DOPE efficiently estimates ATE with complex covariates.
problem Efficient estimation of ATE from complex covariates.
method Proposed DOPE framework for efficient adjustment.
result DOPE retains efficiency even with highly predictive covariates.
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12 results for “Doping”
This paper improves compression of large NLP models using doped Kronecker Products.
problem Accuracy loss when compressing large NLP tasks with Kronecker Products.
method Doping Kronecker Products with an overlay matrix to recover accuracy, and a new regularization scheme called co matrix dropout regularization (CMR).
result Compression of a large language model with LSTM layers of size 25 MB by 25x with 1.4% loss in perplexity score.
Develops model-free methods for event history analysis and efficient covariate adjustment.
problem Estimating treatment effects while accounting for confounding and understanding event history.
method Model-free prediction techniques, Local Covariance Measure (LCM), Debiased Outcome-adapted Propensity Estimator (DOPE), Aalen Covariance Measure (ACM).
result Demonstrates the effectiveness and robustness of the proposed methods in various settings.
Erbium-doped fiber amplifier (EDFA) is an optical amplifier/repeater device used to boost the intensity of optical signals being carried through a fiber optic communication system. A highly accurate EDFA model is important because of its crucial role in optical network management and optimization. The input channels of…
Nanowire field-effect sensors have recently been developed for label-free detection of biomolecules. In this work, we introduce a computational technique based on Bayesian estimation to determine the physical parameters of the sensor and, more importantly, the properties of the analyte molecules. To that end, we first …
Investigates ways to train larger models with fewer resources, finding that test loss depends only on the actual number of trainable parameters.
problem Training larger models for cheaper under hardware constraints.
method Emulates an increase in effective parameters using frozen random parameters or fast structured transforms.
result Scaling laws cannot be deceived by spurious parameters; test loss depends only on the actual number of trainable parameters.
Improved community detection and link prediction with GAE and VGAE.
problem Jointly improving community detection and link prediction with graph autoencoders.
method Introducing a community-preserving message passing scheme and doping encoders with Louvain-based prior communities.
result Empirical effectiveness of Modularity-Aware GAE and VGAE on various real-world graphs.
Modularity-aware GAE and VGAE improve community detection and link prediction.
problem Improving community detection with GAE and VGAE in the absence of node features.
method Introducing a modularity-aware message passing scheme and regularizer to GAE and VGAE encoders.
result Jointly addressing community detection and link prediction with high accuracy is possible.
Recently, the introduction of the generative adversarial network (GAN) and its variants has enabled the generation of realistic synthetic samples, which has been used for enlarging training sets. Previous work primarily focused on data augmentation for semi-supervised and supervised tasks. In this paper, we instead foc…
VLBM learns MDP transitions from limited data, improving OPE performance.
problem Limited coverage of state and action space in offline trajectories.
method VLBM uses variational inference with RSA and branching architecture.
result VLBM outperforms existing OPE methods on deep OPE benchmark.
Study optimizes HTL-free PSCs with MWCNTs, improving efficiency and stability.
problem Optimizing efficiency and degradation in HTL-free perovskite solar cells.
method Machine learning-driven framework integrating experimental validation and numerical simulations.
result Achieved RMSEs of 0.0179 and 0.0117 for efficiency and degradation, respectively.
Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernelcond-mat.mtrl-sci
New method finds graphene nanocrystals with reduced DFT calculations.
problem Efficiently discovering materials with desired properties in high-dimensional chemical space.
method Bayesian optimization with neural network kernel to minimize DFT calculations.
result Reduced computational cost by 20% for discovering materials with target properties.