representation-learning

ViQ presents a visual quantization framework that balances semantic richness and detail preservation in discrete representations, enabling efficient multimodal training with native-resolution inputs by using text-aligned pre-training and proximal representation learning.

This book develops an effective theory for deep neural networks, showing that their predictions are nearly-Gaussian and governed by the depth-to-width ratio, and introduces representation group flow to analyze signal propagation and learning dynamics.

PORTER is a language-grounded structured EHR foundation model that represents clinical events through text descriptions and numeric values, enabling vocabulary-independent transfer across institutions without retraining. On pediatric prediction tasks, PORTER matches fixed-vocabulary models and recovers 97.1% of AUROC when transferred to unseen event descriptions.

This paper compares four tokenization methods (Affine, AIM, JetFormer, VQ-VAE) for astronomical images within a unified transformer framework, using 640,000 galaxy images to evaluate reconstruction quality, physical property prediction, and morphological preservation. It finds that no single method excels across all tasks, highlighting trade-offs in representation learning.

The tweet highlights a research finding that signals of physical plausibility can be extracted from the geometry of frozen image encoders without video training or physics supervision.

DVD-JEPA is an open-source, minimal JEPA world model that learns representations from video by predicting future embeddings rather than pixels. It uses a bouncing DVD logo to demonstrate position recovery, dreaming, and anomaly detection, all running in a browser.

EvoEmbedding is a dynamic embedding model that maintains a continuously updated latent memory to generate adaptive representations for long-context retrieval, outperforming larger specialist models and improving agentic workflows.

PoLAR introduces a geometrically structured latent action representation in hyperbolic space that separates transition extent from mode, improving robotic policy learning performance.

This paper introduces CADE, a framework for time-series question answering that maps each timestep directly into the LLM embedding space and uses a one-directional supervised contrastive loss to align time-series representations with frozen text anchors, outperforming existing baselines on the Time-MQA benchmark.

This paper introduces concept modulation models (CMMs), a unified framework for identifiability and extrapolation in conditional generative models. It shows that feature agreement on observed attributes induces constraints through attribute potentials, enabling algebraic extrapolation criteria that recover and generalize existing results.

Proposes a reinforcement learning framework that uses locally linear embeddings to capture environment dynamics and an attention mechanism to adaptively fuse dynamics-specific and reward-specific features, inspired by neural principles, improving learning efficiency.

This paper proposes Adaptive Binning, a learning-coupled feature-wise coarse-to-fine curriculum for tabular self-supervised learning that adaptively discretizes features, improving representations on medical datasets and establishing a unified benchmark.

Microsoft Research introduces Next-Latent Prediction (NextLat), a self-supervised method that trains transformers to predict their own next latent state, enabling compact world models for reasoning and planning and achieving up to 3.3x faster inference via self-speculative decoding.

This paper proposes a post-training refinement approach using interventional contrastive learning to disentangle speech foundation model representations into separate content and speaker subspaces. The method shows improved out-of-domain speaker verification performance and evidence of successful separation.

MoCo-AIS is a unified contrastive learning framework for computing similarity of vessel trajectories, evaluated on large-scale AIS datasets.

This paper systematically evaluates foundation model representations for multimodal cancer analysis, benchmarking unimodal and multimodal fusion strategies on real-world cohorts, and assessing trustworthiness via conformal prediction.

Introduces Temporal Difference in Vision (TDV), a new paradigm for representation learning that relies solely on causality, eliminating the need for augmentations, masking, or cropping, and matches state-of-the-art methods like DINO and iBOT on dense spatial tasks.

Introduces Temporal Difference in Vision (TDV), a novel visual representation learning paradigm that learns useful representations without augmentations, masking, cropping, or reconstruction, and matches state-of-the-art methods on dense spatial tasks.

This paper formalizes the 'Impedance Mismatch' between foundation models and knowledge graphs, and proposes a theoretical roadmap for neuro-symbolic fusion using structured residual streams, vector symbolic architectures, and orthogonal subspace editing.

Introduces a geometric framework to identify 'AI engrams' – memory traces in deep neural networks – formalizing neuroscientific criteria into a closed-form estimator, enabling surgical memory manipulation in models from MLPs to LLMs.