Deep Model

Deep models, encompassing a broad range of neural network architectures, aim to learn complex patterns from data for various tasks like image classification, time series forecasting, and system identification. Current research emphasizes improving efficiency (e.g., through constant-time learning algorithms and layer caching), enhancing explainability (e.g., via gradient-free methods), and mitigating issues like bias and memorization. These advancements are significant because they improve the reliability, trustworthiness, and applicability of deep models across diverse scientific fields and real-world applications, including healthcare, finance, and autonomous systems.

Papers

January 17, 2024

Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy
Ruiqing Sun, Delong Yang, Shaohui Zhang, Qun Hao
Deep Learning Physic Informed Neural Network Deep Model Efficient Hybrid Sample Reconstruction

January 14, 2024

Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models
Shiming Wang, Zhe Ji, Liyao Xiang, Hao Zhang, Xinbing Wang, Chenghu Zhou, Bo Li
Deep Model Deep Learning Application Model Inversion Attack Adaptive Attack Traditional CrAFT Adversarial Game Inversion Attack

January 12, 2024

Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction
Muhammad Naveed Riaz, Maciej Wielgosz, Abel Garcia Romera, Antonio M. Lopez
Autonomous Driving Data Set Deep Model Ego Vehicle Efficient Deep Learning Model Pedestrian Intention Prediction

January 9, 2024

Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective
Haoyi Xiong, Xuhong Li, Xiaofei Zhang, Jiamin Chen, Xinhao Sun, Yuchen Li, Zeyi Sun, Mengnan Du
Deep Neural Network Explainable AI Explainable Artificial Intelligence XAI Method Deep Model xAI Community Data Mining Perspective

December 31, 2023

Masked Modeling for Self-supervised Representation Learning on Vision and Beyond
Siyuan Li, Luyuan Zhang, Zedong Wang, Di Wu, Lirong Wu, Zicheng Liu, Jun Xia, Cheng Tan, Yang Liu, Baigui Sun, Stan Z. Li
Self Supervised Learning Representation Learning Vision Paper Deep Model Self Supervised Representation Learning Robust Representation Masking Strategy Masked Modeling

December 26, 2023

December 16, 2023

RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification
Muktabh Mayank Srivastava
Zero Shot Deep Model Metric Learning CLIP Model Vision Encoders Computer Vision Application Incremental Training Product Classification

November 23, 2023

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images
Abhishek Singh, Venkatapathy Subramanian, Ayush Maheshwari, Pradeep Narayan, Devi Prasad Shetty, Ganesh Ramakrishnan
Information Extraction Deep Model Document Image Eigen Portfolio Level Annotation

November 22, 2023

REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints
Francesco Corti, Balz Maag, Joachim Schauer, Ulrich Pferschy, Olga Saukh
Deep Model Model Agnostic Resource Efficient Deep Conventional Deep

November 13, 2023

Boolean Variation and Boolean Logic BackPropagation
Van Minh Nguyen
Deep Model Total Variation

October 25, 2023

MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory
Yinan Liang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, Jiwen Lu
Vision Transformer Transformer Architecture Deep Model 32 Bit Microcontrollers Limited Memory CNN Inference

October 23, 2023

Unlocking the Transferability of Tokens in Deep Models for Tabular Data
Qi-Le Zhou, Han-Jia Ye, Le-Ye Wang, De-Chuan Zhan
Pre Trained Model Tabular Data Deep Model Task Transferability K TOKEN Tabular Deep Learning Pre Trained Deep Neural Network

October 17, 2023

Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs
Uri Stern, Daphna Weinshall
Deep Neural Network Anti Forgetting Deep Model DNN Framework Training Free Model Overfitting Modern Deep Learning Model

October 13, 2023

It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models
Lin Chen, Michal Lukasik, Wittawat Jitkrittum, Chong You, Sanjiv Kumar
Deep Learning Model Alignment Problem Deep Model Generalization Error Neural Collapse Variance Information

October 4, 2023

SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
KL Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman, Hamed Pirsiavash
Vision Transformer Deep Model Adversarial Patch Transformer Inference Standard Adversarial Training

October 3, 2023

MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts
Weibin Liao, Haoyi Xiong, Qingzhong Wang, Yan Mo, Xuhong Li, Yi Liu, Zeyu Chen, Siyu Huang, Dejing Dou
Pre Trained Model Deep Model X Ray X Ray Image Continual Pre Training Body Part Medical Imaging Task Self Training Pipeline Self Supervised Continual Learning

October 2, 2023

Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models
Abu Zahid Bin Aziz, Jadie Adams, Shireen Elhabian
Deep Model Shape Prior Statistical Shape Training Method Anatomical Shape

October 1, 2023

Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology
Lucas Rosenblatt, Bin Han, Erin Posthumus, Theresa Crimmins, Bill Howe
Neural Network Deep Learning Deep Model Multi Modal Model Plant Growth Simulation

September 21, 2023

Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data
Muhammad Sulaiman, Mina Farmanbar, Ahmed Nabil Belbachir, Chunming Rong
Deep Model LiDAR Data Multidimensional Local Precision Rate Building Segmentation Shallow Layer Building Extraction Shallow Water Supervised Segmentation Aware Mask

Deep Model

Papers

Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy

Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models

Synthetic Data Generation Framework, Dataset, and Efficient Deep Model for Pedestrian Intention Prediction

Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective

Masked Modeling for Self-supervised Representation Learning on Vision and Beyond

Exploiting the capacity of deep networks only at training stage for nonlinear black-box system identification

M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy

RetailKLIP : Finetuning OpenCLIP backbone using metric learning on a single GPU for Zero-shot retail product image classification

EIGEN: Expert-Informed Joint Learning Aggregation for High-Fidelity Information Extraction from Document Images

REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints

Boolean Variation and Boolean Logic BackPropagation

MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory

Unlocking the Transferability of Tokens in Deep Models for Tabular Data

Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs

It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models

SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers

MUSCLE: Multi-task Self-supervised Continual Learning to Pre-train Deep Models for X-ray Images of Multiple Body Parts

Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models

Top-down Green-ups: Satellite Sensing and Deep Models to Predict Buffelgrass Phenology

Precision in Building Extraction: Comparing Shallow and Deep Models using LiDAR Data