Search Results for author:
Found 10 papers, 4 papers with code
A Fully-Automatic Framework for Parkinson's Disease Diagnosis by Multi-Modality Images
Methods: In this paper, we proposed an automatic, end-to-end, multi-modality diagnosis framework, including segmentation, registration, feature generation and machine learning, to process the information of the striatum for the diagnosis of PD.
Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network
In this paper, we propose a novel convolutional neural network (CNN) to fuse the multi-modality information including T1-MRI and FDG-PDT images around the hippocampal area for the diagnosis of AD.
KLDivNet: An unsupervised neural network for multi-modality image registration
We then embed the KLDivNet into a registration network to achieve the unsupervised deformable registration for multi-modality images.
Cardiac Segmentation on Late Gadolinium Enhancement MRI: A Benchmark Study from Multi-Sequence Cardiac MR Segmentation Challenge
In addition, the paired MS-CMR images could enable algorithms to combine the complementary information from the other sequences for the segmentation of LGE CMR.
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech
Specifically, we first pre-train an encoder-decoder framework in an automatic speech recognition (ASR) objective by using speech-to-text dataset, and then fine-tune ASR encoder on the cleft palate dataset for hypernasality estimation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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SpaceEvo: Hardware-Friendly Search Space Design for Efficient INT8 Inference
The combination of Neural Architecture Search (NAS) and quantization has proven successful in automatically designing low-FLOPs INT8 quantized neural networks (QNN).
ElasticViT: Conflict-aware Supernet Training for Deploying Fast Vision Transformer on Diverse Mobile Devices
However, prior supernet training methods that rely on uniform sampling suffer from the gradient conflict issue: the sampled subnets can have vastly different model sizes (e. g., 50M vs. 2G FLOPs), leading to different optimization directions and inferior performance.
Constraint-aware and Ranking-distilled Token Pruning for Efficient Transformer Inference
Deploying pre-trained transformer models like BERT on downstream tasks in resource-constrained scenarios is challenging due to their high inference cost, which grows rapidly with input sequence length.
Compresso: Structured Pruning with Collaborative Prompting Learns Compact Large Language Models
To this end, Compresso prunes LLaMA-7B to 5. 4B, maintaining original performance and even surpassing LLaMA-7B in reading comprehension by 2. 62%.
LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens
This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance.
