Identifying arbitrary topologies of power networks in real time is a
computationally hard problem due to the number of hypotheses that grows
exponentially with the network size. A new "Learning-to-Infer" variational
inference method is developed for efficient inference of every line status in
the network. Optimizing th... | computer science |
2,249 | Inductive Representation Learning in Large Attributed Graphs | stat.ML | Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many exi... | computer science |
2,250 | Distributional Reinforcement Learning with Quantile Regression | cs.AI | In reinforcement learning an agent interacts with the environment by taking
actions and observing the next state and reward. When sampled
probabilistically, these state transitions, rewards, and actions can all induce
randomness in the observed long-term return. Traditionally, reinforcement
learning algorithms average ... | computer science |
2,251 | Similarity-based Multi-label Learning | stat.ML | Multi-label classification is an important learning problem with many
applications. In this work, we propose a principled similarity-based approach
for multi-label learning called SML. We also introduce a similarity-based
approach for predicting the label set size. The experimental results
demonstrate the effectiveness... | computer science |
2,252 | Partitioning Relational Matrices of Similarities or Dissimilarities
using the Value of Information | cs.AI | In this paper, we provide an approach to clustering relational matrices whose
entries correspond to either similarities or dissimilarities between objects.
Our approach is based on the value of information, a parameterized,
information-theoretic criterion that measures the change in costs associated
with changes in inf... | computer science |
2,253 | Tensorizing Generative Adversarial Nets | cs.LG | Generative Adversarial Network (GAN) and its variants demonstrate
state-of-the-art performance in the class of generative models. To capture
higher dimensional distributions, the common learning procedure requires high
computational complexity and large number of parameters. In this paper, we
present a new generative a... | computer science |
2,254 | Rough extreme learning machine: a new classification method based on
uncertainty measure | cs.LG | Extreme learning machine (ELM) is a new single hidden layer feedback neural
network. The weights of the input layer and the biases of neurons in hidden
layer are randomly generated, the weights of the output layer can be
analytically determined. ELM has been achieved good results for a large number
of classification ta... | computer science |
2,255 | Prototype Matching Networks for Large-Scale Multi-label Genomic Sequence
Classification | cs.LG | One of the fundamental tasks in understanding genomics is the problem of
predicting Transcription Factor Binding Sites (TFBSs). With more than hundreds
of Transcription Factors (TFs) as labels, genomic-sequence based TFBS
prediction is a challenging multi-label classification task. There are two
major biological mechan... | computer science |
2,256 | Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory | stat.ML | In meta-learning an agent extracts knowledge from observed tasks, aiming to
facilitate learning of novel future tasks. Under the assumption that future
tasks are 'related' to previous tasks, representations should be learned in a
way which captures the common structure across learned tasks, while allowing
the learner s... | computer science |
2,257 | Double Q($σ$) and Q($σ, λ$): Unifying Reinforcement
Learning Control Algorithms | cs.AI | Temporal-difference (TD) learning is an important field in reinforcement
learning. Sarsa and Q-Learning are among the most used TD algorithms. The
Q($\sigma$) algorithm (Sutton and Barto (2017)) unifies both. This paper
extends the Q($\sigma$) algorithm to an online multi-step algorithm Q($\sigma,
\lambda$) using eligi... | computer science |
2,258 | KGAN: How to Break The Minimax Game in GAN | cs.LG | Generative Adversarial Networks (GANs) were intuitively and attractively
explained under the perspective of game theory, wherein two involving parties
are a discriminator and a generator. In this game, the task of the
discriminator is to discriminate the real and generated (i.e., fake) data,
whilst the task of the gene... | computer science |
2,259 | Distributed Bayesian Piecewise Sparse Linear Models | cs.AI | The importance of interpretability of machine learning models has been
increasing due to emerging enterprise predictive analytics, threat of data
privacy, accountability of artificial intelligence in society, and so on.
Piecewise linear models have been actively studied to achieve both accuracy and
interpretability. Th... | computer science |
2,260 | Continuous DR-submodular Maximization: Structure and Algorithms | cs.LG | DR-submodular continuous functions are important objectives with wide
real-world applications spanning MAP inference in determinantal point processes
(DPPs), and mean-field inference for probabilistic submodular models, amongst
others. DR-submodularity captures a subclass of non-convex functions that
enables both exact... | computer science |
2,261 | Block-Sparse Recurrent Neural Networks | cs.LG | Recurrent Neural Networks (RNNs) are used in state-of-the-art models in
domains such as speech recognition, machine translation, and language
modelling. Sparsity is a technique to reduce compute and memory requirements of
deep learning models. Sparse RNNs are easier to deploy on devices and high-end
server processors. ... | computer science |
2,262 | Clustering with feature selection using alternating minimization,
Application to computational biology | cs.LG | This paper deals with unsupervised clustering with feature selection. The
problem is to estimate both labels and a sparse projection matrix of weights.
To address this combinatorial non-convex problem maintaining a strict control
on the sparsity of the matrix of weights, we propose an alternating
minimization of the Fr... | computer science |
2,263 | Learning K-way D-dimensional Discrete Code For Compact Embedding
Representations | cs.LG | Embedding methods such as word embedding have become pillars for many
applications containing discrete structures. Conventional embedding methods
directly associate each symbol with a continuous embedding vector, which is
equivalent to applying linear transformation based on "one-hot" encoding of the
discrete symbols. ... | computer science |
2,264 | Information Directed Sampling for Stochastic Bandits with Graph Feedback | cs.LG | We consider stochastic multi-armed bandit problems with graph feedback, where
the decision maker is allowed to observe the neighboring actions of the chosen
action. We allow the graph structure to vary with time and consider both
deterministic and Erd\H{o}s-R\'enyi random graph models. For such a graph
feedback model, ... | computer science |
2,265 | DLPaper2Code: Auto-generation of Code from Deep Learning Research Papers | cs.LG | With an abundance of research papers in deep learning, reproducibility or
adoption of the existing works becomes a challenge. This is due to the lack of
open source implementations provided by the authors. Further, re-implementing
research papers in a different library is a daunting task. To address these
challenges, w... | computer science |
2,266 | Revisiting Simple Neural Networks for Learning Representations of
Knowledge Graphs | cs.AI | We address the problem of learning vector representations for entities and
relations in Knowledge Graphs (KGs) for Knowledge Base Completion (KBC). This
problem has received significant attention in the past few years and multiple
methods have been proposed. Most of the existing methods in the literature use
a predefin... | computer science |
2,267 | Markov Decision Processes with Continuous Side Information | stat.ML | We consider a reinforcement learning (RL) setting in which the agent
interacts with a sequence of episodic MDPs. At the start of each episode the
agent has access to some side-information or context that determines the
dynamics of the MDP for that episode. Our setting is motivated by applications
in healthcare where ba... | computer science |
2,268 | Butterfly Effect: Bidirectional Control of Classification Performance by
Small Additive Perturbation | cs.LG | This paper proposes a new algorithm for controlling classification results by
generating a small additive perturbation without changing the classifier
network. Our work is inspired by existing works generating adversarial
perturbation that worsens classification performance. In contrast to the
existing methods, our wor... | computer science |
2,269 | Intent-Aware Contextual Recommendation System | cs.IR | Recommender systems take inputs from user history, use an internal ranking
algorithm to generate results and possibly optimize this ranking based on
feedback. However, often the recommender system is unaware of the actual intent
of the user and simply provides recommendations dynamically without properly
understanding ... | computer science |
2,270 | Efficient exploration with Double Uncertain Value Networks | cs.LG | This paper studies directed exploration for reinforcement learning agents by
tracking uncertainty about the value of each available action. We identify two
sources of uncertainty that are relevant for exploration. The first originates
from limited data (parametric uncertainty), while the second originates from
the dist... | computer science |
2,271 | Extreme Dimension Reduction for Handling Covariate Shift | cs.LG | In the covariate shift learning scenario, the training and test covariate
distributions differ, so that a predictor's average loss over the training and
test distributions also differ. In this work, we explore the potential of
extreme dimension reduction, i.e. to very low dimensions, in improving the
performance of imp... | computer science |
2,272 | Learning General Latent-Variable Graphical Models with Predictive Belief
Propagation and Hilbert Space Embeddings | cs.LG | In this paper, we propose a new algorithm for learning general
latent-variable probabilistic graphical models using the techniques of
predictive state representation, instrumental variable regression, and
reproducing-kernel Hilbert space embeddings of distributions. Under this new
learning framework, we first convert l... | computer science |
2,273 | Multi-focus Attention Network for Efficient Deep Reinforcement Learning | cs.LG | Deep reinforcement learning (DRL) has shown incredible performance in
learning various tasks to the human level. However, unlike human perception,
current DRL models connect the entire low-level sensory input to the
state-action values rather than exploiting the relationship between and among
entities that constitute t... | computer science |
2,274 | Geometrical Insights for Implicit Generative Modeling | stat.ML | Learning algorithms for implicit generative models can optimize a variety of
criteria that measure how the data distribution differs from the implicit model
distribution, including the Wasserstein distance, the Energy distance, and the
Maximum Mean Discrepancy criterion. A careful look at the geometries induced by
thes... | computer science |
2,275 | Inverse Classification for Comparison-based Interpretability in Machine
Learning | stat.ML | In the context of post-hoc interpretability, this paper addresses the task of
explaining the prediction of a classifier, considering the case where no
information is available, neither on the classifier itself, nor on the
processed data (neither the training nor the test data). It proposes an
instance-based approach wh... | computer science |
2,276 | Obtaining Accurate Probabilistic Causal Inference by Post-Processing
Calibration | cs.AI | Discovery of an accurate causal Bayesian network structure from observational
data can be useful in many areas of science. Often the discoveries are made
under uncertainty, which can be expressed as probabilities. To guide the use of
such discoveries, including directing further investigation, it is important
that thos... | computer science |
2,277 | Building Robust Deep Neural Networks for Road Sign Detection | cs.LG | Deep Neural Networks are built to generalize outside of training set in mind
by using techniques such as regularization, early stopping and dropout. But
considerations to make them more resilient to adversarial examples are rarely
taken. As deep neural networks become more prevalent in mission-critical and
real-time sy... | computer science |
2,278 | Kernel Robust Bias-Aware Prediction under Covariate Shift | cs.LG | Under covariate shift, training (source) data and testing (target) data
differ in input space distribution, but share the same conditional label
distribution. This poses a challenging machine learning task. Robust Bias-Aware
(RBA) prediction provides the conditional label distribution that is robust to
the worstcase lo... | computer science |
2,279 | Learning Structural Weight Uncertainty for Sequential Decision-Making | stat.ML | Learning probability distributions on the weights of neural networks (NNs)
has recently proven beneficial in many applications. Bayesian methods, such as
Stein variational gradient descent (SVGD), offer an elegant framework to reason
about NN model uncertainty. However, by assuming independent Gaussian priors
for the i... | computer science |
2,280 | Deep Learning: A Critical Appraisal | cs.AI | Although deep learning has historical roots going back decades, neither the
term "deep learning" nor the approach was popular just over five years ago,
when the field was reignited by papers such as Krizhevsky, Sutskever and
Hinton's now classic (2012) deep network model of Imagenet. What has the field
discovered in th... | computer science |
2,281 | Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement
Learning with a Stochastic Actor | cs.LG | Model-free deep reinforcement learning (RL) algorithms have been demonstrated
on a range of challenging decision making and control tasks. However, these
methods typically suffer from two major challenges: very high sample complexity
and brittle convergence properties, which necessitate meticulous hyperparameter
tuning... | computer science |
2,282 | Decoupled Learning for Factorial Marked Temporal Point Processes | cs.LG | This paper introduces the factorial marked temporal point process model and
presents efficient learning methods. In conventional (multi-dimensional) marked
temporal point process models, event is often encoded by a single discrete
variable i.e. a marker. In this paper, we describe the factorial marked point
processes w... | computer science |
2,283 | Visual Analytics in Deep Learning: An Interrogative Survey for the Next
Frontiers | cs.HC | Deep learning has recently seen rapid development and significant attention
due to its state-of-the-art performance on previously-thought hard problems.
However, because of the innate complexity and nonlinear structure of deep
neural networks, the underlying decision making processes for why these models
are achieving ... | computer science |
2,284 | Extreme Learning Machine with Local Connections | cs.LG | This paper is concerned with the sparsification of the input-hidden weights
of ELM (Extreme Learning Machine). For ordinary feedforward neural networks,
the sparsification is usually done by introducing certain regularization
technique into the learning process of the network. But this strategy can not
be applied for E... | computer science |
2,285 | PRNN: Recurrent Neural Network with Persistent Memory | cs.LG | Although Recurrent Neural Network (RNN) has been a powerful tool for modeling
sequential data, its performance is inadequate when processing sequences with
multiple patterns. In this paper, we address this challenge by introducing an
external memory and constructing a novel persistent memory augmented RNN
(termed as PR... | computer science |
2,286 | Interpretable Deep Convolutional Neural Networks via Meta-learning | cs.LG | Model interpretability is a requirement in many applications in which crucial
decisions are made by users relying on a model's outputs. The recent movement
for "algorithmic fairness" also stipulates explainability, and therefore
interpretability of learning models. And yet the most successful contemporary
Machine Learn... | computer science |
2,287 | A Unified Approach for Multi-step Temporal-Difference Learning with
Eligibility Traces in Reinforcement Learning | cs.AI | Recently, a new multi-step temporal learning algorithm, called $Q(\sigma)$,
unifies $n$-step Tree-Backup (when $\sigma=0$) and $n$-step Sarsa (when
$\sigma=1$) by introducing a sampling parameter $\sigma$. However, similar to
other multi-step temporal-difference learning algorithms, $Q(\sigma)$ needs
much memory consum... | computer science |
2,288 | Beyond the One Step Greedy Approach in Reinforcement Learning | cs.AI | The famous Policy Iteration algorithm alternates between policy improvement
and policy evaluation. Implementations of this algorithm with several variants
of the latter evaluation stage, e.g, $n$-step and trace-based returns, have
been analyzed in previous works. However, the case of multiple-step lookahead
policy impr... | computer science |
2,289 | Influence-Directed Explanations for Deep Convolutional Networks | cs.LG | We study the problem of explaining a rich class of behavioral properties of
deep neural networks. Distinctively, our influence-directed explanations
approach this problem by peering inside the net- work to identify neurons with
high influence on the property and distribution of interest using an
axiomatically justified... | computer science |
2,290 | State Representation Learning for Control: An Overview | cs.AI | Representation learning algorithms are designed to learn abstract features
that characterize data. State representation learning (SRL) focuses on a
particular kind of representation learning where learned features are in low
dimension, evolve through time, and are influenced by actions of an agent. As
the representatio... | computer science |
2,291 | Deep Reinforcement Learning for Solving the Vehicle Routing Problem | cs.AI | We present an end-to-end framework for solving Vehicle Routing Problem (VRP)
using deep reinforcement learning. In this approach, we train a single model
that finds near-optimal solutions for problem instances sampled from a given
distribution, only by observing the reward signals and following feasibility
rules. Our m... | computer science |
2,292 | Quantifying Uncertainty in Discrete-Continuous and Skewed Data with
Bayesian Deep Learning | cs.LG | Deep Learning (DL) methods have been transforming computer vision with
innovative adaptations to other domains including climate change. For DL to
pervade Science and Engineering (S\&E) applications where risk management is a
core component, well-characterized uncertainty estimates must accompany
predictions. However, ... | computer science |
2,293 | Isolating Sources of Disentanglement in Variational Autoencoders | cs.LG | We decompose the evidence lower bound to show the existence of a term
measuring the total correlation between latent variables. We use this to
motivate our $\beta$-TCVAE (Total Correlation Variational Autoencoder), a
refinement of the state-of-the-art $\beta$-VAE objective for learning
disentangled representations, req... | computer science |
2,294 | Learning Deep Disentangled Embeddings with the F-Statistic Loss | cs.LG | Deep-embedding methods aim to discover representations of a domain that make
explicit the domain's class structure. Disentangling methods aim to make
explicit compositional or factorial structure. We combine these two active but
independent lines of research and propose a new paradigm for discovering
disentangled repre... | computer science |
2,295 | Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps | cs.LG | Recent developments in the field of robot grasping have shown great
improvements in the grasp success rates when dealing with unknown objects. In
this work we improve on one of the most promising approaches, the Grasp Quality
Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We
propose a new arch... | computer science |
2,296 | Online Continuous Submodular Maximization | stat.ML | In this paper, we consider an online optimization process, where the
objective functions are not convex (nor concave) but instead belong to a broad
class of continuous submodular functions. We first propose a variant of the
Frank-Wolfe algorithm that has access to the full gradient of the objective
functions. We show t... | computer science |
2,297 | Convergence of Online Mirror Descent Algorithms | cs.LG | In this paper we consider online mirror descent (OMD) algorithms, a class of
scalable online learning algorithms exploiting data geometric structures
through mirror maps. Necessary and sufficient conditions are presented in terms
of the step size sequence $\{\eta_t\}_{t}$ for the convergence of an OMD
algorithm with re... | computer science |
2,298 | Robust Estimation via Robust Gradient Estimation | stat.ML | We provide a new computationally-efficient class of estimators for risk
minimization. We show that these estimators are robust for general statistical
models: in the classical Huber epsilon-contamination model and in heavy-tailed
settings. Our workhorse is a novel robust variant of gradient descent, and we
provide cond... | computer science |
2,299 | Ordered Preference Elicitation Strategies for Supporting Multi-Objective
Decision Making | cs.LG | In multi-objective decision planning and learning, much attention is paid to
producing optimal solution sets that contain an optimal policy for every
possible user preference profile. We argue that the step that follows, i.e,
determining which policy to execute by maximising the user's intrinsic utility
function over t... | computer science |
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