18 citations · 49 across the 20 of their papers we have counts for
10 papers · 1 filter
Data-Efficient Adaptation of LLMs via Attention Head Reweighting
Tuomas Oikarinen, Zixiao Chen, Charlotte Siska +3
Learning effectively from limited data is critical in domains like security where labeled examples are scarce. Large language models (LLMs) have demonstrated some capabilities for…
CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning
Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1
Catastrophic forgetting remains a fundamental challenge in continual learning, in which models often forget previous knowledge when fine-tuned on a new task. This issue is especial…
Evaluating Neuron Explanations: A Unified Framework with Sanity Checks
Tuomas Oikarinen, Ge Yan, Tsui-Wei Weng
Understanding the function of individual units in a neural network is an important building block for mechanistic interpretability. This is often done by generating a simple text e…
Concept Bottleneck Language Models For protein design
Aya Abdelsalam Ismail, Tuomas Oikarinen, Amy Wang +8
We introduce Concept Bottleneck Protein Language Models (CB-pLM), a generative masked language model with a layer where each neuron corresponds to an interpretable concept. Our arc…
Linear Explanations for Individual Neurons
Tuomas Oikarinen, Tsui-Wei Weng
In recent years many methods have been developed to understand the internal workings of neural networks, often by describing the function of individual neurons in the model. Howeve…
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…
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…
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…
GraphMDN: Leveraging graph structure and deep learning to solve inverse problems
Tuomas P. Oikarinen, Daniel C. Hannah, Sohrob Kazerounian
The recent introduction of Graph Neural Networks (GNNs) and their growing popularity in the past few years has enabled the application of deep learning algorithms to non-Euclidean,…
Robust Deep Reinforcement Learning through Adversarial Loss
Tuomas Oikarinen, Wang Zhang, Alexandre Megretski +2
Recent studies have shown that deep reinforcement learning agents are vulnerable to small adversarial perturbations on the agent's inputs, which raises concerns about deploying suc…