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Found 9 papers, 0 papers with code
AdaSelection: Accelerating Deep Learning Training through Data Subsampling
In this paper, we introduce AdaSelection, an adaptive sub-sampling method to identify the most informative sub-samples within each minibatch to speed up the training of large-scale deep learning models without sacrificing model performance.
Learning Prototype-oriented Set Representations for Meta-Learning
Since our plug-and-play framework can be applied to many meta-learning problems, we further instantiate it to the cases of few-shot classification and implicit meta generative modeling.
Solar Radiation Ramping Events Modeling Using Spatio-temporal Point Processes
Modeling and predicting solar events, particularly the solar ramping event, is critical for improving situational awareness for solar power generation systems.
Goodness-of-Fit Test for Mismatched Self-Exciting Processes
Recently there have been many research efforts in developing generative models for self-exciting point processes, partly due to their broad applicability for real-world applications.
Distributionally Robust Weighted $k$-Nearest Neighbors
When the samples are limited, robustness is especially crucial to ensure the generalization capability of the classifier.
Spatio-Temporal Point Processes with Attention for Traffic Congestion Event Modeling
We present a novel framework for modeling traffic congestion events over road networks.
Deep Fourier Kernel for Self-Attentive Point Processes
We present a novel attention-based model for discrete event data to capture complex non-linear temporal dependence structures.
Sequential Adversarial Anomaly Detection for One-Class Event Data
We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator.
Spectral CUSUM for Online Network Structure Change Detection
Detecting abrupt changes in the community structure of a network from noisy observations is a fundamental problem in statistics and machine learning.
