Meta-learning three-factor plasticity rules for structured credit assignment with sparse feedback

Meta has appointed Aman Jain to lead its public policy efforts in India, bringing over two decades of experience in public policy and business strategy to the role. This strategic move is aimed at enhancing Meta's engagement with Indian regulatory frameworks and stakeholders.

A recent study has introduced differential smoothing as a method to mitigate the diversity collapse often observed in large language models (LLMs) during reinforcement learning fine-tuning. This method aims to enhance both the correctness and diversity of model outputs, addressing a critical issue where outputs lack variety and can lead to diminished performance across tasks.

A new study introduces latent-action world models that learn from both action-conditioned and action-free data, addressing the limitations of traditional models that rely heavily on labeled action trajectories. This approach allows for training on large-scale unlabeled trajectories while requiring only a small set of labeled actions.

A recent study explores the automated recognition of instructional activities and discourse from multimodal classroom data, utilizing AI-driven analysis of 164 hours of video and 68 lesson transcripts. This research aims to replace manual annotation methods, which are resource-intensive and difficult to scale, with more efficient AI techniques for actionable feedback to educators.

The introduction of the D³-Predictor presents a significant advancement in dense prediction by addressing the limitations of existing diffusion models, which are hindered by stochastic noise that disrupts fine-grained spatial cues and geometric structure mappings. This new framework reformulates a pretrained diffusion model to eliminate stochasticity, allowing for a more deterministic mapping from images to geometry.

A novel framework for white balance (WB) correction has been proposed, leveraging a perception-inspired Learnable HSI (LHSI) color space. This approach aims to address the limitations of traditional sRGB-based WB editing, which struggles with color constancy in complex lighting conditions due to fixed nonlinear transformations and entangled color channels.

SplatCo has been introduced as a novel structure-view collaborative Gaussian splatting framework designed for high-fidelity rendering of complex outdoor scenes. This framework integrates a cross-structure collaboration module, a cross-view pruning mechanism, and a structure view co-learning module to enhance detail preservation and rendering efficiency in large-scale unbounded scenes.

A new study introduces a data-efficient fine-tuning strategy for large-scale text-to-video diffusion models, enabling the addition of generative controls over physical camera parameters using sparse, low-quality synthetic data. This approach demonstrates that models fine-tuned on simpler data can outperform those trained on high-fidelity datasets.