24 citations · 104 across the 24 of their papers we have counts for
25 papers
MAG: MAnifold Guided Semi-Supervised Multi-modal In-Context Learning
Zirui Cheng, Xun Xu, Tiankai Chen +7
Few-shot in-context learning (ICL) with multi-modal large language models (MLLMs) enables task adaptation without parameter updates, but its performance is highly sensitive to the…
Accelerating Divisible Load Processing Through Machine Learning: A Practical Framework for Large-Scale Workloads
Bharadwaj Veeravalli
In this paper, we introduce the first machine learning framework for predicting optimal processing times in Single-Level Tree Network (SLTN) architectures for the Divisible Load Th…
CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning
Jingze Ge, Xue Geng, Yun Liu +6
To mitigate the memory constraints associated with fine-tuning large pre-trained models, existing parameter-efficient fine-tuning (PEFT) methods, such as LoRA, rely on low-rank upd…
DART-ing Through the Drift: Dynamic Tracing of Knowledge Neurons for Adaptive Inference-Time Pruning
Abhishek Tyagi, Yunuo Cen, Shrey Dhorajiya +2
Large Language Models (LLMs) exhibit substantial parameter redundancy, particularly in Feed-Forward Networks (FFNs). Existing pruning methods suffer from two primary limitations. F…
Resource-Aware Task Allocator Design: Insights and Recommendations for Distributed Satellite Constellations
Bharadwaj Veeravalli
We present the design of a Resource-Aware Task Allocator (RATA) and an empirical analysis in handling real-time tasks for processing on Distributed Satellite Systems (DSS). We cons…
A Multi-Port Concurrent Communication Model for handling Compute Intensive Tasks on Distributed Satellite System Constellations
Bharadwaj Veeravalli
We develop an integrated Multi-Port Concurrent Communication Divisible Load Theory (MPCC-DLT) framework for relay-centric distributed satellite systems (DSS), capturing concurrent…
Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation
Lechun You, Zhonghua Wu, Weide Liu +5
Current methods for 3D semantic segmentation propose training models with limited annotations to address the difficulty of annotating large, irregular, and unordered 3D point cloud…
SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation
Qilong Wu, Xiaoneng Xiang, Hejia Huang +5
The rapid growth of the financial sector and the rising focus on Environmental, Social, and Governance (ESG) considerations highlight the need for advanced NLP tools. However, open…
LaVida Drive: Vision-Text Interaction VLM for Autonomous Driving with Token Selection, Recovery and Enhancement
Siwen Jiao, Yangyi Fang, Baoyun Peng +2
Recent advancements in Visual Language Models (VLMs) have made them crucial for visual question answering (VQA) in autonomous driving, enabling natural human-vehicle interactions.…
A Timely Survey on Vision Transformer for Deepfake Detection
Zhikan Wang, Zhongyao Cheng, Jiajie Xiong +4
In recent years, the rapid advancement of deepfake technology has revolutionized content creation, lowering forgery costs while elevating quality. However, this progress brings for…
CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Domain adaptive object detection aims to adapt detection models to domains where annotated data is unavailable. Existing methods have been proposed to address the domain gap using…
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing
Hugo Chan-To-Hing, Bharadwaj Veeravalli
Self-supervised frameworks for representation learning have recently stirred up interest among the remote sensing community, given their potential to mitigate the high labeling cos…
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection
Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli +1
Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remain…
HeRAFC: Heuristic Resource Allocation and Optimization in MultiFog-Cloud Environment
Chinmaya Kumar Dehury, Bharadwaj Veeravalli, Satish Narayana Srirama
By bringing computing capacity from a remote cloud environment closer to the user, fog computing is introduced. As a result, users can access the services from more nearby computin…
ACT-Net: Asymmetric Co-Teacher Network for Semi-supervised Memory-efficient Medical Image Segmentation
Ziyuan Zhao, Andong Zhu, Zeng Zeng +2
While deep models have shown promising performance in medical image segmentation, they heavily rely on a large amount of well-annotated data, which is difficult to access, especial…
MMGL: Multi-Scale Multi-View Global-Local Contrastive learning for Semi-supervised Cardiac Image Segmentation
Ziyuan Zhao, Jinxuan Hu, Zeng Zeng +4
With large-scale well-labeled datasets, deep learning has shown significant success in medical image segmentation. However, it is challenging to acquire abundant annotations in cli…
Self-supervised Assisted Active Learning for Skin Lesion Segmentation
Ziyuan Zhao, Wenjing Lu, Zeng Zeng +3
Label scarcity has been a long-standing issue for biomedical image segmentation, due to high annotation costs and professional requirements. Recently, active learning (AL) strategi…
Object-Aware Self-supervised Multi-Label Learning
Xu Kaixin, Liu Liyang, Zhao Ziyuan +2
Multi-label Learning on Image data has been widely exploited with deep learning models. However, supervised training on deep CNN models often cannot discover sufficient discriminat…
A predictive analytics approach for stroke prediction using machine learning and neural networks
Soumyabrata Dev, Hewei Wang, Chidozie Shamrock Nwosu +3
The negative impact of stroke in society has led to concerted efforts to improve the management and diagnosis of stroke. With an increased synergy between technology and medical di…
RRFT: A Rank-Based Resource Aware Fault Tolerant Strategy for Cloud Platforms
Chinmaya Kumar Dehury, Prasan Kumar Sahoo, Bharadwaj Veeravalli
The applications that are deployed in the cloud to provide services to the users encompass a large number of interconnected dependent cloud components. Multiple identical component…
Connection Pruning for Deep Spiking Neural Networks with On-Chip Learning
Thao N. N. Nguyen, Bharadwaj Veeravalli, Xuanyao Fong
Long training time hinders the potential of the deep, large-scale Spiking Neural Network (SNN) with the on-chip learning capability to be realized on the embedded systems hardware.…
Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma
Jiapan Gu, Ziyuan Zhao, Zeng Zeng +10
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the fourth most common cause of cancer-related death worldwide. Understanding the underlying gene…
Deeply Supervised Active Learning for Finger Bones Segmentation
Ziyuan Zhao, Xiaoyan Yang, Bharadwaj Veeravalli +1
Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segm…
Predicting Stroke from Electronic Health Records
Chidozie Shamrock Nwosu, Soumyabrata Dev, Peru Bhardwaj +2
Studies have identified various risk factors associated with the onset of stroke in an individual. Data mining techniques have been used to predict the occurrence of stroke based o…