Search Results for author:
Found 26 papers, 12 papers with code
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 #3 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 #43 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.
