Uncertainty-aware flow matching (UA-Flow) is proposed, a lightweight extension of flow matching that predicts the velocity field together with heteroscedastic uncertainty that tracks sample fidelity more consistently than element-wise baselines and that these signals also carry information about data-manifold support.
This work derives the optimal velocity field targeted by the empirical FM objective on sequential data and shows that the resulting sampler is an ODE whose dynamics describe a nonparametric, memory-augmented continuous-time dynamical system.
Soon Hoe Lim, Shi-Zhen Lin, Michael W. Mahoney et al.· arXiv.org· 4 citations
Governments and development partners often seek Sustainable Development Goal (SDG) "accelerators" whose progress can propagate gains across human well-being, institutions, resources, and environmental priorities. Such claims require evidence on direction, timing, robustness, and trade-offs, not only network connectivit...
Md Muhtasim Munif Fahim, Md Jahid Hasan Imran, Md. Naim Molla et al.· 0 citations
Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging. Health systems now routinely generate large volumes of cardiac MRI, CT and echocardiography together with bulk, single-cell and spatial transcriptomics...
Minh H. N. Le, Tuan Vinh, Thanh-Huy Nguyen et al.· 0 citations
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Chemical reaction networks are widely used to model stochastic dynamics in biophysical processes. Their exact probabilistic description via the chemical master equation becomes intractable for high-dimensional systems due to the exponential growth of the state space. This difficulty is further exacerbated when metastab...
Jiayu Weng, Xinyi Zhu, Jing Liu et al.· 0 citations
The deep learning revolution has enabled major AI advances in physical sciences, particularly in discovering dynamical systems from observational data. Yet the reliability of learned surrogates and symbolically discovered models is often undermined by non-uniqueness: models may fit observed trajectories perfectly, but...
Zakhar Shumaylov, P. Zaika, Philipp Scholl et al.· arXiv.org· 11 citations· ⚡1
Vision-Language-Action (VLA) models are a promising paradigm for field robotics, but their deployment is bottlenecked by strict edge SWaP-C constraints. Compression techniques such as quantization, distillation, and pruning are well-established for LLMs and VLMs. Pruning, however, remains particularly underexplored for...
Jason Jabbour, Dong-Ki Kim, Max Smith et al.· 0 citations
Modern ML research increasingly faces an evaluation bottleneck: building benchmarks and running experiments is costly, slowing iteration. We ask how much can be inferred about evaluation outcomes before experiments, given only a natural-language description of the task and setup. We call this setting anticipatory evalu...
Jungsoo Park, Ethan Mendes, Gabriel Stanovsky et al.· 0 citations
A verifiable and accurate termination criterion for PWS optimization is introduced and like the gradient norm in smooth optimization, this certificate tightly characterizes the optimality gap under the QG condition and can be evaluated without knowledge of any problem parameters.
A grand challenge in modern neuroscience is bridging the gap between the microscale neural circuits and cognitive functions. While extensive knowledge exists about neuronal connectivity and biophysics, how low-level phenomena give rise to abstract behaviors remains largely unresolved. Here, we propose that a mathematic...
Sen Lu, Xiaoyu Zhang, Mingtao Hu et al.· 0 citations
Noise contrastive estimation (NCE) is a popular method for training energy-based models (EBM) with intractable normalisation terms. The key idea of NCE is to learn by comparing unnormalised log-likelihoods of the reference and noisy samples, thus avoiding explicitly computing normalisation terms. However, NCE criticall...
In federated learning (FL), a center trains a shared model from updates that clients compute on their private data. Collecting and training on data is costly, so a strategic client may contribute little data and free-ride on the model trained by the others. We study mechanisms that incentivize data contribution and lea...
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
Research Manager Mike Walker reflects on a career spanning hacker culture, cybersecurity, and AI, sharing lessons about trust, teamwork, leadership, and why solving difficult problems requires curiosity, judgment, agility—and an understanding of the rules. The post Learn the rules: The hacker mindset appeared first on Microsoft Research.
Learn how Google Antigravity and our Cheminformatics Skills accelerate drug discovery workflows, search compounds, and propose novel rigidified macrocycles.
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