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Found 8 papers, 1 papers with code
NGAME: Negative Mining-aware Mini-batching for Extreme Classification
This paper identifies that memory overheads of popular negative mining techniques often force mini-batch sizes to remain small and slow training down.
An Adaptive-Importance-Sampling-Enhanced Bayesian Approach for Topology Estimation in an Unbalanced Power Distribution System
To overcome the above challenges, this paper proposes a Bayesian-inference framework that allows us to simultaneously estimate the topology and the state of a three-phase, unbalanced power distribution system.
Hybrid Encoder: Towards Efficient and Precise Native AdsRecommendation via Hybrid Transformer Encoding Networks
For the sake of efficient recommendation, conventional methods would generate user and advertisement embeddings independently with a siamese transformer encoder, such that approximate nearest neighbour search (ANN) can be leveraged.
Multi-Interest-Aware User Modeling for Large-Scale Sequential Recommendations
User states in different channels are updated by an \emph{erase-and-add} paradigm with interest- and instance-level attention.
DymSLAM:4D Dynamic Scene Reconstruction Based on Geometrical Motion Segmentation
The only input of DymSLAM is stereo video, and its output includes a dense map of the static environment, 3D model of the moving objects and the trajectories of the camera and the moving objects.
Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks
The CTR prediction problem is modeled as a supervised recurrent neural network, which naturally model the user history as a sequence of events.
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
The objective of an online learning algorithm for CMAB is to minimize (\alpha,\beta)-approximation regret, which is the difference between the \alpha{\beta} fraction of the expected reward when always playing the optimal super arm, and the expected reward of playing super arms according to the algorithm.
Influence Diffusion Dynamics and Influence Maximization in Social Networks with Friend and Foe Relationships
Influence diffusion and influence maximization in large-scale online social networks (OSNs) have been extensively studied, because of their impacts on enabling effective online viral marketing.
Social and Information Networks Discrete Mathematics Physics and Society E.1; H.3.3
