Search Results for author:
Found 13 papers, 7 papers with code
Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement
However, existing work on ViTs has restricted their surrogate refinement to backward propagation.
Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models
Large language models have already demonstrated their formidable capabilities in general domains, ushering in a revolutionary transformation.
Improving Integrated Gradient-based Transferable Adversarial Examples by Refining the Integration Path
We propose the Multiple Monotonic Diversified Integrated Gradients (MuMoDIG) attack, which can generate highly transferable adversarial examples on different CNN and ViT models and defenses.
Model Decides How to Tokenize: Adaptive DNA Sequence Tokenization with MxDNA
Foundation models have made significant strides in understanding the genomic language of DNA sequences.
COMET: Benchmark for Comprehensive Biological Multi-omics Evaluation Tasks and Language Models
To address this, we introduce the first comprehensive multi-omics benchmark COMET (Benchmark for Biological COmprehensive Multi-omics Evaluation Tasks and Language Models), designed to evaluate models across single-omics, cross-omics, and multi-omics tasks.
See or Guess: Counterfactually Regularized Image Captioning
Image captioning, which generates natural language descriptions of the visual information in an image, is a crucial task in vision-language research.
BEACON: Benchmark for Comprehensive RNA Tasks and Language Models
RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms.
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
In this paper, we pioneer the exploration of GIAs at the text level, presenting three novel attack designs that inject textual content into the graph.
Toward Understanding BERT-Like Pre-Training for DNA Foundation Models
However, existing pre-training methods for DNA sequences largely rely on direct adoptions of BERT pre-training from NLP, lacking a comprehensive understanding and a specifically tailored approach.
Improved Visual Story Generation with Adaptive Context Modeling
Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation.
Crossing the Gap: Domain Generalization for Image Captioning
In this paper, we introduce a new setting called Domain Generalization for Image Captioning (DGIC), where the data from the target domain is unseen in the learning process.
Boosting Adversarial Transferability via Gradient Relevance Attack
Plentiful adversarial attack researches have revealed the fragility of deep neural networks (DNNs), where the imperceptible perturbations can cause drastic changes in the output.
Generating meta-learning tasks to evolve parametric loss for classification learning
In this paper, we propose a meta-learning approach based on randomly generated meta-learning tasks to obtain a parametric loss for classification learning based on big data.
