25,923 research outputs found
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
In the field of sequential recommendation, deep learning (DL)-based methods
have received a lot of attention in the past few years and surpassed
traditional models such as Markov chain-based and factorization-based ones.
However, there is little systematic study on DL-based methods, especially
regarding to how to design an effective DL model for sequential recommendation.
In this view, this survey focuses on DL-based sequential recommender systems by
taking the aforementioned issues into consideration. Specifically,we illustrate
the concept of sequential recommendation, propose a categorization of existing
algorithms in terms of three types of behavioral sequence, summarize the key
factors affecting the performance of DL-based models, and conduct corresponding
evaluations to demonstrate the effects of these factors. We conclude this
survey by systematically outlining future directions and challenges in this
field.Comment: 36 pages, 17 figures, 6 tables, 104 reference
Compare Contact Model-based Control and Contact Model-free Learning: A Survey of Robotic Peg-in-hole Assembly Strategies
In this paper, we present an overview of robotic peg-in-hole assembly and
analyze two main strategies: contact model-based and contact model-free
strategies. More specifically, we first introduce the contact model control
approaches, including contact state recognition and compliant control two
steps. Additionally, we focus on a comprehensive analysis of the whole robotic
assembly system. Second, without the contact state recognition process, we
decompose the contact model-free learning algorithms into two main subfields:
learning from demonstrations and learning from environments (mainly based on
reinforcement learning). For each subfield, we survey the landmark studies and
ongoing research to compare the different categories. We hope to strengthen the
relation between these two research communities by revealing the underlying
links. Ultimately, the remaining challenges and open questions in the field of
robotic peg-in-hole assembly community is discussed. The promising directions
and potential future work are also considered
'Say EM' for Selecting Probabilistic Models for Logical Sequences
Many real world sequences such as protein secondary structures or shell logs
exhibit a rich internal structures. Traditional probabilistic models of
sequences, however, consider sequences of flat symbols only. Logical hidden
Markov models have been proposed as one solution. They deal with logical
sequences, i.e., sequences over an alphabet of logical atoms. This comes at the
expense of a more complex model selection problem. Indeed, different
abstraction levels have to be explored. In this paper, we propose a novel
method for selecting logical hidden Markov models from data called SAGEM. SAGEM
combines generalized expectation maximization, which optimizes parameters, with
structure search for model selection using inductive logic programming
refinement operators. We provide convergence and experimental results that show
SAGEM's effectiveness.Comment: Appears in Proceedings of the Twenty-First Conference on Uncertainty
in Artificial Intelligence (UAI2005
Noisy Parallel Approximate Decoding for Conditional Recurrent Language Model
Recent advances in conditional recurrent language modelling have mainly
focused on network architectures (e.g., attention mechanism), learning
algorithms (e.g., scheduled sampling and sequence-level training) and novel
applications (e.g., image/video description generation, speech recognition,
etc.) On the other hand, we notice that decoding algorithms/strategies have not
been investigated as much, and it has become standard to use greedy or beam
search. In this paper, we propose a novel decoding strategy motivated by an
earlier observation that nonlinear hidden layers of a deep neural network
stretch the data manifold. The proposed strategy is embarrassingly
parallelizable without any communication overhead, while improving an existing
decoding algorithm. We extensively evaluate it with attention-based neural
machine translation on the task of En->Cz translation
On Multi-resident Activity Recognition in Ambient Smart-Homes
Increasing attention to the research on activity monitoring in smart homes
has motivated the employment of ambient intelligence to reduce the deployment
cost and solve the privacy issue. Several approaches have been proposed for
multi-resident activity recognition, however, there still lacks a comprehensive
benchmark for future research and practical selection of models. In this paper
we study different methods for multi-resident activity recognition and evaluate
them on same sets of data. The experimental results show that recurrent neural
network with gated recurrent units is better than other models and also
considerably efficient, and that using combined activities as single labels is
more effective than represent them as separate labels
Cross-Entropic Learning of a Machine for the Decision in a Partially Observable Universe
Revision of the paper previously entitled "Learning a Machine for the
Decision in a Partially Observable Markov Universe" In this paper, we are
interested in optimal decisions in a partially observable universe. Our
approach is to directly approximate an optimal strategic tree depending on the
observation. This approximation is made by means of a parameterized
probabilistic law. A particular family of hidden Markov models, with input
\emph{and} output, is considered as a model of policy. A method for optimizing
the parameters of these HMMs is proposed and applied. This optimization is
based on the cross-entropic principle for rare events simulation developed by
Rubinstein.Comment: Submitted to EJO
Variational Inference for Data-Efficient Model Learning in POMDPs
Partially observable Markov decision processes (POMDPs) are a powerful
abstraction for tasks that require decision making under uncertainty, and
capture a wide range of real world tasks. Today, effective planning approaches
exist that generate effective strategies given black-box models of a POMDP
task. Yet, an open question is how to acquire accurate models for complex
domains. In this paper we propose DELIP, an approach to model learning for
POMDPs that utilizes amortized structured variational inference. We empirically
show that our model leads to effective control strategies when coupled with
state-of-the-art planners. Intuitively, model-based approaches should be
particularly beneficial in environments with changing reward structures, or
where rewards are initially unknown. Our experiments confirm that DELIP is
particularly effective in this setting
Protein secondary structure prediction using deep convolutional neural fields
Protein secondary structure (SS) prediction is important for studying protein
structure and function. When only the sequence (profile) information is used as
input feature, currently the best predictors can obtain ~80% Q3 accuracy, which
has not been improved in the past decade. Here we present DeepCNF (Deep
Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep
Learning extension of Conditional Neural Fields (CNF), which is an integration
of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can
model not only complex sequence-structure relationship by a deep hierarchical
architecture, but also interdependency between adjacent SS labels, so it is
much more powerful than CNF. Experimental results show that DeepCNF can obtain
~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the
CASP and CAMEO test proteins, greatly outperforming currently popular
predictors. As a general framework, DeepCNF can be used to predict other
protein structure properties such as contact number, disorder regions, and
solvent accessibility
Leveraging exploration in off-policy algorithms via normalizing flows
The ability to discover approximately optimal policies in domains with sparse
rewards is crucial to applying reinforcement learning (RL) in many real-world
scenarios. Approaches such as neural density models and continuous exploration
(e.g., Go-Explore) have been proposed to maintain the high exploration rate
necessary to find high performing and generalizable policies. Soft
actor-critic(SAC) is another method for improving exploration that aims to
combine efficient learning via off-policy updates while maximizing the policy
entropy. In this work, we extend SAC to a richer class of probability
distributions (e.g., multimodal) through normalizing flows (NF) and show that
this significantly improves performance by accelerating the discovery of good
policies while using much smaller policy representations. Our approach, which
we call SAC-NF, is a simple, efficient,easy-to-implement modification and
improvement to SAC on continuous control baselines such as MuJoCo and PyBullet
Roboschool domains. Finally, SAC-NF does this while being significantly
parameter efficient, using as few as 5.5% the parameters for an equivalent SAC
model.Comment: Accepted to 3rd Conference on Robot Learning (CoRL 2019); Keywords:
Exploration, soft actor-critic, normalizing flow, off-policy; maximum
entropy, reinforcement learning; deceptive reward; sparse reward; inverse
autoregressive flo
Using multi-categorization semantic analysis and personalization for semantic search
Semantic search technology has received more attention in the last years.
Compared with the keyword based search, semantic search is used to excavate the
latent semantics information and help users find the information items that
they want indeed. In this paper, we present a novel approach for semantic
search which combines Multi-Categorization Semantic Analysis with
personalization technology. The MCSA approach can classify documents into
multiple categories, which is distinct from the existing approaches of
classifying documents into a single category. Then, the search history and
personal information for users are significantly considered in analysing and
matching the original search result by Term Vector DataBase. A series of
personalization algorithms are proposed to match personal information and
search history. At last, the related experiments are made to validate the
effectiveness and efficiency of our method. The experimental results show that
our method based on MCSA and personalization outperforms some existing methods
with the higher search accuracy and the lower extra time cost.Comment: 15 page
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