Counterfactual Debiasing for Fact Verification - OpenReview 016 namely CLEVER, which is augmentation-free 017 and mitigates biases on the inference stage 018 Specifically, we train a claim-evidence fusion 019 model and a claim-only model independently 020 Then, we obtain the final prediction via sub-021 tracting output of the claim-only model from 022 output of the claim-evidence fusion model,
Weakly-Supervised Affordance Grounding Guided by Part-Level. . . In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric object images without dense labels
LLaVA-OneVision: Easy Visual Task Transfer - OpenReview We present LLaVA-OneVision, a family of open large multimodal models (LMMs) developed by consolidating our insights into data, models, and visual representations in the LLaVA-NeXT blog series
DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks In this paper we propose a new model architecture
Diffusion Generative Modeling for Spatially Resolved Gene. . . Spatial Transcriptomics (ST) allows a high-resolution measurement of RNA sequence abundance by systematically connecting cell morphology depicted in Hematoxylin and eosin (H\ E) stained histology images to spatially resolved gene expressions
Probabilistic Learning to Defer: Handling Missing Expert. . . Recent progress in machine learning research is gradually shifting its focus towards *human-AI cooperation* due to the advantages of exploiting the reliability of human experts and the efficiency of AI models
Thieves on Sesame Street! Model Extraction of BERT-based APIs Finally, we study two defense strategies against model extraction—membership classification and API watermarking—which while successful against some adversaries can also be circumvented by more clever ones