Search Results for author:
Found 16 papers, 5 papers with code
LoRA vs Full Fine-tuning: An Illusion of Equivalence
Second, we show that LoRA models with intruder dimensions, despite achieving similar performance to full fine-tuning on the target task, become worse models of the pre-training distribution and adapt less robustly to multiple tasks sequentially.
Language-guided Skill Learning with Temporal Variational Inference
We present an algorithm for skill discovery from expert demonstrations.
A Vision Check-up for Language Models
Although LLM-generated images do not look like natural images, results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world.
The Truth is in There: Improving Reasoning in Language Models with Layer-Selective Rank Reduction
Transformer-based Large Language Models (LLMs) have become a fixture in modern machine learning.
Learning adaptive planning representations with natural language guidance
Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand.
Pushdown Layers: Encoding Recursive Structure in Transformer Language Models
Recursion is a prominent feature of human language, and fundamentally challenging for self-attention due to the lack of an explicit recursive-state tracking mechanism.
Pseudointelligence: A Unifying Framework for Language Model Evaluation
With large language models surpassing human performance on an increasing number of benchmarks, we must take a principled approach for targeted evaluation of model capabilities.
Grokking of Hierarchical Structure in Vanilla Transformers
When analyzing the relationship between model-internal properties and grokking, we find that optimal depth for grokking can be identified using the tree-structuredness metric of \citet{murty2023projections}.
LaMPP: Language Models as Probabilistic Priors for Perception and Action
Language models trained on large text corpora encode rich distributional information about real-world environments and action sequences.
Characterizing Intrinsic Compositionality in Transformers with Tree Projections
To evaluate this possibility, we describe an unsupervised and parameter-free method to \emph{functionally project} the behavior of any transformer into the space of tree-structured networks.
Correcting Robot Plans with Natural Language Feedback
In this paper, we explore natural language as an expressive and flexible tool for robot correction.
Skill Induction and Planning with Latent Language
We evaluate this approach in the ALFRED household simulation environment, providing natural language annotations for only 10% of demonstrations.
Intelligent Carpet: Inferring 3D Human Pose From Tactile Signals
In this work, leveraging such tactile interactions, we propose a 3D human pose estimation approach using the pressure maps recorded by a tactile carpet as input.
Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales
We posit that machine learning will be the cornerstone of future collection, processing, and analysis of multimodal streams of data in animal communication studies, including bioacoustic, behavioral, biological, and environmental data.
Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller
We study a generalized setup for learning from demonstration to build an agent that can manipulate novel objects in unseen scenarios by looking at only a single video of human demonstration from a third-person perspective.
Multiple Interactions Made Easy (MIME): Large Scale Demonstrations Data for Imitation
In order to make progress and capture the space of manipulation, we would need to collect a large-scale dataset of diverse tasks such as pouring, opening bottles, stacking objects etc.
