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Found 9 papers, 1 papers with code
RAGCache: Efficient Knowledge Caching for Retrieval-Augmented Generation
We implement RAGCache and evaluate it on vLLM, a state-of-the-art LLM inference system and Faiss, a state-of-the-art vector database.
Portrait Diffusion: Training-free Face Stylization with Chain-of-Painting
Face stylization refers to the transformation of a face into a specific portrait style.
Punctate White Matter Lesion Segmentation in Preterm Infants Powered by Counterfactually Generative Learning
In this paper, we propose to leverage the idea of counterfactual reasoning coupled with the auxiliary task of brain tissue segmentation to learn fine-grained positional and morphological representations of PWMLs for accurate localization and segmentation.
Trend-Based SAC Beam Control Method with Zero-Shot in Superconducting Linear Accelerator
To validate the effectiveness of our method, two different typical beam control tasks were performed on China Accelerator Facility for Superheavy Elements (CAFe II) and a light particle injector(LPI) respectively.
LSTMSPLIT: Effective SPLIT Learning based LSTM on Sequential Time-Series Data
In this work, we propose a new approach, LSTMSPLIT, that uses SL architecture with an LSTM network to classify time-series data with multiple clients.
Cross-Modal ASR Post-Processing System for Error Correction and Utterance Rejection
Although modern automatic speech recognition (ASR) systems can achieve high performance, they may produce errors that weaken readers' experience and do harm to downstream tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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FFConv: Fast Factorized Convolutional Neural Network Inference on Encrypted Data
Despite the faster HECNN inference, the mainstream packing schemes Dense Packing (DensePack) and Convolution Packing (ConvPack) introduce expensive rotation overhead, which prolongs the inference latency of HECNN for deeper and wider CNN architectures.
Investigating the Automatic Classification of Algae Using Fusion of Spectral and Morphological Characteristics of Algae via Deep Residual Learning
This high level of accuracy was achieved using a deep residual convolutional neural network that learns the optimal combination of spectral and morphological features.
The feasibility of automated identification of six algae types using neural networks and fluorescence-based spectral-morphological features
A number of morphological and spectral fluorescence features are then extracted from the isolated micro-organism imaging data, and used to train neural network classification models designed for the purpose of identification of the six algae types given an isolated micro-organism.
