activity
20132023
most citedWhen Large Language Models Meet Personalization: Perspectives of Challenges and Opportunities

338 citations · 1.1k across the 73 of their papers we have counts for

collaborators
Showing 2018Show all

7 papers · 1 filter

cs.CL2018★ 38 cited

Sentence-State LSTM for Text Representation

Yue Zhang, Qi Liu, Linfeng Song

Bi-directional LSTMs are a powerful tool for text representation. On the other hand, they have been shown to suffer various limitations due to their sequential nature. We investiga…

cs.LG2018★ 330 cited

Constrained Graph Variational Autoencoders for Molecule Design

Qi Liu, Miltiadis Allamanis, Marc Brockschmidt +1

Graphs are ubiquitous data structures for representing interactions between entities. With an emphasis on the use of graphs to represent chemical molecules, we explore the task of…

cs.LG2018★ 8 cited

Security Analysis and Enhancement of Model Compressed Deep Learning Systems under Adversarial Attacks

Qi Liu, Tao Liu, Zihao Liu +3

DNN is presenting human-level performance for many complex intelligent tasks in real-world applications. However, it also introduces ever-increasing security concerns. For example,…

cs.CL2018★ 1 cited

On Modular Training of Neural Acoustics-to-Word Model for LVCSR

Zhehuai Chen, Qi Liu, Hao Li +1

End-to-end (E2E) automatic speech recognition (ASR) systems directly map acoustics to words using a unified model. Previous works mostly focus on E2E training a single model which…

cs.CV2018★ 19 cited

Feature Distillation: DNN-Oriented JPEG Compression Against Adversarial Examples

Zihao Liu, Qi Liu, Tao Liu +4

Image compression-based approaches for defending against the adversarial-example attacks, which threaten the safety use of deep neural networks (DNN), have been investigated recent…

cs.GR2018★ 2 cited

Light Transport Simulation via Generalized Multiple Importance Sampling

Qi Liu, Yiheng Zhang, Lizhuang Ma

Multiple importance sampling (MIS) is employed to reduce variance of estimators, but when sampling and weighting are not suitable to the integrand, the estimators would have extra…

stat.ME2018

Divide and Recombine for Large and Complex Data: Model Likelihood Functions using MCMC

Qi Liu, Anindya Bhadra, William S. Cleveland

In Divide & Recombine (D&R), big data are divided into subsets, each analytic method is applied to subsets, and the outputs are recombined. This enables deep analysis and practical…