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Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning

Robustness Natural Language Processing

AutoZOOM: Autoencoder-based Zeroth Order Optimization Method for Attacking Black-box Neural Networks

Robustness Optimization

signSGD via Zeroth-Order Oracle

Optimization Robustness

Adversarial T-shirt! Evading Person Detectors in A Physical World

ECCV Computer Vision

Sign-OPT: Defending the hard-label black-box cyber attack

Cybersecurity Adversarial Machine Learning

CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networks

Robustness

On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning

ICLR Machine Learning

Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach

Robustness

Neural Network Robustness Certification with General Activation Functions

Robustness Deep Learning

Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case

ICML Machine Learning

Practical Detection of Trojan Neural Networks: Data-Limited and Data-Free Cases

ECCV

Is There a Trade-Off Between Fairness and Accuracy? A Perspective Using Mismatched Hypothesis Testing

ICML Machine Learning

Self-Progressing Robust Training

AAAI Machine Learning

Zeroth-Order Stochastic Variance Reduction for Nonconvex Optimization

Optimization Deep Learning

Higher-Order Certification For Randomized Smoothing

Optimization

On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method

Optimization Robustness

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

Robustness Explainability

Hidden Cost of Randomized Smoothing

AISTATS Machine Learning

MCUNet: Tiny Deep Learning on IoT Devices

Deep Learning Efficient AI

Training Stronger Baselines for Learning to Optimize

Optimization

Robust Overfitting may be mitigated by properly learned smoothening

ICLR

Deep Analysis of CNN-based Spatio-temporal Representations for Action Recognition

CVPR Computer Vision

RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices

AAAI Machine Learning

Foley Music: Learning to Generate Music from Videos

ECCV Computer Vision

Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport

AAAI Machine Learning

NASTransfer: Analyzing Architecture Transferability in Large Scale Neural Architecture Search

AAAI Computer Vision

Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning

ICLR

An Image Enhancing Pattern-based Sparsity for Real-time Inference on Mobile Devices

ECCV Computer Vision

Graph Universal Adversarial Attacks: A Few Bad Actors Ruin Graph Learning Models

Machine Learning Social Networks

Grounding Physical Concepts of Objects and Events Through Dynamic Visual Reasoning

ICLR Computer Vision

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models

CVPR Computer Vision

Self-supervised Moving Vehicle Tracking with Stereo Sound

Multimodal Learning Computer Vision

RSPNet: Relative Speed Perception for Unsupervised Video Representation Learning.

AAAI Computer Vision

Debiased Contrastive Learning

NeurIPS

The Lottery Ticket Hypothesis for the Pre-trained BERT Networks

Efficient AI

Deep Symbolic Superoptimization Without Human Knowledge

Neuro-Symbolic AI Optimization

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Graph Deep Learning AAAI

ZO-AdaMM: Derivative-free optimization for black-box problems

Optimization NeurIPS

Big-Little-Video-Net: Work smarter, not harder, for video understanding

Computer Vision

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

AI in Finance Graph Deep Learning

On the Convergence of A Class of Adam-Type Algorithms for Non-Convex Optimization

Optimization

Scalable Graph Learning for Anti-Money Laundering: A First Look

AI in Finance Graph Deep Learning

Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders

Generative Models Graph Deep Learning

A content-aware attack generator for AI cybersecurity

Adversarial Machine Learning Cybersecurity

Embedding Compression with Isotropic Iterative Quantization

Natural Language Processing

Online AI planning with graph neural networks and adaptive scheduling

Automated Planning Graph Deep Learning

Learning to learn with distributional signatures for text data

Natural Language Processing Transfer Learning

Fast and efficient black-box testing for AI cybersecurity

Cybersecurity Robustness