104,659 research outputs found
Fast Cross-Validation via Sequential Testing
With the increasing size of today's data sets, finding the right parameter
configuration in model selection via cross-validation can be an extremely
time-consuming task. In this paper we propose an improved cross-validation
procedure which uses nonparametric testing coupled with sequential analysis to
determine the best parameter set on linearly increasing subsets of the data. By
eliminating underperforming candidates quickly and keeping promising candidates
as long as possible, the method speeds up the computation while preserving the
capability of the full cross-validation. Theoretical considerations underline
the statistical power of our procedure. The experimental evaluation shows that
our method reduces the computation time by a factor of up to 120 compared to a
full cross-validation with a negligible impact on the accuracy
How to Retrain Recommender System? A Sequential Meta-Learning Method
Practical recommender systems need be periodically retrained to refresh the
model with new interaction data. To pursue high model fidelity, it is usually
desirable to retrain the model on both historical and new data, since it can
account for both long-term and short-term user preference. However, a full
model retraining could be very time-consuming and memory-costly, especially
when the scale of historical data is large. In this work, we study the model
retraining mechanism for recommender systems, a topic of high practical values
but has been relatively little explored in the research community.
Our first belief is that retraining the model on historical data is
unnecessary, since the model has been trained on it before. Nevertheless,
normal training on new data only may easily cause overfitting and forgetting
issues, since the new data is of a smaller scale and contains fewer information
on long-term user preference. To address this dilemma, we propose a new
training method, aiming to abandon the historical data during retraining
through learning to transfer the past training experience. Specifically, we
design a neural network-based transfer component, which transforms the old
model to a new model that is tailored for future recommendations. To learn the
transfer component well, we optimize the "future performance" -- i.e., the
recommendation accuracy evaluated in the next time period. Our Sequential
Meta-Learning(SML) method offers a general training paradigm that is applicable
to any differentiable model. We demonstrate SML on matrix factorization and
conduct experiments on two real-world datasets. Empirical results show that SML
not only achieves significant speed-up, but also outperforms the full model
retraining in recommendation accuracy, validating the effectiveness of our
proposals. We release our codes at: https://github.com/zyang1580/SML.Comment: Appear in SIGIR 202
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Closed-loop optimization of fast-charging protocols for batteries with machine learning.
Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces
Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport
Monte Carlo simulation is the most accurate method for absorbed dose
calculations in radiotherapy. Its efficiency still requires improvement for
routine clinical applications, especially for online adaptive radiotherapy. In
this paper, we report our recent development on a GPU-based Monte Carlo dose
calculation code for coupled electron-photon transport. We have implemented the
Dose Planning Method (DPM) Monte Carlo dose calculation package (Sempau et al,
Phys. Med. Biol., 45(2000)2263-2291) on GPU architecture under CUDA platform.
The implementation has been tested with respect to the original sequential DPM
code on CPU in phantoms with water-lung-water or water-bone-water slab
geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point
source is used in our validation. The results demonstrate adequate accuracy of
our GPU implementation for both electron and photon beams in radiotherapy
energy range. Speed up factors of about 5.0 ~ 6.6 times have been observed,
using an NVIDIA Tesla C1060 GPU card against a 2.27GHz Intel Xeon CPU
processor.Comment: 13 pages, 3 figures, and 1 table. Paper revised. Figures update
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning
This paper studies the joint learning of action recognition and temporal
localization in long, untrimmed videos. We employ a multi-task learning
framework that performs the three highly related steps of action proposal,
action recognition, and action localization refinement in parallel instead of
the standard sequential pipeline that performs the steps in order. We develop a
novel temporal actionness regression module that estimates what proportion of a
clip contains action. We use it for temporal localization but it could have
other applications like video retrieval, surveillance, summarization, etc. We
also introduce random shear augmentation during training to simulate viewpoint
change. We evaluate our framework on three popular video benchmarks. Results
demonstrate that our joint model is efficient in terms of storage and
computation in that we do not need to compute and cache dense trajectory
features, and that it is several times faster than its sequential ConvNets
counterpart. Yet, despite being more efficient, it outperforms state-of-the-art
methods with respect to accuracy.Comment: WACV 2017 camera ready, minor updates about test time efficienc
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