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Found 11 papers, 8 papers with code
Prompt-free and Efficient Few-shot Learning with Language Models
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Diffusion models have emerged as a powerful paradigm for generation, obtaining strong performance in various continuous domains.
PERFECT: Prompt-free and Efficient Few-shot Learning with Language Models
Current methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM can score.
Variational Information Bottleneck for Effective Low-Resource Fine-Tuning
Moreover, we show that our VIB model finds sentence representations that are more robust to biases in natural language inference datasets, and thereby obtains better generalization to out-of-domain datasets.
Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks
State-of-the-art parameter-efficient fine-tuning methods rely on introducing adapter modules between the layers of a pretrained language model.
Compacter: Efficient Low-Rank Hypercomplex Adapter Layers
In this work, we propose Compacter, a method for fine-tuning large-scale language models with a better trade-off between task performance and the number of trainable parameters than prior work.
ParsiNLU: A Suite of Language Understanding Challenges for Persian
Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
End-to-End Bias Mitigation by Modelling Biases in Corpora
We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data.
Learning-Based Compressive MRI
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms have been proposed that can be used with general Fourier subsampling patterns.
Segment Based 3D Object Shape Priors
This leads to a semantic segmentation as a side product of our shape prior formulation.
Scalable sparse covariance estimation via self-concordance
We consider the class of convex minimization problems, composed of a self-concordant function, such as the $\log\det$ metric, a convex data fidelity term $h(\cdot)$ and, a regularizing -- possibly non-smooth -- function $g(\cdot)$.
