179 citations · 382 across the 62 of their papers we have counts for
23 papers · 1 filter
Improving Proactive AI Assistance with Hierarchical Procedural Understanding
Jin-Seop Lee, TaeYeon Won, SeongJun Jung +6
Proactive AI assistants continuously observe a user's activity and decide whether to provide new guidance or remain silent. They should provide appropriate guidance for the task, d…
MedMix: Specialization-Consistent Federated Sparse MoEs under Modality Heterogeneity
Adiba Orzikulova, Dong Min Kim, Jaehong Yoon +1
Federated multimodal medical AI faces modality heterogeneity at both the client and sample levels: clients may systematically lack access to specific modality types, while individu…
Reliable and Responsible Foundation Models: A Comprehensive Survey
Xinyu Yang, Junlin Han, Rishi Bommasani +49
Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), a…
Avatar Forcing: Real-Time Interactive Head Avatar Generation for Natural Conversation
Taekyung Ki, Sangwon Jang, Jaehyeong Jo +2
Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of trul…
RSQ: Learning from Important Tokens Leads to Better Quantized LLMs
Yi-Lin Sung, Prateek Yadav, Jialu Li +2
Layer-wise quantization is a key technique for efficiently compressing large models without expensive retraining. Previous methods typically quantize the weights of each layer by "…
Adapt-: Scalable Continual Multimodal Instruction Tuning via Dynamic Data Selection
Adyasha Maharana, Jaehong Yoon, Tianlong Chen +1
Visual instruction datasets from various distributors are released at different times and often contain a significant number of semantically redundant text-image pairs, depending o…
Glider: Global and Local Instruction-Driven Expert Router
Pingzhi Li, Prateek Yadav, Jaehong Yoon +4
The availability of performant pre-trained models has led to a proliferation of fine-tuned expert models that are specialized to particular domains. This has enabled the creation o…
BECoTTA: Input-dependent Online Blending of Experts for Continual Test-time Adaptation
Daeun Lee, Jaehong Yoon, Sung Ju Hwang
Continual Test Time Adaptation (CTTA) is required to adapt efficiently to continuous unseen domains while retaining previously learned knowledge. However, despite the progress of C…
Multimodal Representation Learning by Alternating Unimodal Adaptation
Xiaohui Zhang, Jaehong Yoon, Mohit Bansal +1
Multimodal learning, which integrates data from diverse sensory modes, plays a pivotal role in artificial intelligence. However, existing multimodal learning methods often struggle…
Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
Yiyang Zhou, Chenhang Cui, Jaehong Yoon +5
Large vision-language models (LVLMs) have shown remarkable abilities in understanding visual information with human languages. However, LVLMs still suffer from object hallucination…
Continual Learners are Incremental Model Generalizers
Jaehong Yoon, Sung Ju Hwang, Yue Cao
Motivated by the efficiency and rapid convergence of pre-trained models for solving downstream tasks, this paper extensively studies the impact of Continual Learning (CL) models as…
Text-Conditioned Sampling Framework for Text-to-Image Generation with Masked Generative Models
Jaewoong Lee, Sangwon Jang, Jaehyeong Jo +5
Token-based masked generative models are gaining popularity for their fast inference time with parallel decoding. While recent token-based approaches achieve competitive performanc…
Forget-free Continual Learning with Soft-Winning SubNetworks
Haeyong Kang, Jaehong Yoon, Sultan Rizky Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which states that competitive smooth (non-binary) subnetworks exist within a dense network in continual learning tasks, we…
On the Soft-Subnetwork for Few-shot Class Incremental Learning
Haeyong Kang, Jaehong Yoon, Sultan Rizky Hikmawan Madjid +2
Inspired by Regularized Lottery Ticket Hypothesis (RLTH), which hypothesizes that there exist smooth (non-binary) subnetworks within a dense network that achieve the competitive pe…
Personalized Subgraph Federated Learning
Jinheon Baek, Wonyong Jeong, Jiongdao Jin +2
Subgraphs of a larger global graph may be distributed across multiple devices, and only locally accessible due to privacy restrictions, although there may be links between subgraph…
Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization
Jaehong Yoon, Geon Park, Wonyong Jeong +1
In practical federated learning scenarios, the participating devices may have different bitwidths for computation and memory storage by design. However, despite the progress made i…
Representational Continuity for Unsupervised Continual Learning
Divyam Madaan, Jaehong Yoon, Yuanchun Li +2
Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual l…
Online Coreset Selection for Rehearsal-based Continual Learning
Jaehong Yoon, Divyam Madaan, Eunho Yang +1
A dataset is a shred of crucial evidence to describe a task. However, each data point in the dataset does not have the same potential, as some of the data points can be more repres…
Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning
Minyoung Song, Jaehong Yoon, Eunho Yang +1
As deep neural networks are growing in size and being increasingly deployed to more resource-limited devices, there has been a recent surge of interest in network pruning methods,…
Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning
Wonyong Jeong, Jaehong Yoon, Eunho Yang +1
While existing federated learning approaches mostly require that clients have fully-labeled data to train on, in realistic settings, data obtained at the client-side often comes wi…
Federated Continual Learning with Weighted Inter-client Transfer
Jaehong Yoon, Wonyong Jeong, Giwoong Lee +2
There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios. Yet little research ha…
Scalable and Order-robust Continual Learning with Additive Parameter Decomposition
Jaehong Yoon, Saehoon Kim, Eunho Yang +1
While recent continual learning methods largely alleviate the catastrophic problem on toy-sized datasets, some issues remain to be tackled to apply them to real-world problem domai…
Lifelong Learning with Dynamically Expandable Networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee +1
We propose a novel deep network architecture for lifelong learning which we refer to as Dynamically Expandable Network (DEN), that can dynamically decide its network capacity as it…