Graph Neural Networks have demonstrated significant success in graph classification tasks, yet they often require substantial computational resources and strugg...
We introduce the Do-Undo task and benchmark to address a critical gap in vision-language models: understanding and generating physically plausible scene transfo...
Building general-purpose reasoning models with reinforcement learning (RL) entails substantial cross-domain heterogeneity, including large variation in inferenc...
Recent deep learning frameworks in histopathology, particularly multiple instance learning (MIL) combined with pathology foundational models (PFMs), have shown ...
A well-engineered prompt can increase the performance of large language models; automatic prompt optimization techniques aim to increase performance without req...
The Square Kilometre Array (SKA) project will operate one of the world's largest continuous scientific data systems, sustaining petascale imaging under strict p...
Autoregressive models (ARMs) are hindered by slow sequential inference. While masked diffusion models (MDMs) offer a parallel alternative, they suffer from crit...
In this paper, we propose a Differentially Private Stochastic Gradient Push with Compressed communication (termed DP-CSGP) for decentralized learning over direc...
Denoising language models (DLMs) have been proposed as a powerful alternative to traditional language models (LMs) for automatic speech recognition (ASR), motiv...
Large Mixture-of-Experts (MoE) model inference is challenging due to high resource demands and dynamic workloads. Existing solutions often deploy the entire mod...
Much of software engineering (SE) research assumes that progress depends on massive datasets and CPU-intensive optimizers. Yet has this assumption been rigorous...
The study presents the outcomes of research and experimental validation in the domain of automated codebase migration, with a focus on addressing challenges in ...
