Deep neural networks (DNNs) are essential for performing advanced tasks on edge or mobile devices, yet their deployment is often hindered by severe resource con...
We study the basic task of mean estimation in the presence of mean-shift contamination. In the mean-shift contamination model, an adversary is allowed to replac...
Small language models (SLMs) offer compelling advantages in cost, latency, and adaptability, but have so far lagged behind larger models on long-horizon softwar...
Understanding the geometry of learned distributions is fundamental to improving and interpreting diffusion models, yet systematic tools for exploring their land...
Despite the extensive literature on training loss functions, the evaluation of generalization on the validation set remains underexplored. In this work, we cond...
Automated test generation is essential for software quality assurance, with coverage rate serving as a key metric to ensure thorough testing. Recent advancement...
The optimization of complex medical appointment scheduling remains a significant operational challenge in multi-center healthcare environments, where clinical s...
Differentially private federated learning faces a fundamental tension: privacy protection mechanisms that safeguard client data simultaneously create quantifiab...
The rapid advancement of large language models (LLMs) has led to a significant increase in automated tools in the software engineering, capable of performing va...
Scaling long-context capabilities is crucial for Multimodal Large Language Models (MLLMs). However, real-world multimodal datasets are extremely heterogeneous. ...
Neural routing solvers (NRSs) that leverage deep learning to tackle vehicle routing problems have demonstrated notable potential for practical applications. By ...
Studying Gene Expression in Cancer Cells Studying gene expression in a cancer patient’s cells can help clinical biologists understand the cancer’s origin and p...
