This paper proposes a novel contrastive learning scheme for knowledge distillation-based anomaly detection, namely Multiresolution Contrastive Distillation (MCD), which does not require positive/negative pairs with an anchor but operates by pulling/pushing the distance between the teacher and student features.
Jihyun Lee, Hangi Park, Yongmin Seo et al.· 2023 18th International Conf...· 3 citations
Frequency-aware Task Weighting (FTW), a dynamic task-balancing rule that estimates a loss-trajectory stability proxy from the low-frequency energy ratio of recent loss histories, is implemented and examined.
Zhi-Yuan Nie, Zi-Xi Zhou, Xian-Bin Gu· 0 citations
Regularization losses are the primary driver in this setting, delivering the accuracy gains and almost all of the deformation control for free at inference time, while the affine architecture adds a smaller complementary benefit at acceptable cost.
Champollion, a self-supervised learning framework that learns interpretable local representations of cortical folding from structural MRI, is introduced, establishing cortical folding as a rich and largely untapped source of neurodevelopmental information.
Julien Laval, R. Guiavarch, Antoine Dufournet et al.· arXiv.org· 1 citation
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A novel framework that integrates the augmented path signature (APS) descriptor with the T-Mamba model, inspired by the efficacy of state space models (SSMs) in sequence modelling, that enables the model to learn both local temporal patterns and global long-range dependencies, substantially improving verification accur...
IGG (Image Generation informed by Geodesic dynamics), a novel framework that integrates topology-preserving geodesic principles into the diffusion-based generative process, where geometric object changes are learned as smooth and invertible smooth mappings from a given template/source image.
Nian Wu, Nivetha Jayakumar, Jiarui Xing et al.· 0 citations
Graph modeling, a crucial task for representing complex relationships in graph-structured data, has achieved significant success in recent years. However, current graph modeling methods rely on traditional Graph Neural Networks and pre-training approaches to implicitly learn the underlying relational structure of graph...
Thanh-Dat Truong, Sarah Alharbi, Susan Gauch et al.· 0 citations
Shortcut learning denotes the widespread situation in which a classifier exploits spurious correlations rather than diagnostic features. Existing mitigation strategies mostly aim to learn shortcut-invariant representations; their empirical success is limited and they cannot be applied to classifiers using frozen founda...
Together, these results show that VLMs systematically mischaracterize how their predictions depend on available and missing evidence, motivating executable interventions as a behavioral test of multimodal self-explanations.
Aydin Javadov, Daniel Schoess, F. von Wangenheim· 0 citations
A range of ML models are evaluated-Logistic Regression, Decision Tree (DT), Random Forest (RF), Gradient Boosting (GB), Naive Bayes (NB), Support Vector Machine (SVM), and AdaBoost (AdB)-on these datasets, analyzing prediction accuracy, model size, and prediction latency.
S. Bhat, Piotr Szczuko· Scientific Reports· 11 citations
This work presents Hybrid Stratified Replay (HSR), a novel mechanism that restructures batches by stratifying rollouts based on Path Entropy, a rollout-level confidence proxy, and outcome reward, offering a principled solution for stable and efficient RL fine-tuning.
Yi-Meng Ye, Shuang Chen, Wen-Xuan Huang et al.· 0 citations
This paper proposes conditioned initialization, a principled scheme that initializes attention weights to improve the spectral properties of the attention layer and shows that conditioned initialization can potentially reduce the condition number of the attention Jacobian, leading to more stable optimization.
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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