3 citations · 8 across the 6 of their papers we have counts for
6 papers
WILT: A Multi-Turn, Memorization-Robust Inductive Logic Benchmark for LLMs
Eryk Banatt, Jonathan Cheng, Skanda Vaidyanath +1
While large language models have shown impressive capabilities across a wide range of domains, they still encounter significant challenges in reasoning tasks that require gathering…
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement Learning
Akash Velu, Skanda Vaidyanath, Dilip Arumugam
Oftentimes, environments for sequential decision-making problems can be quite sparse in the provision of evaluative feedback to guide reinforcement-learning agents. In the extreme…
Differentiable Weight Masks for Domain Transfer
Samar Khanna, Skanda Vaidyanath, Akash Velu
One of the major drawbacks of deep learning models for computer vision has been their inability to retain multiple sources of information in a modular fashion. For instance, given…
PushWorld: A benchmark for manipulation planning with tools and movable obstacles
Ken Kansky, Skanda Vaidyanath, Scott Swingle +3
While recent advances in artificial intelligence have achieved human-level performance in environments like Starcraft and Go, many physical reasoning tasks remain challenging for m…
LISA: Learning Interpretable Skill Abstractions from Language
Divyansh Garg, Skanda Vaidyanath, Kuno Kim +2
Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making. While it is possible to…
Jigsaw: Large Language Models meet Program Synthesis
Naman Jain, Skanda Vaidyanath, Arun Iyer +4
Large pre-trained language models such as GPT-3, Codex, and Google's language model are now capable of generating code from natural language specifications of programmer intent. We…