Search Results for author:
Found 10 papers, 1 papers with code
Output Constraints as Attack Surface: Exploiting Structured Generation to Bypass LLM Safety Mechanisms
In this work, we reveal a critical control-plane attack surface orthogonal to traditional data-plane vulnerabilities.
M4: Multi-Proxy Multi-Gate Mixture of Experts Network for Multiple Instance Learning in Histopathology Image Analysis
Multiple instance learning (MIL) has been successfully applied for whole slide images (WSIs) analysis in computational pathology, enabling a wide range of prediction tasks from tumor subtyping to inferring genetic mutations and multi-omics biomarkers.
A practical framework for multi-domain speech recognition and an instance sampling method to neural language modeling
Automatic speech recognition (ASR) systems used on smart phones or vehicles are usually required to process speech queries from very different domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+6
Improving Speech Recognition Accuracy of Local POI Using Geographical Models
Secondly, a group of geo-specific language models (Geo-LMs) are integrated into our speech recognition system to improve recognition accuracy of long tail and homophone POI.
Pinpointing the Memory Behaviors of DNN Training
The training of deep neural networks (DNNs) is usually memory-hungry due to the limited device memory capacity of DNN accelerators.
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices
The increasing computational cost of deep neural network models limits the applicability of intelligent applications on resource-constrained edge devices.
LANCE: Efficient Low-Precision Quantized Winograd Convolution for Neural Networks Based on Graphics Processing Units
Accelerating deep convolutional neural networks has become an active topic and sparked an interest in academia and industry.
Background subtraction on depth videos with convolutional neural networks
Background subtraction is a significant component of computer vision systems.
Auto-tuning Neural Network Quantization Framework for Collaborative Inference Between the Cloud and Edge
By analyzing the characteristics of layers in DNNs, an auto-tuning neural network quantization framework for collaborative inference is proposed.
Group Orbit Optimization: A Unified Approach to Data Normalization
In this paper we propose and study an optimization problem over a matrix group orbit that we call \emph{Group Orbit Optimization} (GOO).
