Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inferenc...
Automated seizure detection from electroencephalography (EEG) remains difficult due to the large variability of seizure dynamics across patients, recording cond...
We develop a practical framework for distinguishing diffusive stochastic processes from deterministic signals using only a single discrete time series. Our appr...
Large language models (LLMs) often fail to learn effective long chain-of-thought (Long CoT) reasoning from human or non-Long-CoT LLMs imitation. To understand t...
In safety-critical domains, linguistic ambiguity can have severe consequences; a vague command like 'Pass me the vial' in a surgical setting could lead to catas...
Representing networks as a graph and training a link prediction model using benign connections is an effective method of anomaly-based intrusion detection. Exis...
The rapid deployment of Internet of Things (IoT) devices has led to large-scale sensor networks that monitor environmental and urban phenomena in real time. Com...
As emerging applications demand higher throughput and lower latencies, operators are increasingly deploying millimeter-wave (mmWave) links within x-haul transpo...
We propose DeePM (Deep Portfolio Manager), a structured deep-learning macro portfolio manager trained end-to-end to maximize a robust, risk-adjusted utility. De...
Recent advances in video generation have been dominated by diffusion and flow-matching models, which produce high-quality results but remain computationally int...
The constrained combinatorial multi-armed bandit model has been widely employed to solve problems in wireless networking and related areas, including the proble...
Active learning (AL) plays a critical role in materials science, enabling applications such as the construction of machine-learning interatomic potentials for a...
