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Found 13 papers, 7 papers with code
An Iterative Associative Memory Model for Empathetic Response Generation
Empathetic response generation is to comprehend the cognitive and emotional states in dialogue utterances and generate proper responses.
Exploiting Emotion-Semantic Correlations for Empathetic Response Generation
Based on dynamic emotion-semantic vectors and dependency trees, we propose a dynamic correlation graph convolutional network to guide the model in learning context meanings in dialogue and generating empathetic responses.
DCIR: Dynamic Consistency Intrinsic Reward for Multi-Agent Reinforcement Learning
In this paper, we propose a new approach that enables agents to learn whether their behaviors should be consistent with that of other agents by utilizing intrinsic rewards to learn the optimal policy for each agent.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Editing 3D Scenes via Text Prompts without Retraining
To tackle these issues, we introduce a text-driven editing method, termed DN2N, which allows for the direct acquisition of a NeRF model with universal editing capabilities, eliminating the requirement for retraining.
PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations
We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the forward, inverse, and mixed problems of stochastic differential equations.
PVD-AL: Progressive Volume Distillation with Active Learning for Efficient Conversion Between Different NeRF Architectures
To address this limitation and maximize the potential of each architecture, we propose Progressive Volume Distillation with Active Learning (PVD-AL), a systematic distillation method that enables any-to-any conversions between different architectures.
One is All: Bridging the Gap Between Neural Radiance Fields Architectures with Progressive Volume Distillation
In this paper, we propose Progressive Volume Distillation (PVD), a systematic distillation method that allows any-to-any conversions between different architectures, including MLP, sparse or low-rank tensors, hashtables and their compositions.
Ranked #1 on
Novel View Synthesis
on NeRF
(Average PSNR metric)
Less Emphasis on Difficult Layer Regions: Curriculum Learning for Singularly Perturbed Convection-Diffusion-Reaction Problems
Although Physics-Informed Neural Networks (PINNs) have been successfully applied in a wide variety of science and engineering fields, they can fail to accurately predict the underlying solution in slightly challenging convection-diffusion-reaction problems.
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
Second, to improve the robustness of binary models with contextual dependencies, we compute the contextual dynamic embeddings to determine the binarization thresholds in general binary convolutional blocks.
Improve Deep Image Inpainting by Emphasizing the Complexity of Missing Regions
Deep image inpainting research mainly focuses on constructing various neural network architectures or imposing novel optimization objectives.
Semi-supervised Multi-task Learning for Semantics and Depth
Multi-Task Learning (MTL) aims to enhance the model generalization by sharing representations between related tasks for better performance.
Missingness Augmentation: A General Approach for Improving Generative Imputation Models
However, data augmentation, as a simple yet effective method, has not received enough attention in this area.
PC-GAIN: Pseudo-label Conditional Generative Adversarial Imputation Networks for Incomplete Data
Further, this classifier is incorporated into the generative adversarial framework to help the generator to yield higher quality imputation results.
