Paperpile

Referenced Papers (7)

Making sense of vision and touch: Self-supervised learning of multimodal representations for contact-rich tasks

Dee

IEEE Int. Conf. Robot. Autom.

"The speaker cites this paper as an example of research utilizing multimodal sensor data, such as force-torque and proprioception, for robotic tasks involving physical contact."

Referenced at: 13:00

MIMIC-IV, a freely accessible electronic health record dataset

Alistair E W Johnson, Lucas Bulgarelli, Lu Shen, Alvin Gayles, Ayad Shammout, S Horng

Sci. Data, 2023

"This paper is cited as the source for the MIMIC-IV dataset, which the speaker uses as a key example of combining tabular and time-series data in the medical field to predict patient outcomes."

Referenced at: 15:44

Tutorial on graph representation learning

Hamilton

AAAI

"This tutorial is cited as a reference for the examples of graph modalities presented on the slide and for the broader topic of graph representation learning."

Referenced at: 17:39

DeepSets

Zaheer

NeurIPS 18 Compet.

"This paper is cited as a foundational work on set modalities, introducing the DeepSets model for handling data where the order of elements is not important."

Referenced at: 20:25

Point Cloud GAN

Chun-Liang Li, M Zaheer, Yang Zhang, B Póczos, R Salakhutdinov

ArXiv, 2018

"This paper is cited as an example of applying set-based learning principles to 3D point clouds, which the speaker describes as a form of set data."

Referenced at: 20:25

Efficient estimation of word representations in vector space

Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean

arXiv [cs.CL], 2013

"The speaker cites this paper in the context of unsupervised learning to illustrate dimensionality reduction through word embeddings, where semantic relationships like country-capital pairs are captured."

Referenced at: 32:49

Generative modelling with inverse heat dissipation

Severi Rissanen, Markus Heinonen, Arno Solin

arXiv [cs.CV], 2022

"This paper is cited as the source for images illustrating a de-noising diffusion model, which is presented as an example of an unsupervised or self-supervised generative model."

Referenced at: 33:25