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Found 9 papers, 1 papers with code
SwitchTab: Switched Autoencoders Are Effective Tabular Learners
Self-supervised representation learning methods have achieved significant success in computer vision and natural language processing, where data samples exhibit explicit spatial or semantic dependencies.
Global-Local MAV Detection under Challenging Conditions based on Appearance and Motion
In this paper, we propose a global-local MAV detector that can fuse both motion and appearance features for MAV detection under challenging conditions.
Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems
This survey offers a holistic understanding of the prevailing transferable attacks and their impacts across different domains.
MASTERKEY: Practical Backdoor Attack Against Speaker Verification Systems
In this work, we propose a backdoor attack MASTERKEY, to compromise the SV models.
PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection
We significantly enhance the query efficiency and reduce the cost of a successful untargeted and targeted adversarial attack by 93. 1% and 65. 5% compared with the state-of-the-art black-box attacks, using merely ~300 queries (~5 minutes) and ~1, 500 queries (~25 minutes), respectively.
Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots
The chatbot is fine-tuned with a collection of crafted conversation sequences.
SuperVoice: Text-Independent Speaker Verification Using Ultrasound Energy in Human Speech
Our evaluation shows that SUPERVOICE achieves 0. 58% equal error rate in the speaker verification task, it only takes 120 ms for testing an incoming utterance, outperforming all existing speaker verification systems.
Shared Multi-Task Imitation Learning for Indoor Self-Navigation
Deep imitation learning enables robots to learn from expert demonstrations to perform tasks such as lane following or obstacle avoidance.
Avoidance of Manual Labeling in Robotic Autonomous Navigation Through Multi-Sensory Semi-Supervised Learning
This solution includes a suboptimal sensor policy based on sensor fusion to automatically label states encountered by a robot to avoid human supervision during training.
