Search Results for author:
Found 40 papers, 26 papers with code
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.
Tackling Diverse Minorities in Imbalanced Classification
Imbalanced datasets are commonly observed in various real-world applications, presenting significant challenges in training classifiers.
FinGPT: Democratizing Internet-scale Data for Financial Large Language Models
In light of this, we aim to democratize Internet-scale financial data for LLMs, which is an open challenge due to diverse data sources, low signal-to-noise ratio, and high time-validity.
OpenGSL: A Comprehensive Benchmark for Graph Structure Learning
Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes.
Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model
While the model parameters do contribute to memory usage, the primary memory bottleneck during training arises from storing feature maps, also known as activations, as they are crucial for gradient calculation.
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
In this work, we explore a "pre-train, and search" paradigm for efficient sharding.
Dynamic Datasets and Market Environments for Financial Reinforcement Learning
The financial market is a particularly challenging playground for deep reinforcement learning due to its unique feature of dynamic datasets.
Interactive System-wise Anomaly Detection
Anomaly detection, where data instances are discovered containing feature patterns different from the majority, plays a fundamental role in various applications.
Data-centric Artificial Intelligence: A Survey
Artificial Intelligence (AI) is making a profound impact in almost every domain.
Towards Personalized Preprocessing Pipeline Search
However, the existing systems often have a very small search space for feature preprocessing with the same preprocessing pipeline applied to all the numerical features.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
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.
Bring Your Own View: Graph Neural Networks for Link Prediction with Personalized Subgraph Selection
To bridge the gap, we introduce a Personalized Subgraph Selector (PS2) as a plug-and-play framework to automatically, personally, and inductively identify optimal subgraphs for different edges when performing GNNLP.
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems
Optimization problems with nonlinear cost functions and combinatorial constraints appear in many real-world applications but remain challenging to solve efficiently compared to their linear counterparts.
RSC: Accelerating Graph Neural Networks Training via Randomized Sparse Computations
To this end, we propose Randomized Sparse Computation, which for the first time demonstrate the potential of training GNNs with approximated operations.
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
AutoShard: Automated Embedding Table Sharding for Recommender Systems
This is a significant design challenge of distributed systems named embedding table sharding, i. e., how we should partition the embedding tables to balance the costs across devices, which is a non-trivial task because 1) it is hard to efficiently and precisely measure the cost, and 2) the partition problem is known to be NP-hard.
Auto-PINN: Understanding and Optimizing Physics-Informed Neural Architecture
Physics-informed neural networks (PINNs) are revolutionizing science and engineering practice by bringing together the power of deep learning to bear on scientific computation.
BED: A Real-Time Object Detection System for Edge Devices
DNNs have been an effective tool for data processing and analysis.
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.
Modeling Techniques for Machine Learning Fairness: A Survey
Machine learning models are becoming pervasive in high-stakes applications.
AutoVideo: An Automated Video Action Recognition System
Action recognition is an important task for video understanding with broad applications.
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks
To this end, we analyze the bottleneck of deep GNNs by leveraging the Dirichlet energy of node embeddings, and propose a generalizable principle to guide the training of deep GNNs.
DouZero: Mastering DouDizhu with Self-Play Deep Reinforcement Learning
Games are abstractions of the real world, where artificial agents learn to compete and cooperate with other agents.
Simplifying Deep Reinforcement Learning via Self-Supervision
Supervised regression to demonstrations has been demonstrated to be a stable way to train deep policy networks.
Learning Disentangled Representations for Time Series
Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.
Rank the Episodes: A Simple Approach for Exploration in Procedurally-Generated Environments
Unfortunately, methods based on intrinsic rewards often fall short in procedurally-generated environments, where a different environment is generated in each episode so that the agent is not likely to visit the same state more than once.
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.
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning
Outlier detection is an important data mining task with numerous practical applications such as intrusion detection, credit card fraud detection, and video surveillance.
Towards Deeper Graph Neural Networks with Differentiable Group Normalization
Graph neural networks (GNNs), which learn the representation of a node by aggregating its neighbors, have become an effective computational tool in downstream applications.
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.
PyODDS: An End-to-end Outlier Detection System with Automated Machine Learning
Outlier detection is an important task for various data mining applications.
Multi-Channel Graph Convolutional Networks
To further improve the graph representation learning ability, hierarchical GNN has been explored.
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.
PyODDS: An End-to-End Outlier Detection System
PyODDS is an end-to end Python system for outlier detection with database support.
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.
Multi-label Dataless Text Classification with Topic Modeling
With a few seed words relevant to each category, SMTM conducts multi-label classification for a collection of documents without any labeled document.
