Search Results for author:
Found 19 papers, 8 papers with code
PromptBench: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts
The increasing reliance on Large Language Models (LLMs) across academia and industry necessitates a comprehensive understanding of their robustness to prompts.
Cross-Lingual Paraphrase Identification
Machine Translation
+5
Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
BioBridge: Bridging Biomedical Foundation Models via Knowledge Graphs
Foundation models (FMs) are able to leverage large volumes of unlabeled data to demonstrate superior performance across a wide range of tasks.
Graph Neural Prompting with Large Language Models
While existing work has explored utilizing knowledge graphs (KGs) to enhance language modeling via joint training and customized model architectures, applying this to LLMs is problematic owing to their large number of parameters and high computational cost.
Accurate Differential Operators for Hybrid Neural Fields
Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts.
Variational Causal Inference
Estimating an individual's potential outcomes under counterfactual treatments is a challenging task for traditional causal inference and supervised learning approaches when the outcome is high-dimensional (e. g. gene expressions, impulse responses, human faces) and covariates are relatively limited.
Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics.
Fine-Grained Prototypes Distillation for Few-Shot Object Detection
However, the class-level prototypes are difficult to precisely generate, and they also lack detailed information, leading to instability in performance. New methods are required to capture the distinctive local context for more robust novel object detection.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
Semi-supervised Learning using Adversarial Training with Good and Bad Samples
In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training.
Graph Convolutional Networks for Multi-modality Medical Imaging: Methods, Architectures, and Clinical Applications
Image-based characterization and disease understanding involve integrative analysis of morphological, spatial, and topological information across biological scales.
Training self-supervised peptide sequence models on artificially chopped proteins
In this paper, we propose a new peptide data augmentation scheme, where we train peptide language models on artificially constructed peptides that are small contiguous subsets of longer, wild-type proteins; we refer to the training peptides as "chopped proteins".
Computational Assessment of Hyperpartisanship in News Titles
Through an analysis of the topic distribution, we find that societal issues gradually receive more attention from all media groups.
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines
There is a broad consensus that news media outlets incorporate ideological biases in their news articles.
Adversarial Attacks on Online Learning to Rank with Stochastic Click Models
We propose the first study of adversarial attacks on online learning to rank.
Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets
Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters.
Integration of Graph Neural Network and Neural-ODEs for Tumor Dynamic Prediction
Additionally, we show that the graph encoder is able to effectively utilize multimodal data to enhance tumor predictions.
Pure Exploration in Asynchronous Federated Bandits
We study the federated pure exploration problem of multi-armed bandits and linear bandits, where $M$ agents cooperatively identify the best arm via communicating with the central server.
UMG-CLIP: A Unified Multi-Granularity Vision Generalist for Open-World Understanding
Vision-language foundation models, represented by Contrastive language-image pre-training (CLIP), have gained increasing attention for jointly understanding both vision and textual tasks.
