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Found 10 papers, 3 papers with code
SDR-Former: A Siamese Dual-Resolution Transformer for Liver Lesion Classification Using 3D Multi-Phase Imaging
This study proposes a novel Siamese Dual-Resolution Transformer (SDR-Former) framework, specifically designed for liver lesion classification in 3D multi-phase CT and MR imaging with varying phase counts.
Communication-Efficient Personalized Federated Learning for Speech-to-Text Tasks
To protect privacy and meet legal regulations, federated learning (FL) has gained significant attention for training speech-to-text (S2T) systems, including automatic speech recognition (ASR) and speech translation (ST).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Implicit neural representation has paved the way for new approaches to dynamic scene reconstruction and rendering.
SoK: Comparing Different Membership Inference Attacks with a Comprehensive Benchmark
We have identified three principles for the proposed "comparing different MI attacks" methodology, and we have designed and implemented the MIBench benchmark with 84 evaluation scenarios for each dataset.
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences
Compared with a representative SSBA as a baseline ($SSBA_{Base}$), $CASSOCK$-based attacks have significantly advanced the attack performance, i. e., higher ASR and lower FPR with comparable CDA (clean data accuracy).
PPA: Preference Profiling Attack Against Federated Learning
By observing a user model's gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user's local dataset, and thus the user's preference of the class is exposed.
Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing Linguistic Information and Noisy Data
Recent advancements in end-to-end speech synthesis have made it possible to generate highly natural speech.
Adaptive Pricing in Insurance: Generalized Linear Models and Gaussian Process Regression Approaches
We develop two pricing models: an adaptive Generalized Linear Model (GLM) and an adaptive Gaussian Process (GP) regression model.
Machine Learning Allows Calibration Models to Predict Trace Element Concentration in Soil with Generalized LIBS Spectra
Machine learning has been used to develop the model.
