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This paper conducts a controlled study on advection-aware graph nowcasting for distributed solar ramp forecasting, finding that accurate cloud-motion features are as important as graph structure and introducing a self-supervised estimator that reduces forecast error.
Atelier introduces a self-supervised hypernetwork framework that generates implicit neural representations for cryoEM density maps, enabling local feature extraction and improving performance on downstream annotation tasks.
EvolvingAvatar introduces a causal 3D head generation system that adapts in real-time to conversational contexts using test-time training, enhancing motion statistics in dialogue scenarios.
TT-VidT is a new video pretraining method that efficiently models motion by decoupling the temporal axis, achieving state-of-the-art results on motion-centric benchmarks with reduced computational cost.
This paper systematically evaluates masking strategies for EEG foundation models across MAE and JEPA frameworks, identifying optimal configurations and a novel failure mode, achieving comparable performance to REVE with reduced compute.
A physics-informed self-supervised learning framework is introduced for calibrating and reconstructing positions in multi-wire parallel plate avalanche counters using only detector geometry and charge constraints, eliminating the need for labeled data or dedicated calibration runs.
The paper proposes a general self-supervised learning framework for combinatorial optimization using Frank-Wolfe methods to handle constraints, with strong empirical results on problems like TSP, Maximum Coverage, and QAP.
EMGBlend introduces a self-supervised framework for pretraining on heterogeneous EMG datasets, addressing differences in electrode layouts, frequency support, and data source imbalances to improve gesture recognition and force decoding tasks.
This paper proposes the Activity Chain Encoder (ACE), a self-supervised model that learns daily activity patterns from mobile phone location data without labels, and demonstrates its effectiveness in identifying differentiated activity patterns in urban environments like London.
The paper evaluates ECG biometrics under realistic conditions involving exercise-induced stress and cross-session variability using a Siamese ResNet model, achieving state-of-the-art performance.
Tactile-JEPA is a self-supervised pre-training method for distributed tactile sensors that uses spatial topology to learn representations, improving force estimation and orientation tasks in robotics over prior state-of-the-art.
Toolformer trains language models to self-teach how to use external APIs like calculators and search engines via self-supervised learning, significantly improving zero-shot performance across tasks.
This paper proposes a new unified pre-training framework for medical code sequences that captures hierarchical structures and complex interactions, demonstrating superior performance in clinical event prediction and drug repositioning case studies for Alzheimer's disease.
This paper proposes a multimodal anomaly detection framework for fault detection in mechanical systems using self-supervised cross-modal reconstruction and adaptive thresholding to improve robustness under distribution shifts.
HintMiner is a novel tool that automatically mines hints for user questions from Q&A web posts like Stack Overflow using a language model trained via self-supervised learning, achieving effective performance in evaluations.
MANAS-2 introduces a new EEG foundation model using constrained reconstruction to enhance latent representations and improve performance on downstream tasks across multiple datasets.
The paper proposes MAPA, a self-supervised pretraining method with spatial encodings for neural interfaces, which reduces the labeled data needed for accurate brain-computer interface decoding across subjects.
This paper proposes an adaptive spectral bandwidth control method for kernelized graph construction to align kernel spectral properties with intrinsic manifold dimensions, showing improvements in self-supervised learning embedding tasks on CIFAR-100.
LeVJEPA introduces a more efficient self-supervised video pretraining approach by eliminating the need for target networks and predictors, using a single shared encoder with SIGReg regularizer, and achieving competitive performance with lower compute costs.
This paper proposes Graph-CMMC, a graph-based pseudo-multimodal contrastive learning framework for 12-lead ECG representations, which effectively models inter-lead dependencies and achieves competitive performance in cardiac disease classification.