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Found 13 papers, 6 papers with code
Prompt Perturbation Consistency Learning for Robust Language Models
However, their performance on sequence labeling tasks such as intent classification and slot filling (IC-SF), which is a central component in personal assistant systems, lags significantly behind discriminative models.
Learning to Poison Large Language Models During Instruction Tuning
The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities.
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning
A key novelty of our work is to develop solution accuracy-independent algorithms that do not require large batch gradients (and function evaluations) for solving federated CO problems.
Hijacking Large Language Models via Adversarial In-Context Learning
In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific tasks by utilizing labeled examples as demonstrations in the precondition prompts.
Interpretability-Aware Vision Transformer
Furthermore, if ViTs are not properly trained with the given data and do not prioritize the region of interest, the {\it post hoc} methods would be less effective.
Auto-Prompting SAM for Mobile Friendly 3D Medical Image Segmentation
In addition to the domain gaps between natural and medical images, disparities in the spatial arrangement between 2D and 3D images, the substantial computational burden imposed by powerful GPU servers, and the time-consuming manual prompt generation impede the extension of SAM to a broader spectrum of medical image segmentation applications.
Fairness-aware Vision Transformer via Debiased Self-Attention
Importantly, our DSA framework leads to improved fairness guarantees over prior works on multiple prediction tasks without compromising target prediction performance.
Negative Flux Aggregation to Estimate Feature Attributions
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns.
Learning Compact Features via In-Training Representation Alignment
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i. e., last hidden layer) and a linear classifier (i. e., output layer) that are trained jointly with stochastic gradient descent (SGD) on the loss function (e. g., cross-entropy).
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissue contrast and (2) the limitation of convolutional neural network-based models in capturing long-range global context.
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
We hypothesize that adversarial training can eliminate shortcut features whereas saliency guided training can filter out non-relevant features; both are nuisance features accounting for the performance degradation on OOD test sets.
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification
On-device Deep Neural Networks (DNNs) have recently gained more attention due to the increasing computing power of the mobile devices and the number of applications in Computer Vision (CV), Natural Language Processing (NLP), and Internet of Things (IoTs).
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
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