Argmax

We start talking about diffusion models as a technique for generative deep learning.

We discuss NeurIPS outstanding paper award winning paper, talking about important topics surrounding metrics and reproducibility.

We talk about QMIX https://arxiv.org/abs/1803.11485 as an example of Deep Multi-agent RL.

Todays paper: Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility
and Double Descent from the Decision Boundary Perspective (https://arxiv.org/pdf/2203.08124.pdf)

Summary:
A discussion of reproducibility and double descent through visualizations of decision boundaries.

Highlights of the discussion:

• Relationship between model performance and reproducibility
• Which models are robust and reproducible
• How they calculate the various scores

Todays paper: VICReg (https://arxiv.org/abs/2105.04906)

Summary of the paper
VICReg prevents representation collapse using a mixture of variance, invariance and covariance when calculating the loss. It does not require negative samples and achieves great performance on downstream tasks.

Highlights of discussion

• The VICReg architecture (Figure 1)
• Sensitivity to hyperparameters (Table 7)
• Top 5 metric usefulness

Todays paper: data2vec (https://arxiv.org/abs/2202.03555)

Summary of the paper
A multimodal SSL algorithm that predicts latent representation of different types of input.

Highlights of discussion

• What are the motivations of SSL and multimodal
• How does the student teacher learning work?
• What are similarities and differences between ViT, BYOL, and Reinforcement Learning algorithms.

This is the first episode of Argmax! We talk about our motivations for doing a podcast, and what we hope listeners will get out of it.

Todays paper: Reward is Enough

Summary of the paper
The authors present the Reward is Enough hypothesis: Intelligence, and its associated abilities, can be understood as subserving the maximisation of reward by an agent acting in its environment.

Highlights of discussion

• High level overview of Reinforcement Learning
• How evolution can be encoded as a reward maximization problem
• What is the one reward signal we are trying to optimize?