Search Results for author:
Found 23 papers, 8 papers with code
Co-Speech Gesture Video Generation via Motion-Decoupled Diffusion Model
While previous works mostly generate structural human skeletons, resulting in the omission of appearance information, we focus on the direct generation of audio-driven co-speech gesture videos in this work.
Dynamic Embedding Size Search with Minimum Regret for Streaming Recommender System
The high-throughput data requires the model to be updated in a timely manner for capturing the user interest dynamics, which leads to the emergence of streaming recommender systems.
Dynamically Expandable Graph Convolution for Streaming Recommendation
Personalized recommender systems have been widely studied and deployed to reduce information overload and satisfy users' diverse needs.
FixCaps: An Improved Capsules Network for Diagnosis of Skin Cancer
The group convolution was used to avoid model underfitting in the capsule layer.
Ranked #1 on
Lesion Classification
on HAM10000
MOEF: Modeling Occasion Evolution in Frequency Domain for Promotion-Aware Click-Through Rate Prediction
In this paper, we propose a novel CTR model named MOEF for recommendations under frequent changes of occasions.
DeepScalper: A Risk-Aware Reinforcement Learning Framework to Capture Fleeting Intraday Trading Opportunities
Reinforcement learning (RL) techniques have shown great success in many challenging quantitative trading tasks, such as portfolio management and algorithmic trading.
RMIX: Learning Risk-Sensitive Policies forCooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Feature Shapley: A general framework to discovering useful feature interactions
A machine learning system is typically composed of model and data.
RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents
Current value-based multi-agent reinforcement learning methods optimize individual Q values to guide individuals' behaviours via centralized training with decentralized execution (CTDE).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
RMIX: Risk-Sensitive Multi-Agent Reinforcement Learning
Centralized training with decentralized execution (CTDE) has become an important paradigm in multi-agent reinforcement learning (MARL).
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction
Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge.
TB2J: a python package for computing magnetic interaction parameters
We present TB2J, a Python package for the automatic computation of magnetic interactions, including exchange and Dzyaloshinskii-Moriya interactions, between atoms of magnetic crystals from the results of density functional calculations.
Materials Science
Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without Communication
Thus, the global policy of the whole page could be sub-optimal.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Contextual User Browsing Bandits for Large-Scale Online Mobile Recommendation
First, since we concern the reward of a set of recommended items, we model the online recommendation as a contextual combinatorial bandit problem and define the reward of a recommended set.
Continual Learning from the Perspective of Compression
We compare these approaches in terms of both compression and forgetting and empirically study the reasons that limit the performance of continual learning methods based on variational posterior approximation.
Learning Behaviors with Uncertain Human Feedback
However, previous works rarely consider the uncertainty when humans provide feedback, especially in cases that the optimal actions are not obvious to the trainers.
Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving
In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed.
Learning Efficient Multi-agent Communication: An Information Bottleneck Approach
Under the limited bandwidth constraint, a communication protocol is required to generate informative messages.
PyProcar: A Python library for electronic structure pre/post-processing
A file with a specific property evaluated for each $k$-point in a $k-$mesh and for each band can be used to project other properties such as electron-phonon mean path, Fermi velocity, electron effective mass, etc.
Materials Science
Task Agnostic Continual Learning via Meta Learning
One particular formalism that studies learning under non-stationary distribution is provided by continual learning, where the non-stationarity is imposed by a sequence of distinct tasks.
CSI-Net: Unified Human Body Characterization and Pose Recognition
We build CSI-Net, a unified Deep Neural Network~(DNN), to learn the representation of WiFi signals.
Overcoming Catastrophic Interference using Conceptor-Aided Backpropagation
Catastrophic interference has been a major roadblock in the research of continual learning.
Overcoming Catastrophic Interference by Conceptors
Catastrophic interference has been a major roadblock in the research of continual learning.
