9 papers · 1 filter
Structured Unrestricted-Rank Matrices for Parameter Efficient Fine-tuning
Arijit Sehanobish, Avinava Dubey, Krzysztof Choromanski +4
Recent efforts to scale Transformer models have demonstrated rapid progress across a wide range of tasks (Wei et al., 2022). However, fine-tuning these models for downstream tasks…
Fast Tree-Field Integrators: From Low Displacement Rank to Topological Transformers
Krzysztof Choromanski, Arijit Sehanobish, Somnath Basu Roy Chowdhury +4
We present a new class of fast polylog-linear algorithms based on the theory of structured matrices (in particular low displacement rank) for integrating tensor fields defined on w…
Embodied AI with Two Arms: Zero-shot Learning, Safety and Modularity
Jake Varley, Sumeet Singh, Deepali Jain +5
We present an embodied AI system which receives open-ended natural language instructions from a human, and controls two arms to collaboratively accomplish potentially long-horizon…
Conditional Language Policy: A General Framework for Steerable Multi-Objective Finetuning
Kaiwen Wang, Rahul Kidambi, Ryan Sullivan +17
Reward-based finetuning is crucial for aligning language policies with intended behaviors (e.g., creativity and safety). A key challenge is to develop steerable language models tha…
Optimal Time Complexity Algorithms for Computing General Random Walk Graph Kernels on Sparse Graphs
Krzysztof Choromanski, Isaac Reid, Arijit Sehanobish +1
We present the first linear time complexity randomized algorithms for unbiased approximation of the celebrated family of general random walk kernels (RWKs) for sparse graphs. This…
Linear Transformer Topological Masking with Graph Random Features
Isaac Reid, Kumar Avinava Dubey, Deepali Jain +12
When training transformers on graph-structured data, incorporating information about the underlying topology is crucial for good performance. Topological masking, a type of relativ…