Feed Forward Network (FFN) block structure within a standard Transformer model.

We provide random, cold-source (new drugs), and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone, feature-based models, and topological heuristics.

However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols.

D efender ( RaPiD) problem, aiming to rapidly counter adversarial perturbations without altering the deployed model.

Modern machine learning systems are able to learn extremely complicated relations from data.

A critical approach for efficiently deploying computationally demanding large language models (LLMs) is Key-V alue (KV) caching.

The complexity of automating bias evaluation in textual content poses significant challenges. Traditional approaches in social sciences, such as content analysis, often rely on manual word counts from static lists [Gaucher et al., 2011], which may miss the subtleties and unlisted language cues that advanced NLP technologies can detect.

In this paper, we propose OneRef, a minimalist referring framework built on the modality-shared one-tower transformer that unifies the visual and linguistic feature spaces.

The two-stage fine-tuning (FT) method, linear probing (LP) then fine-tuning (LP-FT), outperforms linear probing and FT alone. This holds true for both in-distribution (ID) and out-of-distribution (OOD) data.

Language Models (VLM) to interpret video anomalies precisely.