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Found 7 papers, 1 papers with code
Towards Better Characterization of Paraphrases
To effectively characterize the nature of paraphrase pairs without expert human annotation, we proposes two new metrics: word position deviation (WPD) and lexical deviation (LD).
Semantic-Aware Contrastive Sentence Representation Learning with Large Language Models
However, to train a contrastive learning model, large numbers of labeled sentences are required to construct positive and negative pairs explicitly, such as those in natural language inference (NLI) datasets.
One-Bit-Aided Modulo Sampling for DOA Estimation
To overcome this fundamental bottleneck, we propose a one-bit-aided (1bit-aided) modulo sampling scheme for direction-of-arrival (DOA) estimation.
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation
Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recognition (NER).
Line Spectral Estimation via Unlimited Sampling
In addition, a two-stage US LSE (USLSE) is proposed, where the line spectral signal is first recovered by iteratively executing DP and OMP, and then the parameters are estimated by applying a state-of-the-art LSE algorithm.
Joint Network Topology Inference via Structured Fusion Regularization
Theoretically, we provide a theoretical analysis of the proposed graph estimator, which establishes a non-asymptotic bound of the estimation error under the high-dimensional setting and reflects the effect of several key factors on the convergence rate of our algorithm.
Testing Unfaithful Gaussian Graphical Models
The global Markov property for Gaussian graphical models ensures graph separation implies conditional independence.
