Search Results for author:
Found 36 papers, 20 papers with code
RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone
Experiments show that our proposed model outperforms state-of-the-art (SOTA) mobile CNN, ViT, ViG, and hybrid architectures in terms of accuracy and/or speed on image classification, object detection, instance segmentation, and semantic segmentation.
Online-LoRA: Task-free Online Continual Learning via Low Rank Adaptation
Catastrophic forgetting is a significant challenge in online continual learning (OCL), especially for non-stationary data streams that do not have well-defined task boundaries.
Skip2-LoRA: A Lightweight On-device DNN Fine-tuning Method for Low-cost Edge Devices
This paper proposes Skip2-LoRA as a lightweight fine-tuning method for deep neural networks to address the gap between pre-trained and deployed models.
CrediRAG: Network-Augmented Credibility-Based Retrieval for Misinformation Detection in Reddit
Fake news threatens democracy and exacerbates the polarization and divisions in society; therefore, accurately detecting online misinformation is the foundation of addressing this issue.
A Tiny Supervised ODL Core with Auto Data Pruning for Human Activity Recognition
As a tinyML solution at a few mW for the human activity recognition, we design a supervised ODL core that supports our automatic data pruning using a 45nm CMOS process technology.
Scaling Graph Convolutions for Mobile Vision
To compete with existing mobile architectures, MobileViG introduces Sparse Vision Graph Attention (SVGA), a fast token-mixing operator based on the principles of GNNs.
Ada-VE: Training-Free Consistent Video Editing Using Adaptive Motion Prior
This enables a greater number of cross-frame attentions over more frames within the same computational budget, thereby enhancing both video quality and temporal coherence.
PP-SAM: Perturbed Prompts for Robust Adaptation of Segment Anything Model for Polyp Segmentation
To this end, we utilize variable perturbed bounding box prompts (BBP) to enrich the learning context and enhance the model's robustness to BBP perturbations during inference.
EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
An efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources.
Ranked #1 on
Medical Image Segmentation
on ISIC 2018
GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs
Our smallest model, GreedyViG-S, achieves 81. 1% top-1 accuracy on ImageNet-1K, 2. 9% higher than Vision GNN and 2. 2% higher than Vision HyperGraph Neural Network (ViHGNN), with less GMACs and a similar number of parameters.
News Source Credibility Assessment: A Reddit Case Study
In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation.
Machine Unlearning for Image-to-Image Generative Models
This paper serves as a bridge, addressing the gap by providing a unifying framework of machine unlearning for image-to-image generative models.
Fast-NTK: Parameter-Efficient Unlearning for Large-Scale Models
The rapid growth of machine learning has spurred legislative initiatives such as ``the Right to be Forgotten,'' allowing users to request data removal.
G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation
The encoder utilizes the self-attention mechanism to capture long-range dependencies, while the decoder refines the feature maps preserving long-range information due to the global receptive fields of the graph convolution block.
Ranked #1 on
Retinal Vessel Segmentation
on DRIVE
(Specificity metric)
Zero-Shot Neural Architecture Search: Challenges, Solutions, and Opportunities
Recently, zero-shot (or training-free) Neural Architecture Search (NAS) approaches have been proposed to liberate NAS from the expensive training process.
MobileViG: Graph-Based Sparse Attention for Mobile Vision Applications
Our work proves that well designed hybrid CNN-GNN architectures can be a new avenue of exploration for designing models that are extremely fast and accurate on mobile devices.
TIPS: Topologically Important Path Sampling for Anytime Neural Networks
Anytime neural networks (AnytimeNNs) are a promising solution to adaptively adjust the model complexity at runtime under various hardware resource constraints.
Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation
Transformers have shown great success in medical image segmentation.
ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
Based on this theoretical analysis, we propose a new zero-shot proxy, ZiCo, the first proxy that works consistently better than #Params.
Medical Image Segmentation via Cascaded Attention Decoding
To address this issue, we propose a novel attention-based decoder, namely CASCaded Attention DEcoder (CASCADE), which leverages the multiscale features of hierarchical vision transformers.
Ranked #1 on
Polyp Segmentation
on Kvasir-SEG
Efficient On-device Training via Gradient Filtering
Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI.
Dynamic Multimodal Fusion
In this work, we propose dynamic multimodal fusion (DynMM), a new approach that adaptively fuses multimodal data and generates data-dependent forward paths during inference.
Ranked #49 on
Semantic Segmentation
on NYU Depth v2
SUGAR: Efficient Subgraph-level Training via Resource-aware Graph Partitioning
Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition.
FLASH: Fast Neural Architecture Search with Hardware Optimization
This paper proposes FLASH, a very fast NAS methodology that co-optimizes the DNN accuracy and performance on a real hardware platform.
How does topology of neural architectures impact gradient propagation and model performance?
In this paper, we reveal that the topology of the concatenation-type skip connections is closely related to the gradient propagation which, in turn, enables a predictable behavior of DNNs’ test performance.
Ranked #34 on
Neural Architecture Search
on CIFAR-10
On the relationship between topology and gradient propagation in deep networks
(ii) Can certain topological characteristics of deep networks indicate a priori (i. e., without training) which models, with a different number of parameters/FLOPS/layers, achieve a similar accuracy?
New Directions in Distributed Deep Learning: Bringing the Network at Forefront of IoT Design
In this paper, we first highlight three major challenges to large-scale adoption of deep learning at the edge: (i) Hardware-constrained IoT devices, (ii) Data security and privacy in the IoT era, and (iii) Lack of network-aware deep learning algorithms for distributed inference across multiple IoT devices.
FedMAX: Mitigating Activation Divergence for Accurate and Communication-Efficient Federated Learning
In this paper, we identify a new phenomenon called activation-divergence which occurs in Federated Learning (FL) due to data heterogeneity (i. e., data being non-IID) across multiple users.
EdgeAI: A Vision for Deep Learning in IoT Era
The significant computational requirements of deep learning present a major bottleneck for its large-scale adoption on hardware-constrained IoT-devices.
How does topology influence gradient propagation and model performance of deep networks with DenseNet-type skip connections?
In this paper, we reveal that the topology of the concatenation-type skip connections is closely related to the gradient propagation which, in turn, enables a predictable behavior of DNNs' test performance.
Memory- and Communication-Aware Model Compression for Distributed Deep Learning Inference on IoT
Therefore, we propose Network of Neural Networks (NoNN), a new distributed IoT learning paradigm that compresses a large pretrained 'teacher' deep network into several disjoint and highly-compressed 'student' modules, without loss of accuracy.
Dream Distillation: A Data-Independent Model Compression Framework
Model compression is eminently suited for deploying deep learning on IoT-devices.
On Network Science and Mutual Information for Explaining Deep Neural Networks
In this paper, we present a new approach to interpret deep learning models.
A Dynamic Network and Representation LearningApproach for Quantifying Economic Growth fromSatellite Imagery
To this end, we propose a new network science- and representation learning-based approach that can quantify economic indicators and visualize the growth of various regions.
Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems
Our results show that these generalized 3D NoCs only incur a 1. 8% (36-tile system) and 1. 1% (64-tile system) average performance loss compared to application-specific NoCs.
Machine Learning and Manycore Systems Design: A Serendipitous Symbiosis
Tight collaboration between experts of machine learning and manycore system design is necessary to create a data-driven manycore design framework that integrates both learning and expert knowledge.
