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Found 6 papers, 4 papers with code
Confidence-aware Denoised Fine-tuning of Off-the-shelf Models for Certified Robustness
We also find that such a fine-tuning can be done by updating a small fraction of parameters of the classifier.
Addressing Prediction Delays in Time Series Forecasting: A Continuous GRU Approach with Derivative Regularization
In addition, we demonstrate the low prediction delay of our method in a variety of datasets.
Data-Efficient Molecular Generation with Hierarchical Textual Inversion
Compared to the conventional textual inversion method in the image domain using a single-level token embedding, our multi-level token embeddings allow the model to effectively learn the underlying low-shot molecule distribution.
Evaluating the Efficacy of Interactive Language Therapy Based on LLM for High-Functioning Autistic Adolescent Psychological Counseling
This study investigates the efficacy of Large Language Models (LLMs) in interactive language therapy for high-functioning autistic adolescents.
Long-term Time Series Forecasting based on Decomposition and Neural Ordinary Differential Equations
Long-term time series forecasting (LTSF) is a challenging task that has been investigated in various domains such as finance investment, health care, traffic, and weather forecasting.
Confidence-aware Training of Smoothed Classifiers for Certified Robustness
Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i. e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs.
