Search Results for author:
Found 14 papers, 4 papers with code
An Adaptive Descriptor Design for Object Recognition in the Wild
These 'picture styles' are directly related to the scene radiance, image pipeline of the camera, and post processing functions.
Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes
(3) We propose highly optimized all-reduce algorithms that achieve up to 3x and 11x speedup on AlexNet and ResNet-50 respectively than NCCL-based training on a cluster with 1024 Tesla P40 GPUs.
Distributed Equivalent Substitution Training for Large-Scale Recommender Systems
We present Distributed Equivalent Substitution (DES) training, a novel distributed training framework for large-scale recommender systems with dynamic sparse features.
Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters
Distributed training techniques have been widely deployed in large-scale deep neural networks (DNNs) training on dense-GPU clusters.
BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
A photo captured with bokeh effect often means objects in focus are sharp while the out-of-focus areas are all blurred.
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results.
APAN: Asynchronous Propagation Attention Network for Real-time Temporal Graph Embedding
Limited by the time complexity of querying k-hop neighbors in a graph database, most graph algorithms cannot be deployed online and execute millisecond-level inference.
Enhancing Balanced Graph Edge Partition with Effective Local Search
The graph edge partition problem, which is to split the edge set into multiple balanced parts to minimize the total number of copied vertices, has been widely studied from the view of optimization and algorithms.
Multi-modal Graph Learning for Disease Prediction
However, it is not easy for these approaches to generalize to unseen samples.
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but is a challenging problem in practice.
Multi-modal Graph Learning for Disease Prediction
For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e. g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL).
PT-Tuning: Bridging the Gap between Time Series Masked Reconstruction and Forecasting via Prompt Token Tuning
In this paper, we first point out that the unification of task objectives and adaptation for task difficulty are critical for bridging the gap between time series masked reconstruction and forecasting.
AesopAgent: Agent-driven Evolutionary System on Story-to-Video Production
In the Horizontal Layer, we introduce a novel RAG-based evolutionary system that optimizes the whole video generation workflow and the steps within the workflow.
