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Found 73 papers, 28 papers with code
Hierarchical Deep Multiagent Reinforcement Learning with Temporal Abstraction
Besides, we propose a new experience replay mechanism to alleviate the issue of the sparse transitions at the high level of abstraction and the non-stationarity of multiagent learning.
A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents
In multiagent domains, coping with non-stationary agents that change behaviors from time to time is a challenging problem, where an agent is usually required to be able to quickly detect the other agent's policy during online interaction, and then adapt its own policy accordingly.
Deep Multi-Agent Reinforcement Learning with Discrete-Continuous Hybrid Action Spaces
Deep Reinforcement Learning (DRL) has been applied to address a variety of cooperative multi-agent problems with either discrete action spaces or continuous action spaces.
FReeNet: Multi-Identity Face Reenactment
This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model.
Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sample-efficient reinforcement learning.
Action Semantics Network: Considering the Effects of Actions in Multiagent Systems
ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.
Face-to-Parameter Translation for Game Character Auto-Creation
Character customization system is an important component in Role-Playing Games (RPGs), where players are allowed to edit the facial appearance of their in-game characters with their own preferences rather than using default templates.
Learning Action-Transferable Policy with Action Embedding
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning.
From Few to More: Large-scale Dynamic Multiagent Curriculum Learning
In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents.
Multi-label Relation Modeling in Facial Action Units Detection
The proposed method consists of the data preprocessing, the feature extraction and the AU classification.
Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks.
Exploring Unknown States with Action Balance
In this paper, we focus on improving the effectiveness of finding unknown states and propose action balance exploration, which balances the frequency of selecting each action at a given state and can be treated as an extension of upper confidence bound (UCB) to deep reinforcement learning.
Unsupervised Facial Action Unit Intensity Estimation via Differentiable Optimization
The generator learns to "render" a face image from a set of facial parameters in a differentiable way, and the feature extractor extracts deep features for measuring the similarity of the rendered image and input real image.
MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration
Meta reinforcement learning (meta-RL) extracts knowledge from previous tasks and achieves fast adaptation to new tasks.
Fast and Robust Face-to-Parameter Translation for Game Character Auto-Creation
With the rapid development of Role-Playing Games (RPGs), players are now allowed to edit the facial appearance of their in-game characters with their preferences rather than using default templates.
Neutral Face Game Character Auto-Creation via PokerFace-GAN
Besides, the neural network based renderer used in previous methods is also difficult to be extended to multi-view rendering cases.
Unsupervised Learning Facial Parameter Regressor for Action Unit Intensity Estimation via Differentiable Renderer
Facial action unit (AU) intensity is an index to describe all visually discernible facial movements.
OptMatch: Optimized Matchmaking via Modeling the High-Order Interactions on the Arena
OptMatch contains an offline learning stage and an online planning stage.
GraphFederator: Federated Visual Analysis for Multi-party Graphs
This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs.
Human-Computer Interaction Cryptography and Security Graphics
Semi-Supervised Learning for In-Game Expert-Level Music-to-Dance Translation
Previous works of this topic consider music-to-dance as a supervised motion generation problem based on time-series data.
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework
In many cases, each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate.
Open-Ended Question Answering
Reinforcement Learning (RL)
+1
Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping
In this paper, we consider the problem of adaptively utilizing a given shaping reward function.
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning
We introduce the Fever Basketball game, a novel reinforcement learning environment where agents are trained to play basketball game.
Intrinsically Guided Exploration in Meta Reinforcement Learning
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal.
Model-based Reinforcement Learning
Recommendation Systems
+2
Personalized Bundle Recommendation in Online Games
In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers.
Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation
To be specific, our framework consists of a speaker-independent stage and a speaker-specific stage.
Easy and Efficient Transformer : Scalable Inference Solution For large NLP model
To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels.
OpenMEVA: A Benchmark for Evaluating Open-ended Story Generation Metrics
Automatic metrics are essential for developing natural language generation (NLG) models, particularly for open-ended language generation tasks such as story generation.
Long Text Generation by Modeling Sentence-Level and Discourse-Level Coherence
Generating long and coherent text is an important but challenging task, particularly for open-ended language generation tasks such as story generation.
Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Flow-Guided One-Shot Talking Face Generation With a High-Resolution Audio-Visual Dataset
To synthesize high-definition videos, we build a large in-the-wild high-resolution audio-visual dataset and propose a novel flow-guided talking face generation framework.
Learning a Facial Expression Embedding Disentangled From Identity
The facial expression analysis requires a compact and identity-ignored expression representation.
GLIB: Towards Automated Test Oracle for Graphically-Rich Applications
We perform an evaluation of \texttt{GLIB} on 20 real-world game apps (with bug reports available) and the result shows that \texttt{GLIB} can achieve 100\% precision and 99. 5\% recall in detecting non-crashing bugs such as game GUI glitches.
Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion
As this keypoint based representation models the motions of facial regions, head, and backgrounds integrally, our method can better constrain the spatial and temporal consistency of the generated videos.
KuiLeiXi: a Chinese Open-Ended Text Adventure Game
Equipped with GPT-2 and the latest GPT-3, AI Dungeon has been seen as a famous example of the powerful text generation capabilities of large-scale pre-trained language models, and a possibility for future games.
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
The empirical results show that the proposed method can preserve a higher cumulative reward than behavior cloning and learn a more consistent policy to the original one.
LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks.
Learning the Representation of Behavior Styles with Imitation Learning
Imitation learning is one of the methods for reproducing expert demonstrations adaptively by learning a mapping between observations and actions.
Analyzing the Implicit Position Encoding Ability of Transformer Decoder
A common limitation of Transformer Encoder's self-attention mechanism is that it cannot automatically capture the information of word order, so one needs to feed the explicit position encodings into the target model.
RL4RS: A Real-World Dataset for Reinforcement Learning based Recommender System
In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms.
Episodic Multi-agent Reinforcement Learning with Curiosity-Driven Exploration
Efficient exploration in deep cooperative multi-agent reinforcement learning (MARL) still remains challenging in complex coordination problems.
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games
With this unified diversity measure, we design the corresponding diversity-promoting objective and population effectivity when seeking the best responses in open-ended learning.
Youling: an AI-Assisted Lyrics Creation System
In the lyrics generation process, \textit{Youling} supports traditional one pass full-text generation mode as well as an interactive generation mode, which allows users to select the satisfactory sentences from generated candidates conditioned on preceding context.
PPPNE: Personalized proximity preserved network embedding
These methods may fail to capture the personalized informativeness of each vertex.
Ranked #1 on
Link Prediction
on PPI
Collaborative filtering via heterogeneous neural networks
These methods may fail to capture the personalized informativeness of each vertex.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.
Off-Beat Multi-Agent Reinforcement Learning
During execution durations, the environment changes are influenced by, but not synchronised with, action execution.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
EUCLID: Towards Efficient Unsupervised Reinforcement Learning with Multi-choice Dynamics Model
Unsupervised reinforcement learning (URL) poses a promising paradigm to learn useful behaviors in a task-agnostic environment without the guidance of extrinsic rewards to facilitate the fast adaptation of various downstream tasks.
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e. g., detection and intensity estimation) has been documented for its time-consuming, labor-intensive, and error-prone annotation.
FlowFace: Semantic Flow-guided Shape-aware Face Swapping
Unlike most previous methods that focus on transferring the source inner facial features but neglect facial contours, our FlowFace can transfer both of them to a target face, thus leading to more realistic face swapping.
TCFimt: Temporal Counterfactual Forecasting from Individual Multiple Treatment Perspective
TCFimt constructs adversarial tasks in a seq2seq framework to alleviate selection and time-varying bias and designs a contrastive learning-based block to decouple a mixed treatment effect into separated main treatment effects and causal interactions which further improves estimation accuracy.
Towards Unbiased Volume Rendering of Neural Implicit Surfaces With Geometry Priors
In this paper, we argue that this limited accuracy is due to the bias of their volume rendering strategies, especially when the viewing direction is close to be tangent to the surface.
StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles
In a nutshell, we aim to attain a speaking style from an arbitrary reference speaking video and then drive the one-shot portrait to speak with the reference speaking style and another piece of audio.
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning
Despite its success, ACL's applicability is limited by (1) the lack of a general student framework for dealing with the varying number of agents across tasks and the sparse reward problem, and (2) the non-stationarity of the teacher's task due to ever-changing student strategies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adaptive Value Decomposition with Greedy Marginal Contribution Computation for Cooperative Multi-Agent Reinforcement Learning
We first illustrate that the proposed value decomposition can consider the complicated interactions among agents and is feasible to learn in large-scale scenarios.
Zero-Shot Text-to-Parameter Translation for Game Character Auto-Creation
In our method, taking the power of large-scale pre-trained multi-modal CLIP and neural rendering, T2P searches both continuous facial parameters and discrete facial parameters in a unified framework.
Diverse 3D Hand Gesture Prediction from Body Dynamics by Bilateral Hand Disentanglement
Considering the asymmetric gestures and motions of two hands, we introduce a Spatial-Residual Memory (SRM) module to model spatial interaction between the body and each hand by residual learning.
DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video
Different from previous works relying on multiple up-sample layers to directly generate pixels from latent embeddings, DINet performs spatial deformation on feature maps of reference images to better preserve high-frequency textural details.
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images.
Ranked #2 on
Surface Normals Estimation
on Stanford-ORB
TalkCLIP: Talking Head Generation with Text-Guided Expressive Speaking Styles
In this work, we propose an expression-controllable one-shot talking head method, dubbed TalkCLIP, where the expression in a speech is specified by the natural language.
2D Semantic Segmentation task 3 (25 classes)
Talking Head Generation
FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping
The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results.
Prioritized Trajectory Replay: A Replay Memory for Data-driven Reinforcement Learning
In summary, our research emphasizes the significance of trajectory-based data sampling techniques in enhancing the efficiency and performance of offline RL algorithms.
Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective
In this paper, we investigate the problem of learning with noisy labels in real-world annotation scenarios, where noise can be categorized into two types: factual noise and ambiguity noise.
EfficientDreamer: High-Fidelity and Robust 3D Creation via Orthogonal-view Diffusion Prior
This limitation leads to the Janus problem, where multi-faced 3D models are generated under the guidance of such diffusion models.
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model
Aligning agent behaviors with diverse human preferences remains a challenging problem in reinforcement learning (RL), owing to the inherent abstractness and mutability of human preferences.
Rethink Baseline of Integrated Gradients from the Perspective of Shapley Value
Thus, we propose a novel baseline construction method called Shapley Integrated Gradients (SIG) that searches for a set of baselines by proportional sampling to partly simulate the computation path of Shapley Value.
Towards Long-term Annotators: A Supervised Label Aggregation Baseline
These annotators could leave substantial historical annotation records on the crowdsourcing platforms, which can benefit label aggregation, but are ignored by previous works.
Towards a Simultaneous and Granular Identity-Expression Control in Personalized Face Generation
We devise a novel diffusion model that can undertake the task of simultaneously face swapping and reenactment.
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning Techniques
To address this, we introduce XRL-Bench, a unified standardized benchmark tailored for the evaluation and comparison of XRL methods, encompassing three main modules: standard RL environments, explainers based on state importance, and standard evaluators.
A Dataset for the Validation of Truth Inference Algorithms Suitable for Online Deployment
In this paper, we introduce a substantial crowdsourcing annotation dataset collected from a real-world crowdsourcing platform.
Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model
To fill such a gap, we introduce a scalable inference solution: Easy and Efficient Transformer (EET), including a series of transformer inference optimization at the algorithm and implementation levels.
