WeCon introduces gated residual fusion in the encoder, residual fusion in the decoder, and efficient preference optimization to match state-of-the-art hypervolume on MOCOPs while cutting inference time by 40%.
Neural Networks , volume =
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
PAMNet achieves state-of-the-art multivariate time series forecasting by explicitly separating and modulating the phase and amplitude of periodic cycles via a lightweight dual-branch network.
citing papers explorer
-
WeCon: An Efficient Weight-Conditioned Neural Solver for Multi-Objective Combinatorial Optimization Problems
WeCon introduces gated residual fusion in the encoder, residual fusion in the decoder, and efficient preference optimization to match state-of-the-art hypervolume on MOCOPs while cutting inference time by 40%.
-
PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
PAMNet achieves state-of-the-art multivariate time series forecasting by explicitly separating and modulating the phase and amplitude of periodic cycles via a lightweight dual-branch network.