Feng Yu

Modular Serial Pipelined Sorting Architecture for Continuous Variable-Length Sequences with a Very Simple Control Strategy

2017 · IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences

A modular serial pipelined sorting architecture for continuous input sequences is presented. It supports continuous sequences, whose lengths can be dynamically changed, and does so using a very simple control strategy. It consists of identical …

Disentangled Item Representation for Recommender Systems

2021 · ACM Transactions on Intelligent Systems and Technology

Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information …

Generic Temporal Reasoning with Differential Analysis and Explanation

2022 · arXiv (Cornell University)

Temporal reasoning is the task of predicting temporal relations of event pairs. While temporal reasoning models can perform reasonably well on in-domain benchmarks, we have little idea of these systems' generalizability due to existing datasets' …

Generic Temporal Reasoning with Differential Analysis and Explanation

2023

Temporal reasoning is the task of predicting temporal relations of event pairs. While temporal reasoning models can perform reasonably well on in-domain benchmarks, we have little idea of these systems’ generalizability due to existing datasets’ …

Deep Graph Contrastive Representation Learning

2020 · arXiv (Cornell University)

Graph representation learning nowadays becomes fundamental in analyzing graph-structured data. Inspired by recent success of contrastive methods, in this paper, we propose a novel framework for unsupervised graph representation learning by leveraging a contrastive objective …

Graph Contrastive Learning with Adaptive Augmentation

2021

Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation on the input graph to obtain two graph views and maximize the …