Search Results for author:
Found 9 papers, 1 papers with code
De-IReps: Searching for improved Re-parameterizing Architecture based on Differentiable Evolution Strategy
Therefore, in this work we design a search space that covers almost all re-parameterization operations.
Delayed Rewards Calibration via Reward Empirical Sufficiency
Therefore, a purify-trained classifier is designed to obtain the distribution and generate the calibrated rewards.
Inductive Granger Causal Modeling for Multivariate Time Series
To bridge this gap, we propose an Inductive GRanger cAusal modeling (InGRA) framework for inductive Granger causality learning and common causal structure detection on multivariate time series, which exploits the shared commonalities underlying the different individuals.
Some estimates of the generalized beukers integral with techniques of partial fraction decomposition
In the second section of this paper, we provide some estimates of the upper and lower bound of the value $J_{3}$, which involves the generalized Beukers integral and is related to $\zeta(5)$.
Number Theory 11J72, 11J82, 11M06
Variational Autoencoders for Highly Multivariate Spatial Point Processes Intensities
To bridge this gap, we introduce a declustering based hidden variable model that leads to an efficient inference procedure via a variational autoencoder (VAE).
Active Lighting Recurrence by Parallel Lighting Analogy for Fine-Grained Change Detection
Besides, we also theoretically prove the invariance of our ALR approach to the ambiguity of normal and lighting decomposition.
Granger Causal Structure Reconstruction from Heterogeneous Multivariate Time Series
Granger causal structure reconstruction is an emerging topic that can uncover causal relationship behind multivariate time series data.
Sequential Scenario-Specific Meta Learner for Online Recommendation
Our approach is based on the insight that having a good generalization from a few examples relies on both a generic model initialization and an effective strategy for adapting this model to newly arising tasks.
Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks
This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks.
