Publications (34)
Generalizable Pose Estimation Using Implicit Scene Representations
Vaibhav Saxena, Kamal Rahimi Malekshan, Linh Tran +1
6-DoF pose estimation is an essential component of robotic manipulation pipelines. However, it usually suffers from a lack of generalization to new instances and object types. Most…
Audio Driven Real-Time Facial Animation for Social Telepresence
Jiye Lee, Chenghui Li, Linh Tran +5
We present an audio-driven real-time system for animating photorealistic 3D facial avatars with minimal latency, designed for social interactions in virtual reality for anyone. Cen…
Excitons bound by photon exchange
Erika Cortese, Linh Tran, Jean-Michel Manceau +5
In contrast to interband excitons in undoped quantum wells, doped quantum wells do not display sharp resonances due to excitonic bound states. In these systems the effective Coulom…
Learning to Infer Entities, Properties and their Relations from Clinical Conversations
Nan Du, Mingqiu Wang, Linh Tran +2
Recently we proposed the Span Attribute Tagging (SAT) Model (Du et al., 2019) to infer clinical entities (e.g., symptoms) and their properties (e.g., duration). It tackles the chal…
Hydra: Preserving Ensemble Diversity for Model Distillation
Linh Tran, Bastiaan S. Veeling, Kevin Roth +7
Ensembles of models have been empirically shown to improve predictive performance and to yield robust measures of uncertainty. However, they are expensive in computation and memory…
MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari Taghanaki, Aliasghar Khani, Fereshte Khani +4
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting…
Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness
Saeid Asgari Taghanaki, Ali Gholami, Fereshte Khani +4
Batch normalization (BN) is a ubiquitous technique for training deep neural networks that accelerates their convergence to reach higher accuracy. However, we demonstrate that BN co…
Contrastive Retrieval Heads Improve Attention-Based Re-Ranking
Linh Tran, Yulong Li, Radu Florian +1
The strong zero-shot and long-context capabilities of recent Large Language Models (LLMs) have paved the way for highly effective re-ranking systems. Attention-based re-rankers lev…
Improving Parameter-Efficient Federated Learning with Differentially Private Refactorization
Linh Tran, Ana Milanova, Stacy Patterson
Federated Learning (FL) with parameter-efficient fine-tuning, such as Low-Rank Adaptation (LoRA), enables scalable model training on distributed data. However, when combined with D…
Privacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Linh Tran, Wei Sun, Stacy Patterson +1
Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated…
VOFA: Visual Object Goal Pushing with Force-Adaptive Control for Humanoids
Zichao Hu, Zifan Xu, Dongsik Chang +6
The ability to push large objects in a goal-directed manner using onboard egocentric perception is an essential skill for humanoid robots to perform complex tasks such as material…
PBM-VFL: Vertical Federated Learning with Feature and Sample Privacy
Linh Tran, Timothy Castiglia, Stacy Patterson +1
We present Poisson Binomial Mechanism Vertical Federated Learning (PBM-VFL), a communication-efficient Vertical Federated Learning algorithm with Differential Privacy guarantees. P…
Local law for eigenvalues of random Hermitian matrices with external source
Linh Tran
We prove a local law for eigenvalues of the random Hermitian matrices with external source where is Wigner matrix and is diagonal matrix with o…
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints
Karl D. D. Willis, Pradeep Kumar Jayaraman, Hang Chu +8
Physical products are often complex assemblies combining a multitude of 3D parts modeled in computer-aided design (CAD) software. CAD designers build up these assemblies by alignin…
To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods
Loc Tran, Linh Tran
To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect the…
The Generalized Mean Information Coefficient
Alexander Luedtke, Linh Tran
Reshef & Reshef recently published a paper in which they present a method called the Maximal Information Coefficient (MIC) that can detect all forms of statistical dependence betwe…
SimCURL: Simple Contrastive User Representation Learning from Command Sequences
Hang Chu, Amir Hosein Khasahmadi, Karl D. D. Willis +5
User modeling is crucial to understanding user behavior and essential for improving user experience and personalized recommendations. When users interact with software, vast amount…
Extracting Symptoms and their Status from Clinical Conversations
Nan Du, Kai Chen, Anjuli Kannan +3
This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly…
Noise-robust classification with hypergraph neural network
Nguyen Trinh Vu Dang, Loc Tran, Linh Tran
This paper presents a novel version of the hypergraph neural network method. This method is utilized to solve the noisy label learning problem. First, we apply the PCA dimensional…
Group-disentangled Representation Learning with Weakly-Supervised Regularization
Linh Tran, Amir Hosein Khasahmadi, Aditya Sanghi +1
Learning interpretable and human-controllable representations that uncover factors of variation in data remains an ongoing key challenge in representation learning. We investigate…
The Medical Scribe: Corpus Development and Model Performance Analyses
Izhak Shafran, Nan Du, Linh Tran +11
There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of prov…
Sparse random graphs: Eigenvalues and Eigenvectors
Linh Tran, Van Vu, Ke Wang
In this paper we prove the semi-circular law for the eigenvalues of regular random graph in the case , complementing a previous result of McKay for f…
How Good is the Bayes Posterior in Deep Neural Networks Really?
Florian Wenzel, Kevin Roth, Bastiaan S. Veeling +7
During the past five years the Bayesian deep learning community has developed increasingly accurate and efficient approximate inference procedures that allow for Bayesian inference…
Reaching a Consensus on Random Networks: The Power of Few
Linh Tran, Van Vu
A community of individuals splits into two camps, Red and Blue. The individuals are connected by a social network, which influences their colors. Everyday, each person changes…
Dynamical Systems under Aluthge transforms
Linh Tran
In this note, we verify that the bounded shadowing property and quasi-hyperbolicity of bounded linear operators on Hilbert spaces are preserved under Aluthge transforms.
COIL: Constrained Optimization in Learned Latent Space: Learning Representations for Valid Solutions
Peter J Bentley, Soo Ling Lim, Adam Gaier +1
Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To addre…
The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
Jakub Swiatkowski, Kevin Roth, Bastiaan S. Veeling +7
Variational Bayesian Inference is a popular methodology for approximating posterior distributions over Bayesian neural network weights. Recent work developing this class of methods…
GAGAN: Geometry-Aware Generative Adversarial Networks
Jean Kossaifi, Linh Tran, Yannis Panagakis +1
Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their te…
Robust variance estimation and inference for causal effect estimation
Linh Tran, Maya Petersen, Joshua Schwab +1
We consider a longitudinal data structure consisting of baseline covariates, time-varying treatment variables, intermediate time-dependent covariates, and a possibly time dependent…
Assessing Historical Structural Oppression Worldwide via Rule-Guided Prompting of Large Language Models
Sreejato Chatterjee, Linh Tran, Quoc Duy Nguyen +6
Traditional efforts to measure historical structural oppression struggle with cross-national validity due to the unique, locally specified histories of exclusion, colonization, and…
Lookahead Anchoring: Preserving Character Identity in Audio-Driven Human Animation
Junyoung Seo, Rodrigo Mira, Alexandros Haliassos +6
Audio-driven human animation models often suffer from identity drift during temporal autoregressive generation, where characters gradually lose their identity over time. One soluti…
Solve fraud detection problem by using graph based learning methods
Loc Tran, Tuan Tran, Linh Tran +1
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss…
A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
Linh Tran, Sanjay Chari, Md. Saikat Islam Khan +3
We present the Differentially Private Blockchain-Based Vertical Federal Learning (DP-BBVFL) algorithm that provides verifiability and privacy guarantees for decentralized applicati…
Cauchy-Schwarz Regularized Autoencoder
Linh Tran, Maja Pantic, Marc Peter Deisenroth
Recent work in unsupervised learning has focused on efficient inference and learning in latent variables models. Training these models by maximizing the evidence (marginal likeliho…