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Found 60 papers, 20 papers with code
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
RL4RS: A Real-World Benchmark for Reinforcement Learning based Recommender System
Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting sequential recommendations to multi-step decision-making 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.
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.
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.
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.
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.
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.
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.
Learning a Facial Expression Embedding Disentangled From Identity
The facial expression analysis requires a compact and identity-ignored expression representation.
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.
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.
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.
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.
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.
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
Intrinsically Guided Exploration in Meta Reinforcement Learning
Deep reinforcement learning algorithms generally require large amounts of data to solve a single task.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
Multi-label Relation Modeling in Facial Action Units Detection
The proposed method consists of the data preprocessing, the feature extraction and the AU classification.
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.
Learning Action-Transferable Policy with Action Embedding
Transfer learning (TL) is a promising way to improve the sample efficiency of reinforcement learning.
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.
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.
Reinforcement Learning Experience Reuse with Policy Residual Representation
Experience reuse is key to sample-efficient reinforcement learning.
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.
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.
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.
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.
