arXiv:2511.04814v1 Announce Type: new Abstract: Antimicrobial peptides have emerged as promising molecules to combat antimicrobial resistance. However, fragmented datasets, inconsistent annotations, and the lack of standardized benchmarks hinder computational approaches and slow down the discovery of new candidates. To address…
arXiv:2410.11551v2 Announce Type: replace Abstract: Training large models with millions or even billions of parameters from scratch incurs substantial computational costs. Parameter Efficient Fine-Tuning (PEFT) methods, particularly Low-Rank Adaptation (LoRA), address this challenge by adapting only a reduced number of…
arXiv:2511.04825v1 Announce Type: new Abstract: We explore the recently introduced persistent reachability homology (PRH) of digraph data, i.e. data in the form of directed graphs. In particular, we study the effectiveness of PRH in network classification task in a key…
arXiv:2502.03366v3 Announce Type: replace Abstract: In classification tasks, softmax functions are ubiquitously used as output activations to produce predictive probabilities. Such outputs only capture aleatoric uncertainty. To capture epistemic uncertainty, approximate Gaussian inference methods have been proposed. We develop a…
arXiv:2511.04834v1 Announce Type: new Abstract: Recent advances in text-to-image generative models have raised concerns about their potential to produce harmful content when provided with malicious input text prompts. To address this issue, two main approaches have emerged: (1) fine-tuning the…
arXiv:2505.17013v5 Announce Type: replace Abstract: In concept erasure, a model is modified to selectively prevent it from generating a target concept. Despite the rapid development of new methods, it remains unclear how thoroughly these approaches remove the target concept from…
arXiv:2511.04838v1 Announce Type: new Abstract: In molecular property prediction, the most valuable compounds (e.g., high potency) often occupy sparse regions of the target space. Standard Graph Neural Networks (GNNs) commonly optimize for the average error, underperforming on these uncommon but…
arXiv:2506.10751v2 Announce Type: replace Abstract: Automated question answering (QA) over electronic health records (EHRs) can bridge critical information gaps for clinicians and patients, yet it demands both precise evidence retrieval and faithful answer generation under limited supervision. In this work,…
arXiv:2511.04844v1 Announce Type: new Abstract: SDE-based methods such as denoising diffusion probabilistic models (DDPMs) have shown remarkable success in real-world sample generation tasks. Prior analyses of DDPMs have been focused on the exponential Euler discretization, showing guarantees that generally depend…
arXiv:2507.20088v2 Announce Type: replace Abstract: We develop a unified theoretical framework for neural architectures whose internal representations evolve as stationary states of dissipative Schr”odinger-type dynamics on learned latent graphs. Each layer is defined by a fixed-point Schr”odinger-type equation depending on…
