11 citations · 29 across the 18 of their papers we have counts for
19 papers
Geometric Encoding for Spatial Reasoning in Vision-Language Models
Antonio Jun, Haoshui Yu, Zhengyi Lu +2
Vision-Language Models (VLMs) are far more reliable at recognizing what appears in a video than at reasoning about its spatial and temporal properties, such as metric distances, ob…
HybridSB-MoE: Dual-Domain Schrödinger Bridges with Scene-Adaptive Expert Routing for Speech Enhancement
Zhengyi Lu, Aswini Sivakumar, Jie Hu +1
Generative speech enhancement faces three gaps: spectral models capture harmonic structure but often disrupt phase, waveform models preserve phase but miss harmonics, and Schröding…
RAG-X: Systematic Diagnosis of Retrieval-Augmented Generation for Medical Question Answering
Aswini Sivakumar, Vijayan Sugumaran, Yao Qiang
Automated question-answering (QA) systems increasingly rely on retrieval-augmented generation (RAG) to ground large language models (LLMs) in authoritative medical knowledge, ensur…
Not All Tokens Are Meant to Be Forgotten
Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +3
Large Language Models (LLMs), pre-trained on massive text corpora, exhibit remarkable human-level language understanding, reasoning, and decision-making abilities. However, they te…
Automatic Calibration for Membership Inference Attack on Large Language Models
Saleh Zare Zade, Yao Qiang, Xiangyu Zhou +4
Membership Inference Attacks (MIAs) have recently been employed to determine whether a specific text was part of the pre-training data of Large Language Models (LLMs). However, exi…
Generative LLM Powered Conversational AI Application for Personalized Risk Assessment: A Case Study in COVID-19
Mohammad Amin Roshani, Xiangyu Zhou, Yao Qiang +4
Large language models (LLMs) have shown remarkable capabilities in various natural language tasks and are increasingly being applied in healthcare domains. This work demonstrates a…
Prompt Perturbation Consistency Learning for Robust Language Models
Yao Qiang, Subhrangshu Nandi, Ninareh Mehrabi +4
Large language models (LLMs) have demonstrated impressive performance on a number of natural language processing tasks, such as question answering and text summarization. However,…
Learning to Poison Large Language Models for Downstream Manipulation
Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +4
The advent of Large Language Models (LLMs) has marked significant achievements in language processing and reasoning capabilities. Despite their advancements, LLMs face vulnerabilit…
FedDRO: Federated Compositional Optimization for Distributionally Robust Learning
Prashant Khanduri, Chengyin Li, Rafi Ibn Sultan +3
Recently, compositional optimization (CO) has gained popularity because of its applications in distributionally robust optimization (DRO) and many other machine learning problems.…
Hijacking Large Language Models via Adversarial In-Context Learning
Xiangyu Zhou, Yao Qiang, Saleh Zare Zade +2
In-context learning (ICL) has emerged as a powerful paradigm leveraging LLMs for specific downstream tasks by utilizing labeled examples as demonstrations (demos) in the preconditi…
Interpretability-Aware Vision Transformer
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformers (ViTs) have become prominent models for solving various vision tasks. However, the interpretability of ViTs has not kept pace with their promising performance.…
AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation
Chengyin Li, Prashant Khanduri, Yao Qiang +3
Segment Anything Model (SAM) is one of the pioneering prompt-based foundation models for image segmentation and has been rapidly adopted for various medical imaging applications. H…
Fairness-aware Vision Transformer via Debiased Self-Attention
Yao Qiang, Chengyin Li, Prashant Khanduri +1
Vision Transformer (ViT) has recently gained significant attention in solving computer vision (CV) problems due to its capability of extracting informative features and modeling lo…
Negative Flux Aggregation to Estimate Feature Attributions
Xin Li, Deng Pan, Chengyin Li +2
There are increasing demands for understanding deep neural networks' (DNNs) behavior spurred by growing security and/or transparency concerns. Due to multi-layer nonlinearity of th…
Learning Compact Features via In-Training Representation Alignment
Xin Li, Xiangrui Li, Deng Pan +2
Deep neural networks (DNNs) for supervised learning can be viewed as a pipeline of the feature extractor (i.e., last hidden layer) and a linear classifier (i.e., output layer) that…
FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images
Chengyin Li, Yao Qiang, Rafi Ibn Sultan +4
Computed Tomography (CT) based precise prostate segmentation for treatment planning is challenging due to (1) the unclear boundary of the prostate derived from CT's poor soft tissu…
Saliency Guided Adversarial Training for Learning Generalizable Features with Applications to Medical Imaging Classification System
Xin Li, Yao Qiang, Chengyin Li +2
This work tackles a central machine learning problem of performance degradation on out-of-distribution (OOD) test sets. The problem is particularly salient in medical imaging based…
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification
Yao Qiang, Supriya Tumkur Suresh Kumar, Marco Brocanelli +1
On-device Deep Neural Networks (DNNs) have recently gained more attention due to the increasing computing power of the mobile devices and the number of applications in Computer Vis…
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Yao Qiang, Xin Li, Dongxiao Zhu
Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representati…