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Found 15 papers, 5 papers with code
PGL at TextGraphs 2020 Shared Task: Explanation Regeneration using Language and Graph Learning Methods
To address this problem, we use a pre-trained language model to recall the top-K relevant explanations for each question.
Learning Multiple Representations with Inconsistency-Guided Detail Regularization for Mask-Guided Matting
Our framework and model introduce the following key aspects: (1) to learn real-world adaptive semantic representation for objects with diverse and complex structures under real-world scenes, we introduce extra semantic segmentation and edge detection tasks on more diverse real-world data with segmentation annotations; (2) to avoid overfitting on low-level details, we propose a module to utilize the inconsistency between learned segmentation and matting representations to regularize detail refinement; (3) we propose a novel background line detection task into our auxiliary learning framework, to suppress interference of background lines or textures.
Task Indicating Transformer for Task-conditional Dense Predictions
The task-conditional model is a distinctive stream for efficient multi-task learning.
YOLO-MED : Multi-Task Interaction Network for Biomedical Images
Object detection and semantic segmentation are pivotal components in biomedical image analysis.
Federated Multi-Task Learning on Non-IID Data Silos: An Experimental Study
The innovative Federated Multi-Task Learning (FMTL) approach consolidates the benefits of Federated Learning (FL) and Multi-Task Learning (MTL), enabling collaborative model training on multi-task learning datasets.
FedHCA$^2$: Towards Hetero-Client Federated Multi-Task Learning
Moreover, we employ learnable Hyper Aggregation Weights for each client to customize personalized parameter updates.
Prompt Guided Transformer for Multi-Task Dense Prediction
Task-conditional architecture offers advantage in parameter efficiency but falls short in performance compared to state-of-the-art multi-decoder methods.
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model with Knowledge-Enhanced Mixture-of-Denoising-Experts
Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation.
Ranked #12 on
Text-to-Image Generation
on MS COCO
ERNIE-Search: Bridging Cross-Encoder with Dual-Encoder via Self On-the-fly Distillation for Dense Passage Retrieval
Our method 1) introduces a self on-the-fly distillation method that can effectively distill late interaction (i. e., ColBERT) to vanilla dual-encoder, and 2) incorporates a cascade distillation process to further improve the performance with a cross-encoder teacher.
ERNIE-SPARSE: Learning Hierarchical Efficient Transformer Through Regularized Self-Attention
We argue that two factors, information bottleneck sensitivity and inconsistency between different attention topologies, could affect the performance of the Sparse Transformer.
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
A unified framework named ERNIE 3. 0 was recently proposed for pre-training large-scale knowledge enhanced models and trained a model with 10 billion parameters.
ERNIE-SPARSE: Robust Efficient Transformer Through Hierarchically Unifying Isolated Information
The first factor is information bottleneck sensitivity, which is caused by the key feature of Sparse Transformer — only a small number of global tokens can attend to all other tokens.
ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation
We trained the model with 10 billion parameters on a 4TB corpus consisting of plain texts and a large-scale knowledge graph.
Decentralized Automotive Radar Spectrum Allocation to Avoid Mutual Interference Using Reinforcement Learning
In this paper, a decentralized spectrum allocation approach is presented to avoid mutual interference among automotive radars.
Cognitive Radar Using Reinforcement Learning in Automotive Applications
The concept of cognitive radar (CR) enables radar systems to achieve intelligent adaption to a changeable environment with feedback facility from receiver to transmitter.
