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18,068 papers
Wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes. While Random Forest and XGBoost are widely used, they predict each location independently from satellite features, without adapting to the varying difficulty of heterogeneou...
To lower the expertise barrier in machine learning, the AutoML community has focused on the CASH problem, which jointly automates algorithm selection and hyperparameter tuning. While traditional methods like Bayesian Optimization (BO) struggle with cold-start issues, Large Language Models (LLMs) can mitigate these thro...
Long-range distillation is introduced, whereby a long-timestep probabilistic ``student''model is trained to forecast directly at long-range using a huge synthetic dataset generated by an autoregressive ``teacher''model, representing the first demonstration that real-world skill can be improved by training on AI-generat...
This work developed an algorithmic framework for variational quantum kernels utilizing resource- efficient quantum kernels utilizing resource- efficient ansatze for complex classification tasks and introduced a parameter scaling technique to accelerate convergence.
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In-context reinforcement learning (ICRL) has emerged as an effective paradigm for test time adaptation to unseen tasks without parameter updates. However, existing ICRL methods can exhibit brittle and unstable adaptions, and the mechanisms underlying such adaptions remain poorly understood. We provide a policy-gradient...
Sig-DEG (Signature-based Differential Equation Generator), a generator for distilling pre-trained diffusion models, combines partial signatures that summarize Brownian motion over subintervals with a recurrent structure to approximate stochastic differential equation solutions to achieve universality.
Recent advances highlight Reinforcement Learning with Verifiable Rewards (RLVR) as a promising approach to enhancing LLM capabilities. However, it remains unclear whether the current practice of RLVR truly expands models' reasoning boundary or mainly amplifies high-reward outputs that base models already know. We intro...
Scheduling echocardiographic examinations in a hospital setting presents significant challenges due to non-deterministic factors such as patient no-shows, variable arrival times, and heterogeneous exam durations, as well as asymmetric resource constraints between fetal and non-fetal patient streams. Fetal cases involve...
It is taken that current research practices of improving one or two aspects in isolation are insufficient and research on Trustworthy AI must account for interactions between aspects and adopt a holistic view across all relevant axes at once.
A model of time series dependencies, that flexibly adapt to different time series regularity, while enabling us to learn these dependencies from observations is proposed.
It is suggested that simple in-training interpolation deserves consideration before introducing more elaborate forgetting-mitigation mechanisms, and Sequential Fine-tuning with Averaging with Averaging with SFA is introduced and evaluated.
Standard deep learning architectures used for classification generate label predictions with a projection head and softmax activation function. Although successful, these methods fail to leverage the relational information between samples for generating label predictions. In recent works, graph-based learning technique...
The new approach, known as FLEX, could lead to more intuitive, interactive household and assistive robots.
By learning how matter moves, the WorldParticle system could assist with developing soft robots, medicines, and more.
A machine-learning approach called CheMeleon provides a practical way to develop new drugs or other molecules with much less detailed, expensive experimentation.
A Blog post by Ai2 on Hugging Face
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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