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12 papers · 2 filters
Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting
Yunyao Cheng, Chenjuan Guo, Kaixuan Chen +6
Accurate time series forecasting is crucial for optimizing resource allocation, industrial production, and urban management, particularly with the growth of cyber-physical and IoT…
New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction
Guillaume Salha-Galvan, Johannes F. Lutzeyer, George Dasoulas +2
Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP). Their performances are less impressive on community detecti…
GENNAPE: Towards Generalized Neural Architecture Performance Estimators
Keith G. Mills, Fred X. Han, Jialin Zhang +6
Predicting neural architecture performance is a challenging task and is crucial to neural architecture design and search. Existing approaches either rely on neural performance pred…
Mitigating Unfairness via Evolutionary Multi-objective Ensemble Learning
Qingquan Zhang, Jialin Liu, Zeqi Zhang +3
In the literature of mitigating unfairness in machine learning, many fairness measures are designed to evaluate predictions of learning models and also utilised to guide the traini…
Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores
Diana Petrescu, Arsany Guirguis, Do Le Quoc +3
Storage disaggregation underlies today's cloud and is naturally complemented by pushing down some computation to storage, thus mitigating the potential network bottleneck between t…
ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling
Zheqi Zhu, Yuchen Shi, Pingyi Fan +2
As a promising learning paradigm integrating computation and communication, federated learning (FL) proceeds the local training and the periodic sharing from distributed clients. D…