DiffFuseNet runs diffusion denoising on shallow encoded features instead of full images, making infrared-visible fusion roughly ten times faster than prior diffusion methods without sacrificing quality.
SGF-MRI uses a fast auxiliary MRI sequence to guide reconstruction of slower scans, speeding up brain and knee MRI while preserving diagnostic detail.
How SGF-MRI Speeds Up Brain and Knee MRI Scans Read More »
TriDeNT is a three-branch self-supervised framework that lets H&E pathology models learn from immunohistochemistry and spatial transcriptomics data they will never see at deployment — with performance gains up to 101%.
TriDeNT Taught a Pathology Model to Learn From Data It Will Never See Again Read More »
HCEP tackles open-set emotion recognition: teaching multimodal AI to admit when an expression matches no known category instead of forcing a wrong label.
Teaching AI to Say “I Don’t Know”: HCEP and the Open-Set Emotion Recognition Problem Read More »
Stochastic Bregman proximal gradient methods drop the Lipschitz assumption behind SGD’s convergence proofs, match optimal sample complexity, and resist gradient explosion where standard optimizers collapse.
MVR-GLF extracts three complementary spectral views from temporal hyperspectral images and fuses them globally and locally, catching changes that single-view detectors miss.
How MVR-GLF Reads Spectral Shape to Catch What Others Miss Read More »
A criterion-wise local differential privacy framework perturbs each rating dimension on-device before it reaches the server — here’s what that privacy costs in recommendation accuracy.
What Happens When You Add Differential Privacy to a Multi-Criteria Recommender System Read More »
SMM-FNet fuses hyperspectral, multispectral, and radar imagery through purpose-built Mamba modules, showing why three sensor sources beat two in remote sensing.
When Two Sensors Are Not Enough: SMM-FNet Fuses Three Remote Sensing Sources With Mamba Read More »
Federated recommenders drift apart when clients simply average parameters. AlignFedRec shares item similarity matrices instead, aligning structure rather than embeddings.
Why Federated Recommenders Break and How Structural Alignment Fixes Them Read More »
RASD-FuNet routes each feature point to a low-rank or attention branch based on a learned confidence score, topping hyperspectral fusion benchmarks at a fraction of the FLOPs.