238,634 research outputs found
Simulated loss of foveal vision eliminates visual search advantage in repeated displays
In the contextual cueing paradigm, incidental visual learning of repeated distractor configurations leads to faster search times in repeated compared to new displays. This contextual cueing is closely linked to the visual exploration of the search arrays as indicated by fewer fixations and more efficient scan paths in repeated search arrays. Here, we examined contextual cueing under impaired visual exploration induced by a simulated central scotoma that causes the participant to rely on extrafoveal vision. We let normal-sighted participants search for the target either under unimpaired viewing conditions or with a gaze-contingent central scotoma masking the currently fixated area. Under unimpaired viewing conditions, participants revealed shorter search times and more efficient exploration of the display for repeated compared to novel search arrays and thus exhibited contextual cueing. When visual search was impaired by the central scotoma, search facilitation for repeated displays was eliminated. These results indicate that a loss of foveal sight, as it is commonly observed in maculopathies, e.g., may lead to deficits in high-level visual functions well beyond the immediate consequences of a scotoma
TPX: Biomedical literature search made easy
TPX is a web-based PubMed search enhancement tool that enables faster article searching using an alysis and exploration features . These features include identification of relevant biomedical concepts from search results with linkouts to source databases, concept
based article categorization, concept assisted search and filtering, query refinement. A distinguishing feature here is the ability to add user-defined concept names and/or concept types for named entity recognition. The tool allows contextual exploration of knowledge sources by providing concept association maps derived from the MEDLINE repository. It also has a full-text search mode that can be configured on request to access local text repositories, incorporating entity co-occurrence search at sentence/paragraph levels. Local text files can also be analyzed on-the-fly
Non-parametric contextual stochastic search
Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. Yet, many stochastic search algorithms require relearning if the task or objective function changes slightly to adapt the solution to the new situation or the new context. In this paper, we consider the contextual stochastic search setup. Here, we want to find multiple good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation of a task or context. Contextual algorithms have been investigated in the field of policy search, however, the search distribution typically uses a parametric model that is linear in the some hand-defined context features. Finding good context features is a challenging task, and hence, non-parametric methods are often preferred over their parametric counter-parts. In this paper, we propose a non-parametric contextual stochastic search algorithm that can learn a non-parametric search distribution for multiple tasks simultaneously. In difference to existing methods, our method can also learn a context dependent covariance matrix that guides the exploration of the search process. We illustrate its performance on several non-linear contextual tasks
Dynamic Control of Explore/Exploit Trade-Off In Bayesian Optimization
Bayesian optimization offers the possibility of optimizing black-box
operations not accessible through traditional techniques. The success of
Bayesian optimization methods such as Expected Improvement (EI) are
significantly affected by the degree of trade-off between exploration and
exploitation. Too much exploration can lead to inefficient optimization
protocols, whilst too much exploitation leaves the protocol open to strong
initial biases, and a high chance of getting stuck in a local minimum.
Typically, a constant margin is used to control this trade-off, which results
in yet another hyper-parameter to be optimized. We propose contextual
improvement as a simple, yet effective heuristic to counter this - achieving a
one-shot optimization strategy. Our proposed heuristic can be swiftly
calculated and improves both the speed and robustness of discovery of optimal
solutions. We demonstrate its effectiveness on both synthetic and real world
problems and explore the unaccounted for uncertainty in the pre-determination
of search hyperparameters controlling explore-exploit trade-off.Comment: Accepted for publication in the proceedings of 2018 Computing
Conferenc
Efficient Beam Alignment in Millimeter Wave Systems Using Contextual Bandits
In this paper, we investigate the problem of beam alignment in millimeter
wave (mmWave) systems, and design an optimal algorithm to reduce the overhead.
Specifically, due to directional communications, the transmitter and receiver
beams need to be aligned, which incurs high delay overhead since without a
priori knowledge of the transmitter/receiver location, the search space spans
the entire angular domain. This is further exacerbated under dynamic conditions
(e.g., moving vehicles) where the access to the base station (access point) is
highly dynamic with intermittent on-off periods, requiring more frequent beam
alignment and signal training. To mitigate this issue, we consider an online
stochastic optimization formulation where the goal is to maximize the
directivity gain (i.e., received energy) of the beam alignment policy within a
time period. We exploit the inherent correlation and unimodality properties of
the model, and demonstrate that contextual information improves the
performance. To this end, we propose an equivalent structured Multi-Armed
Bandit model to optimally exploit the exploration-exploitation tradeoff. In
contrast to the classical MAB models, the contextual information makes the
lower bound on regret (i.e., performance loss compared with an oracle policy)
independent of the number of beams. This is a crucial property since the number
of all combinations of beam patterns can be large in transceiver antenna
arrays, especially in massive MIMO systems. We further provide an
asymptotically optimal beam alignment algorithm, and investigate its
performance via simulations.Comment: To Appear in IEEE INFOCOM 2018. arXiv admin note: text overlap with
arXiv:1611.05724 by other author
Better Optimism By Bayes: Adaptive Planning with Rich Models
The computational costs of inference and planning have confined Bayesian
model-based reinforcement learning to one of two dismal fates: powerful
Bayes-adaptive planning but only for simplistic models, or powerful, Bayesian
non-parametric models but using simple, myopic planning strategies such as
Thompson sampling. We ask whether it is feasible and truly beneficial to
combine rich probabilistic models with a closer approximation to fully Bayesian
planning. First, we use a collection of counterexamples to show formal problems
with the over-optimism inherent in Thompson sampling. Then we leverage
state-of-the-art techniques in efficient Bayes-adaptive planning and
non-parametric Bayesian methods to perform qualitatively better than both
existing conventional algorithms and Thompson sampling on two contextual
bandit-like problems.Comment: 11 pages, 11 figure
Counterfactual Estimation and Optimization of Click Metrics for Search Engines
Optimizing an interactive system against a predefined online metric is
particularly challenging, when the metric is computed from user feedback such
as clicks and payments. The key challenge is the counterfactual nature: in the
case of Web search, any change to a component of the search engine may result
in a different search result page for the same query, but we normally cannot
infer reliably from search log how users would react to the new result page.
Consequently, it appears impossible to accurately estimate online metrics that
depend on user feedback, unless the new engine is run to serve users and
compared with a baseline in an A/B test. This approach, while valid and
successful, is unfortunately expensive and time-consuming. In this paper, we
propose to address this problem using causal inference techniques, under the
contextual-bandit framework. This approach effectively allows one to run
(potentially infinitely) many A/B tests offline from search log, making it
possible to estimate and optimize online metrics quickly and inexpensively.
Focusing on an important component in a commercial search engine, we show how
these ideas can be instantiated and applied, and obtain very promising results
that suggest the wide applicability of these techniques
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