Search Results for author:
Found 12 papers, 6 papers with code
Pyramid Vector Quantization for LLMs
In this work, we aim to further exploit this spherical geometry of the weights when performing quantization by considering Pyramid Vector Quantization (PVQ) for large language models.
Zero-Shot Learning of Causal Models
In this work, we propose to learn a \emph{single} model capable of inferring in a zero-shot manner the causal generative processes of datasets.
AVID: Adapting Video Diffusion Models to World Models
We evaluate AVID on video game and real-world robotics data, and show that it outperforms existing baselines for diffusion model adaptation. 1 Our results demonstrate that if utilized correctly, pretrained video models have the potential to be powerful tools for embodied AI.
A Fixed-Point Approach for Causal Generative Modeling
We propose a novel formalism for describing Structural Causal Models (SCMs) as fixed-point problems on causally ordered variables, eliminating the need for Directed Acyclic Graphs (DAGs), and establish the weakest known conditions for their unique recovery given the topological ordering (TO).
The Essential Role of Causality in Foundation World Models for Embodied AI
The study of causality lends itself to the construction of veridical world models, which are crucial for accurately predicting the outcomes of possible interactions.
Learned Causal Method Prediction
In this work, we propose CAusal Method Predictor (CAMP), a framework for predicting the best method for a given dataset.
Towards Causal Foundation Model: on Duality between Causal Inference and Attention
These results provide compelling evidence that our method has the potential to serve as a stepping stone for the development of causal foundation models.
Understanding Causality with Large Language Models: Feasibility and Opportunities
We assess the ability of large language models (LLMs) to answer causal questions by analyzing their strengths and weaknesses against three types of causal question.
Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning
In this work, we further extend the existing body of work and develop a novel gradient-based approach to learning an ADMG with non-linear functional relations from observational data.
Scaling Federated Learning for Fine-tuning of Large Language Models
We perform an extensive sweep over the number of clients, ranging up to 32, to evaluate the impact of distributed compute on task performance in the federated averaging setting.
Perceiving Music Quality with GANs
By using the human rated dataset we show that the discriminator score correlates significantly with the subjective ratings, suggesting that the proposed method can be used to create a no-reference musical audio quality assessment measure.
Towards Machine Learning on data from Professional Cyclists
Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions.
