A pair selection strategy based on negative similarity dynamics strengthens contrastive supervision in gloss-free sign language translation by reducing noisy negatives.
Effective approaches to attention- based neural machine translation
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
representative citing papers
Sequential machine learning on jet declustering history trees outperforms static models at identifying jet quenching in heavy-ion collision simulations.
Attention gates added to U-Net automatically focus on target organs in CT images and improve segmentation performance on abdominal datasets.
Pith review generated a malformed one-line summary.
A gated-fusion CSI predictor using GRU, attention, and DSLH reaches -13.84 dB NMSE with 26% fewer parameters and 2.3x higher throughput than a LinFormer baseline on 3GPP channels.
Sentence-level models outperform skeleton-based approaches for narrative coherence despite a new SSN network improving on cosine and Euclidean baselines.
Gemma 2 models achieve leading performance at their sizes by combining established Transformer modifications with knowledge distillation for the 2B and 9B variants.
citing papers explorer
-
Resource-Efficient CSI Prediction: A Gated Fusion and Factorized Projection Approach
A gated-fusion CSI predictor using GRU, attention, and DSLH reaches -13.84 dB NMSE with 26% fewer parameters and 2.3x higher throughput than a LinFormer baseline on 3GPP channels.