18 citations · 19 across the 8 of their papers we have counts for
8 papers
Corrupting Neuron Explanations of Deep Visual Features
Divyansh Srivastava, Tuomas Oikarinen, Tsui-Wei Weng
The inability of DNNs to explain their black-box behavior has led to a recent surge of explainability methods. However, there are growing concerns that these explainability methods…
Promoting Robustness of Randomized Smoothing: Two Cost-Effective Approaches
Linbo Liu, Trong Nghia Hoang, Lam M. Nguyen +1
Randomized smoothing has recently attracted attentions in the field of adversarial robustness to provide provable robustness guarantees on smoothed neural network classifiers. Howe…
The Importance of Prompt Tuning for Automated Neuron Explanations
Justin Lee, Tuomas Oikarinen, Arjun Chatha +3
Recent advances have greatly increased the capabilities of large language models (LLMs), but our understanding of the models and their safety has not progressed as fast. In this pa…
Concept-Monitor: Understanding DNN training through individual neurons
Mohammad Ali Khan, Tuomas Oikarinen, Tsui-Wei Weng
In this work, we propose a general framework called Concept-Monitor to help demystify the black-box DNN training processes automatically using a novel unified embedding space and c…
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Ligong Han, Seungwook Han, Shivchander Sudalairaj +8
Transformations based on domain expertise (expert transformations), such as random-resized-crop and color-jitter, have proven critical to the success of contrastive learning techni…
Label-Free Concept Bottleneck Models
Tuomas Oikarinen, Subhro Das, Lam M. Nguyen +1
Concept bottleneck models (CBM) are a popular way of creating more interpretable neural networks by having hidden layer neurons correspond to human-understandable concepts. However…