Shuai Zhang

NeuRec: On Nonlinear Transformation for Personalized Ranking

2018

Modeling user-item interaction patterns is an important task for personalized recommendations. Many recommender systems are based on the assumption that there exists a linear relationship between users and items while neglecting the intricacy and non-linearity …

Efficient multiplier-less inference of deep autoencoders on wearable healthcare systems.

2019 · Ubiquitous Computing

This paper presents an efficient multiplier-less inference (MLI) approach of deep autoencoders (DAE) for wearable healthcare systems. It employs a novel grouped multiplier block (GMB) module to reduce computational/hardwired complexity of DAE during inference process. …

Machine Learning Assisted Side-Channel-Attack Countermeasure and Its Application on a 28-nm AES Circuit

2019 · IEEE Journal of Solid-State Circuits

Hardware countermeasure of side channel attack (SCA) becomes necessary to protect crypto circuits. Many countermeasures endured large area and power consumption. We propose a SCA-resistant methodology based on machine learning, which compensates the Hamming distance …

HyperML

2020

This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the …

A Practical Chinese Dependency Parser Based on A Large-scale Dataset

2020 · arXiv (Cornell University)

Dependency parsing is a longstanding natural language processing task, with its outputs crucial to various downstream tasks. Recently, neural network based (NN-based) dependency parsing has achieved significant progress and obtained the state-of-the-art results. As we …

One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition

2020 · arXiv (Cornell University)

The RNN-Transducers and improved attention-based encoder-decoder models are widely applied to streaming speech recognition. Compared with these two end-to-end models, the CTC model is more efficient in training and inference. However, it cannot capture the …

Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition

2020 · arXiv (Cornell University)

Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models' performance. In this paper, we propose a decoupled transformer model …

Design and development of environmental protection intelligent second-hand trading system in universities

2020 · 2020 7th International Forum on Electrical Engineering and Automation (IFEEA)

The increasing popularity of online shopping not only facilitates people's life, but also stimulates people's desire for consumption, causing college students to buy a lot of idle items, resulting in a waste of resources. By …

First-Principles Equation of State Database for Warm Dense Matter Computation

2020 · arXiv (Cornell University)

We put together a first-principles equation of state (FPEOS) database for matter at extreme conditions by combining results from path integral Monte Carlo and density functional molecular dynamics simulations of the elements H, He, B, …

Triplet Attention

2021

The Transformer model has benefited various real-world applications, where the self-attention mechanism with dot-products shows superior alignment ability on building long dependency. However, the pair-wisely attended self-attention limits further performance improvement on challenging tasks. To …

Knowledge Router: Learning Disentangled Representations for Knowledge Graphs

2021

The design of expressive representations of entities and relations in a knowledge graph is an important endeavor. While many of the existing approaches have primarily focused on learning from relational patterns and structural information, the …

Automated Hyperparameter Optimization of Gradient Boosting Decision Tree Approach for Gold Mineral Prospectivity Mapping in the Xiong’ershan Area

2022 · Minerals

The weak classifier ensemble algorithms based on the decision tree model, mainly include bagging (e.g., fandom forest-RF) and boosting (e.g., gradient boosting decision tree, eXtreme gradient boosting), the former reduces the variance for the overall …

Challenges and Opportunities of a Vastly Distributed Cloud Computing Infrastructure – In the Context of the Dong Shu Xi Suan (DSXS) Project of China

2023

It has been widely recognized that placing cloud data centers near clean energy sources or in cooler environments may reduce the operational cost and carbon footprint. But when the scale of the system, in terms …

Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression

2024 · arXiv (Cornell University)

We propose a novel one-step supervised imitation learning (IL) framework called Adversarial Density Regression (ADR). This IL framework aims to correct the policy learned on unknown-quality to match the expert distribution by utilizing demonstrations, without …

Anomaly Detection Service for Sensor Stream Data based on Lag-correlation Analysis

2024

The sensor streams influence and correlate with each other, and the hidden correlation can be used to identify and explain abnormal problems. This paper proposed one kind of anomaly detection service based on lag-correlation analysis. …

LHQ-SVC: Lightweight and High Quality Singing Voice Conversion Modeling

2024 · arXiv (Cornell University)

Singing Voice Conversion (SVC) has emerged as a significant subfield of Voice Conversion (VC), enabling the transformation of one singer's voice into another while preserving musical elements such as melody, rhythm, and timbre. Traditional SVC …

NRPP: A Learning Graph Representation Approach for Network Robustness Prediction

2024

In the field of modern network science, robustness is a key factor in evaluating the characteristics of complex networks. Connectivity robustness and controllability robustness are two important measures. They refer to a network's ability to …

DReSS: Data-driven Regularized Structured Streamlining for Large Language Models

2025 · arXiv (Cornell University)

Large language models (LLMs) have achieved significant progress across various domains, but their increasing scale results in high computational and memory costs. Recent studies have revealed that LLMs exhibit sparsity, providing the potential to reduce …

GENNEXT: The Next Generation of IR and Recommender Systems with Language Agents, Generative Models, and Conversational AI

2025

We present GENNEXT, a workshop dedicated to exploring the integration of language agents, generative models, and conversational AI within information retrieval (IR) and recommender systems (RS). Building on the success of our recent RecSys'24 workshop, …

Multi-Granularity Content-Aware Network with Semantic Integration for Unsupervised Anomaly Detection

2025 · Applied Sciences

Unsupervised anomaly detection has been widely applied to industrial scenarios. Recently, transformer-based methods have also been developed and have produced good performance. Although the global dependencies in anomaly images are considered, the typical patch partition …

AutoSVD++

2017

Collaborative filtering (CF) has been successfully used to provide users with personalized products and services. However, dealing with the increasing sparseness of user-item matrix still remains a challenge. To tackle such issue, hybrid CF such …

Deep Learning based Recommender System: A Survey and New Perspectives

2017 · arXiv (Cornell University)

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, …

Symmetric Metric Learning with Adaptive Margin for Recommendation

2020 · Proceedings of the AAAI Conference on Artificial Intelligence

Metric learning based methods have attracted extensive interests in recommender systems. Current methods take the user-centric way in metric space to ensure the distance between user and negative item to be larger than that between …

Deep Learning Based Recommender System

2019 · ACM Computing Surveys

With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along …