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Found 9 papers, 7 papers with code
Dynamic Uncertainty Ranking: Enhancing In-Context Learning for Long-Tail Knowledge in LLMs
Large language models (LLMs) can learn vast amounts of knowledge from diverse domains during pre-training.
Parameter Competition Balancing for Model Merging
Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model.
Knowledge Fusion By Evolving Weights of Language Models
Fine-tuning pre-trained language models, particularly large language models, demands extensive computing resources and can result in varying performance outcomes across different domains and datasets.
CADE: Cosine Annealing Differential Evolution for Spiking Neural Network
This paper attempts to tackle the challenges by introducing Cosine Annealing Differential Evolution (CADE), designed to modulate the mutation factor (F) and crossover rate (CR) of differential evolution (DE) for the SNN model, i. e., Spiking Element Wise (SEW) ResNet.
Towards Stability of Parameter-free Optimization
To tackle this challenge, in this paper, we propose a novel parameter-free optimizer, \textsc{AdamG} (Adam with the golden step size), designed to automatically adapt to diverse optimization problems without manual tuning.
Safe and Robust Watermark Injection with a Single OoD Image
Training a high-performance deep neural network requires large amounts of data and computational resources.
Revisiting Data-Free Knowledge Distillation with Poisoned Teachers
Data-free knowledge distillation (KD) helps transfer knowledge from a pre-trained model (known as the teacher model) to a smaller model (known as the student model) without access to the original training data used for training the teacher model.
Backdoor Defense for Data-Free Distillation with Poisoned Teachers
Data-free Knowledge Distillation
Turning the Curse of Heterogeneity in Federated Learning into a Blessing for Out-of-Distribution Detection
We propose to take advantage of such heterogeneity and turn the curse into a blessing that facilitates OoD detection in FL.
Federated Adversarial Debiasing for Fair and Transferable Representations
While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework.
