Human rationales in supervision for Telugu sentiment analysis improve model alignment with human reasoning and often produce gains in predictive performance.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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PatchTST uses subseries patching and channel-independent Transformers to deliver significantly better long-term multivariate time series forecasting and strong self-supervised transfer performance.
PhysicsFormer applies a lightweight Transformer PINN with pseudo-sequential representations to convection, Burgers, lid-driven cavity, and inverse Navier-Stokes problems, reporting near-zero error in parameter identification and flow reconstruction from sparse noisy data.
citing papers explorer
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Human-Centered Supervision for Sentiment Analysis in Telugu: A Systematic Inquiry Beyond Accuracy
Human rationales in supervision for Telugu sentiment analysis improve model alignment with human reasoning and often produce gains in predictive performance.
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A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
PatchTST uses subseries patching and channel-independent Transformers to deliver significantly better long-term multivariate time series forecasting and strong self-supervised transfer performance.
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A Simple but Efficient Transformer-Based Physics-Informed Neural Network for Incompressible Navier--Stokes Equations
PhysicsFormer applies a lightweight Transformer PINN with pseudo-sequential representations to convection, Burgers, lid-driven cavity, and inverse Navier-Stokes problems, reporting near-zero error in parameter identification and flow reconstruction from sparse noisy data.