Neural-network variational Monte Carlo provides an accurate route to solving the electronic Schr\"odinger equation, but many neural wavefunctions are optimized for a single nuclear geometry, requiring separate calculations to map out a potential energy surface. We introduce NucleoNet, a nuclear-aware determinant ansatz...
Qiao-You Gao, Yang Huang, En-Ze Hou et al.· 0 citations
The increasing demand for GPU-accelerated machine learning inference has exposed limitations in centralized GPU infrastructures, including high cost, limited elasticity, and long wait times under resource constraints. At the same time, modern Apple Silicon-based macOS devices are equipped with powerful GPUs that remain...
Jianhua Li, Yun Xing, Girisha De Silva et al.· 0 citations
Comparing distributions under severe sample imbalance requires effectively using abundant majority observations while retaining sensitivity to differences involving the smaller sample. This challenge is particularly relevant for complex, non-Euclidean data, such as graphs, whose structural characteristics call for flex...
We study dictionary learning for probability measure-valued data by embedding measures into Hilbert spaces using features derived from optimal transport geometry. We learn data-adapted dictionaries through sparse autoencoder parameterizations, producing fast, scalable representations of probability measures as sparse f...
Brendan Mallery, Shuchin Aeron, James Murphy· 0 citations
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We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underlying features. This setting is motivated by applications such as medical testing, where additional cost can provide more precise measurements of patient's features for pre...
Sanghwan Kim, Kyoungseok Jang, Seyoung Yun· 0 citations
Federated vision-language models (FedVLMs) represent one of the emerging frontiers of machine learning, aiming to integrate federated learning within the fine-tuning pipelines of vision-language models (VLMs). In this work, we introduce BLEND, a new framework designed to jointly enhance the personalization and generali...
Payam Abdisarabshali, Fardis Nadimi, Kasra Borazjani et al.· 0 citations
Predictive-State Flows (PSF) is introduced, which partitions a sequence into blocks and associates each block with a predictive state computed solely from the preceding context, and suggests that latent design for continuous DLMs should consider not only how compactly text is represented, but also how generative roles...
Jin-Jin He, Xin-Song Feng, Lu-Cheng Fu et al.· 0 citations
The determinant-ratio point process (DRPP), which models repulsion and attraction via a ratio of the determinants of two symmetric positive-definite kernels, is proposed, which preserves the feature-based interpretation of standard DPPs while allowing the two effects to be specified separately.
Hideaki Ishibashi, Takahiro Kawashima, H. Hino· 0 citations
Mixed-integer programming (MIP) solvers are widely used, yet their performance can vary substantially even under seemingly neutral perturbations. Such variability complicates benchmarking, and the interpretation of computational studies. We introduce the Weighted Variability Score (WVS), a robust multi-component metric...
Mourchid Adegbindin, Brenda L Dietrich, Jamol J. Pender et al.· 1 citation
This work studies an entropy-based estimator of Kullback-Leibler (KL) divergence constructed from normalized importance weights. We prove a universal finite-sample bound showing that its expectation can exceed the target-proposal KL divergence by at most $\log 2$ for any sample size. We then characterize the KL diverge...
Buu Phan, L. Mackey, David Alvarez-Melis· 0 citations
Bayesian simulation-based inference (SBI) can struggle to recover increasingly concentrated posteriors as observation size grows. We consider learning sufficient summary statistics jointly with the posterior as a general principle for scalable inference in various SBI methods. A common measure taken in SBI is dimension...
Percy S. Zhai, Max Pachman, Peverel D. Shipley et al.· 0 citations
Fewer latent tokens promise cheaper diffusion. Yet aggressive latent compression creates a reconstruction-generation dilemma: adding channels improves reconstruction but makes generation harder. We introduce APEX, short for Asynchronous Prefix denoising for EXtreme compression, which pairs a hierarchical latent space w...
David Dinkevich, Nisan Chiprut, Yoav HaCohen et al.· 0 citations
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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