Deep Learning

Deep learning, a subfield of machine learning, focuses on training artificial neural networks with multiple layers to extract complex patterns from data. Current research emphasizes improving model robustness against noisy or adversarial inputs, exploring efficient architectures like Vision Transformers and convolutional LSTMs for various tasks (e.g., image classification, time series forecasting), and integrating physics-informed approaches for enhanced interpretability and reliability. These advancements are significantly impacting diverse fields, from automated industrial inspection and medical image analysis to improved weather forecasting and more efficient content moderation systems.

Papers

October 15, 2023

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October 9, 2023

Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness
Bhavya Vasudeva, Kameron Shahabi, Vatsal Sharan
Deep Learning Native Robustness Strong Generalization Distribution Generalization Simplicity Bias Biased Prediction Better Out of Distribution Generalization

Deep Learning

Papers

Score-Based Methods for Discrete Optimization in Deep Learning

FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategy

Hawkeye: A PyTorch-based Library for Fine-Grained Image Recognition with Deep Learning

Foundation Ark: Accruing and Reusing Knowledge for Superior and Robust Performance

Surveying the Landscape of Text Summarization with Deep Learning: A Comprehensive Review

A Hybrid Transfer Learning Assisted Decision Support System for Accurate Prediction of Alzheimer Disease

Adam-family Methods with Decoupled Weight Decay in Deep Learning

Designing Observables for Measurements with Deep Learning

A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches

Human-Centered Evaluation of XAI Methods

NeuroInspect: Interpretable Neuron-based Debugging Framework through Class-conditional Visualizations

Neural Harmonium: An Interpretable Deep Structure for Nonlinear Dynamic System Identification with Application to Audio Processing

LLMs as Potential Brainstorming Partners for Math and Science Problems

Growing ecosystem of deep learning methods for modeling protein$\unicode{x2013}$protein interactions

Pi-DUAL: Using Privileged Information to Distinguish Clean from Noisy Labels

Data efficient deep learning for medical image analysis: A survey

Watt For What: Rethinking Deep Learning's Energy-Performance Relationship

Deep Learning for Automatic Detection and Facial Recognition in Japanese Macaques: Illuminating Social Networks

Deep Learning: A Tutorial

Mitigating Simplicity Bias in Deep Learning for Improved OOD Generalization and Robustness