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Found 13 papers, 7 papers with code
Learning to Summarize from LLM-generated Feedback
Developing effective text summarizers remains a challenge due to issues like hallucinations, key information omissions, and verbosity in LLM-generated summaries.
CUPID: A Real-Time Session-Based Reciprocal Recommendation System for a One-on-One Social Discovery Platform
However, conventional session-based approaches struggle with high latency due to the demands of modeling sequential user behavior for each recommendation process.
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
To this end, we introduce Base-Anchored Preference Optimization (BAPO), a simple yet effective approach that utilizes the initial responses of reference model to mitigate forgetting while accommodating personalized alignment.
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning
Additionally, we observe that as data heterogeneity increases, the gap between higher feature norms for observed classes, obtained from local models, and feature norms of unobserved classes widens, in contrast to the behavior of classifier weight norms.
Instructive Decoding: Instruction-Tuned Large Language Models are Self-Refiner from Noisy Instructions
Notably, utilizing 'opposite' as the noisy instruction in ID, which exhibits the maximum divergence from the original instruction, consistently produces the most significant performance gains across multiple models and tasks.
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
FedSOL is designed to identify gradients of local objectives that are inherently orthogonal to directions affecting the proximal objective.
The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions
This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy
Cell segmentation is a fundamental task for computational biology analysis.
Self-Contrastive Learning
This paper proposes a novel contrastive learning framework, called Self-Contrastive (SelfCon) Learning, that self-contrasts within multiple outputs from the different levels of a multi-exit network.
Self-Contrastive Learning: Single-viewed Supervised Contrastive Framework using Sub-network
To this end, we propose Self-Contrastive (SelfCon) learning, which self-contrasts within multiple outputs from the different levels of a single network.
Preservation of the Global Knowledge by Not-True Distillation in Federated Learning
In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models.
MixCo: Mix-up Contrastive Learning for Visual Representation
Contrastive learning has shown remarkable results in recent self-supervised approaches for visual representation.
SIPA: A Simple Framework for Efficient Networks
With the success of deep learning in various fields and the advent of numerous Internet of Things (IoT) devices, it is essential to lighten models suitable for low-power devices.
