DateLogicQA: Benchmarking Temporal Biases in Large Language Models Paper • 2412.13377 • Published 5 days ago • 2
Are Vision Language Models Texture or Shape Biased and Can We Steer Them? Paper • 2403.09193 • Published Mar 14
On the Interplay of Convolutional Padding and Adversarial Robustness Paper • 2308.06612 • Published Aug 12, 2023
An Extended Study of Human-like Behavior under Adversarial Training Paper • 2303.12669 • Published Mar 22, 2023
The Power of Linear Combinations: Learning with Random Convolutions Paper • 2301.11360 • Published Jan 26, 2023
Does Medical Imaging learn different Convolution Filters? Paper • 2210.13799 • Published Oct 25, 2022
Adversarial Robustness through the Lens of Convolutional Filters Paper • 2204.02481 • Published Apr 5, 2022
CNN Filter DB: An Empirical Investigation of Trained Convolutional Filters Paper • 2203.15331 • Published Mar 29, 2022
Swan and ArabicMTEB: Dialect-Aware, Arabic-Centric, Cross-Lingual, and Cross-Cultural Embedding Models and Benchmarks Paper • 2411.01192 • Published Nov 2 • 3
How Do Training Methods Influence the Utilization of Vision Models? Paper • 2410.14470 • Published Oct 18 • 4
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models Paper • 2410.06154 • Published Oct 8 • 16
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models Paper • 2410.02740 • Published Oct 3 • 52