A structure-preserving PINN framework for the nonlinear KdV equation, a prototypical model for nonlinear and dispersive wave propagation, that embeds the conservation of mass and Hamiltonian energy directly into the loss function, ensuring physically consistent and energy-stable evolution throughout training and prediction.
Victory Obieke, Emmanuel E. Oguadimma· arXiv.org· 4 citations
A cross-fidelity knowledge distillation and adaptive fusion network (CFKD-AFN), which leverages abundant but low-fidelity simulation data to enhance the prediction on scarce but high-fidelity trial data, and is extended to an interpretable variant for exploratory analysis of feature-attribution patterns associated with treatment outcomes.
Wen-Jing Chen, Lian-Sheng Zhuang, Zi-Ying Luo et al.· arXiv.org· 0 citations
Molecular graph representation learning is widely used in chemical and biomedical research, and reusing widely available and well-validated pre-trained 2D encoders, while incorporating molecular domain knowledge during downstream adaptation, offers a more practical alternative.
This paper proposes a novel flow matching method that overcomes the limitations of existing multi-marginal trajectory inference algorithms, using a GAN-inspired adversarial loss to fit neurally parametrised interpolant curves between source and target points such that the marginal distributions at intermediate time points are close to the observed distributions.
Oskar Kviman, Kirill Tamogashev, Nicola Branchini et al.· arXiv.org· 2 citations
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This paper proposes the first general method for modelling Schr\"odinger bridges when one (or both) distributions are given by their unnormalised densities, with no access to data samples, and applies the newly developed algorithm to the problem of sampling posterior distributions in latent spaces of generative models, thus creating a data-free image-to-image translation method.
Kirill Tamogashev, Esmeralda S. Whitammer· arXiv.org· 4 citations
This work introduces SHAKE-GNN, a novel scalable graph-level GNN framework based on a hierarchy of Kirchhoff Forests, a class of random spanning forests used to construct stochastic multi-resolution decompositions of graphs, enabling flexible trade-offs between efficiency and performance.
Recent advances in self-supervised learning for EEG representation have largely relied on masked reconstruction, where models are trained to recover randomly masked signal segments. While effective at modeling local dependencies, the training objective of masked reconstruction does not compel the model to capture global generative constraints essential for characterizing neural activity. To address this limitation, we propose EEGDM, a novel self-supervised framework that leverages latent diffusion models to generate EEG signals as an objective. Unlike masked reconstruction, diffusion-based generation progressively denoises signals from noise to realism, compelling the model to capture holistic temporal patterns and cross-channel relationships. Specifically, EEGDM incorporates an EEG encoder that distills raw signals and their channel augmentations into a compact representation, which serves as conditional information to guide the diffusion denoising process, thereby enabling the encoder and diffusion model to be jointly optimized through the generative objective. This design endows EEGDM with a compact latent space, which not only offers ample control over the generative process but also can be leveraged for downstream tasks. Experimental results show that EEGDM (1) reconstructs high-quality EEG signals, (2) learns robust representations, and (3) achieves competitive performance across diverse downstream tasks, thus exploring a new direction for self-supervised EEG representation learning.
Shaocong Wang, Tong Liu, Ming Li et al.· arXiv.org· 2 citations
The empirical evaluations on standard benchmarks and in a comparison study with widely used explanation methods show that attention weights can be meaningfully incorporated into the studied XAI frameworks, highlighting their value in enriching transformer explainability.
Marte Eggen, Jacob Lysnæs-Larsen, Inga Strümke· arXiv.org· 1 citation
Agnostics introduces Agnostics, a language-agnostic post-training pipeline that eliminates per-language engineering, and releases the language-agnostic training datasets, making RL post-training in any programming language as simple as editing a short YAML file.
Aleksander Boruch-Gruszecki, Yangtian Zi, Zi-Xuan Wu et al.· arXiv.org· 5 citations
StructSynth is introduced, a framework that treats a dependency graph as a generation plan---determining the generation order, conditioning context, and scope of each black-box LLM call, and achieves state-of-the-art downstream utility and the best privacy-risk ranking among fourteen compared generators in low-data settings.
Si-Yi Liu, Yujian Zheng, Haoyang Li et al.· 0 citations
The evaluation of ctdGAN with 14 imbalanced datasets demonstrated its strong ability in generating high fidelity samples and improving classification accuracy, and several other improvements, including a simple, yet effective cluster-wise scaling technique that captures multiple feature modes without affecting data dimensionality.
Leonidas Akritidis, Panayiotis Bozanis· IEEE Transactions on Knowled...· 3 citations
A cycle consistency-based data-driven training framework that supports unsupervised learning and significantly reduces reliance on manual intervention, demonstrating potential advantages in non-injective regression tasks.
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
MIT News · Artificial Intelligence· news.mit.eduAug 24, 2026
