186 citations · 412 across the 34 of their papers we have counts for
34 papers
AutoSeqRec: Autoencoder for Efficient Sequential Recommendation
Sijia Liu, Jiahao Liu, Hansu Gu +4
Sequential recommendation demonstrates the capability to recommend items by modeling the sequential behavior of users. Traditional methods typically treat users as sequences of ite…
Certified Robustness for Large Language Models with Self-Denoising
Zhen Zhang, Guanhua Zhang, Bairu Hou +5
Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses,…
Fairness Improves Learning from Noisily Labeled Long-Tailed Data
Jiaheng Wei, Zhaowei Zhu, Gang Niu +4
Both long-tailed and noisily labeled data frequently appear in real-world applications and impose significant challenges for learning. Most prior works treat either problem in an i…
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
Yuguang Yao, Jiancheng Liu, Yifan Gong +4
Numerous adversarial attack methods have been developed to generate imperceptible image perturbations that can cause erroneous predictions of state-of-the-art machine learning (ML)…
Text-Visual Prompting for Efficient 2D Temporal Video Grounding
Yimeng Zhang, Xin Chen, Jinghan Jia +2
In this paper, we study the problem of temporal video grounding (TVG), which aims to predict the starting/ending time points of moments described by a text sentence within a long u…
Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks
Shuai Zhang, Meng Wang, Pin-Yu Chen +3
Due to the significant computational challenge of training large-scale graph neural networks (GNNs), various sparse learning techniques have been exploited to reduce memory and sto…
Towards Understanding How Self-training Tolerates Data Backdoor Poisoning
Soumyadeep Pal, Ren Wang, Yuguang Yao +1
Recent studies on backdoor attacks in model training have shown that polluting a small portion of training data is sufficient to produce incorrect manipulated predictions on poison…
TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization
Bairu Hou, Jinghan Jia, Yihua Zhang +4
Robustness evaluation against adversarial examples has become increasingly important to unveil the trustworthiness of the prevailing deep models in natural language processing (NLP…
DialGuide: Aligning Dialogue Model Behavior with Developer Guidelines
Prakhar Gupta, Yang Liu, Di Jin +6
Dialogue models are able to generate coherent and fluent responses, but they can still be challenging to control and may produce non-engaging, unsafe results. This unpredictability…
CLAWSAT: Towards Both Robust and Accurate Code Models
Jinghan Jia, Shashank Srikant, Tamara Mitrovska +4
We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models. Different from existing works, we show that code obfusca…
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices
Yimeng Zhang, Akshay Karkal Kamath, Qiucheng Wu +6
In this paper, we propose a data-model-hardware tri-design framework for high-throughput, low-cost, and high-accuracy multi-object tracking (MOT) on High-Definition (HD) video stre…
Fairness Reprogramming
Guanhua Zhang, Yihua Zhang, Yang Zhang +4
Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the…
Improving Bot Response Contradiction Detection via Utterance Rewriting
Di Jin, Sijia Liu, Yang Liu +1
Though chatbots based on large neural models can often produce fluent responses in open domain conversations, one salient error type is contradiction or inconsistency with the prec…
The NLP Sandbox: an efficient model-to-data system to enable federated and unbiased evaluation of clinical NLP models
Yao Yan, Thomas Yu, Kathleen Muenzen +14
Objective The evaluation of natural language processing (NLP) models for clinical text de-identification relies on the availability of clinical notes, which is often restricted due…
Queried Unlabeled Data Improves and Robustifies Class-Incremental Learning
Tianlong Chen, Sijia Liu, Shiyu Chang +2
Class-incremental learning (CIL) suffers from the notorious dilemma between learning newly added classes and preserving previously learned class knowledge. That catastrophic forget…
Data-Efficient Double-Win Lottery Tickets from Robust Pre-training
Tianlong Chen, Zhenyu Zhang, Sijia Liu +3
Pre-training serves as a broadly adopted starting point for transfer learning on various downstream tasks. Recent investigations of lottery tickets hypothesis (LTH) demonstrate suc…
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for Free
Tianlong Chen, Zhenyu Zhang, Yihua Zhang +3
Trojan attacks threaten deep neural networks (DNNs) by poisoning them to behave normally on most samples, yet to produce manipulated results for inputs attached with a particular t…
CryoRL: Reinforcement Learning Enables Efficient Cryo-EM Data Collection
Quanfu Fan, Yilai Li, Yuguang Yao +4
Single-particle cryo-electron microscopy (cryo-EM) has become one of the mainstream structural biology techniques because of its ability to determine high-resolution structures of…
Proactive Image Manipulation Detection
Vishal Asnani, Xi Yin, Tal Hassner +2
Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize…
Optimizer Amalgamation
Tianshu Huang, Tianlong Chen, Sijia Liu +3
Selecting an appropriate optimizer for a given problem is of major interest for researchers and practitioners. Many analytical optimizers have been proposed using a variety of theo…
RMSMP: A Novel Deep Neural Network Quantization Framework with Row-wise Mixed Schemes and Multiple Precisions
Sung-En Chang, Yanyu Li, Mengshu Sun +4
This work proposes a novel Deep Neural Network (DNN) quantization framework, namely RMSMP, with a Row-wise Mixed-Scheme and Multi-Precision approach. Specifically, this is the firs…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Shuai Zhang, Meng Wang, Sijia Liu +2
The \textit{lottery ticket hypothesis} (LTH) states that learning on a properly pruned network (the \textit{winning ticket}) improves test accuracy over the original unpruned netwo…
An Open Natural Language Processing Development Framework for EHR-based Clinical Research: A case demonstration using the National COVID Cohort Collaborative (N3C)
Sijia Liu, Andrew Wen, Liwei Wang +28
While we pay attention to the latest advances in clinical natural language processing (NLP), we can notice some resistance in the clinical and translational research community to a…