用 AI 生成的网站
用 AI 生成的网站
用 AI 生成的网站
Quantifying social behavior in laboratory animals is fundamental to neuroscience but remains hindered by manual annotation’s subjectivity. The Multi-Agent Behavior (MABe) challenge addresses this by benchmarking automated recognition from pose data, yet faces challenges like extreme class imbalance, complex topology, and cross-laboratory domain shifts.
In this work, we propose Ego-GAT-SqueezeNet, a unified framework for multi-agent behavior understanding. First, we introduce an egocentric alignment strategy to invariantize agent features against translation and rotation. Second, we employ a Graph Attention Network (GAT) to explicitly model the dynamic spatial topology. Crucially, we integrate a Squeezeformer backbone that leverages efficient downsampling to capture long-range dependencies in high-frequency sequences. For environmental heterogeneity, we utilize Feature-wise Linear Modulation (FiLM) to dynamically recalibrate features based on laboratory and subject identities. Our approach achieves an F1-score of 0.7702 on the validation set, outperforming baselines by identifying rare social actions across diverse experimental setups.
Accurate pasture biomass estimation is critical for precision grazing management yet remains challenged by the trade-off between the scalability of remote sensing and the reliability of manual sampling. To address this, we introduce PastureNet, a novel hierarchical ensemble framework that estimates biomass directly from high-resolution RGB images. Unlike traditional approaches, PastureNet synergizes diverse inductive biases by integrating three state-of-the-art Vision Transformers: DINOv3 (object-centric), SigLIP 2 (semantic-aligned), and EVA-02 (texture-sensitive). A key innovation is the integration of Zero-shot Semantic Concept Scores to inject explicit ecological domain knowledge (e.g., clover presence) into the regression pipeline, alongside a Matrix Reconciliation post-processing step that ensures biological consistency across biomass components. Evaluated on a heterogeneous Australian dataset, our method achieves a Weighted R2 of 0.70, significantly outperforming CNN baselines (0.47) and demonstrating robust generalization without requiring physical metadata at inference time.
随着基于注意力机制的大模型面临数据、算力、电力的限制，与对模型可解释性、可控性、推理能力的更高要求，深度学习领域出现了显著的“回溯现象”：人们纷纷将目光投向了前深度学习时代的经典算法思想。如，OpenAI 在 2025 年 11 月发布了通过稀疏电路来理解神经网络的文章：通过稀疏电路来理解神经网络 | OpenAI；还有像清华大学孙茂松老师团队在 2025 年 12 月发布的论文H-Neurons：大语言模型中幻觉相关神经元的存在、作用及其起源，基于 L1 稀疏线性回归器 Lasso 研究的幻觉相关神经元在神经网络的分布。
本文旨在深入探讨AdaBoost、主成分分析（PCA）、稀疏编码和粒子滤波这四大经典算法的基本思想在 2025 年大模型时代的重生与进化。通过对近三年论文的梳理与分析，得出结论：这些经典算法在本质上与现代大模型的对齐（Alignment）、高效微调（PEFT）、可解释性（Interpretability）及复杂推理（Reasoning）殊途同归。AdaBoost 的间隔理论与误差修正思想不仅解释了深度学习中的“良性过拟合”现象，更通过贝叶斯奖励模型集成（BRME）解决了 RLHF 中的奖励黑客问题；PCA 的低秩假设与流形理论直接催生了 LoRA-XS 等高效微调方法及 KV Cache 压缩技术，并揭示了模型本质上的线性特征；稀疏编码的基向量分解思想通过稀疏自编码器（SAE）破解了神经元超级叠加的可解释性难题，并推动了 MoE 架构与 Sparse-Linear Attention (SLA) 的演进；而粒子滤波的序列状态估计思想则为思维链（CoT）推理提供了概率论框架，并赋予视频生成模型掌握处理不确定性的物理世界模拟能力。这些经典思想正成为大模型从 System 1 向 System 2 跃迁的关键基石。