FAF: A Feature-Adaptive Framework for Few-Shot Time Series Forecasting
arXiv:2506.19567v1 Announce Type: cross Abstract: Multi-task and few-shot time series forecasting tasks are commonly encountered in scenarios such as the…
arXiv:2506.19567v1 Announce Type: cross Abstract: Multi-task and few-shot time series forecasting tasks are commonly encountered in scenarios such as the…
arXiv:2506.18729v2 Announce Type: replace-cross Abstract: We propose MuseControlLite, a lightweight mechanism designed to fine-tune text-to-music generation models for precise conditioning…
arXiv:2506.18920v1 Announce Type: new Abstract: In this work, we investigate how autonomous agents, organized into tribes, learn to use communication…
arXiv:2501.15225v2 Announce Type: replace-cross Abstract: While many advanced LLMs are designed to handle long sequence data, we can still observe…
arXiv:2505.12260v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs)-based hybrid retrieval uses LLMs to encode queries and documents into low-dimensional…
arXiv:2506.17350v1 Announce Type: cross Abstract: Backdoor attacks have emerged as a critical security threat against deep neural networks in recent…
arXiv:2506.17369v1 Announce Type: cross Abstract: In the era of large language models (LLMs), code benchmarks have become an important research…
arXiv:2506.18240v1 Announce Type: cross Abstract: Here in this work, we present a novel Quadratic Binary Optimization (QBO) model for quantized…
arXiv:2506.15591v2 Announce Type: replace-cross Abstract: It is a challenging problem to reproduce rich spatial details while maintaining temporal consistency in…
arXiv:2506.16782v1 Announce Type: cross Abstract: Fairness in machine learning (ML) has become a rapidly growing area of research. But why,…