Search Results for author:
Found 15 papers, 4 papers with code
Grade Like a Human: Rethinking Automated Assessment with Large Language Models
While large language models (LLMs) have been used for automated grading, they have not yet achieved the same level of performance as humans, especially when it comes to grading complex questions.
BehaviorGPT: Smart Agent Simulation for Autonomous Driving with Next-Patch Prediction
Simulating realistic interactions among traffic agents is crucial for efficiently validating the safety of autonomous driving systems.
RAEE: A Training-Free Retrieval-Augmented Early Exiting Framework for Efficient Inference
Finally, based on the pre-built retrieval database, RAEE leverages the retrieved similar data's exiting information to guide the backbone model to exit at the layer, which is predicted by the approximated distribution.
IHC Matters: Incorporating IHC analysis to H&E Whole Slide Image Analysis for Improved Cancer Grading via Two-stage Multimodal Bilinear Pooling Fusion
Immunohistochemistry (IHC) plays a crucial role in pathology as it detects the over-expression of protein in tissue samples.
Pre-processing matters: A segment search method for WSI classification
Our study analyzes the impact of pre-processing parameters on inference and training across single- and multiple-domain datasets.
On the Compressibility of Quantized Large Language Models
Deploying Large Language Models (LLMs) on edge or mobile devices offers significant benefits, such as enhanced data privacy and real-time processing capabilities.
ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion
Retrieval-based augmentations (RA) incorporating knowledge from an external database into language models have greatly succeeded in various knowledge-intensive (KI) tasks.
Natural Language Understanding
Neural Architecture Search
+5
Timely Fusion of Surround Radar/Lidar for Object Detection in Autonomous Driving Systems
Surround Radar/Lidar can provide 360-degree view sampling with the minimal cost, which are promising sensing hardware solutions for autonomous driving systems.
Moby: Empowering 2D Models for Efficient Point Cloud Analytics on the Edge
3D object detection plays a pivotal role in many applications, most notably autonomous driving and robotics.
NFL: Robust Learned Index via Distribution Transformation
Based on the characteristics of the transformed keys, we propose a robust After-Flow Learned Index (AFLI).
A Fast Transformer-based General-Purpose Lossless Compressor
To ease this problem, this paper targets on cutting down the execution time of deep-learning-based compressors.
Variational Nested Dropout
Nested dropout is a variant of dropout operation that is able to order network parameters or features based on the pre-defined importance during training.
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over the Simplex
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex
In this work, assuming that the exact posterior or a decent approximation is obtained, we propose a generic framework to approximate the output probability distribution induced by model posterior with a parameterized model and in an amortized fashion.
EasyConvPooling: Random Pooling with Easy Convolution for Accelerating Training and Testing
Convolution operations dominate the overall execution time of Convolutional Neural Networks (CNNs).
