Search Results for author:
Found 9 papers, 2 papers with code
Adaptive Skeleton Graph Decoding
Large language models (LLMs) have seen significant adoption for natural language tasks, owing their success to massive numbers of model parameters (e. g., 70B+); however, LLM inference incurs significant computation and memory costs.
Learn To be Efficient: Build Structured Sparsity in Large Language Models
The emergence of activation sparsity in LLMs provides a natural approach to reduce this cost by involving only parts of the parameters for inference.
Leveraging Hierarchical Feature Sharing for Efficient Dataset Condensation
In this paper, we recognize that images share common features in a hierarchical way due to the inherent hierarchical structure of the classification system, which is overlooked by current data parameterization methods.
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
CALICO's efficacy is substantiated by extensive evaluations on 3D object detection and BEV map segmentation tasks, where it delivers significant performance improvements.
Coverage-centric Coreset Selection for High Pruning Rates
We then propose a novel one-shot coreset selection method, Coverage-centric Coreset Selection (CCS), that jointly considers overall data coverage upon a distribution as well as the importance of each example.
Understanding and Diagnosing Vulnerability under Adversarial Attacks
Moreover, we design the first diagnostic method to quantify the vulnerability contributed by each layer, which can be used to identify vulnerable parts of model architectures.
Efficient Adversarial Training with Transferable Adversarial Examples
Adversarial training is an effective defense method to protect classification models against adversarial attacks.
Analyzing the Interpretability Robustness of Self-Explaining Models
Recently, interpretable models called self-explaining models (SEMs) have been proposed with the goal of providing interpretability robustness.
Robust Classification using Robust Feature Augmentation
Existing deep neural networks, say for image classification, have been shown to be vulnerable to adversarial images that can cause a DNN misclassification, without any perceptible change to an image.
