Breast histopathology analysis increasingly relies on distributed learning because direct data pooling across institutions is often restricted by privacy, governance, and communication constraints. This study compares server-based Federated Averaging (FedAvg), fully decentralized gossip learning, and Hybrid Gossip-FedA...
Yusuf Ozturk, Enes Goltekin, Bengisu Atli et al.· 0 citations
The Neverwhere Benchmark Suite is developed, comprised of over sixty 3D Gaussian Splatting reconstructions of urban indoor and outdoor scenes, to encourage large-scale and reproducible robot evaluation by making it easier to create and integrate Gaussian splats-based reconstructions into simulated continuous testing se...
Zi-Yu Chen, Henghui Bao, Hao-Ran Chang et al.· 0 citations
Real-world datasets often exhibit long-tailed class distributions, where a few head classes contain a large number of training samples while a large number of tail classes have only a few. The composition of each mini-batch, determined by the sampling strategy, governs which classes contribute to the stochastic gradien...
This work recognizes the involved Key Postures (KPs) and motions in the Adavu using Convolutional Neural Network (CNN) and Support Vector Machine (SVM), respectively, and compares these KP and motion sequences with the ground truth to find the best match using the Edit Distance algorithm.
This article describes an open image dataset for developing and evaluating active-fire segmentation methods in satellite imagery. The dataset contains 2,148 image-mask pairs from 25 California wildfires, with acquisitions spanning July 2020 to August 2026. Each image is a 512x512-pixel, three-channel composite derived...
This work proposes an architecture?interpretable foundation model based on the deep dictionary network (DDN) for unified multi-organ ULDCT denoising, and achieves state-of-the-art performance and consistently surpasses competing ULDCT methods across all mul
Bao-Shun Shi, Shuang-Yi Yang, Ke Jiang et al.· 0 citations
Across structured, unstructured, and particle data, the sample-based formulation improves reconstruction accuracy with measurably fewer primitives in comparison to prior formulations, achieving up to 4.8 dB higher PSNR with an approximate 44x reduction in primitive count.
Michael R. Martin, J. Insley, Victor A. Mateevitsi et al.· 0 citations
This work presents EMODY Flow, a lightweight flow-matching framework that attaches to a frozen Qwen-3 Omni model and reuses its internal Mimi audio-codecs to condition two parallel DiT generators - one for SMPL-X body pose, one for FLAME facial expressions.
H. Agarwal, Xavier Alameda-Pineda, Olivier Perrotin· 0 citations
This work asks whether a finite collection can instead be stored intensionally, as the decoded orbit of one compact law that returns exactly to its start, as the decoded orbit of one compact law that returns exactly to its start.
This paper presents a novel sparsity-driven continual learning framework that operates in two stages: it first identifies a sparse, task-critical parameter mask via a sparsity-inducing objective, then performs mask-constrained fine-tuning by only optimizing parameters selected by the mask.
Yunxiang Fu, Meng Lou, Zicheng Liao et al.· 0 citations
Noise2Noise (N2N) trains denoisers on pairs of independently corrupted observations, eliminating clean references. We stress-test two natural conjectures about why the L1 loss outperforms L2 here. First, the hypothesis that the L1 loss confers robustness via parameter sparsity confuses the loss with Lasso regularizatio...
Ding-Yan Shang, Zhen-Yu Xu, You Wang et al.· 0 citations
OPD-Aha is introduced, which reconstructs the distillation target directly from this isolated visual preference rather than relying on the fragile teacher-student discrepancy, yielding broad and consistent improvements across diverse fine-grained perception and complex multimodal reasoning benchmarks.
Chen-Hao Qiu, Da-Wei Li, Ye-Chao Zhang et al.· 0 citations
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.
