3 citations · 3 across the 3 of their papers we have counts for
4 papers
Morpheus-2.0: Multiscale Probabilistic Forecasting with Dual-Axis Attention
Aakriti, Manoj Cherukumalli, Prathamesh Patil +5
A universal forecaster is trained once and applied everywhere, so it must serve tasks whose schemas, forecast horizons, and temporal resolutions all differ. The number of targets a…
Dynamic Linear Coregionalization for Realistic Synthetic Multivariate Time Series
Annita Vapsi, Penghang Liu, Saheed Obitayo +8
Synthetic data is essential for training foundation models for time series (FMTS), but most generators assume static correlations, and are typically missing realistic inter-channel…
HOSL: Hybrid-Order Split Learning for Memory-Constrained Edge Training
Aakriti Lnu, Zhe Li, Dandan Liang +3
Split learning (SL) enables collaborative training of large language models (LLMs) between resource-constrained edge devices and compute-rich servers by partitioning model computat…
AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning
Krishnateja Killamsetty, Guttu Sai Abhishek, Aakriti +4
Deep neural networks have seen great success in recent years; however, training a deep model is often challenging as its performance heavily depends on the hyper-parameters used. I…