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17,191 papers
This work establishes a robust dataset and evaluation benchmark for prosody-aware computational Sanskrit recitation by Benchmarking state-of-the-art TTS architectures via an automated UTMOS (neural MOS) predictor demonstrates that XTTS-v2 achieves the highest perceptual quality.
This work introduces WhatIf-Bench, a benchmark measuring how well forecasters use covariates to forecast a target's response to future events, and reveals generalization to Novel futures as the core challenge, and offers a testbed toward arbitrary scenario analysis.
A modification of the gradient boosting algorithm, which is called parallel gradient boosting, designed to circumvent all limitations of classical implementations, is studied, which provides predictions of similar quality to state-of-the-art boosting libraries such as XGBoost, while being faster by several orders of ma...
Across three visual tasks, it is shown that pathway selection is task-dependent, and that data distribution and prompt design can also modulate which pathway is used to solve the image-based query.
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Giskard is presented, a scalable protocol for private and Byzantine-robust decentralized aggregation that reduces per-party communication complexity by close to three orders of magnitude over the closest competitor while exhibiting comparable robustness under up to $n/4 Byzantine parties.
Medical vision-language models (VLMs) are evaluated on public benchmarks whose images and question-answer pairs have been freely available for years, yet reported accuracy assumes that these examples were not in the training data. We test this assumption for open VLMs on SLAKE-En, PathVQA, and VQA-RAD, and on a public...
SAHG (Sector-Anisotropic Hyperbolic Graph) learns a direction-dependent curvature field $\gamma(u)$ that adapts geometric resolution across structural directions, and uses sector prototypes to convert angular concentration and alignment into classifier-readable features.
We revisit a neural negative binomial model for weekly earthquake counts on a 3 x 3 degree grid over the Tian Shan and Pamir, and ask which of its conclusions survive a careful evaluation. At the completeness magnitude of 4.5, the 144-week test period holds 2448 cell-weeks; six cell-weeks, in five distinct weeks, have...
This study presents a reproducible framework to build ML models for coarse soundscape classification and introduces CoarseSoundNet, a deep learning model trained to distinguish biophony, geophony, and anthropophony under realistic terrestrial PAM conditions.
This work introduces Projection agents, a GCO methodology built on the Wolpertinger framework and adapted to a shared GNN-based latent space for observations and actions, and releases LaGCO-RL, a Python library that automates latent action-space construction and supports existing RL-GCO solutions, promoting reproducibi...
The results suggest that training explicitly for saliency is a promising route towards chain-of-thought reasoning that is more faithful to the model's actual decision process.
This paper defines this iterative inference process as Markovian generation chains, where each step takes a specific prompt template and the previous output as input, without including any prior data.
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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