Higher-order interactions beyond pairwise relationships in large complex networks are often modeled as hypergraphs. Analyzing hypergraph properties such as tria...
Distributed attention is a fundamental problem for scaling context window for Large Language Models (LLMs). The state-of-the-art method, Ring-Attention, suffers...
As foundation models grow in size, fine-tuning them becomes increasingly expensive. While GPU spot instances offer a low-cost alternative to on-demand resources...
Locating the files and functions requiring modification in large open-source software (OSS) repositories is challenging due to their scale and structural comple...
The rapid growth of large language models (LLMs) and the continuous release of new GPU products have significantly increased the demand for distributed training...
Free-viewpoint video (FVV) enables immersive viewing experiences by allowing users to view scenes from arbitrary perspectives. As a prominent reconstruction tec...
Extending well-structured transition systems to incorporate a probabilistic scheduling rule, we define a new class of stochastic well-structured transition syst...
Hybrid AI-HPC workflows combine large-scale simulation, training, high-throughput inference, and tightly coupled, agent-driven control within a single execution...
As LLMs advance their reasoning capabilities about the physical world, the absence of rigorous benchmarks for evaluating their ability to generate scientificall...
While Ethereum has successfully achieved dynamic availability together with safety, a fundamental delay remains between transaction execution and immutable fina...
Data-driven analysis of business processes has a long tradition in research. However, recently the term of process mining is mostly used when referring to data-...
State-of-the-art video generative models typically learn the distribution of video latents in the VAE space and map them to pixels using a VAE decoder. While th...
