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06/28/2023
On the Exploitability of Instruction Tuning
Instruction tuning is an effective technique to align large language mod...
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06/23/2023
Bring Your Own Data! Self-Supervised Evaluation for Large Language Models
With the rise of Large Language Models (LLMs) and their ubiquitous deplo...
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06/07/2023
On the Reliability of Watermarks for Large Language Models
As LLMs become commonplace, machine-generated text has the potential to ...
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01/06/2023
Model-Agnostic Hierarchical Attention for 3D Object Detection
Transformers as versatile network architectures have recently seen great...
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12/13/2022
What do Vision Transformers Learn? A Visual Exploration
Vision transformers (ViTs) are quickly becoming the de-facto architectur...
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09/15/2022
Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models
Pre-trained vision-language models (e.g., CLIP) have shown promising zer...
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08/03/2021
Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability
Conventional saliency maps highlight input features to which neural netw...
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02/26/2021
Improving Robustness of Learning-based Autonomous Steering Using Adversarial Images
For safety of autonomous driving, vehicles need to be able to drive unde...
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10/14/2020
Towards Accurate Quantization and Pruning via Data-free Knowledge Transfer
When large scale training data is available, one can obtain compact and ...
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09/18/2020
Prepare for the Worst: Generalizing across Domain Shifts with Adversarial Batch Normalization
Adversarial training is the industry standard for producing models that ...
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04/20/2020
