3 citations · 3 across the 5 of their papers we have counts for
6 papers
Benchmarking IoT Time-Series AD with Event-Level Augmentations
Dmitry Zhevnenko, Ilya Makarov, Aleksandr Kovalenko +6
Anomaly detection (AD) for safety-critical IoT time series should be judged at the event level: reliability and earliness under realistic perturbations. Yet many studies still emph…
Evaluating Generalization Capabilities of LLM-Based Agents in Mixed-Motive Scenarios Using Concordia
Chandler Smith, Marwa Abdulhai, Manfred Diaz +83
Large Language Model (LLM) agents have demonstrated impressive capabilities for social interaction and are increasingly being deployed in situations where they might engage with bo…
One Small Step in Latent, One Giant Leap for Pixels: Fast Latent Upscale Adapter for Your Diffusion Models
Aleksandr Razin, Danil Kazantsev, Ilya Makarov
Diffusion models struggle to scale beyond their training resolutions, as direct high-resolution sampling is slow and costly, while post-hoc image super-resolution (ISR) introduces…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
Enhancing GNNs Performance on Combinatorial Optimization by Recurrent Feature Update
Daria Pugacheva, Andrei Ermakov, Igor Lyskov +2
Combinatorial optimization (CO) problems are crucial in various scientific and industrial applications. Recently, researchers have proposed using unsupervised Graph Neural Networks…
A Semi-Supervised Deep Learning Approach to Dataset Collection for Query-By-Humming Task
Amantur Amatov, Dmitry Lamanov, Maksim Titov +3
Query-by-Humming (QbH) is a task that involves finding the most relevant song based on a hummed or sung fragment. Despite recent successful commercial solutions, implementing QbH s…