Unnamed: 0 int64 0 41k | title stringlengths 4 274 | category stringlengths 5 18 | summary stringlengths 22 3.66k | theme stringclasses 8
values |
|---|---|---|---|---|
2,800 | Combining Symbolic and Function Evaluation Expressions In Neural
Programs | cs.LG | Neural programming involves training neural networks to learn programs from
data. Previous works have failed to achieve good generalization performance,
especially on programs with high complexity or on large domains. This is
because they mostly rely either on black-box function evaluations that do not
capture the stru... | computer science |
2,801 | tau-FPL: Tolerance-Constrained Learning in Linear Time | cs.LG | Learning a classifier with control on the false-positive rate plays a
critical role in many machine learning applications. Existing approaches either
introduce prior knowledge dependent label cost or tune parameters based on
traditional classifiers, which lack consistency in methodology because they do
not strictly adh... | computer science |
2,802 | Time Series Segmentation through Automatic Feature Learning | cs.LG | Internet of things (IoT) applications have become increasingly popular in
recent years, with applications ranging from building energy monitoring to
personal health tracking and activity recognition. In order to leverage these
data, automatic knowledge extraction - whereby we map from observations to
interpretable stat... | computer science |
2,803 | An Empirical Analysis of Proximal Policy Optimization with
Kronecker-factored Natural Gradients | cs.AI | In this technical report, we consider an approach that combines the PPO
objective and K-FAC natural gradient optimization, for which we call PPOKFAC.
We perform a range of empirical analysis on various aspects of the algorithm,
such as sample complexity, training speed, and sensitivity to batch size and
training epochs... | computer science |
2,804 | Faster Algorithms for Large-scale Machine Learning using Simple Sampling
Techniques | cs.LG | Now a days, the major challenge in machine learning is the `Big~Data'
challenge. The big data problems due to large number of data points or large
number of features in each data point, or both, the training of models have
become very slow. The training time has two major components: Time to access
the data and time to... | computer science |
2,805 | Active Learning of Strict Partial Orders: A Case Study on Concept
Prerequisite Relations | cs.LG | Strict partial order is a mathematical structure commonly seen in relational
data. One obstacle to extracting such type of relations at scale is the lack of
large-scale labels for building effective data-driven solutions. We develop an
active learning framework for mining such relations subject to a strict order.
Our a... | computer science |
2,806 | Cross-Domain Transfer in Reinforcement Learning using Target Apprentice | cs.AI | In this paper, we present a new approach to Transfer Learning (TL) in
Reinforcement Learning (RL) for cross-domain tasks. Many of the available
techniques approach the transfer architecture as a method of speeding up the
target task learning. We propose to adapt and reuse the mapped source task
optimal-policy directly ... | computer science |
2,807 | Optimal Convergence for Distributed Learning with Stochastic Gradient
Methods and Spectral-Regularization Algorithms | stat.ML | We study generalization properties of distributed algorithms in the setting
of nonparametric regression over a reproducing kernel Hilbert space (RKHS). We
first investigate distributed stochastic gradient methods (SGM), with
mini-batches and multi-passes over the data. We show that optimal
generalization error bounds c... | computer science |
2,808 | Hybrid Gradient Boosting Trees and Neural Networks for Forecasting
Operating Room Data | cs.LG | Time series data constitutes a distinct and growing problem in machine
learning. As the corpus of time series data grows larger, deep models that
simultaneously learn features and classify with these features can be
intractable or suboptimal. In this paper, we present feature learning via long
short term memory (LSTM) ... | computer science |
2,809 | Bayesian Neural Networks | cs.LG | This paper describes and discusses Bayesian Neural Network (BNN). The paper
showcases a few different applications of them for classification and
regression problems. BNNs are comprised of a Probabilistic Model and a Neural
Network. The intent of such a design is to combine the strengths of Neural
Networks and Stochast... | computer science |
2,810 | MaskGAN: Better Text Generation via Filling in the______ | stat.ML | Neural text generation models are often autoregressive language models or
seq2seq models. These models generate text by sampling words sequentially, with
each word conditioned on the previous word, and are state-of-the-art for
several machine translation and summarization benchmarks. These benchmarks are
often defined ... | computer science |
2,811 | COBRA: A Fast and Simple Method for Active Clustering with Pairwise
Constraints | cs.AI | Clustering is inherently ill-posed: there often exist multiple valid
clusterings of a single dataset, and without any additional information a
clustering system has no way of knowing which clustering it should produce.
This motivates the use of constraints in clustering, as they allow users to
communicate their interes... | computer science |
2,812 | Pretraining Deep Actor-Critic Reinforcement Learning Algorithms With
Expert Demonstrations | cs.AI | Pretraining with expert demonstrations have been found useful in speeding up
the training process of deep reinforcement learning algorithms since less
online simulation data is required. Some people use supervised learning to
speed up the process of feature learning, others pretrain the policies by
imitating expert dem... | computer science |
2,813 | Scalable Lévy Process Priors for Spectral Kernel Learning | stat.ML | Gaussian processes are rich distributions over functions, with generalization
properties determined by a kernel function. When used for long-range
extrapolation, predictions are particularly sensitive to the choice of kernel
parameters. It is therefore critical to account for kernel uncertainty in our
predictive distri... | computer science |
2,814 | Causal Learning and Explanation of Deep Neural Networks via Autoencoded
Activations | cs.AI | Deep neural networks are complex and opaque. As they enter application in a
variety of important and safety critical domains, users seek methods to explain
their output predictions. We develop an approach to explaining deep neural
networks by constructing causal models on salient concepts contained in a CNN.
We develop... | computer science |
2,815 | Short-term Memory of Deep RNN | cs.LG | The extension of deep learning towards temporal data processing is gaining an
increasing research interest. In this paper we investigate the properties of
state dynamics developed in successive levels of deep recurrent neural networks
(RNNs) in terms of short-term memory abilities. Our results reveal interesting
insigh... | computer science |
2,816 | Adaptive Representation Selection in Contextual Bandit with Unlabeled
History | cs.AI | We consider an extension of the contextual bandit setting, motivated by
several practical applications, where an unlabeled history of contexts can
become available for pre-training before the online decision-making begins. We
propose an approach for improving the performance of contextual bandit in such
setting, via ad... | computer science |
2,817 | Blind Pre-Processing: A Robust Defense Method Against Adversarial
Examples | cs.LG | Deep learning algorithms and networks are vulnerable to perturbed inputs
which is known as the adversarial attack. Many defense methodologies have been
investigated to defend against such adversarial attack. In this work, we
propose a novel methodology to defend the existing powerful attack model. We
for the first time... | computer science |
2,818 | DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk
Prediction | cs.LG | We train and validate a semi-supervised, multi-task LSTM on 57,675
person-weeks of data from off-the-shelf wearable heart rate sensors, showing
high accuracy at detecting multiple medical conditions, including diabetes
(0.8451), high cholesterol (0.7441), high blood pressure (0.8086), and sleep
apnea (0.8298). We compa... | computer science |
2,819 | Applying Cooperative Machine Learning to Speed Up the Annotation of
Social Signals in Large Multi-modal Corpora | cs.HC | Scientific disciplines, such as Behavioural Psychology, Anthropology and
recently Social Signal Processing are concerned with the systematic exploration
of human behaviour. A typical work-flow includes the manual annotation (also
called coding) of social signals in multi-modal corpora of considerable size.
For the invo... | computer science |
2,820 | Learning Robust Options | cs.AI | Robust reinforcement learning aims to produce policies that have strong
guarantees even in the face of environments/transition models whose parameters
have strong uncertainty. Existing work uses value-based methods and the usual
primitive action setting. In this paper, we propose robust methods for learning
temporally ... | computer science |
2,821 | Using Discretization for Extending the Set of Predictive Features | cs.LG | To date, attribute discretization is typically performed by replacing the
original set of continuous features with a transposed set of discrete ones.
This paper provides support for a new idea that discretized features should
often be used in addition to existing features and as such, datasets should be
extended, and n... | computer science |
2,822 | Predicting Customer Churn: Extreme Gradient Boosting with Temporal Data | stat.ML | Accurately predicting customer churn using large scale time-series data is a
common problem facing many business domains. The creation of model features
across various time windows for training and testing can be particularly
challenging due to temporal issues common to time-series data. In this paper,
we will explore ... | computer science |
2,823 | On the Connection between Differential Privacy and Adversarial
Robustness in Machine Learning | stat.ML | Adversarial examples in machine learning has been a topic of intense research
interest, with attacks and defenses being developed in a tight back-and-forth.
Most past defenses are best-effort, heuristic approaches that have all been
shown to be vulnerable to sophisticated attacks. More recently, rigorous
defenses that ... | computer science |
2,824 | Path Consistency Learning in Tsallis Entropy Regularized MDPs | cs.AI | We study the sparse entropy-regularized reinforcement learning (ERL) problem
in which the entropy term is a special form of the Tsallis entropy. The optimal
policy of this formulation is sparse, i.e.,~at each state, it has non-zero
probability for only a small number of actions. This addresses the main
drawback of the ... | computer science |
2,825 | Learning Multiple Levels of Representations with Kernel Machines | cs.LG | We propose a connectionist-inspired kernel machine model with three key
advantages over traditional kernel machines. First, it is capable of learning
distributed and hierarchical representations. Second, its performance is highly
robust to the choice of kernel function. Third, the solution space is not
limited to the s... | computer science |
2,826 | Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep
Neural Networks | cs.LG | In general, neural networks are not currently capable of learning tasks in a
sequential fashion. When a novel, unrelated task is learnt by a neural network,
it substantially forgets how to solve previously learnt tasks. One of the
original solutions to this problem is pseudo-rehearsal, which involves learning
the new t... | computer science |
2,827 | Global Model Interpretation via Recursive Partitioning | cs.LG | In this work, we propose a simple but effective method to interpret black-box
machine learning models globally. That is, we use a compact binary tree, the
interpretation tree, to explicitly represent the most important decision rules
that are implicitly contained in the black-box machine learning models. This
tree is l... | computer science |
2,828 | Efficient Model-Based Deep Reinforcement Learning with Variational State
Tabulation | cs.LG | Modern reinforcement learning algorithms reach super-human performance in
many board and video games, but they are sample inefficient, i.e. they
typically require significantly more playing experience than humans to reach an
equal performance level. To improve sample efficiency, an agent may build a
model of the enviro... | computer science |
2,829 | Efficient Exploration through Bayesian Deep Q-Networks | cs.AI | We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling
based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for
targeted exploration in high dimensions through posterior sampling but is
usually computationally expensive. We address this limitation by introducing
uncertainty only at th... | computer science |
2,830 | Learning to Search with MCTSnets | cs.AI | Planning problems are among the most important and well-studied problems in
artificial intelligence. They are most typically solved by tree search
algorithms that simulate ahead into the future, evaluate future states, and
back-up those evaluations to the root of a search tree. Among these algorithms,
Monte-Carlo tree ... | computer science |
2,831 | Progressive Reinforcement Learning with Distillation for Multi-Skilled
Motion Control | cs.LG | Deep reinforcement learning has demonstrated increasing capabilities for
continuous control problems, including agents that can move with skill and
agility through their environment. An open problem in this setting is that of
developing good strategies for integrating or merging policies for multiple
skills, where each... | computer science |
2,832 | Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical
Care | cs.LG | Patients in the intensive care unit (ICU) require constant and close
supervision. To assist clinical staff in this task, hospitals use monitoring
systems that trigger audiovisual alarms if their algorithms indicate that a
patient's condition may be worsening. However, current monitoring systems are
extremely sensitive ... | computer science |
2,833 | Reinforcement Learning from Imperfect Demonstrations | cs.AI | Robust real-world learning should benefit from both demonstrations and
interactions with the environment. Current approaches to learning from
demonstration and reward perform supervised learning on expert demonstration
data and use reinforcement learning to further improve performance based on the
reward received from ... | computer science |
2,834 | Admissible Time Series Motif Discovery with Missing Data | cs.LG | The discovery of time series motifs has emerged as one of the most useful
primitives in time series data mining. Researchers have shown its utility for
exploratory data mining, summarization, visualization, segmentation,
classification, clustering, and rule discovery. Although there has been more
than a decade of exten... | computer science |
2,835 | A Unified View of Causal and Non-causal Feature Selection | cs.AI | In this paper, we unify causal and non-causal feature selection methods based
on the Bayesian network framework. We first show that the objectives of causal
and non-causal feature selection methods are equal and are to find the Markov
blanket of a class attribute, the theoretically optimal feature set for
classificatio... | computer science |
2,836 | Combining Linear Non-Gaussian Acyclic Model with Logistic Regression
Model for Estimating Causal Structure from Mixed Continuous and Discrete Data | cs.LG | Estimating causal models from observational data is a crucial task in data
analysis. For continuous-valued data, Shimizu et al. have proposed a linear
acyclic non-Gaussian model to understand the data generating process, and have
shown that their model is identifiable when the number of data is sufficiently
large. Howe... | computer science |
2,837 | Scalable Alignment Kernels via Space-Efficient Feature Maps | cs.LG | String kernels are attractive data analysis tools for analyzing string data.
Among them, alignment kernels are known for their high prediction accuracies in
string classifications when tested in combination with SVMs in various
applications. However, alignment kernels have a crucial drawback in that they
scale poorly d... | computer science |
2,838 | Sim-To-Real Optimization Of Complex Real World Mobile Network with
Imperfect Information via Deep Reinforcement Learning from Self-play | cs.AI | Mobile network that millions of people use every day is one of the most
complex systems in real world. Optimization of mobile network to meet exploding
customer demand and reduce CAPEX/OPEX poses greater challenges than in prior
works. Learning to solve complex problems in real world to benefit everyone and
make the wo... | computer science |
2,839 | Simultaneous Modeling of Multiple Complications for Risk Profiling in
Diabetes Care | cs.LG | Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in
multiple complications. Risk prediction and profiling of T2DM complications is
critical for healthcare professionals to design personalized treatment plans
for patients in diabetes care for improved outcomes. In this paper, we study
the risk of ... | computer science |
2,840 | Accelerated Primal-Dual Policy Optimization for Safe Reinforcement
Learning | cs.AI | Constrained Markov Decision Process (CMDP) is a natural framework for
reinforcement learning tasks with safety constraints, where agents learn a
policy that maximizes the long-term reward while satisfying the constraints on
the long-term cost. A canonical approach for solving CMDPs is the primal-dual
method which updat... | computer science |
2,841 | Subspace Network: Deep Multi-Task Censored Regression for Modeling
Neurodegenerative Diseases | cs.LG | Over the past decade a wide spectrum of machine learning models have been
developed to model the neurodegenerative diseases, associating biomarkers,
especially non-intrusive neuroimaging markers, with key clinical scores
measuring the cognitive status of patients. Multi-task learning (MTL) has been
commonly utilized by... | computer science |
2,842 | Robust Maximization of Non-Submodular Objectives | stat.ML | We study the problem of maximizing a monotone set function subject to a
cardinality constraint $k$ in the setting where some number of elements $\tau$
is deleted from the returned set. The focus of this work is on the worst-case
adversarial setting. While there exist constant-factor guarantees when the
function is subm... | computer science |
2,843 | Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic
Corrections | cs.LG | The paper describes a new algorithm to generate minimal, stable, and symbolic
corrections to an input that will cause a neural network with ReLU neurons to
change its output. We argue that such a correction is a useful way to provide
feedback to a user when the neural network produces an output that is different
from a... | computer science |
2,844 | Clipped Action Policy Gradient | cs.LG | Many continuous control tasks have bounded action spaces and clip
out-of-bound actions before execution. Policy gradient methods often optimize
policies as if actions were not clipped. We propose clipped action policy
gradient (CAPG) as an alternative policy gradient estimator that exploits the
knowledge of actions bei... | computer science |
2,845 | Learning to Explain: An Information-Theoretic Perspective on Model
Interpretation | cs.LG | We introduce instancewise feature selection as a methodology for model
interpretation. Our method is based on learning a function to extract a subset
of features that are most informative for each given example. This feature
selector is trained to maximize the mutual information between selected
features and the respon... | computer science |
2,846 | Variational Inference for Policy Gradient | cs.LG | Inspired by the seminal work on Stein Variational Inference and Stein
Variational Policy Gradient, we derived a method to generate samples from the
posterior variational parameter distribution by \textit{explicitly} minimizing
the KL divergence to match the target distribution in an amortize fashion.
Consequently, we a... | computer science |
2,847 | Intrinsic Motivation and Mental Replay enable Efficient Online
Adaptation in Stochastic Recurrent Networks | cs.AI | Autonomous robots need to interact with unknown, unstructured and changing
environments, constantly facing novel challenges. Therefore, continuous online
adaptation for lifelong-learning and the need of sample-efficient mechanisms to
adapt to changes in the environment, the constraints, the tasks, or the robot
itself a... | computer science |
2,848 | Projection-Free Online Optimization with Stochastic Gradient: From
Convexity to Submodularity | stat.ML | Online optimization has been a successful framework for solving large-scale
problems under computational constraints and partial information. Current
methods for online convex optimization require either a projection or exact
gradient computation at each step, both of which can be prohibitively expensive
for large-scal... | computer science |
2,849 | Vector Field Based Neural Networks | cs.LG | A novel Neural Network architecture is proposed using the mathematically and
physically rich idea of vector fields as hidden layers to perform nonlinear
transformations in the data. The data points are interpreted as particles
moving along a flow defined by the vector field which intuitively represents
the desired move... | computer science |
2,850 | Teacher Improves Learning by Selecting a Training Subset | stat.ML | We call a learner super-teachable if a teacher can trim down an iid training
set while making the learner learn even better. We provide sharp super-teaching
guarantees on two learners: the maximum likelihood estimator for the mean of a
Gaussian, and the large margin classifier in 1D. For general learners, we
provide a ... | computer science |
2,851 | Cakewalk Sampling | stat.ML | Combinatorial optimization is a common theme in computer science which
underlies a considerable variety of problems. In contrast to the continuous
setting, combinatorial problems require special solution strategies, and it's
hard to come by generic schemes like gradient methods for continuous domains.
We follow a stand... | computer science |
2,852 | Addressing Function Approximation Error in Actor-Critic Methods | cs.AI | In value-based reinforcement learning methods such as deep Q-learning,
function approximation errors are known to lead to overestimated value
estimates and suboptimal policies. We show that this problem persists in an
actor-critic setting and propose novel mechanisms to minimize its effects on
both the actor and critic... | computer science |
2,853 | Real-Time Bidding with Multi-Agent Reinforcement Learning in Display
Advertising | stat.ML | Real-time advertising allows advertisers to bid for each impression for a
visiting user. To optimize a specific goal such as maximizing the revenue led
by ad placements, advertisers not only need to estimate the relevance between
the ads and user's interests, but most importantly require a strategic response
with respe... | computer science |
2,854 | Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs | stat.ML | The loss functions of deep neural networks are complex and their geometric
properties are not well understood. We show that the optima of these complex
loss functions are in fact connected by simple curves, such as a polygonal
chain with only one bend, over which training and test accuracy are nearly
constant. We intro... | computer science |
2,855 | DeepSOFA: A Real-Time Continuous Acuity Score Framework using Deep
Learning | cs.LG | Traditional methods for assessing illness severity and predicting in-hospital
mortality among critically ill patients require manual, time-consuming, and
error-prone calculations that are further hindered by the use of static
variable thresholds derived from aggregate patient populations. These coarse
frameworks do not... | computer science |
2,856 | DiGrad: Multi-Task Reinforcement Learning with Shared Actions | cs.LG | Most reinforcement learning algorithms are inefficient for learning multiple
tasks in complex robotic systems, where different tasks share a set of actions.
In such environments a compound policy may be learnt with shared neural network
parameters, which performs multiple tasks concurrently. However such compound
polic... | computer science |
2,857 | Model-Based Value Estimation for Efficient Model-Free Reinforcement
Learning | cs.LG | Recent model-free reinforcement learning algorithms have proposed
incorporating learned dynamics models as a source of additional data with the
intention of reducing sample complexity. Such methods hold the promise of
incorporating imagined data coupled with a notion of model uncertainty to
accelerate the learning of c... | computer science |
2,858 | Learning Longer-term Dependencies in RNNs with Auxiliary Losses | cs.LG | Despite recent advances in training recurrent neural networks (RNNs),
capturing long-term dependencies in sequences remains a fundamental challenge.
Most approaches use backpropagation through time (BPTT), which is difficult to
scale to very long sequences. This paper proposes a simple method that improves
the ability ... | computer science |
2,859 | Learning Flexible and Reusable Locomotion Primitives for a Microrobot | cs.RO | The design of gaits for robot locomotion can be a daunting process which
requires significant expert knowledge and engineering. This process is even
more challenging for robots that do not have an accurate physical model, such
as compliant or micro-scale robots. Data-driven gait optimization provides an
automated alter... | computer science |
2,860 | Semi-Supervised Online Structure Learning for Composite Event
Recognition | cs.AI | Online structure learning approaches, such as those stemming from Statistical
Relational Learning, enable the discovery of complex relations in noisy data
streams. However, these methods assume the existence of fully-labelled training
data, which is unrealistic for most real-world applications. We present a novel
appro... | computer science |
2,861 | Essentially No Barriers in Neural Network Energy Landscape | stat.ML | Training neural networks involves finding minima of a high-dimensional
non-convex loss function. Knowledge of the structure of this energy landscape
is sparse. Relaxing from linear interpolations, we construct continuous paths
between minima of recent neural network architectures on CIFAR10 and CIFAR100.
Surprisingly, ... | computer science |
2,862 | DAGs with NO TEARS: Smooth Optimization for Structure Learning | stat.ML | Estimating the structure of directed acyclic graphs (DAGs, also known as
Bayesian networks) is a challenging problem since the search space of DAGs is
combinatorial and scales superexponentially with the number of nodes. Existing
approaches rely on various local heuristics for enforcing the acyclicity
constraint and ar... | computer science |
2,863 | Recurrent Predictive State Policy Networks | stat.ML | We introduce Recurrent Predictive State Policy (RPSP) networks, a recurrent
architecture that brings insights from predictive state representations to
reinforcement learning in partially observable environments. Predictive state
policy networks consist of a recursive filter, which keeps track of a belief
about the stat... | computer science |
2,864 | Sever: A Robust Meta-Algorithm for Stochastic Optimization | cs.LG | In high dimensions, most machine learning methods are brittle to even a small
fraction of structured outliers. To address this, we introduce a new
meta-algorithm that can take in a base learner such as least squares or
stochastic gradient descent, and harden the learner to be resistant to
outliers. Our method, Sever, p... | computer science |
2,865 | Efficient Algorithms for Outlier-Robust Regression | cs.LG | We give the first polynomial-time algorithm for performing linear or
polynomial regression resilient to adversarial corruptions in both examples and
labels.
Given a sufficiently large (polynomial-size) training set drawn i.i.d. from
distribution D and subsequently corrupted on some fraction of points, our
algorithm o... | computer science |
2,866 | DeepMoTIon: Learning to Navigate Like Humans | cs.RO | We present a novel human-aware navigation approach, where the robot learns to
mimic humans to navigate safely in crowds. The presented model referred to as
DeepMoTIon, is trained with pedestrian surveillance data to predict human
velocity. The robot processes LiDAR scans via the trained network to navigate
to the targe... | computer science |
2,867 | Attention-based Graph Neural Network for Semi-supervised Learning | stat.ML | Recently popularized graph neural networks achieve the state-of-the-art
accuracy on a number of standard benchmark datasets for graph-based
semi-supervised learning, improving significantly over existing approaches.
These architectures alternate between a propagation layer that aggregates the
hidden states of the local... | computer science |
2,868 | ARMDN: Associative and Recurrent Mixture Density Networks for eRetail
Demand Forecasting | cs.LG | Accurate demand forecasts can help on-line retail organizations better plan
their supply-chain processes. The challenge, however, is the large number of
associative factors that result in large, non-stationary shifts in demand,
which traditional time series and regression approaches fail to model. In this
paper, we pro... | computer science |
2,869 | Learning the Joint Representation of Heterogeneous Temporal Events for
Clinical Endpoint Prediction | cs.AI | The availability of a large amount of electronic health records (EHR)
provides huge opportunities to improve health care service by mining these
data. One important application is clinical endpoint prediction, which aims to
predict whether a disease, a symptom or an abnormal lab test will happen in the
future according... | computer science |
2,870 | Sylvester Normalizing Flows for Variational Inference | stat.ML | Variational inference relies on flexible approximate posterior distributions.
Normalizing flows provide a general recipe to construct flexible variational
posteriors. We introduce Sylvester normalizing flows, which can be seen as a
generalization of planar flows. Sylvester normalizing flows remove the
well-known single... | computer science |
2,871 | Deep Learning Reconstruction of Ultra-Short Pulses | cs.AI | Ultra-short laser pulses with femtosecond to attosecond pulse duration are
the shortest systematic events humans can create. Characterization (amplitude
and phase) of these pulses is a key ingredient in ultrafast science, e.g.,
exploring chemical reactions and electronic phase transitions. Here, we propose
and demonstr... | computer science |
2,872 | Composable Deep Reinforcement Learning for Robotic Manipulation | cs.LG | Model-free deep reinforcement learning has been shown to exhibit good
performance in domains ranging from video games to simulated robotic
manipulation and locomotion. However, model-free methods are known to perform
poorly when the interaction time with the environment is limited, as is the
case for most real-world ro... | computer science |
2,873 | Simple random search provides a competitive approach to reinforcement
learning | cs.LG | A common belief in model-free reinforcement learning is that methods based on
random search in the parameter space of policies exhibit significantly worse
sample complexity than those that explore the space of actions. We dispel such
beliefs by introducing a random search method for training static, linear
policies for... | computer science |
2,874 | Variance Reduction for Policy Gradient with Action-Dependent Factorized
Baselines | cs.LG | Policy gradient methods have enjoyed great success in deep reinforcement
learning but suffer from high variance of gradient estimates. The high variance
problem is particularly exasperated in problems with long horizons or
high-dimensional action spaces. To mitigate this issue, we derive a bias-free
action-dependent ba... | computer science |
2,875 | Natural Gradient Deep Q-learning | cs.LG | This paper presents findings for training a Q-learning reinforcement learning
agent using natural gradient techniques. We compare the original deep Q-network
(DQN) algorithm to its natural gradient counterpart (NGDQN), measuring NGDQN
and DQN performance on classic controls environments without target networks.
We find... | computer science |
2,876 | Explanation Methods in Deep Learning: Users, Values, Concerns and
Challenges | cs.AI | Issues regarding explainable AI involve four components: users, laws &
regulations, explanations and algorithms. Together these components provide a
context in which explanation methods can be evaluated regarding their adequacy.
The goal of this chapter is to bridge the gap between expert users and lay
users. Different... | computer science |
2,877 | Inference in Probabilistic Graphical Models by Graph Neural Networks | cs.LG | A useful computation when acting in a complex environment is to infer the
marginal probabilities or most probable states of task-relevant variables.
Probabilistic graphical models can efficiently represent the structure of such
complex data, but performing these inferences is generally difficult.
Message-passing algori... | computer science |
2,878 | Causal Inference on Discrete Data via Estimating Distance Correlations | stat.ML | In this paper, we deal with the problem of inferring causal directions when
the data is on discrete domain. By considering the distribution of the cause
$P(X)$ and the conditional distribution mapping cause to effect $P(Y|X)$ as
independent random variables, we propose to infer the causal direction via
comparing the di... | computer science |
2,879 | Structured Output Learning with Abstention: Application to Accurate
Opinion Prediction | cs.LG | Motivated by Supervised Opinion Analysis, we propose a novel framework
devoted to Structured Output Learning with Abstention (SOLA). The structure
prediction model is able to abstain from predicting some labels in the
structured output at a cost chosen by the user in a flexible way. For that
purpose, we decompose the p... | computer science |
2,880 | Deep Reinforcement Learning with Model Learning and Monte Carlo Tree
Search in Minecraft | cs.AI | Deep reinforcement learning has been successfully applied to several
visual-input tasks using model-free methods. In this paper, we propose a
model-based approach that combines learning a DNN-based transition model with
Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned
transition model pre... | computer science |
2,881 | CNN-LTE: a Class of 1-X Pooling Convolutional Neural Networks on Label
Tree Embeddings for Audio Scene Recognition | cs.NE | We describe in this report our audio scene recognition system submitted to
the DCASE 2016 challenge. Firstly, given the label set of the scenes, a label
tree is automatically constructed. This category taxonomy is then used in the
feature extraction step in which an audio scene instance is represented by a
label tree e... | computer science |
2,882 | Deep Transfer Learning: A new deep learning glitch classification method
for advanced LIGO | cs.CV | The exquisite sensitivity of the advanced LIGO detectors has enabled the
detection of multiple gravitational wave signals. The sophisticated design of
these detectors mitigates the effect of most types of noise. However, advanced
LIGO data streams are contaminated by numerous artifacts known as glitches:
non-Gaussian n... | computer science |
2,883 | Integrating E-Commerce and Data Mining: Architecture and Challenges | cs.LG | We show that the e-commerce domain can provide all the right ingredients for
successful data mining and claim that it is a killer domain for data mining. We
describe an integrated architecture, based on our expe-rience at Blue Martini
Software, for supporting this integration. The architecture can dramatically
reduce t... | computer science |
2,884 | Generalized Prediction Intervals for Arbitrary Distributed
High-Dimensional Data | cs.CV | This paper generalizes the traditional statistical concept of prediction
intervals for arbitrary probability density functions in high-dimensional
feature spaces by introducing significance level distributions, which provides
interval-independent probabilities for continuous random variables. The
advantage of the trans... | computer science |
2,885 | Pose Estimation from a Single Depth Image for Arbitrary Kinematic
Skeletons | cs.CV | We present a method for estimating pose information from a single depth image
given an arbitrary kinematic structure without prior training. For an arbitrary
skeleton and depth image, an evolutionary algorithm is used to find the optimal
kinematic configuration to explain the observed image. Results show that our
appro... | computer science |
2,886 | Linearized Additive Classifiers | cs.CV | We revisit the additive model learning literature and adapt a penalized
spline formulation due to Eilers and Marx, to train additive classifiers
efficiently. We also propose two new embeddings based two classes of orthogonal
basis with orthogonal derivatives, which can also be used to efficiently learn
additive classif... | computer science |
2,887 | Learning in Riemannian Orbifolds | cs.LG | Learning in Riemannian orbifolds is motivated by existing machine learning
algorithms that directly operate on finite combinatorial structures such as
point patterns, trees, and graphs. These methods, however, lack statistical
justification. This contribution derives consistency results for learning
problems in structu... | computer science |
2,888 | A Combinatorial Algorithm to Compute Regularization Paths | cs.LG | For a wide variety of regularization methods, algorithms computing the entire
solution path have been developed recently. Solution path algorithms do not
only compute the solution for one particular value of the regularization
parameter but the entire path of solutions, making the selection of an optimal
parameter much... | computer science |
2,889 | A Generalized Method for Integrating Rule-based Knowledge into Inductive
Methods Through Virtual Sample Creation | cs.LG | Hybrid learning methods use theoretical knowledge of a domain and a set of
classified examples to develop a method for classification. Methods that use
domain knowledge have been shown to perform better than inductive learners.
However, there is no general method to include domain knowledge into all
inductive learning ... | computer science |
2,890 | Efficient Inference in Fully Connected CRFs with Gaussian Edge
Potentials | cs.CV | Most state-of-the-art techniques for multi-class image segmentation and
labeling use conditional random fields defined over pixels or image regions.
While region-level models often feature dense pairwise connectivity,
pixel-level models are considerably larger and have only permitted sparse graph
structures. In this pa... | computer science |
2,891 | An Entropy-based Learning Algorithm of Bayesian Conditional Trees | cs.LG | This article offers a modification of Chow and Liu's learning algorithm in
the context of handwritten digit recognition. The modified algorithm directs
the user to group digits into several classes consisting of digits that are
hard to distinguish and then constructing an optimal conditional tree
representation for eac... | computer science |
2,892 | Learning Social Affordance for Human-Robot Interaction | cs.RO | In this paper, we present an approach for robot learning of social affordance
from human activity videos. We consider the problem in the context of
human-robot interaction: Our approach learns structural representations of
human-human (and human-object-human) interactions, describing how body-parts of
each agent move w... | computer science |
2,893 | Context Encoders: Feature Learning by Inpainting | cs.CV | We present an unsupervised visual feature learning algorithm driven by
context-based pixel prediction. By analogy with auto-encoders, we propose
Context Encoders -- a convolutional neural network trained to generate the
contents of an arbitrary image region conditioned on its surroundings. In order
to succeed at this t... | computer science |
2,894 | A Hybrid Loss for Multiclass and Structured Prediction | cs.LG | We propose a novel hybrid loss for multiclass and structured prediction
problems that is a convex combination of a log loss for Conditional Random
Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs).
We provide a sufficient condition for when the hybrid loss is Fisher consistent
for classificat... | computer science |
2,895 | Collaborative Representation for Classification, Sparse or Non-sparse? | cs.CV | Sparse representation based classification (SRC) has been proved to be a
simple, effective and robust solution to face recognition. As it gets popular,
doubts on the necessity of enforcing sparsity starts coming up, and primary
experimental results showed that simply changing the $l_1$-norm based
regularization to the ... | computer science |
2,896 | Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification | cs.CV | Rectified activation units (rectifiers) are essential for state-of-the-art
neural networks. In this work, we study rectifier neural networks for image
classification from two aspects. First, we propose a Parametric Rectified
Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU
improves model fitti... | computer science |
2,897 | Latent Hierarchical Model for Activity Recognition | cs.RO | We present a novel hierarchical model for human activity recognition. In
contrast to approaches that successively recognize actions and activities, our
approach jointly models actions and activities in a unified framework, and
their labels are simultaneously predicted. The model is embedded with a latent
layer that is ... | computer science |
2,898 | Boosting Convolutional Features for Robust Object Proposals | cs.CV | Deep Convolutional Neural Networks (CNNs) have demonstrated excellent
performance in image classification, but still show room for improvement in
object-detection tasks with many categories, in particular for cluttered scenes
and occlusion. Modern detection algorithms like Regions with CNNs (Girshick et
al., 2014) rely... | computer science |
2,899 | Large Margin Boltzmann Machines and Large Margin Sigmoid Belief Networks | cs.LG | Current statistical models for structured prediction make simplifying
assumptions about the underlying output graph structure, such as assuming a
low-order Markov chain, because exact inference becomes intractable as the
tree-width of the underlying graph increases. Approximate inference algorithms,
on the other hand, ... | computer science |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.