283 citations · 588 across the 29 of their papers we have counts for
30 papers
What makes Models Compositional? A Theoretical View: With Supplement
Parikshit Ram, Tim Klinger, Alexander G. Gray
Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of exi…
Enhancing In-context Learning via Linear Probe Calibration
Momin Abbas, Yi Zhou, Parikshit Ram +4
In-context learning (ICL) is a new paradigm for natural language processing that utilizes Generative Pre-trained Transformer (GPT)-like models. This approach uses prompts that incl…
Compositional Program Generation for Few-Shot Systematic Generalization
Tim Klinger, Luke Liu, Soham Dan +3
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Neural machine learning models, including the now ubiqui…
End-to-end Differentiable Clustering with Associative Memories
Bishwajit Saha, Dmitry Krotov, Mohammed J. Zaki +1
Clustering is a widely used unsupervised learning technique involving an intensive discrete optimization problem. Associative Memory models or AMs are differentiable neural network…
Model Sparsity Can Simplify Machine Unlearning
Jinghan Jia, Jiancheng Liu, Parikshit Ram +5
In response to recent data regulation requirements, machine unlearning (MU) has emerged as a critical process to remove the influence of specific examples from a given model. Altho…
What Is Missing in IRM Training and Evaluation? Challenges and Solutions
Yihua Zhang, Pranay Sharma, Parikshit Ram +3
Invariant risk minimization (IRM) has received increasing attention as a way to acquire environment-agnostic data representations and predictions, and as a principled solution for…