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Found 22 papers, 13 papers with code
CODA: Temporal Domain Generalization via Concept Drift Simulator
In real-world applications, machine learning models often become obsolete due to shifts in the joint distribution arising from underlying temporal trends, a phenomenon known as the "concept drift".
DiscoverPath: A Knowledge Refinement and Retrieval System for Interdisciplinarity on Biomedical Research
The exponential growth in scholarly publications necessitates advanced tools for efficient article retrieval, especially in interdisciplinary fields where diverse terminologies are used to describe similar research.
Hessian-aware Quantized Node Embeddings for Recommendation
To address the gradient mismatch problem in STE, we further consider the quantized errors and its second-order derivatives for better stability.
Tackling Diverse Minorities in Imbalanced Classification
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers.
Towards Assumption-free Bias Mitigation
Those studies face challenges, either in inaccurate predictions of sensitive attributes or the need to mitigate unequal distribution of manually defined non-sensitive attributes related to bias.
Context-aware Domain Adaptation for Time Series Anomaly Detection
We formulate context sampling into the Markov decision process and exploit deep reinforcement learning to optimize the time series domain adaptation process via context sampling and design a tailored reward function to generate domain-invariant features that better align two domains for anomaly detection.
Data-centric Artificial Intelligence: A Survey
Artificial Intelligence (AI) is making a profound impact in almost every domain.
Data-centric AI: Perspectives and Challenges
The role of data in building AI systems has recently been significantly magnified by the emerging concept of data-centric AI (DCAI), which advocates a fundamental shift from model advancements to ensuring data quality and reliability.
Mitigating Relational Bias on Knowledge Graphs
Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning.
DreamShard: Generalizable Embedding Table Placement for Recommender Systems
Although prior work has explored learning-based approaches for the device placement of computational graphs, embedding table placement remains to be a challenging problem because of 1) the operation fusion of embedding tables, and 2) the generalizability requirement on unseen placement tasks with different numbers of tables and/or devices.
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Towards Similarity-Aware Time-Series Classification
Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner.
AutoVideo: An Automated Video Action Recognition System
Action recognition is an important task for video understanding with broad applications.
Simplifying Deep Reinforcement Learning via Self-Supervision
Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.
TODS: An Automated Time Series Outlier Detection System
We present TODS, an automated Time Series Outlier Detection System for research and industrial applications.
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback.
Policy-GNN: Aggregation Optimization for Graph Neural Networks
It is a challenging task to develop an effective aggregation strategy for each node, given complex graphs and sparse features.
Dual Policy Distillation
In this work, we introduce dual policy distillation(DPD), a student-student framework in which two learners operate on the same environment to explore different perspectives of the environment and extract knowledge from each other to enhance their learning.
RLCard: A Toolkit for Reinforcement Learning in Card Games
The goal of RLCard is to bridge reinforcement learning and imperfect information games, and push forward the research of reinforcement learning in domains with multiple agents, large state and action space, and sparse reward.
Experience Replay Optimization
Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand.
Superhighway: Bypass Data Sparsity in Cross-Domain CF
Cross-domain collaborative filtering (CF) aims to alleviate data sparsity in single-domain CF by leveraging knowledge transferred from related domains.
Representation Learning for Image-based Music Recommendation
Image perception is one of the most direct ways to provide contextual information about a user concerning his/her surrounding environment; hence images are a suitable proxy for contextual recommendation.
