Echocardiography is the most widely used non-invasive cardiac imaging modality, providing essential information for cardiovascular diagnosis. Interpreting an echocardiogram requires synthesizing complementary evidence across multiple heart views to identify abnormalities and produce structured clinical reports. While r...
Darya Taratynova, Ahmed Aly, Numan Saeed et al.· 0 citations
Evidence for reliable cross-subject five-vowel decoding is limited within this dataset and protocol, and the benchmark provides a reproducible chain from source rows to retained epochs, predictions, participant-level metrics, multiplicity-adjusted inference, and bounded diagnostic analyses.
Xiao-Yan Li, Ze-Yan Tao· Journal of Neural Engineerin...· 0 citations
This work proposes a physics-based noise synthesis framework tailored to CCD noise formation in the telescope, and demonstrates the effectiveness of this framework in both photometric and scientific accuracy.
CadKnitter is able to generate a complementary CAD part that follows both the geometric constraints of the given CAD model and the semantic constraints of the desired design text prompt, which outperforms other state-of-the-art baselines by a clear margin.
Tri Le, Khang Nguyen, Bao-Ru Huang et al.· arXiv.org· 2 citations
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It is shown decoder-only MDMs, despite a larger modeling space, can achieve significant inference speedups and comparable perplexity with techniques like temperature annealing, offering a path to reduced inference compute.
Shuchen Xue, Tianyu Xie, Tianyang Hu et al.· arXiv.org· 17 citations· ⚡1
VSI is not a universal best abstention signal; it is a sample-intrinsic indicator of vision-ignoring failure, best used as a conditional ensemble component.
FoCUS is presented, a controllable image captioning method that lets users steer captions toward specific semantic emphases through natural-language control prompts, and SCoPE, a benchmark with contrastive Include/Avoid constraints for measuring both target content coverage and out-of-scope suppression.
Jongyeop Hyun, Taeyoung Kim, Hyounghun Kim· 0 citations
The Veronese soft-argmax is validated in a Hough transform-based network across all resolvable lines, confirming uniform and seam-free recovery and the squared chordal distance between lines in projective space is derived, enabling a geometrically precise training objective.
Benjamin El-Zein, Dominik Eckert, Paul Zech et al.· 0 citations
A closed-loop threshold-aware training strategy in which the dismissal threshold is recalculated during training and used to penalize cancer-positive images that approach the dismissal region supports closed-loop threshold-aware training for high-recall selective dismissal.
Parham Hajishafiezahramini, Matthew Hamilton, Edward Kendall et al.· 0 citations
CORD is proposed, the first post-fit adapter to impose exact prediction preservation by repairing the full calibrated probability vector by removing the preservation constraint from calibrator fitting and assigns exact recovery of the original decision to subsequent output repair.
Subpopulation-Aware Generative Enhancement (SAGE), a two-stage generative augmentation framework, is introduced, using cluster-derived sub-labels and class labels to fine-tune a conditional generative model and text encoder, generating targeted synthetic data to fill underrepresented regions in the training set and con...
Yi-Ming Luo, Rong-Qiang Zhao, Jie Liu· 0 citations
This work proposes Class-wise Reliability-Aware Distillation (CRAD), which, per class, first discards teachers that disagree with the peer consensus and then takes a weighted average of the rest, weighting each teacher by its per-class reliability (precision, or inverse variance).
Baraa Bilbeisi, Meng-Chen Fan, Bao-Cheng Geng et al.· 1 citation
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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