Search papers, labs, and topics across Lattice.
33 papers published across 2 labs.
AI's role in medical education has exploded post-2021, with generative AI and learner-facing applications dominating a field now heavily concentrated in the US and a few elite institutions.
LLM agents significantly outperform numerical optimizers in four out of five black-box optimization domains, but adding more numerical tools doesn't always help.
A single neural density functional accurately predicts ion correlations in polymer electrolytes across varying concentrations and geometries, generalizing beyond training data and outperforming existing architectures.
Reverse-mode gradients slash Bayesian inference time for expensive simulators, especially when parameters vastly outnumber outputs.
Spatio-temporal Bayesian optimization for point-process data is now scalable and efficient, achieving accurate peak discovery with minimal evaluations.
AI's role in medical education has exploded post-2021, with generative AI and learner-facing applications dominating a field now heavily concentrated in the US and a few elite institutions.
LLM agents significantly outperform numerical optimizers in four out of five black-box optimization domains, but adding more numerical tools doesn't always help.
A single neural density functional accurately predicts ion correlations in polymer electrolytes across varying concentrations and geometries, generalizing beyond training data and outperforming existing architectures.
Reverse-mode gradients slash Bayesian inference time for expensive simulators, especially when parameters vastly outnumber outputs.
Spatio-temporal Bayesian optimization for point-process data is now scalable and efficient, achieving accurate peak discovery with minimal evaluations.
State-preserving constraints in Bayesian optimization are transformed into an efficient, anisotropic ellipsoid in design space, enabling robust optimization of complex engineering systems.
A training-free change detection method achieves near-optimal performance by integrating diffusion models to precisely estimate Hyvärinen scores, slashing detection delays by over 90% in simulations.
A score-based method accelerates cluster DAG learning from interventions by two orders of magnitude, matching state-of-the-art edge recovery even on dense graphs.
Unifying factual support, counterfactual sensitivity, and exemplar tolerance reveals how GNNs truly reason about molecular properties, not just predict them.
Learnable shape parameterizations unlock superior sample efficiency and design exploration in Bayesian optimization, outperforming hand-crafted approaches across diverse engineering domains.
Research reports can now be continuously updated with over 60% fewer search calls and 33% less token consumption, maintaining high quality over time.
LLMs adapt neural operator configurations using both a strong task-dependent prior and real-time experimental feedback, outperforming Bayesian optimization.
Generative models can now capture the full spectrum of solutions in high-dimensional physical systems with symmetry-breaking bifurcations, a feat previously intractable.
Kernel methods finally offer a scalable and interpretable alternative to black-box ANNs for learning Koopman embeddings, matching or surpassing ANN performance on complex dynamical systems.
Large observational studies can finally boost the precision of small RCTs for treatment effect estimation without sacrificing unbiasedness or making strong confounding assumptions.
Auxiliary "ghost tasks" make any linear differential equation system effectively parametrizable for Gaussian Processes, solving a key limitation in physics-informed ML.
LLMs can be steered to reliably interpret complex genomic variants by grounding their reasoning in deterministic biological computations and evidence verification, avoiding hallucinated TF binding changes and fabricated experimental support.
Chronologically consistent language models trained on scientific literature and patents outperform general domain models and even predecessors on temporal science and technology tasks, even with limited historical data.
Spectral learning unlocks $\mathcal{O}(d\log d)$ covariance estimation for dense Gaussian dependencies, outperforming prior methods in forecasting and prediction tasks.
Multiplicative composition of Fourier-Chebyshev features unlocks richer PDE solution representations than implicit MLP composition.
AI agents are now building climate models that outperform published research and offer novel hypotheses for long-standing scientific debates.
Pretraining neural CFD surrogates across diverse domains unlocks substantial gains in zero- and few-shot generalization, drastically reducing data requirements for new applications.
Generative AI now predicts complex 3D urban wind and temperature fields with LES-level accuracy in seconds, unlocking rapid, turbulence-aware urban design.
A unified, open-source framework and a massive 3.3 TB dataset now enable rigorous ML-driven prediction of complex multiphase flows in porous media, accelerating research in critical energy and engineering applications.
Physics-aligned electronic ground-state models shatter out-of-distribution generalization barriers for MLIPs, achieving near-chemical accuracy on reactive systems with minimal data.
Model selection for scientific forecasting is fundamentally protocol-dependent, with optimal choices reversing based on dataset size, system complexity, and evaluation metrics like state refresh versus closed-loop performance.
Machine learning agents discover new exact values for notoriously difficult-to-compute link invariants like slice genus and unknotting number.
Generative orthogonal learning unlocks rate double robustness and quasi-oracle efficiency for estimating distributional treatment effects under complex time-varying confounding.
Bayesian inference for complex switching stochastic differential equations is now broadly tractable, even with non-analytic transition densities.
A single information-theoretic framework elegantly unifies multi-objective, multi-fidelity, and constrained Bayesian optimization, bypassing separate heuristics for each.
The asymptotic behavior of RBF kernel MLEs is finally understood, revealing minimax optimal convergence rates for all key parameters.
AI is poised to revolutionize ARPES, moving beyond data analysis to enable fully autonomous, self-driving experimental labs.
Kinetic Langevin diffusions slash posterior sampling time for imaging inverse problems by 10x while maintaining reconstruction quality, even with complex diffusion model priors.
