Li Shen

A Memory Allocation Model for Cassandra Database

2016 · Advanced science and technology letters

Cassandra database system is one of the universal databases. To achieve high performance, we should allocate memory space rationally according to actual demands. To solve this problem, we firstly analyze the reading and writing process …

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

2020 · JAMA Network Open

Importance: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives. Objective: To evaluate whether AI can overcome …

Towards Practical Adam: Non-Convexity, Convergence Theory, and Mini-Batch Acceleration

2021 · arXiv (Cornell University)

Adam is one of the most influential adaptive stochastic algorithms for training deep neural networks, which has been pointed out to be divergent even in the simple convex setting via a few simple counterexamples. Many …

The Unreasonable Effectiveness of Random Pruning: Return of the Most Naive Baseline for Sparse Training

2022 · TU/e Research Portal

Random pruning is arguably the most naive way to attain sparsity in neural networks, but has been deemed uncompetitive by either post-training pruning or sparse training. In this paper, we focus on sparse training and …

Instructed Diffuser with Temporal Condition Guidance for Offline Reinforcement Learning

2023 · arXiv (Cornell University)

Recent works have shown the potential of diffusion models in computer vision and natural language processing. Apart from the classical supervised learning fields, diffusion models have also shown strong competitiveness in reinforcement learning (RL) by …

SPORT: A Subgraph Perspective on Graph Classification with Label Noise

2024 · ACM Transactions on Knowledge Discovery from Data

Graph neural networks (GNNs) have achieved great success recently on graph classification tasks using supervised end-to-end training. Unfortunately, extensive noisy graph labels could exist in the real world because of the complicated processes of manual …

Accessing the topological properties of human brain functional sub-circuits in Echo State Networks

2024 · arXiv (Cornell University)

Recent years have witnessed an emerging trend in neuromorphic computing that centers around the use of brain connectomics as a blueprint for artificial neural networks. Connectomics-based neuromorphic computing has primarily focused on embedding human brain …

Graph Convolutional Mixture-of-Experts Learner Network for Long-Tailed Domain Generalization

2025 · IEEE Transactions on Circuits and Systems for Video Technology

The goal of single domain generalization is to use data from a single domain (source domain) to train a model, which is then deployed over several unknown domains for testing (target domains). This study introduces …

Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging

2025 · IEEE Transactions on Pattern Analysis and Machine Intelligence

Multi-task learning (MTL) leverages a shared model to accomplish multiple tasks and facilitate knowledge transfer. Recent research on task arithmetic-based MTL demonstrates that merging the parameters of independently fine-tuned models can effectively achieve MTL. However, …

Analogical Reasoning on Chinese Morphological and Semantic Relations

2018

Analogical reasoning is effective in capturing linguistic regularities. This paper proposes an analogical reasoning task on Chinese. After delving into Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28 explicit semantic relations. A …