8,503 research outputs found
A network approach to topic models
One of the main computational and scientific challenges in the modern age is
to extract useful information from unstructured texts. Topic models are one
popular machine-learning approach which infers the latent topical structure of
a collection of documents. Despite their success --- in particular of its most
widely used variant called Latent Dirichlet Allocation (LDA) --- and numerous
applications in sociology, history, and linguistics, topic models are known to
suffer from severe conceptual and practical problems, e.g. a lack of
justification for the Bayesian priors, discrepancies with statistical
properties of real texts, and the inability to properly choose the number of
topics. Here we obtain a fresh view on the problem of identifying topical
structures by relating it to the problem of finding communities in complex
networks. This is achieved by representing text corpora as bipartite networks
of documents and words. By adapting existing community-detection methods --
using a stochastic block model (SBM) with non-parametric priors -- we obtain a
more versatile and principled framework for topic modeling (e.g., it
automatically detects the number of topics and hierarchically clusters both the
words and documents). The analysis of artificial and real corpora demonstrates
that our SBM approach leads to better topic models than LDA in terms of
statistical model selection. More importantly, our work shows how to formally
relate methods from community detection and topic modeling, opening the
possibility of cross-fertilization between these two fields.Comment: 22 pages, 10 figures, code available at https://topsbm.github.io
Building Machines That Learn and Think Like People
Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans in some respects. Despite their biological inspiration and
performance achievements, these systems differ from human intelligence in
crucial ways. We review progress in cognitive science suggesting that truly
human-like learning and thinking machines will have to reach beyond current
engineering trends in both what they learn, and how they learn it.
Specifically, we argue that these machines should (a) build causal models of
the world that support explanation and understanding, rather than merely
solving pattern recognition problems; (b) ground learning in intuitive theories
of physics and psychology, to support and enrich the knowledge that is learned;
and (c) harness compositionality and learning-to-learn to rapidly acquire and
generalize knowledge to new tasks and situations. We suggest concrete
challenges and promising routes towards these goals that can combine the
strengths of recent neural network advances with more structured cognitive
models.Comment: In press at Behavioral and Brain Sciences. Open call for commentary
proposals (until Nov. 22, 2016).
https://www.cambridge.org/core/journals/behavioral-and-brain-sciences/information/calls-for-commentary/open-calls-for-commentar
Simulation-Based Inference for Global Health Decisions
The COVID-19 pandemic has highlighted the importance of in-silico
epidemiological modelling in predicting the dynamics of infectious diseases to
inform health policy and decision makers about suitable prevention and
containment strategies. Work in this setting involves solving challenging
inference and control problems in individual-based models of ever increasing
complexity. Here we discuss recent breakthroughs in machine learning,
specifically in simulation-based inference, and explore its potential as a
novel venue for model calibration to support the design and evaluation of
public health interventions. To further stimulate research, we are developing
software interfaces that turn two cornerstone COVID-19 and malaria epidemiology
models COVID-sim, (https://github.com/mrc-ide/covid-sim/) and OpenMalaria
(https://github.com/SwissTPH/openmalaria) into probabilistic programs, enabling
efficient interpretable Bayesian inference within those simulators
The Mechanics of Embodiment: A Dialogue on Embodiment and Computational Modeling
Embodied theories are increasingly challenging traditional views of cognition by arguing that conceptual representations that constitute our knowledge are grounded in sensory and motor experiences, and processed at this sensorimotor level, rather than being represented and processed abstractly in an amodal conceptual system. Given the established empirical foundation, and the relatively underspecified theories to date, many researchers are extremely interested in embodied cognition but are clamouring for more mechanistic implementations. What is needed at this stage is a push toward explicit computational models that implement sensory-motor grounding as intrinsic to cognitive processes. In this article, six authors from varying backgrounds and approaches address issues concerning the construction of embodied computational models, and illustrate what they view as the critical current and next steps toward mechanistic theories of embodiment. The first part has the form of a dialogue between two fictional characters: Ernest, the �experimenter�, and Mary, the �computational modeller�. The dialogue consists of an interactive sequence of questions, requests for clarification, challenges, and (tentative) answers, and touches the most important aspects of grounded theories that should inform computational modeling and, conversely, the impact that computational modeling could have on embodied theories. The second part of the article discusses the most important open challenges for embodied computational modelling
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