3,526 research outputs found
Hyperparameter Importance Across Datasets
With the advent of automated machine learning, automated hyperparameter
optimization methods are by now routinely used in data mining. However, this
progress is not yet matched by equal progress on automatic analyses that yield
information beyond performance-optimizing hyperparameter settings. In this
work, we aim to answer the following two questions: Given an algorithm, what
are generally its most important hyperparameters, and what are typically good
values for these? We present methodology and a framework to answer these
questions based on meta-learning across many datasets. We apply this
methodology using the experimental meta-data available on OpenML to determine
the most important hyperparameters of support vector machines, random forests
and Adaboost, and to infer priors for all their hyperparameters. The results,
obtained fully automatically, provide a quantitative basis to focus efforts in
both manual algorithm design and in automated hyperparameter optimization. The
conducted experiments confirm that the hyperparameters selected by the proposed
method are indeed the most important ones and that the obtained priors also
lead to statistically significant improvements in hyperparameter optimization.Comment: \c{opyright} 2018. Copyright is held by the owner/author(s).
Publication rights licensed to ACM. This is the author's version of the work.
It is posted here for your personal use, not for redistribution. The
definitive Version of Record was published in Proceedings of the 24th ACM
SIGKDD International Conference on Knowledge Discovery & Data Minin
Implicit learning of temporal behavior in complex dynamic environments
Humans can automatically detect and learn to exploit repeated aspects (regularities) of the environment. Timing research suggests that such learning is not only used to anticipate what will happen, but also when it will happen. However, in timing experiments, the intervals to be timed are presented in isolation from other stimuli and explicitly cued, contrasting with naturalistic environments in which intervals are embedded in a constant stream of events and individuals are hardly aware of them. It is unclear whether laboratory findings from timing research translate to a more ecologically valid, implicit environment. Here we show in a game-like experiment, specifically designed to measure naturalistic behavior, that participants implicitly use regular intervals to anticipate future events, even when these intervals are constantly interrupted by irregular yet behaviorally relevant events. This finding extends previous research by showing that individuals not only detect such regularities but can also use this knowledge to decide when to act in a complex environment. Furthermore, this finding demonstrates that this type of learning can occur independently from the ordinal sequence of motor actions, which contrasts this work with earlier motor learning studies. Taken together, our results demonstrate that regularities in the time between events are implicitly monitored and used to predict and act on what happens when, thereby showing that laboratory findings from timing research can generalize to naturalistic environments. Additionally, with the development of our game-like experiment, we demonstrate an approach to test cognitive theories in less controlled, ecologically more valid environments
Unique behavioral and neurochemical effects induced by repeated adolescent consumption of caffeine-mixed alcohol in C57BL/6 mice.
The number of highly caffeinated products has increased dramatically in the past few years. Among these products, highly caffeinated energy drinks are the most heavily advertised and purchased, which has resulted in increased incidences of co-consumption of energy drinks with alcohol. Despite the growing number of adolescents and young adults reporting caffeine-mixed alcohol use, knowledge of the potential consequences associated with co-consumption has been limited to survey-based results and in-laboratory human behavioral testing. Here, we investigate the effect of repeated adolescent (post-natal days P35-61) exposure to caffeine-mixed alcohol in C57BL/6 mice on common drug-related behaviors such as locomotor sensitivity, drug reward and cross-sensitivity, and natural reward. To determine changes in neurological activity resulting from adolescent exposure, we monitored changes in expression of the transcription factor ΔFosB in the dopaminergic reward pathway as a sign of long-term increases in neuronal activity. Repeated adolescent exposure to caffeine-mixed alcohol exposure induced significant locomotor sensitization, desensitized cocaine conditioned place preference, decreased cocaine locomotor cross-sensitivity, and increased natural reward consumption. We also observed increased accumulation of ΔFosB in the nucleus accumbens following repeated adolescent caffeine-mixed alcohol exposure compared to alcohol or caffeine alone. Using our exposure model, we found that repeated exposure to caffeine-mixed alcohol during adolescence causes unique behavioral and neurochemical effects not observed in mice exposed to caffeine or alcohol alone. Based on similar findings for different substances of abuse, it is possible that repeated exposure to caffeine-mixed alcohol during adolescence could potentially alter or escalate future substance abuse as means to compensate for these behavioral and neurochemical alterations. © 2016 Robins et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Validation of Observed Bedload Transport Pathways Using Morphodynamic Modeling
Phenomena related to braiding, including local scour and fill, channel bar development, migration
and avulsion, make numerical morphodynamic modeling of braided rivers challenging. This paper investigates
the performance of a Delft3D model, in a 2D depth-averaged formulation, to simulate the
morphodynamics of an anabranch of the Rees River (New Zealand). Model performance is evaluated using
data from field surveys collected on the falling limb of a major high flow, and using several sediment
transport formulas. Initial model results suggest that there is generally good agreement between observed and
modeled bed levels. However, some discrepancies in the bed level estimations were noticed, leading to bed
level, water depth and water velocity estimation errors
Practical sand transport formula for non-breaking waves and currents
Open Access funded by Engineering and Physical Sciences Research Council Under a Creative Commons license Acknowledgements This work is part of the SANTOSS project (‘SANd Transport in OScillatory flows in the Sheet-flow regime’) funded by the UK's EPSRC (GR/T28089/01) and STW in The Netherlands (TCB.6586). JW acknowledges Deltares strategic research funding under project number 1202359.09. Richard Soulsby is gratefully acknowledged for valuable discussions and feedback on the formula during the SANTOSS project.Peer reviewedPostprin
Are LSTMs Good Few-Shot Learners?
Deep learning requires large amounts of data to learn new tasks well,
limiting its applicability to domains where such data is available.
Meta-learning overcomes this limitation by learning how to learn. In 2001,
Hochreiter et al. showed that an LSTM trained with backpropagation across
different tasks is capable of meta-learning. Despite promising results of this
approach on small problems, and more recently, also on reinforcement learning
problems, the approach has received little attention in the supervised few-shot
learning setting. We revisit this approach and test it on modern few-shot
learning benchmarks. We find that LSTM, surprisingly, outperform the popular
meta-learning technique MAML on a simple few-shot sine wave regression
benchmark, but that LSTM, expectedly, fall short on more complex few-shot image
classification benchmarks. We identify two potential causes and propose a new
method called Outer Product LSTM (OP-LSTM) that resolves these issues and
displays substantial performance gains over the plain LSTM. Compared to popular
meta-learning baselines, OP-LSTM yields competitive performance on
within-domain few-shot image classification, and performs better in
cross-domain settings by 0.5% to 1.9% in accuracy score. While these results
alone do not set a new state-of-the-art, the advances of OP-LSTM are orthogonal
to other advances in the field of meta-learning, yield new insights in how LSTM
work in image classification, allowing for a whole range of new research
directions. For reproducibility purposes, we publish all our research code
publicly.Comment: Accepted at Machine Learning Journal, Special Issue of the ECML PKDD
2023 Journal Trac
Morphological response to a North Sea bed depression induced by gas mining
Gas mining leads to saucer-like surface depressions. In the North Sea, gas is currently mined at several offshore locations. The associated bed depression has a similar spatial extent as offshore tidal sandbanks, which are large-scale bed patterns covering a significant part of the North Sea bottom. The morphological time scales of bed depressions and tidal sandbanks are similar, so that significant interaction between these features is expected. In this paper we allow the bed depression to become morphologically active. A simple depression model based on a homogeneous soil is tuned with data of a bed depression near the Dutch barrier island of Ameland. Next, this subsidence model is included in a morphodynamic model. We show that this model is able to explain tidal sandbanks, which represent natural bed behavior. Here we approximate the solution by an expansion up to first order. The zeroth-order solution of the model is a flat bed with a spatially uniform, time-independent current. The first-order solution is investigated using a Fourier transformation. In general, we observe significant interaction between the bed depression and the natural sandbank formation process. The process of induced bed depression triggers and intensifies the natural morphological behavior of the offshore seabed. The model also shows essential differences between modeling a morphodynamically active marine bottom depression and a bottom depression below the threshold for sediment motion. The maximum bed level depression in the active case is significantly larger, and the circular shape of depression contours is affected by stretching toward the preferred orientation of the tidal sandbank formation process
- …