Search Results for author:
Found 16 papers, 8 papers with code
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Our work highlights several novel perspectives and opportunities for implementing randomized algorithms for deep learning on modern CPUs.
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i. e., Kuaishou/Kwai Platform.
Personal Fixations-Based Object Segmentation with Object Localization and Boundary Preservation
In this paper, we focus on Personal Fixations-based Object Segmentation (PFOS) to address issues in previous studies, such as the lack of appropriate dataset and the ambiguity in fixations-based interaction.
An annotated grain kernel image database for visual quality inspection
We present a machine vision-based database named GrainSet for the purpose of visual quality inspection of grain kernels.
Identifying the Defective: Detecting Damaged Grains for Cereal Appearance Inspection
Cereal grain plays a crucial role in the human diet as a major source of essential nutrients.
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection
In this paper, we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection.
Binarized Neural Networks on the ImageNet Classification Task
We trained Binarized Neural Networks (BNNs) on the high resolution ImageNet ILSVRC-2102 dataset classification task and achieved a good performance.
Attention Deep Model with Multi-Scale Deep Supervision for Person Re-Identification
Besides, most of the multi-scale models embedding the multi-scale feature learning block into the feature extraction deep network, which reduces the efficiency of inference network.
Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference
Modern deep neural networks increasingly make use of features such as dynamic control flow, data structures and dynamic tensor shapes.
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation
In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model.
Physics-Guided Graph Neural Networks for Real-time AC/DC Power Flow Analysis
The increasing scale of alternating current and direct current (AC/DC) hybrid systems necessitates a faster power flow analysis tool than ever.
DeRisk: An Effective Deep Learning Framework for Credit Risk Prediction over Real-World Financial Data
Despite the tremendous advances achieved over the past years by deep learning techniques, the latest risk prediction models for industrial applications still rely on highly handtuned stage-wised statistical learning tools, such as gradient boosting and random forest methods.
Design of Recognition and Evaluation System for Table Tennis Players' Motor Skills Based on Artificial Intelligence
Based on this, this paper improves wearable devices of table tennis sport, and realizes the pattern recognition and evaluation of table tennis players' motor skills through artificial intelligence.
Doubly Robust Proximal Causal Learning for Continuous Treatments
To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments.
The Blessings of Multiple Treatments and Outcomes in Treatment Effect Estimation
To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes.
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models.
