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Found 12 papers, 1 papers with code
A Survey on Efficient Inference for Large Language Models
This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
FlashDecoding++: Faster Large Language Model Inference on GPUs
A single and static dataflow may lead to a 50. 25% performance loss for GEMMs of different shapes in LLM inference.
Contrastive Learning for Low-light Raw Denoising
Inspired by the success of contrastive learning used in some high-level computer vision tasks, we bring in this idea to the low-level denoising task.
Bokeh Rendering Based on Adaptive Depth Calibration Network
This allows the camera to capture images with shallow depth-of-field, in which only a small area of the image is in sharp focus, while the rest of the image is blurred.
Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading
We assessed the effectiveness of FACL in cancer diagnosis and Gleason grading tasks using 19, 461 whole-slide images of prostate cancer from multiple centers.
TAOTF: A Two-stage Approximately Orthogonal Training Framework in Deep Neural Networks
In the first stage, we propose a novel algorithm called polar decomposition-based orthogonal initialization (PDOI) to find a good initialization for the orthogonal optimization.
CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS
Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum).
A Fair Federated Learning Framework With Reinforcement Learning
More importantly, we apply the Gini coefficient and validation accuracy of clients in each communication round to construct a reward function for the reinforcement learning.
Mixed-UNet: Refined Class Activation Mapping for Weakly-Supervised Semantic Segmentation with Multi-scale Inference
Deep learning techniques have shown great potential in medical image processing, particularly through accurate and reliable image segmentation on magnetic resonance imaging (MRI) scans or computed tomography (CT) scans, which allow the localization and diagnosis of lesions.
RCMNet: A deep learning model assists CAR-T therapy for leukemia
Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset.
Point Cloud Color Constancy
In this paper, we present Point Cloud Color Constancy, in short PCCC, an illumination chromaticity estimation algorithm exploiting a point cloud.
Driving maneuvers prediction based on cognition-driven and data-driven method
To overcome this challenge, we propose a novel method that combines both the cognition-driven model and the data-driven model.
