arXiv:2509.16491v1 Announce Type: new Abstract: Foundation models pretrained on physiological data such as photoplethysmography (PPG) signals are increasingly used to improve heart rate (HR) prediction across diverse settings. Fine-tuning these models for local deployment is often seen as a practical…
arXiv:2503.14963v3 Announce Type: replace Abstract: Multimodal Contrastive Learning (MCL) advances in aligning different modalities and generating multimodal representations in a joint space. By leveraging contrastive learning across diverse modalities, large-scale multimodal data enhances representational quality. However, a critical yet often…
arXiv:2509.16499v1 Announce Type: new Abstract: The widespread use of diffusion models has led to an abundance of AI-generated data, raising concerns about model collapse — a phenomenon in which recursive iterations of training on synthetic data lead to performance degradation.…
arXiv:2505.18877v2 Announce Type: replace Abstract: Low-Rank Adaptation (LoRA) lowers the computational and memory overhead of fine-tuning large models by updating a low-dimensional subspace of the pre-trained weight matrix. Albeit efficient, LoRA exhibits suboptimal convergence and noticeable performance degradation, due to…
arXiv:2509.16502v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) has significantly mitigated the hallucinations of Large Language Models (LLMs) by grounding the generation with external knowledge. Recent extensions of RAG to graph-based retrieval offer a promising direction, leveraging the structural knowledge…
arXiv:2507.18519v2 Announce Type: replace Abstract: Bisimulation metric has long been regarded as an effective control-related representation learning technique in various reinforcement learning tasks. However, in this paper, we identify two main issues with the conventional bisimulation metric: 1) an inability…
arXiv:2509.16508v1 Announce Type: new Abstract: When existing retrieval-augmented generation (RAG) solutions are intended to be used for new knowledge domains, it is necessary to update their encoders, which are taken to be pretrained large language models (LLMs). However, fully finetuning…
arXiv:2508.19318v2 Announce Type: replace Abstract: Deep Reinforcement Learning (DRL) has emerged as an efficient approach to resource allocation due to its strong capability in handling complex decision-making tasks. However, only limited research has explored the training of DRL models with…
arXiv:2509.16516v1 Announce Type: new Abstract: In this paper, we introduce a novel weighted co-training approach that is guided by Large Language Models (LLMs). Namely, in our co-training approach, we use LLM labels on unlabeled data as target labels and co-train…
arXiv:2309.03086v3 Announce Type: replace-cross Abstract: We suggest a new algorithm to estimate representations of compact Lie groups from finite samples of their orbits. Different from other reported techniques, our method allows the retrieval of the precise representation type as a…
