Neural Information Processing Systems http://nips.cc/

Network intrusion detection system Continual learning with shallow methods Detailed illustration of configuration changes Datasets details Data preprocessing and feature selection Task formulation Task similarity via optimal transport dataset distance Training time comparison of the proposed ECBRS with the baselines Additional experiments with anomaly detection datasets Ablation studies Implementation, hardware details, and hyperparameter selection Occurrence of task dissimilarity between two different tasks is rare Limitations and broader impact A.1 Network intrusion detection system NID comprises two parts: the training module and the anomaly detection engine. The training can be periodic or triggered by an event like decay in intrusion detection accuracy. These features are fed to the anomaly detection engine to identify anomaly pattern(s). In our work, shallow methods are the non-neural network-based approaches. BWT is the influence that learning a task ' t ' has on the performance of BWT occurs when learning a task diminishes proficiency in prior tasks.

Work done at Georgia Institute of Technology. Work done at Sutter Health.

Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure. Accurate prognosis prediction is vital for timely interventions and resource optimization. We present a transformer-based framework for predicting CKD progression using multi-modal electronic health records (EHR) from the Seoul National University Hospital OMOP Common Data Model. Our approach (\textbf{ProQ-BERT}) integrates demographic, clinical, and laboratory data, employing quantization-based tokenization for continuous lab values and attention mechanisms for interpretability. The model was pretrained with masked language modeling and fine-tuned for binary classification tasks predicting progression from stage 3a to stage 5 across varying follow-up and assessment periods. Evaluated on a cohort of 91,816 patients, our model consistently outperformed CEHR-BERT, achieving ROC-AUC up to 0.995 and PR-AUC up to 0.989 for short-term prediction. These results highlight the effectiveness of transformer architectures and temporal design choices in clinical prognosis modeling, offering a promising direction for personalized CKD care.

This work investigates how semantics influence localisation performance and robustness in a learned self-supervised, contrastive semantic localisation framework. After training a localisation network on both original and perturbed maps, we conduct a thorough post-hoc introspection analysis to probe whether the model filters environmental noise and prioritises distinctive landmarks over routine clutter. We validate various interpretability methods and present a comparative reliability analysis. Integrated gradients and Attention Weights consistently emerge as the most reliable probes of learned behaviour. A semantic class ablation further reveals an implicit weighting in which frequent objects are often down-weighted. Overall, the results indicate that the model learns noise-robust, semantically salient relations about place definition, thereby enabling explainable registration under challenging visual and structural variations.

Network intrusion detection system Continual learning with shallow methods Detailed illustration of configuration changes Datasets details Data preprocessing and feature selection Task formulation Task similarity via optimal transport dataset distance Training time comparison of the proposed ECBRS with the baselines Additional experiments with anomaly detection datasets Ablation studies Implementation, hardware details, and hyperparameter selection Occurrence of task dissimilarity between two different tasks is rare Limitations and broader impact A.1 Network intrusion detection system NID comprises two parts: the training module and the anomaly detection engine. The training can be periodic or triggered by an event like decay in intrusion detection accuracy. These features are fed to the anomaly detection engine to identify anomaly pattern(s). In our work, shallow methods are the non-neural network-based approaches. BWT is the influence that learning a task ' t ' has on the performance of BWT occurs when learning a task diminishes proficiency in prior tasks.

Deep neural networks (DNNs) are increasingly applied to safety-critical tasks in resource-constrained environments, such as video-based driver action and intention recognition. While last layer probabilistic deep learning (LL-PDL) methods can detect out-of-distribution (OOD) instances, their performance varies. As an alternative to last layer approaches, we propose extending pre-trained DNNs with transformation layers to produce multiple latent representations to estimate the uncertainty. W e evaluate our latent uncertainty representation (LUR) and repulsively trained LUR (RLUR) approaches against eight PDL methods across four video-based driver action and intention recognition datasets, comparing classification performance, calibration, and uncertainty-based OOD detection. W e also contribute 28,000 frame-level action labels and 1,194 video-level intention labels for the NuScenes dataset. Our results show that LUR and RLUR achieve comparable in-distribution classification performance to other LL-PDL approaches. F or uncertainty-based OOD detection, LUR matches top-performing PDL methods while being more efficient to train and easier to tune than approaches that require Markov-Chain Monte Carlo sampling or repulsive training procedures.