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17,191 papers
The baselines show that a random update of the same size leaves that rank almost unchanged while wrecking the model, and that an edit built on the effective rank of the update has no measurable effect in the authors' tests.
CF-NODE outperforms all four methods with statistically significant improvements over the second-best method, improves AUC on every target domain, and attains the highest average AUC and macro-F1, delivering stronger grade discrimination together with more balanced recognition across DR severity levels.
The signal generation capabilities of a novel data augmentation framework are examined in aerospace structures. First, the framework learns class-conditional temporal and spectral characteristics of the measured structural responses. It uses conditional generative adversarial network training and Monte Carlo dropout un...
Transformer Residual Activation Codebook Encoding (Trace), a framework that eliminates the received data downstream computation and enables communication--computation overlap and reduces a key latency bottleneck in distributed Transformer inference.
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BOLT (bound-oriented learning for transport-coupled scheduling) builds a lower bound from chain, machine-capacity, and fleet-capacity relaxations that certifies a completed schedule's suboptimality gap without a solver.
The proposed Correlation-Guided and Dynamics-Trained Clustered Echo State Network (CGDT-CESN) is proposed, which comprises a modular reservoir architecture with each cluster distinctly specializing in one of the variables in the multivariate time series.
This work tests whether learning a specific factual binding produces a later relearning advantage beyond familiarity with its entity, answer vocabulary, format, or query slot, and whether that advantage remains positive after adjustment for measured pre-relearning behavior.
This work interprets absorbing diffusion as the infinite-contrast limit of a trust axis, analogous to the signal-to-noise gap between known and unknown regions in diffusion inpainting, and suggests that detectability and damage depend jointly on departure from the data conditional.
This study investigates whether Large Language Models, conditioned on physical diagnostic descriptions of simulation errors, can propose informative parameter updates more efficiently than conventional derivative-free optimizers, and introduces a hybrid workflow that uses the LLM as a warm-start, passing accumulated ev...
Introduction: Near-perfect crop-label accuracy can conceal uncertainty, perturbation sensitivity, and weak explanations. This study evaluates these reliability dimensions without treating benchmark classification as an agronomic recommendation. Materials and methods: Ten classifiers were evaluated using five matched st...
A conceptual framework linking data quality assurance, feature engineering, model training and reliability testing, and deployment to an agent population is proposed, closed by a cross-agent monitoring and feedback loop.
It is concluded that horizontally scalable, log-based messaging, combined with fault-tolerant, in-memory and micro-batch computation and durable, replicated storage constituted the technical foundation that made real-time, ML-driven enterprise analytics feasible within this period and that this layered architecture con...
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.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
Prabha Jha used her experience with MIT Open Learning’s program for senior executives to develop AI solutions for her employer, Verizon, and to write a book.
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