arXiv:2607. 17264v1 Announce Type: new Abstract: Disentangled representation learning is a powerful paradigm for robust attribute prediction.
arXiv:2606. 05109v1 Announce Type: new Abstract: To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information.
To leverage the full potential of multimodal data, we need representations that go beyond the state-of-the-art alignment and fusion approaches and exploit all cross-modal interactions without sacrificing modality-specific information. Learning disentangled representations is a principled way to identify these underlying shared and unique factors that are hidden in observational data.
arXiv:2410. 11251v2 Announce Type: replace Abstract: A hallmark of intelligent agents is the ability to learn reusable skills purely from unsupervised interaction with the environment.
arXiv:2503. 09679v2 Announce Type: replace Abstract: Meta-learning represents a strong class of approaches for solving few-shot learning tasks.
arXiv:2601. 21688v2 Announce Type: replace-cross Abstract: Disentangled representation learning aims to map independent factors of variation to independent representation components.
The paper introduces Skill Abstraction with Interpretable Latents (SAIL), a method that models human skill as a persistent, multi‑dimensional construct inferred from naturalistic behavior over time. SAIL produces a robust skill embedding that blends expert and novice bases, learns transferable subskills through counterfactual subskill swaps, and supports skill‑informed behavior prediction across various in‑domain contexts. Experiments on racing and baseball demonstrate that SAIL achieves strong predictive performance, improves behaviorally grounded disentanglement compared to baselines, and enhances downstream AI coaching outcomes.
arXiv:2505. 20853v3 Announce Type: replace-cross Abstract: Fusing heterogeneous information remains a persistent challenge in modern data analysis.
Model merging provides an efficient way to construct multi-task generalist models without additional training, but its performance often degrades under severe task interference. Task interference in m...
arXiv:2607. 24023v1 Announce Type: new Abstract: Brain-Machine Interfaces (BMIs) provide a direct communication pathway between the brain and external devices, enabling humans to control assistive and robotic technologies, with potential applications in rehabilitation, human motor augmentation, and human-centered robotics.
arXiv:2602. 01619v2 Announce Type: replace-cross Abstract: Unsupervised Skill Discovery (USD) aims to autonomously learn a diverse set of skills without relying on extrinsic rewards.
arXiv:2502. 20681v3 Announce Type: replace-cross Abstract: Transformers may exhibit two-stage training dynamics during the real-world training process.
arXiv:2608.30366v1 Announce Type: new Abstract: The loss landscape of Deep Neural Networks (DNNs) exhibits highly complex and non-convex properties. Recent studies have revealed the phenomenon of mod...