129 research outputs found
Object-oriented Neural Programming (OONP) for Document Understanding
We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. An OONP parser models semantic parsing as a decision
process: a neural net-based Reader sequentially goes through the document, and
during the process it builds and updates an intermediate ontology to summarize
its partial understanding of the text it covers. OONP supports a rich family of
operations (both symbolic and differentiable) for composing the ontology, and a
big variety of forms (both symbolic and differentiable) for representing the
state and the document. An OONP parser can be trained with supervision of
different forms and strength, including supervised learning (SL) ,
reinforcement learning (RL) and hybrid of the two. Our experiments on both
synthetic and real-world document parsing tasks have shown that OONP can learn
to handle fairly complicated ontology with training data of modest sizes.Comment: accepted by ACL 201
Competing for Shareable Arms in Multi-Player Multi-Armed Bandits
Competitions for shareable and limited resources have long been studied with
strategic agents. In reality, agents often have to learn and maximize the
rewards of the resources at the same time. To design an individualized
competing policy, we model the competition between agents in a novel
multi-player multi-armed bandit (MPMAB) setting where players are selfish and
aim to maximize their own rewards. In addition, when several players pull the
same arm, we assume that these players averagely share the arms' rewards by
expectation. Under this setting, we first analyze the Nash equilibrium when
arms' rewards are known. Subsequently, we propose a novel SelfishMPMAB with
Averaging Allocation (SMAA) approach based on the equilibrium. We theoretically
demonstrate that SMAA could achieve a good regret guarantee for each player
when all players follow the algorithm. Additionally, we establish that no
single selfish player can significantly increase their rewards through
deviation, nor can they detrimentally affect other players' rewards without
incurring substantial losses for themselves. We finally validate the
effectiveness of the method in extensive synthetic experiments.Comment: ICML 202
Autoregressive Diffusion Model for Graph Generation
Diffusion-based graph generative models have recently obtained promising
results for graph generation. However, existing diffusion-based graph
generative models are mostly one-shot generative models that apply Gaussian
diffusion in the dequantized adjacency matrix space. Such a strategy can suffer
from difficulty in model training, slow sampling speed, and incapability of
incorporating constraints. We propose an \emph{autoregressive diffusion} model
for graph generation. Unlike existing methods, we define a node-absorbing
diffusion process that operates directly in the discrete graph space. For
forward diffusion, we design a \emph{diffusion ordering network}, which learns
a data-dependent node absorbing ordering from graph topology. For reverse
generation, we design a \emph{denoising network} that uses the reverse node
ordering to efficiently reconstruct the graph by predicting the node type of
the new node and its edges with previously denoised nodes at a time. Based on
the permutation invariance of graph, we show that the two networks can be
jointly trained by optimizing a simple lower bound of data likelihood. Our
experiments on six diverse generic graph datasets and two molecule datasets
show that our model achieves better or comparable generation performance with
previous state-of-the-art, and meanwhile enjoys fast generation speed.Comment: 18 page
CodeExp: Explanatory Code Document Generation
Developing models that can automatically generate detailed code explanation
can greatly benefit software maintenance and programming education. However,
existing code-to-text generation models often produce only high-level summaries
of code that do not capture implementation-level choices essential for these
scenarios. To fill in this gap, we propose the code explanation generation
task. We first conducted a human study to identify the criteria for
high-quality explanatory docstring for code. Based on that, we collected and
refined a large-scale code docstring corpus and formulated automatic evaluation
metrics that best match human assessments. Finally, we present a multi-stage
fine-tuning strategy and baseline models for the task. Our experiments show
that (1) our refined training dataset lets models achieve better performance in
the explanation generation tasks compared to larger unrefined data (15x
larger), and (2) fine-tuned models can generate well-structured long docstrings
comparable to human-written ones. We envision our training dataset,
human-evaluation protocol, recommended metrics, and fine-tuning strategy can
boost future code explanation research. The code and annotated data are
available at https://github.com/subercui/CodeExp.Comment: Accepted in Findings of EMNLP 202
Virtual Synchronous Generator Control Using Twin Delayed Deep Deterministic Policy Gradient Method
This paper presents a data-driven approach that adaptively tunes the parameters of a virtual synchronous generator to achieve optimal frequency response against disturbances. In the proposed approach, the control variables, namely, the virtual moment of inertia and damping factor, are transformed into actions of a reinforcement learning agent. Different from the state-of-the-art methods, the proposed study introduces the settling time parameter as one of the observations in addition to the frequency and rate of change of frequency (RoCoF). In the reward function, preset indices are considered to simultaneously ensure bounded frequency deviation, low RoCoF, fast response, and quick settling time. To maximize the reward, this study employs the Twin-Delayed Deep Deterministic Policy Gradient (TD3) algorithm. TD3 has an exceptional capacity for learning optimal policies and is free of overestimation bias, which may lead to suboptimal policies. Finally, numerical validation in MATLAB/Simulink and real-time simulation using RTDS confirm the superiority of the proposed method over other adaptive tuning methods
Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease
The COVID-19 pandemic has highlighted the urgent need for the identification of new antiviral drug therapies for a variety of diseases. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2, while other related human coronaviruses cause diseases ranging from severe respiratory infections to the common cold. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of a range of human coronavirus diseases. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cell-based experimental assessment reveals several clinically-relevant drug repurposing candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.We gratefully acknowledge funding that supported this research support from the Ryerson University Faculty of Science (CNA), as well as funding support in the form of a CIFAR Catalyst Grant (JPJ and CNA), an NSERC Alliance Grant (CNA) and the Ryerson COVID-19 SRC Response Fund award (CNA). BW is partly supported by CIFAR AI Chairs Program. This work was also supported by a Mitacs award (BW), the European Union’s Horizon 2020 research and innovation program under a Marie Sklodowska-Curie grant (ER), by the CIFAR Azrieli Global Scholar program (JPJ), by the Ontario Early Researcher Awards program (JPJ and CNA), and by the Canada Research Chairs program (JPJ). We also thank Dr. James Rini (University of Toronto) for the kind gift of the 9.8E12 antibody used to detect the 229E Spike protein, and Dr. Scott Gray-Owen (University of Toronto) for the kind gift of the NL63 human coronavirus.Peer reviewe
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