Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
2,600 | Bayesian Reinforcement Learning: A Survey | cs.AI | Bayesian methods for machine learning have been widely investigated, yielding
principled methods for incorporating prior information into inference
algorithms. In this survey, we provide an in-depth review of the role of
Bayesian methods for the reinforcement learning (RL) paradigm. The major
incentives for incorporati... | computer science |
2,601 | Online and Distributed learning of Gaussian mixture models by Bayesian
Moment Matching | cs.AI | The Gaussian mixture model is a classic technique for clustering and data
modeling that is used in numerous applications. With the rise of big data,
there is a need for parameter estimation techniques that can handle streaming
data and distribute the computation over several processors. While online
variants of the Exp... | computer science |
2,602 | Fast Learning of Clusters and Topics via Sparse Posteriors | stat.ML | Mixture models and topic models generate each observation from a single
cluster, but standard variational posteriors for each observation assign
positive probability to all possible clusters. This requires dense storage and
runtime costs that scale with the total number of clusters, even though
typically only a few clu... | computer science |
2,603 | Structured Inference Networks for Nonlinear State Space Models | stat.ML | Gaussian state space models have been used for decades as generative models
of sequential data. They admit an intuitive probabilistic interpretation, have
a simple functional form, and enjoy widespread adoption. We introduce a unified
algorithm to efficiently learn a broad class of linear and non-linear state
space mod... | computer science |
2,604 | Funneled Bayesian Optimization for Design, Tuning and Control of
Autonomous Systems | cs.AI | Bayesian optimization has become a fundamental global optimization algorithm
in many problems where sample efficiency is of paramount importance. Recently,
there has been proposed a large number of new applications in fields such as
robotics, machine learning, experimental design, simulation, etc. In this
paper, we foc... | computer science |
2,605 | A new selection strategy for selective cluster ensemble based on
Diversity and Independency | stat.ML | This research introduces a new strategy in cluster ensemble selection by
using Independency and Diversity metrics. In recent years, Diversity and
Quality, which are two metrics in evaluation procedure, have been used for
selecting basic clustering results in the cluster ensemble selection. Although
quality can improve ... | computer science |
2,606 | Error Asymmetry in Causal and Anticausal Regression | cs.AI | It is generally difficult to make any statements about the expected
prediction error in an univariate setting without further knowledge about how
the data were generated. Recent work showed that knowledge about the real
underlying causal structure of a data generation process has implications for
various machine learni... | computer science |
2,607 | Safe, Multi-Agent, Reinforcement Learning for Autonomous Driving | cs.AI | Autonomous driving is a multi-agent setting where the host vehicle must apply
sophisticated negotiation skills with other road users when overtaking, giving
way, merging, taking left and right turns and while pushing ahead in
unstructured urban roadways. Since there are many possible scenarios, manually
tackling all po... | computer science |
2,608 | Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation | cs.LG | We present a fast variational Bayesian algorithm for performing non-negative
matrix factorisation and tri-factorisation. We show that our approach achieves
faster convergence per iteration and timestep (wall-clock) than Gibbs sampling
and non-probabilistic approaches, and do not require additional samples to
estimate t... | computer science |
2,609 | Improving Sampling from Generative Autoencoders with Markov Chains | cs.LG | We focus on generative autoencoders, such as variational or adversarial
autoencoders, which jointly learn a generative model alongside an inference
model. Generative autoencoders are those which are trained to softly enforce a
prior on the latent distribution learned by the inference model. We call the
distribution to ... | computer science |
2,610 | Inference Compilation and Universal Probabilistic Programming | cs.AI | We introduce a method for using deep neural networks to amortize the cost of
inference in models from the family induced by universal probabilistic
programming languages, establishing a framework that combines the strengths of
probabilistic programming and deep learning methods. We call what we do
"compilation of infer... | computer science |
2,611 | Ways of Conditioning Generative Adversarial Networks | cs.LG | The GANs are generative models whose random samples realistically reflect
natural images. It also can generate samples with specific attributes by
concatenating a condition vector into the input, yet research on this field is
not well studied. We propose novel methods of conditioning generative
adversarial networks (GA... | computer science |
2,612 | Detecting Dependencies in Sparse, Multivariate Databases Using
Probabilistic Programming and Non-parametric Bayes | stat.ML | Datasets with hundreds of variables and many missing values are commonplace.
In this setting, it is both statistically and computationally challenging to
detect true predictive relationships between variables and also to suppress
false positives. This paper proposes an approach that combines probabilistic
programming, ... | computer science |
2,613 | Reinforcement Learning Approach for Parallelization in Filters
Aggregation Based Feature Selection Algorithms | cs.LG | One of the classical problems in machine learning and data mining is feature
selection. A feature selection algorithm is expected to be quick, and at the
same time it should show high performance. MeLiF algorithm effectively solves
this problem using ensembles of ranking filters. This article describes two
different wa... | computer science |
2,614 | Hierarchical compositional feature learning | cs.LG | We introduce the hierarchical compositional network (HCN), a directed
generative model able to discover and disentangle, without supervision, the
building blocks of a set of binary images. The building blocks are binary
features defined hierarchically as a composition of some of the features in the
layer immediately be... | computer science |
2,615 | Optimal Binary Autoencoding with Pairwise Correlations | cs.LG | We formulate learning of a binary autoencoder as a biconvex optimization
problem which learns from the pairwise correlations between encoded and decoded
bits. Among all possible algorithms that use this information, ours finds the
autoencoder that reconstructs its inputs with worst-case optimal loss. The
optimal decode... | computer science |
2,616 | Recursive Decomposition for Nonconvex Optimization | cs.AI | Continuous optimization is an important problem in many areas of AI,
including vision, robotics, probabilistic inference, and machine learning.
Unfortunately, most real-world optimization problems are nonconvex, causing
standard convex techniques to find only local optima, even with extensions like
random restarts and ... | computer science |
2,617 | Importance Sampling with Unequal Support | cs.LG | Importance sampling is often used in machine learning when training and
testing data come from different distributions. In this paper we propose a new
variant of importance sampling that can reduce the variance of importance
sampling-based estimates by orders of magnitude when the supports of the
training and testing d... | computer science |
2,618 | Learning to reinforcement learn | cs.LG | In recent years deep reinforcement learning (RL) systems have attained
superhuman performance in a number of challenging task domains. However, a
major limitation of such applications is their demand for massive amounts of
training data. A critical present objective is thus to develop deep RL methods
that can adapt rap... | computer science |
2,619 | Faster variational inducing input Gaussian process classification | cs.LG | Gaussian processes (GP) provide a prior over functions and allow finding
complex regularities in data. Gaussian processes are successfully used for
classification/regression problems and dimensionality reduction. In this work
we consider the classification problem only. The complexity of standard methods
for GP-classif... | computer science |
2,620 | Learning Interpretability for Visualizations using Adapted Cox Models
through a User Experiment | stat.ML | In order to be useful, visualizations need to be interpretable. This paper
uses a user-based approach to combine and assess quality measures in order to
better model user preferences. Results show that cluster separability measures
are outperformed by a neighborhood conservation measure, even though the former
are usua... | computer science |
2,621 | Learning From Graph Neighborhoods Using LSTMs | cs.LG | Many prediction problems can be phrased as inferences over local
neighborhoods of graphs. The graph represents the interaction between entities,
and the neighborhood of each entity contains information that allows the
inferences or predictions. We present an approach for applying machine learning
directly to such graph... | computer science |
2,622 | An Efficient Training Algorithm for Kernel Survival Support Vector
Machines | cs.LG | Survival analysis is a fundamental tool in medical research to identify
predictors of adverse events and develop systems for clinical decision support.
In order to leverage large amounts of patient data, efficient optimisation
routines are paramount. We propose an efficient training algorithm for the
kernel survival su... | computer science |
2,623 | Programs as Black-Box Explanations | stat.ML | Recent work in model-agnostic explanations of black-box machine learning has
demonstrated that interpretability of complex models does not have to come at
the cost of accuracy or model flexibility. However, it is not clear what kind
of explanations, such as linear models, decision trees, and rule lists, are the
appropr... | computer science |
2,624 | Exploration for Multi-task Reinforcement Learning with Deep Generative
Models | cs.AI | Exploration in multi-task reinforcement learning is critical in training
agents to deduce the underlying MDP. Many of the existing exploration
frameworks such as $E^3$, $R_{max}$, Thompson sampling assume a single
stationary MDP and are not suitable for system identification in the multi-task
setting. We present a nove... | computer science |
2,625 | Neural Combinatorial Optimization with Reinforcement Learning | cs.AI | This paper presents a framework to tackle combinatorial optimization problems
using neural networks and reinforcement learning. We focus on the traveling
salesman problem (TSP) and train a recurrent network that, given a set of city
coordinates, predicts a distribution over different city permutations. Using
negative t... | computer science |
2,626 | Low-dimensional Data Embedding via Robust Ranking | cs.AI | We describe a new method called t-ETE for finding a low-dimensional embedding
of a set of objects in Euclidean space. We formulate the embedding problem as a
joint ranking problem over a set of triplets, where each triplet captures the
relative similarities between three objects in the set. By exploiting recent
advance... | computer science |
2,627 | Joint Causal Inference from Observational and Experimental Datasets | cs.LG | We introduce Joint Causal Inference (JCI), a powerful formulation of causal
discovery from multiple datasets that allows to jointly learn both the causal
structure and targets of interventions from statistical independences in pooled
data. Compared with existing constraint-based approaches for causal discovery
from mul... | computer science |
2,628 | Coupled Compound Poisson Factorization | cs.LG | We present a general framework, the coupled compound Poisson factorization
(CCPF), to capture the missing-data mechanism in extremely sparse data sets by
coupling a hierarchical Poisson factorization with an arbitrary data-generating
model. We derive a stochastic variational inference algorithm for the resulting
model ... | computer science |
2,629 | Multiclass MinMax Rank Aggregation | cs.LG | We introduce a new family of minmax rank aggregation problems under two
distance measures, the Kendall {\tau} and the Spearman footrule. As the
problems are NP-hard, we proceed to describe a number of constant-approximation
algorithms for solving them. We conclude with illustrative applications of the
aggregation metho... | computer science |
2,630 | Deep Generalized Canonical Correlation Analysis | cs.LG | We present Deep Generalized Canonical Correlation Analysis (DGCCA) -- a
method for learning nonlinear transformations of arbitrarily many views of
data, such that the resulting transformations are maximally informative of each
other. While methods for nonlinear two-view representation learning (Deep CCA,
(Andrew et al.... | computer science |
2,631 | Reflexive Regular Equivalence for Bipartite Data | cs.LG | Bipartite data is common in data engineering and brings unique challenges,
particularly when it comes to clustering tasks that impose on strong structural
assumptions. This work presents an unsupervised method for assessing similarity
in bipartite data. Similar to some co-clustering methods, the method is based
on regu... | computer science |
2,632 | Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC
Classes | cs.LG | In this work we study the quantitative relation between the recursive
teaching dimension (RTD) and the VC dimension (VCD) of concept classes of
finite sizes. The RTD of a concept class $\mathcal C \subseteq \{0, 1\}^n$,
introduced by Zilles et al. (2011), is a combinatorial complexity measure
characterized by the worst... | computer science |
2,633 | Cosine Normalization: Using Cosine Similarity Instead of Dot Product in
Neural Networks | cs.LG | Traditionally, multi-layer neural networks use dot product between the output
vector of previous layer and the incoming weight vector as the input to
activation function. The result of dot product is unbounded, thus increases the
risk of large variance. Large variance of neuron makes the model sensitive to
the change o... | computer science |
2,634 | Boosted Generative Models | cs.LG | We propose a novel approach for using unsupervised boosting to create an
ensemble of generative models, where models are trained in sequence to correct
earlier mistakes. Our meta-algorithmic framework can leverage any existing base
learner that permits likelihood evaluation, including recent deep expressive
models. Fur... | computer science |
2,635 | Learning What Data to Learn | cs.LG | Machine learning is essentially the sciences of playing with data. An
adaptive data selection strategy, enabling to dynamically choose different data
at various training stages, can reach a more effective model in a more
efficient way. In this paper, we propose a deep reinforcement learning
framework, which we call \em... | computer science |
2,636 | Bridging the Gap Between Value and Policy Based Reinforcement Learning | cs.AI | We establish a new connection between value and policy based reinforcement
learning (RL) based on a relationship between softmax temporal value
consistency and policy optimality under entropy regularization. Specifically,
we show that softmax consistent action values correspond to optimal entropy
regularized policy pro... | computer science |
2,637 | Provably Optimal Algorithms for Generalized Linear Contextual Bandits | cs.LG | Contextual bandits are widely used in Internet services from news
recommendation to advertising, and to Web search. Generalized linear models
(logistical regression in particular) have demonstrated stronger performance
than linear models in many applications where rewards are binary. However, most
theoretical analyses ... | computer science |
2,638 | The Statistical Recurrent Unit | cs.LG | Sophisticated gated recurrent neural network architectures like LSTMs and
GRUs have been shown to be highly effective in a myriad of applications. We
develop an un-gated unit, the statistical recurrent unit (SRU), that is able to
learn long term dependencies in data by only keeping moving averages of
statistics. The SR... | computer science |
2,639 | Learning to Optimize Neural Nets | cs.LG | Learning to Optimize is a recently proposed framework for learning
optimization algorithms using reinforcement learning. In this paper, we explore
learning an optimization algorithm for training shallow neural nets. Such
high-dimensional stochastic optimization problems present interesting
challenges for existing reinf... | computer science |
2,640 | Unsupervised Basis Function Adaptation for Reinforcement Learning | cs.AI | When using reinforcement learning (RL) algorithms to evaluate a policy it is
common, given a large state space, to introduce some form of approximation
architecture for the value function (VF). The exact form of this architecture
can have a significant effect on the accuracy of the VF estimate, however, and
determining... | computer science |
2,641 | Contextual Multi-armed Bandits under Feature Uncertainty | cs.AI | We study contextual multi-armed bandit problems under linear realizability on
rewards and uncertainty (or noise) on features. For the case of identical noise
on features across actions, we propose an algorithm, coined {\em NLinRel},
having $O\left(T^{\frac{7}{8}} \left(\log{(dT)}+K\sqrt{d}\right)\right)$ regret
bound f... | computer science |
2,642 | Right for the Right Reasons: Training Differentiable Models by
Constraining their Explanations | cs.LG | Neural networks are among the most accurate supervised learning methods in
use today, but their opacity makes them difficult to trust in critical
applications, especially when conditions in training differ from those in test.
Recent work on explanations for black-box models has produced tools (e.g. LIME)
to show the im... | computer science |
2,643 | Bayesian Optimization with Gradients | stat.ML | Bayesian optimization has been successful at global optimization of
expensive-to-evaluate multimodal objective functions. However, unlike most
optimization methods, Bayesian optimization typically does not use derivative
information. In this paper we show how Bayesian optimization can exploit
derivative information to ... | computer science |
2,644 | Understanding Black-box Predictions via Influence Functions | stat.ML | How can we explain the predictions of a black-box model? In this paper, we
use influence functions -- a classic technique from robust statistics -- to
trace a model's prediction through the learning algorithm and back to its
training data, thereby identifying training points most responsible for a given
prediction. To ... | computer science |
2,645 | Minimax Regret Bounds for Reinforcement Learning | stat.ML | We consider the problem of provably optimal exploration in reinforcement
learning for finite horizon MDPs. We show that an optimistic modification to
value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} +
H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states,
$A$ the number of action... | computer science |
2,646 | Efficient Online Learning for Optimizing Value of Information: Theory
and Application to Interactive Troubleshooting | cs.AI | We consider the optimal value of information (VoI) problem, where the goal is
to sequentially select a set of tests with a minimal cost, so that one can
efficiently make the best decision based on the observed outcomes. Existing
algorithms are either heuristics with no guarantees, or scale poorly (with
exponential run ... | computer science |
2,647 | Deep Exploration via Randomized Value Functions | stat.ML | We study the use of randomized value functions to guide deep exploration in
reinforcement learning. This offers an elegant means for synthesizing
statistically and computationally efficient exploration with common practical
approaches to value function learning. We present several reinforcement
learning algorithms that... | computer science |
2,648 | Sample and Computationally Efficient Learning Algorithms under S-Concave
Distributions | stat.ML | We provide new results for noise-tolerant and sample-efficient learning
algorithms under $s$-concave distributions. The new class of $s$-concave
distributions is a broad and natural generalization of log-concavity, and
includes many important additional distributions, e.g., the Pareto distribution
and $t$-distribution.... | computer science |
2,649 | Unsupervised Basis Function Adaptation for Reinforcement Learning | cs.LG | When using reinforcement learning (RL) algorithms to evaluate a policy it is
common, given a large state space, to introduce some form of approximation
architecture for the value function (VF). The exact form of this architecture
can have a significant effect on the accuracy of the VF estimate, however, and
determining... | computer science |
2,650 | Smart Augmentation - Learning an Optimal Data Augmentation Strategy | cs.AI | A recurring problem faced when training neural networks is that there is
typically not enough data to maximize the generalization capability of deep
neural networks(DNN). There are many techniques to address this, including data
augmentation, dropout, and transfer learning. In this paper, we introduce an
additional met... | computer science |
2,651 | Inverse Reinforcement Learning from Incomplete Observation Data | cs.LG | Inverse reinforcement learning (IRL) aims to explain observed strategic
behavior by fitting reinforcement learning models to behavioral data. However,
traditional IRL methods are only applicable when the observations are in the
form of state-action paths. This assumption may not hold in many real-world
modelling settin... | computer science |
2,652 | The Top 10 Topics in Machine Learning Revisited: A Quantitative
Meta-Study | cs.LG | Which topics of machine learning are most commonly addressed in research?
This question was initially answered in 2007 by doing a qualitative survey
among distinguished researchers. In our study, we revisit this question from a
quantitative perspective. Concretely, we collect 54K abstracts of papers
published between 2... | computer science |
2,653 | Reliable Decision Support using Counterfactual Models | stat.ML | Decision-makers are faced with the challenge of estimating what is likely to
happen when they take an action. For instance, if I choose not to treat this
patient, are they likely to die? Practitioners commonly use supervised learning
algorithms to fit predictive models that help decision-makers reason about
likely futu... | computer science |
2,654 | On the Reliable Detection of Concept Drift from Streaming Unlabeled Data | stat.ML | Classifiers deployed in the real world operate in a dynamic environment,
where the data distribution can change over time. These changes, referred to as
concept drift, can cause the predictive performance of the classifier to drop
over time, thereby making it obsolete. To be of any real use, these classifiers
need to d... | computer science |
2,655 | Semi-Supervised Generation with Cluster-aware Generative Models | stat.ML | Deep generative models trained with large amounts of unlabelled data have
proven to be powerful within the domain of unsupervised learning. Many real
life data sets contain a small amount of labelled data points, that are
typically disregarded when training generative models. We propose the
Cluster-aware Generative Mod... | computer science |
2,656 | Multi-Advisor Reinforcement Learning | cs.LG | We consider tackling a single-agent RL problem by distributing it to $n$
learners. These learners, called advisors, endeavour to solve the problem from
a different focus. Their advice, taking the form of action values, is then
communicated to an aggregator, which is in control of the system. We show that
the local plan... | computer science |
2,657 | Treatment-Response Models for Counterfactual Reasoning with
Continuous-time, Continuous-valued Interventions | stat.ML | Treatment effects can be estimated from observational data as the difference
in potential outcomes. In this paper, we address the challenge of estimating
the potential outcome when treatment-dose levels can vary continuously over
time. Further, the outcome variable may not be measured at a regular frequency.
Our propos... | computer science |
2,658 | Dynamic Safe Interruptibility for Decentralized Multi-Agent
Reinforcement Learning | cs.AI | In reinforcement learning, agents learn by performing actions and observing
their outcomes. Sometimes, it is desirable for a human operator to
\textit{interrupt} an agent in order to prevent dangerous situations from
happening. Yet, as part of their learning process, agents may link these
interruptions, that impact the... | computer science |
2,659 | Value Directed Exploration in Multi-Armed Bandits with Structured Priors | cs.LG | Multi-armed bandits are a quintessential machine learning problem requiring
the balancing of exploration and exploitation. While there has been progress in
developing algorithms with strong theoretical guarantees, there has been less
focus on practical near-optimal finite-time performance. In this paper, we
propose an ... | computer science |
2,660 | DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks | cs.AI | A key enabler for optimizing business processes is accurately estimating the
probability distribution of a time series future given its past. Such
probabilistic forecasts are crucial for example for reducing excess inventory
in supply chains. In this paper we propose DeepAR, a novel methodology for
producing accurate p... | computer science |
2,661 | Simultaneous Policy Learning and Latent State Inference for Imitating
Driver Behavior | cs.LG | In this work, we propose a method for learning driver models that account for
variables that cannot be observed directly. When trained on a synthetic
dataset, our models are able to learn encodings for vehicle trajectories that
distinguish between four distinct classes of driver behavior. Such encodings
are learned wit... | computer science |
2,662 | Knowledge Fusion via Embeddings from Text, Knowledge Graphs, and Images | cs.AI | We present a baseline approach for cross-modal knowledge fusion. Different
basic fusion methods are evaluated on existing embedding approaches to show the
potential of joining knowledge about certain concepts across modalities in a
fused concept representation. | computer science |
2,663 | Semi-supervised Bayesian Deep Multi-modal Emotion Recognition | cs.AI | In emotion recognition, it is difficult to recognize human's emotional states
using just a single modality. Besides, the annotation of physiological
emotional data is particularly expensive. These two aspects make the building
of effective emotion recognition model challenging. In this paper, we first
build a multi-vie... | computer science |
2,664 | A quantitative assessment of the effect of different algorithmic schemes
to the task of learning the structure of Bayesian Networks | cs.LG | One of the most challenging tasks when adopting Bayesian Networks (BNs) is
the one of learning their structure from data. This task is complicated by the
huge search space of possible solutions and turned out to be a well-known
NP-hard problem and, hence, approximations are required. However, to the best
of our knowled... | computer science |
2,665 | Experimental results : Reinforcement Learning of POMDPs using Spectral
Methods | cs.AI | We propose a new reinforcement learning algorithm for partially observable
Markov decision processes (POMDP) based on spectral decomposition methods.
While spectral methods have been previously employed for consistent learning of
(passive) latent variable models such as hidden Markov models, POMDPs are more
challenging... | computer science |
2,666 | Improving drug sensitivity predictions in precision medicine through
active expert knowledge elicitation | cs.AI | Predicting the efficacy of a drug for a given individual, using
high-dimensional genomic measurements, is at the core of precision medicine.
However, identifying features on which to base the predictions remains a
challenge, especially when the sample size is small. Incorporating expert
knowledge offers a promising alt... | computer science |
2,667 | Context Attentive Bandits: Contextual Bandit with Restricted Context | cs.AI | We consider a novel formulation of the multi-armed bandit model, which we
call the contextual bandit with restricted context, where only a limited number
of features can be accessed by the learner at every iteration. This novel
formulation is motivated by different online problems arising in clinical
trials, recommende... | computer science |
2,668 | Long-term Blood Pressure Prediction with Deep Recurrent Neural Networks | cs.LG | Existing methods for arterial blood pressure (BP) estimation directly map the
input physiological signals to output BP values without explicitly modeling the
underlying temporal dependencies in BP dynamics. As a result, these models
suffer from accuracy decay over a long time and thus require frequent
calibration. In t... | computer science |
2,669 | Monaural Audio Speaker Separation with Source Contrastive Estimation | cs.SD | We propose an algorithm to separate simultaneously speaking persons from each
other, the "cocktail party problem", using a single microphone. Our approach
involves a deep recurrent neural networks regression to a vector space that is
descriptive of independent speakers. Such a vector space can embed empirically
determi... | computer science |
2,670 | Discrete Sequential Prediction of Continuous Actions for Deep RL | cs.LG | It has long been assumed that high dimensional continuous control problems
cannot be solved effectively by discretizing individual dimensions of the
action space due to the exponentially large number of bins over which policies
would have to be learned. In this paper, we draw inspiration from the recent
success of sequ... | computer science |
2,671 | Learning Probabilistic Programs Using Backpropagation | cs.LG | Probabilistic modeling enables combining domain knowledge with learning from
data, thereby supporting learning from fewer training instances than purely
data-driven methods. However, learning probabilistic models is difficult and
has not achieved the level of performance of methods such as deep neural
networks on many ... | computer science |
2,672 | Learning Hard Alignments with Variational Inference | cs.AI | There has recently been significant interest in hard attention models for
tasks such as object recognition, visual captioning and speech recognition.
Hard attention can offer benefits over soft attention such as decreased
computational cost, but training hard attention models can be difficult because
of the discrete la... | computer science |
2,673 | Optimal Warping Paths are unique for almost every Pair of Time Series | cs.LG | Update rules for learning in dynamic time warping spaces are based on optimal
warping paths between parameter and input time series. In general, optimal
warping paths are not unique resulting in adverse effects in theory and
practice. Under the assumption of squared error local costs, we show that no
two warping paths ... | computer science |
2,674 | VAE with a VampPrior | cs.LG | Many different methods to train deep generative models have been introduced
in the past. In this paper, we propose to extend the variational auto-encoder
(VAE) framework with a new type of prior which we call "Variational Mixture of
Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture
distribu... | computer science |
2,675 | Ensemble Sampling | stat.ML | Thompson sampling has emerged as an effective heuristic for a broad range of
online decision problems. In its basic form, the algorithm requires computing
and sampling from a posterior distribution over models, which is tractable only
for simple special cases. This paper develops ensemble sampling, which aims to
approx... | computer science |
2,676 | Shallow Updates for Deep Reinforcement Learning | cs.AI | Deep reinforcement learning (DRL) methods such as the Deep Q-Network (DQN)
have achieved state-of-the-art results in a variety of challenging,
high-dimensional domains. This success is mainly attributed to the power of
deep neural networks to learn rich domain representations for approximating the
value function or pol... | computer science |
2,677 | Poincaré Embeddings for Learning Hierarchical Representations | cs.AI | Representation learning has become an invaluable approach for learning from
symbolic data such as text and graphs. However, while complex symbolic datasets
often exhibit a latent hierarchical structure, state-of-the-art methods
typically learn embeddings in Euclidean vector spaces, which do not account for
this propert... | computer science |
2,678 | Continual Learning in Generative Adversarial Nets | cs.LG | Developments in deep generative models have allowed for tractable learning of
high-dimensional data distributions. While the employed learning procedures
typically assume that training data is drawn i.i.d. from the distribution of
interest, it may be desirable to model distinct distributions which are
observed sequenti... | computer science |
2,679 | Safe Model-based Reinforcement Learning with Stability Guarantees | stat.ML | Reinforcement learning is a powerful paradigm for learning optimal policies
from experimental data. However, to find optimal policies, most reinforcement
learning algorithms explore all possible actions, which may be harmful for
real-world systems. As a consequence, learning algorithms are rarely applied on
safety-crit... | computer science |
2,680 | Semi-supervised Learning with GANs: Manifold Invariance with Improved
Inference | cs.LG | Semi-supervised learning methods using Generative Adversarial Networks (GANs)
have shown promising empirical success recently. Most of these methods use a
shared discriminator/classifier which discriminates real examples from fake
while also predicting the class label. Motivated by the ability of the GANs
generator to ... | computer science |
2,681 | State Space Decomposition and Subgoal Creation for Transfer in Deep
Reinforcement Learning | cs.AI | Typical reinforcement learning (RL) agents learn to complete tasks specified
by reward functions tailored to their domain. As such, the policies they learn
do not generalize even to similar domains. To address this issue, we develop a
framework through which a deep RL agent learns to generalize policies from
smaller, s... | computer science |
2,682 | AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks | cs.LG | New types of machine learning hardware in development and entering the market
hold the promise of revolutionizing deep learning in a manner as profound as
GPUs. However, existing software frameworks and training algorithms for deep
learning have yet to evolve to fully leverage the capability of the new wave of
silicon.... | computer science |
2,683 | Contextual Explanation Networks | cs.LG | We introduce contextual explanation networks (CENs)---a class of models that
learn to predict by generating and leveraging intermediate explanations. CENs
are deep networks that generate parameters for context-specific probabilistic
graphical models which are further used for prediction and play the role of
explanation... | computer science |
2,684 | Learning Disentangled Representations with Semi-Supervised Deep
Generative Models | stat.ML | Variational autoencoders (VAEs) learn representations of data by jointly
training a probabilistic encoder and decoder network. Typically these models
encode all features of the data into a single variable. Here we are interested
in learning disentangled representations that encode distinct aspects of the
data into sepa... | computer science |
2,685 | Hyperparameter Optimization: A Spectral Approach | cs.LG | We give a simple, fast algorithm for hyperparameter optimization inspired by
techniques from the analysis of Boolean functions. We focus on the
high-dimensional regime where the canonical example is training a neural
network with a large number of hyperparameters. The algorithm --- an iterative
application of compresse... | computer science |
2,686 | InfoVAE: Information Maximizing Variational Autoencoders | cs.LG | It has been previously observed that variational autoencoders tend to ignore
the latent code when combined with a decoding distribution that is too
flexible. This undermines the purpose of unsupervised representation learning.
In this paper, we additionally show that existing training criteria can lead to
extremely poo... | computer science |
2,687 | Symmetry Learning for Function Approximation in Reinforcement Learning | stat.ML | In this paper we explore methods to exploit symmetries for ensuring sample
efficiency in reinforcement learning (RL), this problem deserves ever
increasing attention with the recent advances in the use of deep networks for
complex RL tasks which require large amount of training data. We introduce a
novel method to dete... | computer science |
2,688 | Decoupling Learning Rules from Representations | cs.AI | In the artificial intelligence field, learning often corresponds to changing
the parameters of a parameterized function. A learning rule is an algorithm or
mathematical expression that specifies precisely how the parameters should be
changed. When creating an artificial intelligence system, we must make two
decisions: ... | computer science |
2,689 | Gradient descent GAN optimization is locally stable | cs.LG | Despite the growing prominence of generative adversarial networks (GANs),
optimization in GANs is still a poorly understood topic. In this paper, we
analyze the "gradient descent" form of GAN optimization i.e., the natural
setting where we simultaneously take small gradient steps in both generator and
discriminator par... | computer science |
2,690 | An Overview of Multi-Task Learning in Deep Neural Networks | cs.LG | Multi-task learning (MTL) has led to successes in many applications of
machine learning, from natural language processing and speech recognition to
computer vision and drug discovery. This article aims to give a general
overview of MTL, particularly in deep neural networks. It introduces the two
most common methods for... | computer science |
2,691 | Bayesian Conditional Generative Adverserial Networks | cs.LG | Traditional GANs use a deterministic generator function (typically a neural
network) to transform a random noise input $z$ to a sample $\mathbf{x}$ that
the discriminator seeks to distinguish. We propose a new GAN called Bayesian
Conditional Generative Adversarial Networks (BC-GANs) that use a random
generator function... | computer science |
2,692 | Modified Frank-Wolfe Algorithm for Enhanced Sparsity in Support Vector
Machine Classifiers | cs.LG | This work proposes a new algorithm for training a re-weighted L2 Support
Vector Machine (SVM), inspired on the re-weighted Lasso algorithm of Cand\`es
et al. and on the equivalence between Lasso and SVM shown recently by Jaggi. In
particular, the margin required for each training vector is set independently,
defining a... | computer science |
2,693 | Consistent feature attribution for tree ensembles | cs.AI | Note that a newer expanded version of this paper is now available at:
arXiv:1802.03888
It is critical in many applications to understand what features are important
for a model, and why individual predictions were made. For tree ensemble
methods these questions are usually answered by attributing importance values
to... | computer science |
2,694 | Robust and Efficient Transfer Learning with Hidden-Parameter Markov
Decision Processes | stat.ML | We introduce a new formulation of the Hidden Parameter Markov Decision
Process (HiP-MDP), a framework for modeling families of related tasks using
low-dimensional latent embeddings. Our new framework correctly models the joint
uncertainty in the latent parameters and the state space. We also replace the
original Gaussi... | computer science |
2,695 | Observational Learning by Reinforcement Learning | cs.LG | Observational learning is a type of learning that occurs as a function of
observing, retaining and possibly replicating or imitating the behaviour of
another agent. It is a core mechanism appearing in various instances of social
learning and has been found to be employed in several intelligent species,
including humans... | computer science |
2,696 | A-NICE-MC: Adversarial Training for MCMC | stat.ML | Existing Markov Chain Monte Carlo (MCMC) methods are either based on
general-purpose and domain-agnostic schemes which can lead to slow convergence,
or hand-crafting of problem-specific proposals by an expert. We propose
A-NICE-MC, a novel method to train flexible parametric Markov chain kernels to
produce samples with... | computer science |
2,697 | Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of
learning relational order via reinforcement learning procedure? | cs.AI | In this article, we extend the conventional framework of
convolutional-Restricted-Boltzmann-Machine to learn highly abstract features
among abitrary number of time related input maps by constructing a layer of
multiplicative units, which capture the relations among inputs. In many cases,
more than two maps are strongly... | computer science |
2,698 | Towards Understanding Generalization of Deep Learning: Perspective of
Loss Landscapes | cs.LG | It is widely observed that deep learning models with learned parameters
generalize well, even with much more model parameters than the number of
training samples. We systematically investigate the underlying reasons why deep
neural networks often generalize well, and reveal the difference between the
minima (with the s... | computer science |
2,699 | Hashing Over Predicted Future Frames for Informed Exploration of Deep
Reinforcement Learning | cs.LG | In reinforcement learning (RL) tasks, an efficient exploration mechanism
should be able to encourage an agent to take actions that lead to less frequent
states which may yield higher accumulative future return. However, both knowing
about the future and evaluating the frequentness of states are non-trivial
tasks, espec... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.